© 2009 by christian hampp - university of...
TRANSCRIPT
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UTILITY OF CURRENT SURVEILLANCE SYSTEMS TO DETECT RESPIRATORY
SYNCYTIAL VIRUS SEASONS AND IMPLICATIONS FOR IMMUNOPROPHYLAXIS
By
CHRISTIAN HAMPP
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2009
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© 2009 by Christian Hampp
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To my family:
my sisters Bärbel and Ute,
my mother Else,
and to the memory of my father Gottfried
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ACKNOWLEDGEMENTS
I want to thank my adviser, Almut Winterstein, for her continuous support and guidance.
She has been an outstanding mentor and has never stopped challenging me. I would also like to
thank my supervisory committee members Nabih Asal, Teresa Kauf, Earlene Lipowski and
Eileen Schneider for their expertise, advice, and encouragement. My gratitude goes to all faculty
members and staff in the department of Pharmaceutical Outcomes and Policy for their support
and for everything they taught me during my time at the University of Florida. Abraham
Hartzema, Carole Kimberlin and Richard Segal were great sources of inspiration and always
willing to provide their advice. Many thanks also to Jon Shuster for advice on statistical
analyses. Huazhi Liu, the department’s former data analyst, earned my appreciation through his
support and advice with data programming.
For the provision of data, I thank the staff at Public Health Statistics, Office of Vital
Statistics and at the Bureau of Epidemiology, both within the Florida Department of Health. I
appreciate access to Medicaid data from the Centers for Medicare and Medicaid Services and the
help of Gerrie Barosso at the University of Minnesota’s Research Data Assistance Center to
facilitate data access. Cathy Panozzo at the Centers for Disease Control and Prevention deserves
my gratitude for technical assistance and the provision of their surveillance dataset.
I’d like to acknowledge that the study was funded by a grant from the Florida Agency for
Healthcare Administration, AHCA. It was conducted in collaboration with the University of
Florida Center for Medicaid and the Uninsured.
I want to thank my family for their support, guidance and encouragement throughout my
life, and my girlfriend Hee-Jung for her love and emotional support. Finally, I thank my fellow
graduate students and my friends for their friendship and the right amount of distraction.
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TABLE OF CONTENTS
page
ACKNOWLEDGEMENTS .............................................................................................................4
LIST OF TABLES ...........................................................................................................................7
LIST OF FIGURES .........................................................................................................................8
ABSTRACT ...................................................................................................................................10
CHAPTER
1 INTRODUCTION ..................................................................................................................12
Background .............................................................................................................................12 Need for Study .................................................................................................................13
Purpose of Study ..............................................................................................................14 Research Questions and Hypotheses ......................................................................................15
2 LITERATURE REVIEW .......................................................................................................20
Respiratory Syncytial Virus ....................................................................................................20 RSV Disease Epidemiology ............................................................................................21
RSV Infections ................................................................................................................22 Diagnostic Tests for RSV ................................................................................................22
The National Respiratory and Enteric Virus Surveillance System .........................................24
RSV Seasonality .....................................................................................................................25
RSV Prevention ......................................................................................................................26 RSV Risk Factors and Indications for Immunoprophylaxis ............................................28
Prior Authorization Requirements ...................................................................................29
3 METHODS .............................................................................................................................33
Datasets ...................................................................................................................................33 NREVSS ..........................................................................................................................33 Florida Department of Health RSV Surveillance Data ...................................................33 Medicaid Analytic eXtract Claims Dataset .....................................................................34 State Birth Certificates ....................................................................................................35
Study Population .....................................................................................................................36
Part I: Validation of CDC’s Current RSV Season Definition ...............................................39
Part II: RSV Epidemiology between Four US States and Five Regions in Florida ..............43 Part III: Latitude as a Factor in RSV Epidemiology in Florida .............................................44 Part IV: Patient Factors and Seasonality ...............................................................................45 Part V: Timing of Prophylaxis with Palivizumab vs. RSV Seasonality ................................46 Part VI: Optimizing Timing of Prophylaxis ..........................................................................47
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4 RESULTS ...............................................................................................................................51
Sample Characteristics ............................................................................................................51 Part I: Validation of CDC’s Current RSV Season Definition ...............................................53 Part II: RSV Epidemiology between Four US States and Five Regions in Florida ..............56
Part III: Latitude as a Factor in RSV Epidemiology in Florida .............................................57 Part IV: Patient Factors and Seasonality ...............................................................................58 Part V: Timing of Prophylaxis with Palivizumab vs. RSV Seasonality ................................59 Part VI: Optimizing Timing of Prophylaxis ..........................................................................60
5 DISCUSSION .........................................................................................................................91
Part I: Validation of CDC’s Current RSV Season Definition ...............................................91 Part II: RSV Epidemiology between Four US States and Five Regions in Florida ..............93
Part III: Latitude as a Factor in RSV Epidemiology in Florida .............................................94 Part IV: Patient Factors and Seasonality ...............................................................................94 Part V: Timing of Prophylaxis with Palivizumab vs. RSV Seasonality ................................95 Part VI: Optimizing Timing of Prophylaxis ..........................................................................96
External Validity .....................................................................................................................98 Study Limitations..................................................................................................................100
Future Research ....................................................................................................................102 Summary and Conclusions ...................................................................................................103
APPENDIX ..................................................................................................................................106
A. Operational Definitions ..............................................................................................106 Palivizumab Exposure ...................................................................................................106
Risk Factors for RSV .....................................................................................................106 Chronic lung disease ..............................................................................................106
Prematurity .............................................................................................................107 Congenital heart disease .........................................................................................107
Cystic fibrosis .........................................................................................................108 Severe combined or acquired immunodeficiency ..................................................108
Down syndrome .....................................................................................................108 Asthma ...................................................................................................................108 Transplant ...............................................................................................................109 Malignancy .............................................................................................................109 Immunosuppression or antineoplastic agents .........................................................109
Hospitalizations .............................................................................................................109
RSV hospitalization ................................................................................................109
Specific non-RSV bronchiolitis or pneumonia ......................................................110 Unspecific bronchiolitis or pneumonia ..................................................................110
B. Supplemental Tables ..................................................................................................111
LIST OF REFERENCES .............................................................................................................113
BIOGRAPHICAL SKETCH .......................................................................................................121
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LIST OF TABLES
Table page
2-1 Historical landmarks in the NREVSS ....................................................................................31
4-1 Cohort characteristics .............................................................................................................62
4-2 Risk factors for RSV hospitalizations in Florida ....................................................................63
4-3 Palivizumab exposure and RSV hospitalizations by state and risk category .........................64
4-4 Areas under the curve by state and region ..............................................................................65
4-5 Test characteristics at the threshold of 10% median proportion positive laboratory tests .....66
4-6 Test characteristics at optimal thresholds of median proportion positive laboratory tests .....66
4-7 Mean of absolute differences and direction of difference between season onset
according to clinical dataset and surveillance dataset under different definitions for
season onset .......................................................................................................................67
4-8 Mean of absolute differences and direction of difference between season offset
according to clinical dataset and surveillance dataset under different definitions for
season offset .......................................................................................................................68
4-9 Extent of seasonality and seasonality index in each state and regions in Florida ..................69
4-10 Variation in seasons within each state and regions in Florida ..............................................70
4-11 Comparison of season characteristics between regions in Florida .......................................71
4-12 Linear regression analysis of the effects of latitude on season characteristics in Florida ....71
4-13 Mean of absolute differences and direction of difference between onset of palivizumab
utilization and onset of RSV season according to different determinants of RSV
season .................................................................................................................................72
4-14 Mean of absolute differences and direction of difference between offset of
palivizumab utilization and offset of RSV season according to different determinants
of RSV season ....................................................................................................................73
5-1 Cost of prophylaxis per avoided RSV hospitalization..........................................................105
B-1 List of counties in Florida ....................................................................................................111
B-2 Coordinates of Florida regions.............................................................................................112
B-3 Week numbers and corresponding calendar months, shown for the year 2000...................112
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LIST OF FIGURES
Figure page
2-1 Map of RSV regions in Florida ..............................................................................................32
3-1 Season detection based on clinical dataset .............................................................................49
3-3 Cut-off values for areas under the ROC curve .......................................................................50
4-1 Flowchart of sample selection and resulting sample size .......................................................74
4-2 RSV hospitalization rates and resulting seasons in A) California, B) Florida, C) Illinois
and D) Texas ......................................................................................................................75
4-3 RSV hospitalization rates and resulting seasons in the regions of Florida. A) Northwest,
B) North, C) Central, D) Southwest and E) Southeast ......................................................76
4-4 NREVSS laboratory tests and resulting RSV season in A) California, B) Florida,
C) Illinois and D) Texas.. ...................................................................................................78
4-5 NREVSS laboratory tests and resulting RSV season in the regions of Florida.
A) Northwest, B) North, C) Central, D) Southwest and E) Southeast...............................79
4-6 Receiver operating characteristics curves for each state ........................................................81
4-7 Receiver operating characteristics curves for each region in Florida .....................................82
4-8 Linear effect of latitude on season characteristics in Florida. A) Week of season onset,
B) Week of season offset, C) Season duration and D) Peak week ....................................83
4-9 Heterogeneous effects of RSV risk factors on risk for RSV hospitalizations off-season
vs. on-season ......................................................................................................................84
4-10 Palivizumab utilization and RSV seasonality in A) California, B) Florida, C) Illinois
and D) Texas ......................................................................................................................85
4-11 Palivizumab utilization and RSV seasonality in the regions of Florida. A) Northwest,
B) North, C) Central, D) Southwest and E) Southeast ......................................................86
4-12 A) Monthly RSV hospitalization incidence rates, B) Numbers needed to treat with
palivizumab by age in the high risk cohort in California...................................................88
4-13 A) Monthly RSV hospitalization incidence rates, B) Numbers needed to treat with
palivizumab by age in the high risk cohort in Florida .......................................................88
4-14 A) Monthly RSV hospitalization incidence rates, B) Numbers needed to treat with
palivizumab by age in the high risk cohort in Illinois .......................................................89
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4-15 A) Monthly RSV hospitalization incidence rates, B) Numbers needed to treat with
palivizumab by age in the high risk cohort in Texas .........................................................89
4-16 A) Monthly RSV hospitalization incidence rates, B) Numbers needed to treat with
palivizumab in the high-risk cohort for each region in Florida .........................................90
5-1 Distribution of diagnostic codes for bronchiolitis and pneumonia related
hospitalizations ................................................................................................................105
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Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
UTILITY OF CURRENT SURVEILLANCE SYSTEMS TO DETECT RESPIRATORY
SYNCYTIAL VIRUS SEASONS AND IMPLICATIONS FOR IMMUNOPROPHYLAXIS
By
Christian Hampp
August 2009
Chair: Almut Winterstein
Major: Pharmaceutical Sciences
To inform timing of immunoprophylaxis for respiratory syncytial virus (RSV), the Centers
for Disease Control and Prevention (CDC) monitors the weekly numbers and results of RSV
tests from a sample of laboratories. Our objective was to validate the CDC’s use of a 10%
threshold of median proportion of positive tests (MPP) to identify RSV seasons. Additionally, to
help policy makers optimize resources, we provide monthly RSV incidences and numbers
needed to treat (NNT) with palivizumab.
Medicaid fee-forservice recipients under 2 years of age from California, Florida, Illinois
and Texas (1999-2004) were categorized for each week as high-risk or low-risk for RSV
infection based on ICD-9 codes, pharmacy claims and birth certificates (Florida only). Subjects
were continuously eligible from birth and in ambulatory care for 4 weeks before the current
week. The statewide weekly incidence rates of RSV hospitalizations were measured for each risk
category and adjusted for RSV prophylaxis. Weeks were categorized as on-season if the RSV
incidence rate in high-risk children exceeded the season peak of the incidence rate in low-risk
children. Receiver operating characteristics (ROC) curves were used to measure the ability of
MPP to discriminate between on-season and off-season weeks.
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In California, Florida, Illinois and Texas, the areas under the ROC curves were 0.98 (95%
confidence interval, 0.96 – 0.99), 0.92 (0.88 – 0.95), 0.88 (0.83 – 0.92) and 0.92 (0.88 – 0.95),
respectively. Requiring at least 5 positive tests in addition to the 10% MPP threshold optimized
accuracy. Season onset was detected on average 3.4 (0.0 – 7.2) weeks apart from hospitalization
onset and offset was 2.2 (0.0 – 4.3) weeks apart from hospitalization offset. Performance of
surveillance was limited in Florida’s southwest and southeast regions. NNTs differed widely
between states, calendar months and age groups and confirmed regional differences in burden of
RSV within Florida.
The 10% MPP with at least 5 positive tests is a valid threshold even for geographically
diverse states. Higher NNTs for older children highlight the reduced benefit of
immunoprophylaxis in the second year of life. NNTs can provide valuable detail about the local
burden of RSV and their use should be considered by third party payers.
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CHAPTER 1
INTRODUCTION
Background
Respiratory syncytial virus (RSV) is the most frequent cause of lower respiratory tract
infections among infants and children. In the United States, RSV causes annually up to 125,000
hospitalizations for bronchiolitis among infants under 1 year.1 While no vaccination is available,
palivizumab (Synagis, MedImmune, Inc., Gaithersburg, MD), a humanized monoclonal
antibody, is able to reduce RSV-related hospitalizations.2, 3
According to the label, monthly
injections are necessary to provide protection throughout an RSV season.4 The major limiting
factor to the widespread use of RSV prophylaxis is the high drug cost, which often results in
expenses of more than $10,000 to immunize one infant through a six-month season.5 These costs
limit prophylaxis to patients at increased risk for infection such as children with chronic lung
disease (CLD), congenital heart disease (CHD) and certain preterm infants.6
Another option for cost-containment is the restriction of prophylaxis to a clearly-defined
RSV season of high viral activity. The Centers for Disease Control and Prevention (CDC)
monitors RSV activity through its National Respiratory and Enteric Virus Surveillance System
(NREVSS).7 A nationwide sample of laboratories report the number of specimens tested for RSV
and the number of positive tests. These data are updated every week and published on the
NREVSS website.
While the RSV season peaks in November/December in most countries of the northern
hemisphere, regions closer to the equator show less seasonal variability and observe cases year-
round. Furthermore, even within countries, RSV outbreaks differ based on latitude and proximity
to a coast.8 In the southern United States, the RSV season starts earlier and lasts longer compared
to the rest of the nation.9, 10
Differences in seasonality have been identified even within a single
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state: Florida’s southeast experiences earlier and longer seasons compared to the rest of the
state.11
Need for Study
Critical for the initiation of RSV prophylaxis with palivizumab is not only the knowledge
about a patient’s risk status but also about the appropriate timing and duration of
immunoprophylaxis. The CDC developed an RSV season definition that is based on the
proportion of positive RSV tests among all tests in its nationwide sample of laboratories. When
this proportion exceeds 10% in two consecutive weeks, season onset is assumed in the first
week. While this definition may seem reasonable, the relationship between this 10% threshold
and the burden of disease as measured, for instance, in RSV-related hospital admissions, has
never been formally established.
The NREVSS measures RSV activity through the use of population-based tests, thus
providing season estimates for the population as whole, albeit not necessarily representative.
Subjects tested for RSV are, naturally, patients with a suspected infection. The majority of
infections, however, occur among children at low risk,12
simply because they constitute the
largest part of the infant population. Yet, the definition is used to provide immunization
recommendations for high-risk infants. Susceptibility at periods of lower viral activity may
account for a different temporal pattern in RSV hospitalizations among high-risk children. Such
a scenario could occur if infants experience longer RSV seasons than older children.
Finally, state-specific surveillance suggests that the RSV season in Florida differs from the
rest of the nation with regard to onset and duration, which can be attributed to differences in
latitude and climate. Of note, when the CDC introduced its season definition, none of the 74
originally contributing laboratories were based in Florida.13
Seasonality in Florida has been
described as almost year-round in the southeast using the 10% definition, though with a large
14
variation in the absolute number in positive tests.11
This variation is likely correlated with
pronounced temporal differences in the burden of disease, but the actual RSV incidence rate for
each of the months above this threshold is unclear.14
Purpose of Study
By establishing a validated RSV season definition, this study can maximize the acceptance
of the CDC’s surveillance data. A definition with face and construct validity can help health
plans and providers select the optimal timing for RSV prophylaxis. The application of a
validated definition to different states provides knowledge about regional differences in the onset
and duration of the RSV season. Physicians can use this state-specific information to prevent
RSV infections most efficiently with immunoprophylaxis at the appropriate time.15
Since April 2008, the Florida Medicaid program has required prior authorization (PA) for
palivizumab. Florida Medicaid accounts for regional differences in seasonality by allowing
different periods of prophylaxis in different regions.16, 17
However, the regional validity of the
NREVSS and the 10% threshold may differ from the statewide validity due to a smaller number
of labs and therefore, RSV tests. This study provides information about the regional validity of
the surveillance system and the appropriateness of the different regional immunization
recommendations in Florida.
The investigation of patient factors as determinants of RSV seasonality was another
objective of this study. If the temporal occurrence of RSV infections differs between constituents
of the high-risk population, immunization recommendations may have to be adjusted.
Another objective of this study was to investigate past patterns of palivizumab utilization
and contrast these patterns with disease occurrence as measured with the current season
definition based on surveillance data, clinical information from claims data and a fixed
immunization schedule ignoring surveillance data. Therefore, we provide insight about whether
15
providers based timing of immunoprophylaxis on NREVSS data or on their clinical observations
of disease burden, or neither of these.
Since application of the season definition provides only dichotomous information and the
burden of disease may differ between months categorized as on-season, this study also provides
monthly incidences of RSV hospitalizations for children at high-risk together with a detailed
picture of the numbers needed to treat (NNT) with palivizumab to avoid one RSV
hospitalization. In addition, we provide NNTs for different age groups to further inform about
differences in the need for prophylaxis even among children with indications.
Overall, a validated season definition can provide the necessary evidence to guide
providers’ decision making about the optimal timing and duration of RSV immunization. From a
payer’s perspective, this study can aid in optimizing reimbursement policies as region-, month-
and age-specific NNTs can be used optimize RSV prophylaxis.
Research Questions and Hypotheses
This dissertation consists of six parts. Where formal statistical hypothesis testing applied,
statistical significance was assumed at an α level of 0.05. H0 refers to null hypothesis and HA to
alternative hypothesis.
Part I: Validation of CDC’s current RSV season definition
Research Question 1: Is the current RSV season definition used by the CDC able to
detect season onset and offset as measured by RSV-related hospital admissions in a pediatric
population in California, Florida, Illinois and Texas and in the 5 regions of Florida?
Research Question 1a: Is the use of percent positive laboratory tests at varying
thresholds able to discriminate between explicitly-defined weeks of high and low disease
incidence?
16
Hypothesis 1a: HA: The area under the receiver operating characteristics curve of percent
positive lab tests exceeds 0.7. H0: The AUC does not exceed 0.7.
Research Question 1b: Does the currently used threshold of 10% positive tests optimize
sensitivity and specificity?
Hypothesis 1b1: HA: Sensitivity at the optimal threshold is significantly higher than
sensitivity at the 10% threshold. H0: There is no significant difference.
Hypothesis 1b2: HA: Specificity at the optimal threshold is significantly higher than
specificity at the 10% threshold. H0: There is no significant difference.
Research Question 1c: Compared to using only the 10% or optimal threshold, can
additional criteria improve season definitions with regard to accuracy of detecting onset and
offset?
Hypothesis 1c1: HA: A definition with additional criteria significantly reduces the mean
absolute difference to season onset according to RSV hospitalizations compared to a definition
purely based on the threshold. H0: There is no significant mean difference.
Hypothesis 1c2: HA: A definition with additional criteria significantly reduces the mean
absolute difference to season offset according to RSV hospitalizations compared to a definition
purely based on the threshold. H0: There is no significant mean difference.
Part II: RSV epidemiology between four US states and five regions in Florida
Research Question 2a: Across 4 US States, does the degree of seasonality differ; i.e.,
how pronounced are differences in hospitalization rates during and outside seasons?
Hypothesis 2a: HA: At least one state’s seasonality index differs from any of the other
states’ indices. H0: there is no difference in the seasonality index between the four states.
17
Research Question 2b: Across 5 regions in Florida, does the degree of seasonality differ;
i.e., how pronounced are differences in hospitalization rates during and outside seasons?
Hypothesis 2b: HA: At least one region’s seasonality index differs from any of the other
regions’ indices. H0: there is no difference in the seasonality index between the 5 regions.
Research Question 2c: For each of the states and for the regions in Florida, do seasons
have a consistent pattern over time or do onset, peak and offset weeks fluctuate over time?
Hypothesis 2c1: HA: The range of season onset in one state/region over 5 seasons is
significantly shorter than 8 weeks. H0: The range can reach 8 weeks.
Hypothesis 2c2: HA: The range of peak weeks in one state/region over 5 seasons is
significantly shorter than 8 weeks. H0: The range can reach 8 weeks.
Hypothesis 2c3: HA: The range of season offset in one state/region over 5 seasons is
significantly shorter than 8 weeks. H0: The range can reach 8 weeks.
Part III: Latitude as a factor in RSV epidemiology in Florida
Research Question 3a: Do season onset, offset, duration, peak weeks or peak RSV
incidence rate differ between regions in Florida?
Hypothesis 3a1: HA: At least one region differs in week of onset from any other region.
H0: There is no difference in week of onset between regions in Florida.
Hypothesis 3a2: HA: At least one region differs in week of offset from any other region.
H0: There is no difference in week of offset between regions in Florida.
Hypothesis 3a3: HA: At least one region differs in season duration from any other region.
H0: There is no difference in season duration between regions in Florida.
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Hypothesis 3a4: HA: At least one region differs in timing of peak RSV activity from any
other region. H0: There is no difference in timing of peak RSV activity between regions in
Florida.
Hypothesis 3a5: HA: At least one region differs in the peak RSV incidence rate from any
other region. H0: There is no difference in the peak RSV incidence rate between regions in
Florida.
Research Question 3b: Are differences in season onset, offset, duration, peak week or
peak RSV incidence rate, if identified in research question 3a, a factor of latitude?
Hypothesis 3b1: HA: Latitude of the regions is associated with season onset. H0: Latitude
is not significantly associated with season onset.
Hypothesis 3b2: HA: Latitude of the regions is associated with season offset. H0: Latitude
is not significantly associated with season offset.
Hypothesis 3b3: HA: Latitude of the regions is associated with season duration. H0:
Latitude is not significantly associated with season duration.
Hypothesis 3b4: HA: Latitude of the regions is associated with timing of peak week. H0:
Latitude is not significantly associated with timing of peak week.
Hypothesis 3b5: HA: Latitude of the regions is associated with the peak RSV incidence
rate. H0: Latitude is not significantly associated with the peak RSV incidence rate.
Part IV: Patient factors and seasonality
Research Question 4a: Do risk factors affect the risk for RSV hospitalizations differently
during season compared to outside of the season?
19
Hypothesis 4a: HA: There is a significant interaction term between any of the risk factors
and an on-season/off-season indicator in the prediction of risk for RSV hospitalizations. H0:
There is no significant interaction.
Part V: Timing of prophylaxis with palivizumab vs. RSV seasonality
Research Question 5: For each state and for each region in Florida: is onset (offset) of
palivizumab utilization closest to season onset (offset) as defined by:
A. Observed hospitalization rates,
B. The NREVSS season definition, or
C. A fixed immunization schedule.
Hypothesis 5a: HA: There is a significant difference between the mean absolute
difference of A,B or C and onset of palivizumab utilization. H0: There is no significant
difference between the mean absolute difference of A, B or C and onset of palivizumab
utilization.
Hypothesis 5b: HA: There is a significant difference between the mean absolute
difference of A,B or C and offset of palivizumab utilization. H0: There is no significant
difference between the mean absolute difference of A, B or C and offset of palivizumab
utilization.
Part VI: Optimizing timing of prophylaxis
Research Question 6: For each state and for each region in Florida, what are the
incidence rates for RSV hospitalization and the NNTs with palivizumab for high-risk children in
each calendar month, taking patient age into account?
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CHAPTER 2
LITERATURE REVIEW
Respiratory Syncytial Virus
The respiratory syncytial virus was first described in 1956 and named chimpanzee coryza
agent (CCA) by Morris and colleagues who examined 20 chimpanzees with respiratory
symptoms at the Walter Reed Army Institute of Research.18
There, a laboratory worker who had
close contact with infected chimpanzees developed upper respiratory symptoms and was tested
positive for CCA antibodies. The same researchers identified CCA antibodies in a sample of
adolescents and young adults who were barrack mates of the worker, which suggests that they
may have experienced an infection with CCA or a closely related agent earlier. Accordingly,
another group of researchers hypothesized that the virus was of human origin and introduced to
the chimpanzees, causing the outbreak.19
One year later, in 1957, Chanock and colleagues
identified two respiratory viruses, named Long and Snyder after the hosting patients, and found
them indistinguishable from CCA. Due to their shared characteristic of producing syncytial areas
in tissue culture, the researchers grouped these agents together and coined the term respiratory
syncytial virus.19, 20
The RS virus is an enveloped, single-stranded RNA virus classified in the genus
Pneumovirus within the family Paramyxoviridae.21, 22
Two major strains of RSV, A and B, have
been identified and differ mainly in their attachment glycoprotein G. Both strains circulate
parallel, with A being the dominant strain.22
It has been suggested that strain A causes more
severe infections and fluctuation in circulating strains could explain variation in severity between
different seasons.23
21
RSV Disease Epidemiology
RSV is the most frequent cause of lower respiratory tract infections among infants and
children. By one estimate, RSV causes annually up to 125,000 hospitalizations for bronchiolitis
or pneumonia among children younger than 1 year in the United States.1 However, a more recent
study reports an annual number of 57,275 RSV hospitalizations for children under the age of 5
years.24
The same study further estimates that 2.1 million children under the age of 5 experience
RSV infections each year; 3% of these infections lead to hospitalizations, 25% are treated in
emergency departments and 73% in pediatric practices.
Annual RSV- related mortality for underlying pneumonia and influenza deaths has been
estimated as 3.1 per 100,000 infants younger than 1 year in the US, amounting to approximately
124 RSV-related deaths yearly.25
RSV is associated with all-cause death in 5.4 per 100,000
infant years or 214 deaths in this age group. Another study found that the majority of RSV-
related deaths is not associated with typical RSV risk factors. Specifically, among infants who
died from RSV-related causes before reaching the age of 5 years, only 9.9% had underlying
congenital heart disease, 5.5% chronic lung disease and 4.2% were born prematurely.
Consequently, the authors concluded that immunoprophylaxis of high-risk infants would not
prevent the majority of RSV-related deaths.26
The incidence of RSV-related deaths is lower for older children, adolescents and adults,
but increases with older age.25
Pneumonia and influenza deaths associated with RSV occur at a
rate of 7.2 per 100,000 person years above the age of 65 which amounts to 2,388 annual deaths.
RSV associated all-cause mortality was estimated at 29.6 per 100,000 person years or 9,812
annual deaths between the seasons 1990/91 and 1998/99. Eighty-eight percent of RSV-related
pneumonia or influenza deaths occurred in the elderly; however, this may be due to their overall
higher mortality rate.
22
RSV Infections
The clinical picture of RSV infections appears differently in neonates and infants
compared to older children and adults.21
Infections in the first 4-6 weeks of life are rare, which
may be related to the presence of maternal antibodies.21, 23
Infected children between 6 weeks
and 2 years of age usually develop symptomatic lower respiratory tract infections, including
bronchiolitis and pneumonia, but also acute otitis media. Symptoms typically appear after an
incubation period of 5 days and resolve after a few days to one week.21
Older children and adults
often experience mild to moderate upper respiratory tract infections which are consequences of
recurrent RSV infections, suggesting an incomplete acquired immunity after a primary
infection.22
Following RSV bronchiolitis, infant patients often exhibit recurrent wheezing up to
ten years after infection, and an increased incidence of asthma in children subsequent to RSV
hospitalization has been reported.21, 27, 28
Conversely, a recent study found that RSV infections
increased the incidence of asthma up to 8-fold only during the first 2 months after RSV
hospitalization, and there was no increased risk one year after the infection.29
Diagnostic Tests for RSV
RSV infections cannot be distinguished from non-RSV-related lower respiratory tract
infections based on clinical signs and symptoms, which necessitates the use of laboratory tests.
Three approaches to testing are available: cell culture, serology and examination of respiratory
secretions.30
Cell culture allows examination of viral co-infections and genetic and antigenic
change of the RS virus, but its long assay time of 2-5 days decreases utility in clinical practice.
The use of serology is limited by a large number of infected patients who remain serologically
negative. Most commonly, laboratories employ antigen-based assays because they are
inexpensive and easy to perform. In fact, all tests reported to the NREVSS are antigen-based.31
Different methodologies are available, including immunofluorescent antibody test, enzyme
23
linked immunoassay, direct immunoassay and optical immunoassay. Antigen detection is rapid
with a turn-around time of 15-30 minutes. The different methods of antigen detection vary in
sensitivity and specificity with a lack in these attributes if the viral concentration is low: in
elderly or immunocompromised patients and outside the main RSV season. A newer method for
analysis of secretions includes molecular essays using polymerase chain reaction. Due to high
sensitivity and very high specificity they are considered gold standard in diagnosing RSV, but
widespread utilization is limited by assay cost and labor-intensiveness.30, 32
Sensitivity and specificity of antigen-based tests: Sensitivity of antigen based tests can
vary between 59 and 98% with enzyme-linked immunoassay performing the poorest.30
Specificity can range between 75 and 100%; again 75% is associated with enzyme-linked
immunoassay. A study has found that sensitivity decreases with age: 72% sensitivity in a sample
of less than one year-olds compared to 19% among children older than 1 year.33
This study did
not identify differences in sensitivity and specificity with regard to RSV season for the younger
age group but a further drop in sensitivity outside the season among the older age group. Since
the test results reported to the NREVSS are not limited to infants under 1 year of age, it can be
assumed that sensitivity, especially at periods of low viral activity may be suboptimal. This
would result in an underestimation of RSV burden outside high-risk periods.
On an aggregate level however, respiratory lab tests from two single center studies,
including tests for RSV, correlated well with clinical case counts in the same populations.34, 35
Furthermore, Light et al. demonstrated that virus circulation as measured by the Florida
Department of Health (DoH) RSV surveillance system through laboratory tests parallels the
incidence of RSV hospitalization claims in the Florida Medicaid database.36
A correlation
24
between the percentage of positive lab tests and clinical burden of disease is essential for the use
of the NREVSS data to detect RSV season onset and offset.
The National Respiratory and Enteric Virus Surveillance System
The NREVSS monitors temporal and geographic activity of respiratory and enteric viruses.
These viruses include RSV, human parainfluenza viruses, respiratory and enteric adenoviruses,
rotavirus, and since 2007, rhinovirus, enterovirus and human metapneumovirus. Influenza
specimen information, also reported to NREVSS, is integrated with CDC influenza surveillance
data. Collaborating university, community hospital, commercial, and state and county public
health laboratories report virus detections, isolations, and electron microscopy report results on a
weekly basis. Annual summaries from NREVSS are published in Morbidity and Mortality
Weekly Reports (MMWR).37
The first reference to RSV surveillance by the CDC appeared in an MMWR from 1984
with qualitative rather than quantitative information and unclear temporal and geographic
coverage.38
A more systematic approach coincided with the first mentioning of the NREVSS in
1990 including data from 95 laboratories in 49 states.39
The same publication announced for the
season 1990-91 a switch from a monthly postcard reporting system to a weekly telephone-based
reporting system through a computer polling service with automatic tabulation allowing the
publication of available results in the following week.39
The information supplied by each
participating laboratory consists of the weekly number of specimens tested for RSV and the
number of positive tests. In 1993, the CDC provided a first definition for virus activity: weeks
with ≥10% of specimens tested positive.40
This definition was refined in the following season as:
onset is the “first of two consecutive weeks when at least half of participating labs reported any
RSV detections or isolations”.41
An update to this definition from 1998 is still in use: “when at
least half of labs report any RSV detections for at least 2 consecutive weeks and when greater
25
than 10% of all specimens (…) are positive”.42
Further changes to the system include treating
Florida separately from 2005 due to its unique RSV pattern 43
and using inclusion criteria for
participating labs for the 2006-07 season, requiring that each lab “reported ≥30 weeks of data,
tested ≥15 specimens per week during winter months and reported ≥2% of specimens positive
annually”. Lastly, from 2007-08, the system has experienced a substantial increase in the number
of participating laboratories by using commercially available data from Surveillance Data, Inc.,
which is supported by MedImmune, Inc., the manufacturer of palivizumab (see table 2-1).
RSV Seasonality
The RSV season spans from late fall to spring with peaks in November/December in most
countries of the northern hemisphere, but regions closer to the equator show less of a seasonal
variability with year-round case occurrences. Even within countries, RSV outbreaks differ based
on latitude and proximity to the coast.8 In the southern United States, the RSV season starts
earlier and lasts longer than in the rest of the nation.9, 10, 44
More specifically, the impact of
latitude and proximity to the coast may even be detectable within single states such as Florida,
with a considerable north-south extension and large coast line. In fact, longer seasons have been
reported for southeast Florida compared to other parts of the state based on surveillance data.11
Immunization recommendations are further complicated by annual variation in season
onset and duration in the United States, necessitating current information on viral activity.9, 44
A
biennial pattern of RSV seasonality has been indentified in Sweden, with earlier seasons
alternating with later seasons and higher hospital admission rates in earlier seasons.45, 46
A
similar pattern was found in Germany47
and Finland48
, however this biennial pattern has not been
shown for the US.
The CDC’s season definition has been widely applied.9-11, 13, 35, 36, 44, 49, 50
It has been
acknowledged that the 10% threshold is arbitrary49
and that its suitability for defining months for
26
RSV prophylaxis is unclear.51
Two studies supported by MedImmune applied a slightly modified
season definition (≥10% of tests positive in a given month) to the Florida DoH RSV surveillance
system11
and test data from three hospitals.49
These studies identified RSV activity almost year-
round in Florida, reaching epidemic levels in most months. An editorial published with the
former study found that even across time periods above this threshold, the absolute number of
cases is highly variable.52
More specifically, we found and expressed that, in selected months
(May through August 2001) the absolute statewide number of positive tests reported in that study
was less than 10 as compared to 143 in January indicating a large difference in the burden of
disease.14
A study from 1998 using laboratory data from two hospitals in Jacksonville, Florida
claimed to have used the CDC’s definition, however looking at the first of 2 months ≥10%
instead of the first of 2 weeks as the CDC does.10
This study reaches similar conclusions with
almost year-round RSV activity in Florida; yet again, many summer months show less than one
tenth of positive tests of some winter months and still exceed the 10% epidemic threshold. All
these observations highlight the problem that a dichotomous categorization into on-season and
off-season periods can mask large differences in the burden of disease even during season.
Whether the potentially much lower incidence of RSV in the summer months identified in the
three quoted studies warrants prophylaxis, despite exceeding the 10% threshold, is not clear.
RSV Prevention
While no vaccination is available for RSV,23, 53
two preventive agents, respiratory syncytial
virus immune globulin intravenous (RSV-IGIV, RespiGam, MedImmune, Inc., Gaithersburg,
MD) and palivizumab (Synagis, MedImmune, Inc.) are indicated to reduce the risk of RSV-
related hospitalization. RSV-IGIV was approved by the US Food and Drug Administration in
January 1996.54
The second-generation product, palivizumab, was approved in 1998. 55
Palivizumab is a humanized monoclonal antibody and can thus avoid the risk for infections
27
potentially associated with the older, pooled human blood product RSV-IGIV. Also, the smaller
injection volume and less burdensome intramuscular application of palivizumab contributed to
its replacement of intravenous RSV-IGIV from the market.55-57
As a consequence, the
manufacturer discontinued the production of RSV-IGIV at the end of 2003.56
A new agent for the prevention of RSV infections, motavizumab (MedImmune, Inc.), has
reached phase-III of drug development, and its sponsor submitted a Biologics License
Application to the FDA in January 2008.58
At this time, no published clinical trial results have
been identified as evidence for its efficacy.
Currently, palivizumab is the only available pharmaceutical product for the prevention of
RSV-related hospitalizations. Palivizumab has proven its efficacy in clinical trials, with varying
estimates for different indications. IMPACT-RSV reports a relative risk reduction for RSV
hospitalizations (RRR) of 39% for children with CLD, 78% for premature infants and 55% for
the combined group. 2 In another trial, children with CHD experienced a 45% reduction in RSV-
related hospitalizations.59
To date, a reduction in RSV-related mortality has not been
demonstrated for palivizumab.6, 60
The cost of RSV prevention with palivizumab is significant. The average wholesale price
of one 50mg vial is $926.48,61
sufficient for one monthly dose for a 3-kg infant at 15mg/kg. With
increasing age and body weight, higher monthly doses at higher total cost are required. One
study found that the average dose in a Medicaid population of 0-2 year-old children costs about
$1,700.5 If 6 doses are administered as is common in Florida, total cost of immunoprophylaxis
for one child averages more than $10,000 for a single season and significantly more if
palivizumab is administered year-round. Costs per avoided hospitalization are often found to
exceed expenses of the actual hospitalization by far, suggesting unfavorable cost-benefit.5, 62
In
28
the light of these considerations, recommendations limit immunoprophylaxis to patients at
highest risk for infection during seasons of high viral activity.6
RSV Risk Factors and Indications for Immunoprophylaxis
The American Academy of Pediatrics has defined indications that describe children at high
risk for RSV infections6 and recommends immunoprophylaxis for:
Children less than two years of age with chronic lung disease;
Children with a gestational age of less than 28 weeks if they are not more than 12 months
old at season onset (for this study: Prematurity I);
Children with gestational age of 29-32 weeks if they are not more than 6 months old at
season onset (Prematurity II);
Children with a gestational age of 32-35 weeks if additional risk factors such as day-care
attendance or smoking parents are present (Prematurity III);
Children with hemodynamically significant cyanotic and acyanotic congenital heart
disease;
Children with cystic fibrosis (CF); and
Children with severe immunodeficiencies which include severe combined
immunodeficiency (SCID) and acquired immunodeficiency syndrome (AIDS).
The last two indications are not based on strong evidence for effectiveness (expert opinion
only) and more carefully phrased in the guideline.
These recommendations are supported by several studies that report increased RSV
incidences in some of the above risk groups, yet to a varying extent. For instance, Boyce et al.
(2000) report a risk for RSV hospitalization in infants younger than 6 months with CHD of 120.8
per 1,000 infant-years of RSV season compared to 44.1/1000 for low-risk infants in the
Tennessee Medicaid population. In contrast, Duppenthaler et al. (2004) estimate a rate of only 25
RSV hospitalizations per 1,000 infant-years in the same age group with CHD compared to
29
18/1000 without CHD in Switzerland.12, 63
Not included in the AAP guideline, the presence of
Down syndrome has been suggested to be associated with increased risk for RSV infections.64
Furthermore, a few small sample studies suggest a more severe course of RSV infections in
immunocompromised children with liver transplant65, 66
or after chemotherapy for cancer.67, 68
Whether malignancy actually increases the infection risk for RSV is disputed,69
and conclusive
evidence is lacking.
Internationally, RSV prevention guidelines differ in scope; for example, the Swedish
guideline70
is more restrictive than the rather inclusive AAP guideline in the US.6 The former
recommends immunoprophylaxis for infants with extreme prematurity up to 26 weeks’
gestational age only up to 6 months of age or up to 24 months of age with CLD and prematurity
up to 36 weeks’ gestational age.
Prior Authorization Requirements
To limit expenditure for RSV immunoprophylaxis, third party payers typically restrict
reimbursement for prophylaxis to children who meet certain criteria and to a limited time period
of high risk. A common instrument is a requirement for PA where providers have to confirm the
presence of risk factors to the third party payer to request reimbursement. Reimbursement
policies in the 4 study states during the study period are detailed here:
California: California Medicaid has always required PA for palivizumab since its market
introduction in 1998. Requirements changed with updates in AAP guidelines (personal
communication, California Medicaid Pharmacy Benefit Division, 02/23/2009). The current PA
guideline is in agreement with the AAP guideline, with the exception that it allows for utilization
up to the age of 48 months in immunodeficient children. 71
It allows for the administration of up
to six doses between October and May.
30
Florida: Prior to 2008, Florida Medicaid did not restrict access to palivizumab for
children under the age of 2 years, neither with regard to risk factors nor timing of immunization.
Since April 2008, Florida has used a PA system that allows palivizumab utilization for high risk
children according to the AAP guideline for different time periods in 5 regions and up to year-
round in the southeast of Florida (personal communication with Anne C. Wells, Bureau Chief,
Florida Medicaid Pharmacy Services, 02/02/2009).17
The 5 regions are illustrated in figure 2-1;
an overview of the county composition of each region is provided in the appendix (table B-1).
Illinois: Until 2005, no PA requirement was in place for children under 4 years of age,
and immunoprophylaxis was not restricted to season-months. During this time, PA was required
for children older than 4 years of age. Of note, palivizumab has even been used for ventilated
children up to the age of 18 years. Since 2005, the PA requirement mirrors the AAP guideline
(personal communication with Brad Berberet, Assistant Director, Prior Authorization,
Department of Pharmacy Practice, University of Illinois at Chicago, 02/23/2009).
Texas: Texas Medicaid has had a PA requirement in place during the study period and
beyond. The PA was closely modeled after AAP guidelines, allowing prophylaxis for children
with CLD, prematurity or CHD during season months according to NREVSS (personal
communication with Judy Devore, Special Assistant to the Medicaid/CHIP Medical Director,
Texas Health & Human Services Commission, 02/24/2009).
31
Table 2-1. Historical landmarks in the NREVSS
Season Update to the surveillance system MMWR Reference
1983-84 RSV surveillance first mentioned in available MMWR,
no explicit season definition
01/1984 38
1989-90 “Expanded Surveillance System”: first reference to
NREVSS with 94 participating labs
Change from monthly postcard to weekly telephone-based
system announced for 1990/91
11/1990 39
1992-93 Definition of activity: “weeks with >10% of specimens
positive”
01/1993 40
1993-94 Onset: “first of two consecutive weeks when at least half of
participating labs reported any RSV detections or
isolations”
Offset: no definition
12/1993 41
1994-95 Definition as above, “This definition generally indicates a
mean percentage of specimens positive by antigen
detection in excess of 10%.”
12/1994 72
1997-98 Onset: “when at least half of labs report any RSV
detections for at least 2 consecutive weeks and when
greater than 10% of all specimens (…) are positive.”
12/1998 42
1998-99 Same as above, in addition:
Community outbreaks: “greater than 2 consecutive weeks
with greater than 10% positive tests, by city.”
12/1999 73
2005-06
2006-07
2007-08
Florida is added as a separate region.
National activity: defined as above (1997-98)
Regional activity: “median date that indicates the first of 2
consecutive weeks a participating lab reports >10%
(…) positive (…) and the last week >10% positive
tests preceding 2 consecutive weeks of <10%
positive tests.”
Labs have to meet inclusion criteria: “reported ≥30 weeks
of data, tested ≥ 15 specimens per week during
winter months and reported ≥2% of specimens
positive annually.”
National and Regional season
Onset:“first of 2 consecutive weeks during which the
median percentage of specimens positive for RSV
antigen is ≥10%”
Offset: “last of 2 consecutive weeks during which the
median percentage of positive specimens is ≥10%.”
Expansion of available lab data by inclusion of data from
Surveillance Data, Inc. for the season 2007-2008,
with support from MedImmune, Inc.
Labs have to meet inclusion criteria: “reported >30 weeks
and averaged >10 antigen detection tests per week.”
12/2006
12/2007
12/2008
43
74
31
32
Figure 2-1. Map of RSV regions in Florida,7575757475
reprinted with permission of the Florida
Department of Health
33
CHAPTER 3
METHODS
Datasets
This study was based on two datasets, a surveillance dataset and a clinical, patient-level
dataset based on medical and pharmacy claims. The surveillance dataset was comprised of
NREVSS RSV surveillance data for the states of California, Illinois and Texas, and DoH
surveillance data for the state of Florida. The clinical dataset consisted of Medicaid data for the
same four states, commonly referred to as Medicaid Analytic eXtract (MAX) data, provided by
the Centers for Medicare and Medicaid Services (CMS). The claims dataset for Florida was
merged with Vital Statistics’ birth certificates data to obtain gestational age estimates. Each
dataset covered the years 1999-2004, thus including 5 RSV seasons. The choice of the 4 states
provided a large sample size as well as geographic diversity with the intent to increase external
validity. Characteristics of the source datasets are described below.
NREVSS
The RSV component of the NREVSS collects weekly information on RSV tests from a
sample of laboratories as described in greater detail above. The resulting dataset consists of a
laboratory identification number and the city and county where the lab is located. Laboratories
report the test type, the number of total tests and the number of positive tests recorded for a given
week.
Florida Department of Health RSV Surveillance Data
As mentioned above, Florida had not been a separate part of the NREVSS before 2005,
and only a small number of Florida based laboratories reported to the CDC before that. To
investigate seasonality on a regional level within the state, these data had to be supplemented.
The Florida DoH has its own RSV surveillance system in place, using the same laboratory
34
survey approach as the CDC, but with a larger sample in Florida. Geographic detail is provided
at the city level and for 5 regions within the state (figure 2-1, see appendix for the regional
classification of counties).75
Medicaid Analytic eXtract Claims Dataset
For each of the four states, eligibility, inpatient, outpatient and pharmacy datasets for
Medicaid recipients 0-2 years of age were requested. Eligibility and demographic information
was updated for each month. The MAX dataset has already been reconciled by CMS to display
for each transaction a “final action claim” which eliminates the need to remove duplicate claims
that are generated as results of reimbursement negotiations. Since MAX data do not include
claims for managed care enrollees, we restricted the study cohort to children who were only in
the fee-for service (FFS) or primary care case management (PCCM) program. Furthermore, if
potential study subjects were enrolled in a behavioral or dental plan, we included them if they
were also FFS or PCCM enrollees. We refer to this entire group as the FFS sample.
Internal Validity: The validity of Medicaid claims data with regard to measuring the
incidence of RSV-related hospitalizations has not been investigated. Several articles comment on
the general utility and limitations of Medicaid data in epidemiologic research. The greatest
strength lies in the size of the represented population, allowing for the investigation of effects of
rare exposure or rare outcomes with the ability to adjust for multiple confounders.76, 77
With
regard to diagnostic validity, it has been recognized that hospital-based information is superior to
other Medicaid datasets.76
Kiyota et al. (2004) calculated a positive predictive value of 94.1% for
Medicare administrative claims of discharge diagnoses for acute myocardial infarction validated
against hospital records.78
Another study looked at gross diagnostic errors in Medicaid data such
as childbirth or pregnancy-related codes among recipients older than 60 years and concluded that
these errors were not widespread in Medicaid claims datasets.79
Members of that study group
35
also looked at Medicaid data obtained from CMS, including our study states California and
Florida and investigated longitudinal patterns of inpatient and prescription claims to identify
breaks. They found a linear trend in these claims, suggesting no paucity of data.80
Furthermore,
they found that the proportion of prescriptions with a valid national drug code (NDC) ranged
from 97-99% between the states indicating a high degree of coding validity.
RSV hospitalizations were chosen over alternative endpoints such as emergency
department visits, physician office visits or death for consistency with the existing literature, the
label of palivizumab which lists the prevention of RSV-related hospitalizations as a primary
indication, and the public health importance of RSV hospitalizations as outlined above.
State Birth Certificates
Our approach to detecting RSV seasonality in the study sample required the establishment
of high- and low-risk cohorts of Medicaid recipients with regard to their propensity for RSV
infection. Part of the high-risk classification used gestational age to define premature birth.
Medical claims data are not a valid source of gestational age; therefore, we supplemented this
information with birth certificates.
Birth certificate data are collected within in 24-48 hrs after birth by the hospital, which is
where 99% of births in the US are delivered.81
Gestational age is calculated based on the
mother’s report of the last menstrual period (LMP) or, when missing, imputed from the clinical
estimate (CE) at birth. LMP and CE based estimates were found to agree within 2 weeks in
89.1% of infants. A study based in California validated LMP estimates against a CE estimate
using ultrasound at week 15-20 and found that LMP has a false-positive rate of 15% for
identifying preterm birth and a missed true cases at a rate of 20.5%.82
Although not perfect, this
level of accuracy is acceptable for our study, and vital statistics birth data are the only sources for
36
gestational age estimates on a population level. Birth certificates were matched to Medicaid
eligibility data based on social security number and date of birth or name and date of birth.
Study Population
To be included in the claims dataset, children had to be younger than 2 years of age with
continuous eligibility to Medicaid between birth and the current week. For Florida, the
availability of a birth certificate with a gestational age between 15 and 50 weeks was further
required to exclude invalid estimates. Subjects had to be in ambulatory care for at least 4 weeks
before the start of each week examined. This requirement ensured the ability to detect
immunoprophylaxis in ambulatory care since prophylaxis administered in the hospital is not
identifiable from claims data due to the aggregate nature of inpatient charges in the Medicaid
dataset. As a consequence, this requirement essentially removed the first month of life from the
analysis. As mentioned before, the first 4-6 weeks of life have a lower RSV incidence.
A range of risk factors for RSV infection have been discussed in the literature and are
described above. To avoid misclassification, high-risk was defined according to the consensus
that led to the indications for immunoprophylaxis based on grade-I evidence in the AAP
guideline.6 To create a low-risk category, we excluded all children with any potential clinical risk
factors for RSV described in the literature. To address the paucity of evidence for some of these
risk factors, we constructed a logistic regression model based on the Florida dataset with RSV
hospitalization as the dependent variable (equation 3-1, for operational definitions of each
variable, see appendix). Adjusting for demographic characteristics, calendar year and month as
well as the presence of immunoprophylaxis, we determined the contribution of each potential
risk factor to the risk for RSV hospitalizations. The model used subject-weeks as the unit of
analysis.
37
Logit [P(Y=1)] = α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 + β9X9
+ β10X10 + β11X11 + β12X12 + β13X13 + β14X14 + β15X15 + β16X16
+ β17X17 + β18X18 + β19X19 (3-1)
Where:
Y: RSV hospitalization
X1: Calendar year (1999-2004)
X2: Calendar month (1-12)
X3: Age (0-6, 7-12, 13-24 months)
X4: Sex
X5: Race (White, Black, Native American, Asian, Hispanic, Unknown)
X6: Birth month (1-12)
X7: Prematurity I
X8: Prematurity II
X9: Prematurity III
X10: CLD
X11: CHD
X12: CF
X13: Immunodeficiency
X14: Down syndrome
X15: Asthma
X16: Transplant
X17: Malignancy
X18: Immunosuppression
X19: Palivizumab prophylaxis
The model created (t-1) dummy variables (where t = the number of levels) for each
categorical variable
Based on the results of the logistic regression analysis, a low-risk category was created,
excluding all clinical factors that exhibited a trend towards an association with increased risk for
RSV infections, even if statistical significance was not met. Subjects were excluded based on
clinical factors and not demographic characteristics that may also have been associated with
RSV infections. An exclusion based on demographic risk factors, potentially excluding boys who
are at higher risk than girls, would have led to a cohort that lacks comparability to the high-risk
cohort and reduces generalizability of the study’s findings. We refer to the excluded children as
the elevated risk cohort and present their immunization and RSV hospitalization rates for
comparison.
38
The high-risk cohort included (operational definitions in the appendix):
Children younger than 2 years with CLD who received medication for CLD within 6
months of the current week; and
Children younger than 2 years with hemodynamically significant cyanotic or acyanotic
congenital heart disease.
In addition in Florida:
Children with a gestational age of less than 28 weeks if they were not older than 12
months at the beginning of the current week; and
Children with gestational age of 29-32 weeks if they were not older than 6 months at the
beginning of the current week.
The low-risk cohort included all children who were not part of the high-risk cohort at a
given week and who did not have any of the following between birth and the current week:
Cystic fibrosis,
Severe combined or acquired immunodeficiency,
Down syndrome,
Asthma,
Transplant,
Immunosuppression and
Malignancy.
In addition in Florida:
Gestational age of 32-35 weeks and less than 6 months of age at the beginning of the
current week.
We included asthma only if, in addition to a diagnostic code, a prescription for an asthma
medication was billed between 120 days and 14 days before the current week. This ensured that
the condition was current and prevented the exclusion of children who may have had asthma
codes only as a consequence of early symptoms of an RSV infection.
39
The remainder of the methods section addresses each of the parts I-VI outlined in the
introduction. Unless otherwise specified, SAS 9.1.3 (SAS Institute, Cary, NC) was used for data
analyses and graphs were created in Microsoft Excel 2007 (Microsoft Corp. Redmond, WA).
This study has received approval from the institutional review boards of the University of
Florida, CMS and the Florida DoH.
Part I: Validation of CDC’s Current RSV Season Definition
Calculation of RSV incidence rates: To detect RSV seasons from claims data, weekly
incidence rates among low-risk and high-risk infants were calculated. This calculation was
complicated by the fact that immunoprophylaxis differentially reduces the incidence rate of RSV
hospitalizations for high-risk versus low-risk children due to different rates of palivizumab
utilization. Simply excluding palivizumab recipients would have distorted the cohorts since the
antibody is not given at random but targeted to higher-risk patients, even within the high-risk
cohort. Therefore, two cohorts of pseudo non-recipients were created as if none had received
prophylaxis. The incidence rate in each cohort was based on the assumption that prophylaxis
reduced the relative risk of an RSV hospitalization by 50%, the mean of the 45% and 55% RRR
shown in the clinical trials.2, 3
As a result, the observed incidence rate of the exposed was
assumed to be half the incidence rate had they not been exposed. The corrected weekly incidence
rate (Ic) for each cohort was calculated from the number of cases among the truly unexposed
(AU) and a corrected (1/RRR) case-number among the exposed (AE) divided by the sum of
person time for the unexposed (NU*TU) and the exposed (NE*TE) (equation 3-2).
(3-2)
AU + AE* 1/RRR
NU*TU+NE*TEIc =
40
The denominator for the incidence rate was the number of children eligible in a given
week; the numerator was the number of weeks with RSV-related hospital admissions identified
by inpatient claims with ICD-9 CM codes (International Classification of Diseases, 9th Revision,
Clinical Modification) for RSV infections (see appendix). We identified palivizumab exposure as
the presence of a claim for palivizumab (based on NDC or procedure codes, see appendix)
between 28 days before and 3 days after the current week’s start date. This ensured that a subject
was exposed for at least half of the current week.
Season definitions: A key component in the validation of a season defined by laboratory
tests is the definition of a gold standard for comparison. Since the intended use of the
surveillance system is to detect a clinically significant outbreak in the population, a population-
based standard derived from the Medicaid claims dataset was needed. This standard had to
provide estimates for season onset and offset which required their own definitions.
The American Academy of Pediatrics’ Committee on Infectious Diseases and Committee
on Fetus and Newborn recommends immunoprophylaxis only for high-risk children during the
RSV season.6 Consequently, children at low risk for RSV do not have an indication for
prophylaxis at any time of the year. Thus, it can be argued that the peak RSV incidence rate
among low-risk children does not reach a level that is considered high-risk. Therefore, the peak
incidence of low-risk children shall be considered a threshold below which RSV risk is
considered “low”. The season for high-risk children was defined for this study as every week
where the RSV incidence rate among high-risk children exceeded this threshold. To attenuate the
impact of outliers, we used a 2-period central moving average that calculated a current week’s
RSV incidence rate as the average of the preceding and following 2 weeks’ incidence rates,
excluding the current week’s rate. Season onset was defined at the first of two consecutive weeks
41
where the high-risk incidence exceeded the peak of the low-risk incidence. Accordingly, season
offset occurred at the last week before two consecutive weeks where the high-risk incidence was
below the peak incidence of the low-risk cohort (figure 3-1).
We used this approach for statewide data in the 4 states; however, the smaller size of the
high-risk cohort in the individual regions of Florida would have introduced significant
misclassification. To illustrate, with a small denominator, even a single RSV case could have led
to a large RSV incidence, causing us to consider the corresponding week as an on-season week.
To overcome this challenge, we used a different approach to identify clinical RSV seasons in the
regions of Florida. For each year (July-June) we extracted the overall RSV incidence rate at
which the statewide model detected season onset in Florida. We also extracted the incidence rate
at season offset and considered the average of the onset and offset incidence rate as the threshold
incidence for that season. This threshold was applied to each region in Florida. Season onset was
defined as the first of two consecutive weeks above this threshold. Accordingly, season offset
was defined as the last week before two consecutive weeks below this threshold.
Validation of the NREVSS RSV season definition: For this analysis, we restricted the
observation period to include 5 full seasons. A year started in July (week 27) and ended in June
(week 26), thus avoiding partial RSV seasons at the beginning and end of the observation period.
In Florida, we were only able to observe 4 seasons (starting July, 2000) because DoH
surveillance data were not available before the week 1999-42. Each week of the observation
period was categorized as on-season/off-season based on the claims dataset as gold standard and
compared to the median proportion positive (MPP) RSV tests from the surveillance dataset. The
MPP was calculated as the value that divided the laboratories into 2 halves with regard to
proportion positive in a given state/region for a given week.
42
When testing the validity of the season definition, two concepts have to be delineated from
each other and were tested separately:
Is the test (e.g. MPP lab tests) able to differentiate weeks of clinical high burden from
weeks of low burden of disease?
Is the threshold (e.g. 10%) the right choice to optimize sensitivity and specificity for
correctly identifying RSV seasons?
To answer these questions, receiver operating characteristic (ROC) curves were plotted
with test sensitivity on the y-axis and 1-specificty on the x-axis. In addition, separately for each
state, and for regions in Florida, the following 4 test characteristics were calculated (figure 3-2):
Sensitivity: among true on-season weeks, % of weeks classified as on-season
Specificity: among true off-season weeks, % of weeks classified as off-season
Positive Predictive Value (PPV): among weeks classified as on-season, % true on-season
weeks
Negative Predictive Value (NPV): among weeks classified as off-season, % true off-
season weeks
For each observed MPP in each week, the resulting (specificity, 1-sensitivity) point was
plotted and connecting all points yielded the ROC curve. An important attribute of ROC curves
is the area under the curve (AUC). The AUC is a measure of accuracy of a test and varies
between 0.5 for a non-discriminating test (a random guess) and 1.0 for a test with perfect
accuracy.83
A classification of test accuracy has been commonly applied (figure 3-3)84-86
and
defines an AUC above 70% as fair.
Separately for each state and region, we calculated the AUC and concluded that the MPP
was adequate if its discriminatory ability exceeded AUC ≥0.7. Next, we identified the optimal
threshold of MPP. For the detection of RSV seasonality and optimization of resource usage, it
would be equally important to detect on-season weeks as such (sensitivity) as it is to detect off-
season weeks as such (specificity). Therefore, we identified the optimal threshold as the point
43
where sensitivity and specificity are optimized simultaneously. The optimum is part of the output
of a SAS macro that we used for our analysis.87
Confidence intervals for sensitivity, specificity,
PPV and NPV were calculated using the Wilson score method.88
We tested whether the optimal
threshold provided significantly higher sensitivity or specificity than the currently used 10%
MPP threshold by inspecting the 95% confidence intervals for overlap.
The analysis up to this point can only give information about the accuracy of the observed
MPP as a predictor of an RSV season for a randomly-chosen week. However, the season
definition as used by the NREVSS has additional requirements such as onset is the first of two
consecutive weeks where the 10% threshold is exceeded. With this requirement, the MPP
threshold is not the only predictor. Therefore we tested the accuracy of the actual season
definition with the added requirements. We also tested alternative definitions with more
requirements, namely that in a given state/region and week, at least 3 or at least 5 tests have to be
positive. With this approach we aimed to eliminate the impact of outliers especially at times of
infrequent testing. For all these potential definitions we tested two thresholds: 10% MPP and the
optimal MPP for each state/region as derived from the ROC analysis. To test accuracy, we
calculated the mean of absolute differences between season onset and offset according to each
definition and according to RSV hospitalizations. We used one-way analysis of variance to test
whether any of the definitions differed from the others with regard to accuracy. To inform about
the temporal direction of the difference, we also calculated the mean of actual instead of absolute
differences.
Part II: RSV Epidemiology between Four US States and Five Regions in Florida
For each state and for each region in Florida, we calculated a seasonality index based on
seasons according to RSV hospitalizations. This seasonality index is the incidence rate ratio of
44
the incidence rate of RSV hospitalizations during season divided by the incidence rate outside of
the season. Again, we used the Wilson score method to calculate 95% confidence intervals.88
To analyze annual variability of seasons within a state/region, we calculated the mean
weeks of onset, peak and offset and their standard deviations. We tested the hypothesis that the
range of season onset (peak, offset) in one geographical area does not exceed 8 weeks, a range
that we considered low variation. The model we used tests whether the week of onset (peak,
offset) is uniformly distributed over a k-week period, and we used the actual standard deviation
to construct a 95% confidence interval for k as follows. A simulation calculated standard
deviations of 100,000 samples of 5 values each from a range of 1-k. This simulation provided
standard deviations under the null hypothesis that onset (offset, peak) occurs randomly in a range
of k weeks. The proportion of occurrences where the actual standard deviation of season onset
(offset, peak) is smaller than the simulated standard deviation under the null hypotheses provides
evidence against the null hypothesis. For each geographic area, we varied the value of k until the
proportion of simulated standard deviations that were smaller than actual standard deviations was
less than 5%. The resulting k was then interpreted as the upper bound of a one-sided 95%
confidence interval. If the upper bound was larger than 8, we rejected the alternative hypothesis
that season offset (peak, offset) has a range of not more than 8 weeks.
Part III: Latitude as a Factor in RSV Epidemiology in Florida
For each region in Florida, we used a one-way analysis of variance to examine separately
whether at least one region differs from any other region in the parameters week of onset, offset
and peak, season duration and peak RSV incidence. Next, ArcGIS 9.1 (ESRI, Redlands, CA) was
used to determine coordinates of centroids of the five surveillance regions in Florida to allow
assessment of the influence of latitude on the season parameters above. We only included season
parameters where the analysis of variance showed significant variation between regions. Latitude
45
of the centroids (table B-2, appendix) served as an independent variable (X) in a linear regression
model with week of onset (offset, peak) or duration (in weeks) or peak RSV incidence rate as
dependent variable (Y):
Y= α + β*X (3-3)
The slope of the regression equation (β) was interpreted as change in timing of onset
(offset, peak) relative to change in latitude. One degree increase of latitude corresponds to
moving 69 miles (111km) away from the equator.89
JMP 7.0.2 (SAS Institute, Cary, NC) was
used to conduct the analysis and create graphs.
Part IV: Patient Factors and Seasonality
This analysis determined whether high-risk indications increase the odds for RSV
hospitalization differently in off-season versus on-season periods. Subject-weeks for each state
and on a regional level in Florida were categorized as on-season or off-season according to RSV
hospitalizations as described in part I. To gain statistical power, we concatenated all states into a
single dataset. We created a logistic regression model (equation 3-4) including all subject-weeks
where children were in ambulatory care in the 4 weeks preceding the first day of the current
week. Apart from the RSV risk factors as described above (equation 3-1), the model further
included interaction terms between the season indicator and the high-risk indications CLD and
CHD to determine whether these factors predict the probability of RSV infection differentially
between on-season and off-season periods. We further included interaction terms between the
season indicator and age (0-6, 7-12 and 13-24 months) since age is another strong predictor for
RSV hospitalization.
Logit [P(Y=1)] = α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 + β9X9
+ β10X10 + β11X11 + β12X12 + β13X13 + β14X14 + β15X15 + β16X16
+ β17X17 + β18X18 + β19X1X4 + β20X1X5 + β21X1X9 + β22X1X10
(3-4)
46
Where:
Y: RSV hospitalization
X1: RSV season (yes/no)
X2: Calendar year (1999-2004)
X3: Calendar month (1-12)
X4: Age 0-6 months
X5: Age 7-12 months
X6: Sex
X7: Race (White, Black, Native American, Asian, Hispanic, Unknown)
X8: Birth month (1-12)
X9: CLD
X10: CHD
X11: CF
X12: Immunodeficiency
X13: Down syndrome
X14: Asthma
X15: Transplant
X16: Malignancy
X17: Immunosuppression
X18: Palivizumab prophylaxis
X19: Interaction RSV season * Age 0-6 months
X20: Interaction RSV season * Age 7-12 months
X21: Interaction RSV season * CLD
X22: Interaction RSV season * CHD
The model analysis created (t-1) dummy variables (where t = the number of levels) for
each categorical variable
For each of the variables included in interaction terms, we calculated odds ratios and 95%
confidence intervals stratified by off-season and on-season and plotted the results in a forest plot
to facilitate comparison (SigmaPlot 10.0, Systat Software, Inc., San Jose, CA).
Part V: Timing of Prophylaxis with Palivizumab vs. RSV Seasonality
From the Medicaid pharmacy claims dataset separate by state, region and year, the weekly
number of palivizumab prescriptions was identified (NDC and procedure codes: see appendix)
and divided by the number of children eligible to the study in that week. We detected onset of
utilization as the first of two consecutive weeks where the utilization rate exceeded 50% of the
peak utilization rate in the respective geographical area for that season (July-June). We chose
47
50% over any lower threshold to detect strong population coverage. Offset was defined as the
week preceding two consecutive weeks with a utilization rate below 50% of the peak utilization
rate. Annual patterns in utilization were contrasted with seasons identified from:
Observed RSV hospitalization rates,
The current 10% threshold of the NREVSS season definition, and
A fixed immunization schedule.
For the latter, we used the first week of October and the first week of November as
comparators for utilization onset and the last week of March and April as comparators for
utilization offset. To determine which of these comparators is closest to the onset (offset) of
utilization, we calculated the mean absolute difference between utilization onset (offset) and
RSV season onset (offset) according to the comparators. Confidence intervals for means were
calculated using the t-distribution with n-1 degrees of freedom instead of the z-distribution to
account for the small number of observations.90
One-way analysis of variance was used to test
whether any of the comparators was closer to utilization onset (offset) than any of the others and
we used the lowest point estimate of the mean of absolute differences to determine which of the
comparators was most likely to have triggered immunoprophylaxis. To inform about the
temporal direction of the difference, we also calculated the mean of actual instead of absolute
differences.
Part VI: Optimizing Timing of Prophylaxis
The goal of this analysis was to provide information on the actual burden of disease in
high-risk children for each calendar month. We calculated monthly RSV-hospitalization
incidence rates for high-risk children in the Medicaid claims dataset as follows: the denominator
was defined as the number of subject-months; the numerator as the number of subject-months
where at least one RSV-related hospital admission identified by inpatient claims occurred. As
48
described above in part I, a corrected incidence rate (here monthly) was calculated to account for
a reduced RSV incidence as a consequence of immunoprophylaxis.
We calculated the NNT according to equation 3-5:
(3-5)
The absolute risk reduction (ARR) was calculated based on the incidence rate multiplied
with the RRR, for which we assumed the effectiveness estimate of 50% as described in part I.
For each state, we created a matrix of monthly RSV incidence rates and of monthly NNTs
for three age categories: 0-6 months, 7-12 months and 13-24 months. We also calculated RSV
incidence rates and NNT for the regions in Florida, but we collapsed the age categories into a
single category since the case numbers were too small for a more detailed analysis.
NNT=1
absolute risk reduction
49
Figure 3-1. Season detection based on clinical dataset
For each week: Season according to RSV hospitalizations
Yes No
Season according
to surveillance
Yes A - True positive B - False positive PPV: A/(A+B)
No C - False negative D - True negative NPV: D/(C+D)
Sensitivity: A/(A+C) Specificity: D/(B+D)
Figure 3-2. Calculation of test characteristics
Low risk
infants
High risk
infants
Season Onset Season Offset
Low risk
threshold
Timeline: calendar weeks
Wee
kly
RS
V h
osp
ital
izat
ion
in
cid
ence
50
Area under the curve
1 Perfect
0.9-<1 Excellent
0.8-<0.9 Good
0.7-<0.8 Fair
0.6-<0.7 Poor
<0.6 Fail
Figure 3-3. Cut-off values for areas under the ROC curve
51
CHAPTER 4
RESULTS
Sample Characteristics
After applying all inclusion and exclusion criteria, the final sample consisted of a total of
109,665,551 subject-weeks from 2,654,647 children (figure 4-1). In California, the final FFS
sample with 4 weeks of ambulatory care preceding the current week represented 40.4% of the
original population of all Medicaid recipients born between 1999 and 2004. In Florida, birth
certificates were matched to 79.1% of subjects, and the final sample represented 55.6% of the
original population. In Illinois and Texas, the final sample retained 77.4% and 62.0% of the
original populations, respectively. The number of continuously-eligible weeks in California was
smaller than in the other states, suggesting that with increasing age, more children were moved
to managed care and therefore lost study eligibility, which is also evidenced by the younger
average age in California compared to other states (table 4-1).
Results from the logistic regression model based on the Florida dataset confirm that the
AAP indications CLD, prematurity I and II, and CHD are associated with a significant increase
in risk for RSV (table 4-2). Cystic fibrosis, Down syndrome, asthma and immunosuppression
were also associated with an increased risk for RSV. Transplants, malignancy and
immunodeficiency did not reach statistical significance since their 95% confidence intervals
included unity; however, case numbers were small. Since all the above factors had point
estimates above 1 and may therefore be associated with increased risk, we categorized subjects
with any of these risk factors into the cohort of elevated risk if they did not fall into the high-risk
cohort.
Cohort characteristics are presented in table 4-1. Briefly, the proportion of males was
similar across states at about 51%, but the racial compositions differed widely. While 61.9% and
52
65.4% of the California and Texas sample were Hispanic, this applies to less than a third of the
samples in Florida and Illinois, where the proportions of Whites and Blacks were higher than in
California or Texas. The average age was comparable in Florida, Illinois and Texas; but age was
lower in California. Between the four states, differences in the incidence of RSV risk factors
were present. All risk factors except Down syndrome were less prevalent in California, an
observation that may be related to the younger average age if some of the risk factors become
apparent later in life.
Results of the incidences of RSV hospitalizations and palivizumab exposure by risk
category can be found in table 4-3. Consistent with the description of sample demographics, we
found fewer children at high risk in California. Florida had the largest proportion of high-risk
children (2.51%) which was a consequence of including prematurity as a risk factor only in
Florida. The highest incidence of palivizumab exposure among high-risk children was also
observed in Florida, potentially as a result of longer seasons. The overall incidence rates of RSV
hospitalizations were fairly consistent between the states ranging from 0.09% of high-risk weeks
with an RSV hospitalization in Illinois to 0.12% in Florida and Texas. A similar pattern was
observed for children at elevated risk, their proportion was smallest in California and largest in
Florida, which also had the highest rate of palivizumab injections in this cohort. The number of
RSV hospitalizations was again similar between the states, ranging from 0.04% of weeks at
elevated risk in California and Illinois to 0.05% in Florida and Texas. Finally, the largest
proportion of children at low risk was found in California, palivizumab exposure among low risk
children was highest in Texas and the incidence of RSV hospitalizations was again similar across
states at 0.03% in Illinois and 0.04% in the other states.
53
Part I: Validation of CDC’s Current RSV Season Definition
Weekly incidence rates of RSV hospitalizations for the high-risk and low-risk cohorts
together with the resulting RSV seasons are plotted in figure 4-2 for each state. We observed a
pattern of very distinct seasons in California with almost no activity outside of the season.
Florida experienced longer and less regular seasons with residual activity outside the seasons.
RSV seasons in Illinois and Texas seemed more consistent over time compared to Florida. Texas
showed some activity outside the season although much less pronounced than in Florida.
Figure 4-3 presents the weekly RSV incidence rates and seasons based on hospitalizations
for all children regardless of risk category in the regions of Florida after applying the seasonal
incidence thresholds derived from the high-risk/low-risk comparison on the state level. The
northwest, north and central regions showed a distinct seasonal pattern with few cases off-
season. The southwest showed a less regular pattern with periods of lower activity that were,
nevertheless, considered on-season. Finally, the southeast region experienced the least
pronounced seasonality: periods of higher and lower activity were detectable, yet again some
periods with lower activity were still considered on-season. For each of the Florida regions, the
incidence rate of RSV hospitalizations reached the highest peak in the first study season (99/01)
with smaller peaks thereafter.
Figure 4-4 summarizes the surveillance dataset for the 4 states. In the figure, the 10% MPP
threshold is highlighted in red, and the optimal threshold, as explained below, in green. Only 4
seasons are displayed for Florida since state-specific surveillance was initiated later.
Observations correspond to results of the RSV seasonality according to hospitalizations.
California experienced very concise seasons with almost no activity outside of the season despite
year-round testing. Florida experienced longer seasons with some activity outside of the season.
Illinois and Texas also showed a distinct seasonal pattern; however, some outliers with a high
54
MPP outside of the season could be traced back to single positive tests during periods where
testing was infrequent.
Figure 4-5 shows results of surveillance in the regions in Florida. Only 3 seasons were
available in the northwest and 4 in each of the remaining regions. This figure illustrates the
difficulty of establishing a clear-cut season when the frequency of tests was low, for example in
the north and northeast regions. Here, off-season periods coincided with a number of outliers
with a high MPP that were again a consequence of few positive tests at times of rare testing.
Conversely, in the southwest and southeast regions, tests were performed year-round which
reduced the impact of outliers outside the season. Nevertheless, the southeastern region did
experience some RSV activity year-round even though the 10% MPP threshold was not always
reached.
Results of the ROC analysis are presented in figure 4-6 for the 4 states and in figure 4-7 for
the regions in Florida. Highlighted are specificity/1-sensitivity pairs for the currently used 10%
threshold and the point that optimizes sensitivity and specificity. The corresponding point
estimates and 95 % confidence intervals of AUCs are listed in table 4-4. With the exception of
Illinois, the statewide AUCs reached the threshold of excellence (>0.9). In Florida, none of the
regional AUCs reached the statewide AUC. In the north, southwest and southeast regions, AUCs
were statistically significantly lower than the statewide estimate. All regional estimates fell into
the “good” category with the exception of the southeast, which performed only fair with the
lower bound of the 95 % confidence interval reaching into the category “poor”.
Table 4-5 shows further test characteristics of the approach of using 10% MPP to identify
RSV seasons. Here again, statewide tests performed well with sensitivity and specificity
exceeding 0.70, however the north, central, southeast and southwest regions of Florida
55
experienced poor test specificity indicating that approximately half of the off-season weeks were
categorized as on-season by using the 10% MPP threshold as the sole criterion.
Optimal MPP thresholds as a result of the ROC analysis are presented in table 4-6. With
the exception of Florida, they lie below the 10% threshold. Both sensitivity and specificity were
improved for the regions in Florida, yet sensitivity in the southeast and specificity in the north
regions were still less than excellent. The small NPV value for the southeast and southwest
regions did not improve with the use of the optimal threshold. The small NPV indicates that
many weeks categorized as off-season were in fact on-season weeks, but since the number of off-
season weeks was small in these regions, the absolute amount of misclassification by the
surveillance system is limited.
To evaluate the validity of the NREVSS for indicating RSV seasons, we investigated how
the definition has been applied, specifically with the requirement that two consecutive weeks had
to exceed the threshold to start a season. Table 4-7 shows the accuracy of this approach and
reports the mean difference and direction of difference between season onset according to
NREVSS compared to onset based on RSV hospitalizations. The currently used 10% threshold
differed on average by 4.6 weeks (95% CI, 0.2 - 9.1) from the actual season onset in the states.
This difference was not systematic as evidenced by the direction of -0.6 weeks which means that
on average, NREVSS onset preceded the RSV hospitalization onset by 0.6 weeks. In fact, none
of the definitions differed systematically in a certain direction when applied statewide as
indicated by direction estimates for the mean statewide difference that did not exceed 1 week in
either direction. In the regions of Florida, season onset according to the current season definition
differed by 6.1 weeks (95% CI, 0.3 - 11.8) from the actual onset and preceded it on average by
4.6 weeks. Using the optimal threshold for each state and region did not lead to major
56
improvement, however adding further requirements did. Using the 10% MPP threshold in
addition to requiring at least 5 positive tests in a given week for a surveillance area improved
accuracy to a mean difference of 3.4 weeks (95% CI, 0.0 - 7.2) in the states and 4.5 weeks (95%
CI 0.0 - 9.8) in the regions of Florida. Using the optimal MPP threshold with the requirement of
5 positive tests led to a further but marginal improvement to 3.2 weeks (95% CI, 0.0 - 7.0) in
states and 3.9 weeks (95% CI, 0.0 - 9.3) in Florida’s regions. Nevertheless, these differences did
not reach statistical significance. Table 4-8 shows a similar pattern for the prediction of season
offset with the added requirement of 5 positive tests performing the best but differences between
the definitions were less pronounced. Of note, with a mean difference exceeding 6 weeks, the
definitions were performing much poorer in the regions of Florida compared to the state level.
All definitions except the current CDC definition predicted an earlier offset compared to the
actual offset in the regions of Florida.
Part II: RSV Epidemiology between Four US States and Five Regions in Florida
Table 4-9 provides evidence for a pronounced difference in the seasonality indices between
states. California and Illinois had a large seasonality index, indicating that the risk of an RSV
infection during a season was more than 12 times the risk of an infection outside of a season. The
risk for an on-season infection was 9.7 times (95% CI, 9.0 - 10.1) higher in Texas and only 3.6
times (95% CI, 3.3 - 3.8) higher in Florida. The latter was a result of a low on-season activity in
Florida (0.060 RSV hospitalizations per 100 subject-weeks; 95% CI, 0.059 - 0.062) combined
with a high off-season activity (0.017; 95% CI, 0.016 - 0.018). The on-season activity in Florida
only reached half of the activity in California and Texas, a difference that was statistically
significant as evidenced by non-overlapping confidence intervals. Similarly, the off-season
activity in Florida was significantly higher than in any other state. The regions in Florida differed
widely with regard to their seasonality indices; however this difference was largely driven by
57
differences in the off-season activity. The northwest and north regions had an off-season activity
comparable with California and Texas resulting in a seasonality index in the northeast that was
only slightly below the Texas estimate. Moving south in Florida increased the off-season RSV
activity to 0.023 RSV hospitalizations per 100 subject-weeks (95% CI, 0.020 - 0.026) in the
southeast leading to a seasonality index of only 2.5 (95% CI, 2.2 - 2.8), which suggests that the
risk for an RSV infection during a season was only increased by 2.5-fold compared to off-season
periods.
Table 4-10 shows how seasons varied within geographic areas between different years.
With the exception of Florida, variation in season onset was limited to about 2 months or nine
weeks, but the upper limit of the 95% confidence interval for the range of season onset well
exceeded the null hypothesis threshold of 8 weeks and we can conclude that there was significant
variation in season onset in each state and each region in Florida. With the exception of
California, peak weeks showed a similar picture. In California peak weeks varied over a range of
only 4 weeks (upper limit, one-sided 95 % confidence interval: 8) indicating a fairly stable peak
activity. Also, RSV season offset in California was fairly stable: it varied only over 4 weeks
however with a larger confidence interval. Between onset, offset and peaks, we observed the
largest statewide variation in Florida. A large contributor to this effect were the central,
southwest and southeast regions of Florida which showed a pronounced variation with regard to
season offset, reaching a range of 21 weeks in the southwest.
Part III: Latitude as a Factor in RSV Epidemiology in Florida
Differences in the RSV seasons between the regions of Florida were pronounced. Table 4-
11 indicates that all season parameters except peak incidence rate differed significantly between
the regions. The earliest season onset was observed in the southeast region for the 28th
week of
the year (95% CI, 26.4 - 30.8) the latest onset occurred in the northwest on average during the
58
45th
week (95% CI, 43.0 - 47.4). The earliest offset was observed in the north (8.2; 95% CI, 3.0 -
13.4) and the latest in the southwest (20.4; 95% CI, 15.2 - 25.6). Seasons were shortest in the
north with an average duration of 20.6 weeks (95% CI, 14.5 - 26.7) and longest in the southeast
with an average duration of 42.8 weeks (95% CI, 36.7 - 48.9). The southeast also experienced an
early peak week (39.6; 95% CI, 37.3 - 41.9) while the other regions peaked closer to the end of
the year. Results from the linear regression analysis show that latitude was a strong linear
predictor for these differences. With each degree increase in latitude, season onset occurred 3.23
weeks later (95% CI, 2.49 - 3.97) (table 4-12). Moving north one degree in Florida was
associated with a 2.10 weeks earlier offset (95% CI, -3.55 – (-0.65)). These effects added up to a
5.03 weeks shorter season (95% CI, -6.61, -(-3.45)) for each degree north. The peak week
occurred 2.31 weeks later (95% CI, 1.50 - 3.12) for each degree north but the peak incidences
did not change (model R2=0.00). These associations are illustrated in figure 4-8.
Part IV: Patient Factors and Seasonality
Of the 4 interaction terms tested, 2 showed statistical significance: age 7-12 months and
CLD. While age 7-12 months was associated with a 25% higher risk for RSV hospitalizations
on-season vs. off-season (OR= 1.25; 95% CI, 1.13 - 1.39), CLD was associated with a 35%
lower risk during seasons compared to off-season (OR=0.65; 95% CI, 0.53 - 0.80). Of note, this
is not an indicator that CLD lowered the risk for RSV during seasons; it merely means that the
effect of CLD on RSV risk was stronger outside of the season. The status of the tested variables
as risk factors for RSV was confirmed and figure 4-9 which shows for all risk factors,
independent of the RSV season, that their odds ratios exceeded one. The figure also shows that
being in the youngest age category was associated with a similar risk on-season and off-season,
which also applies to CHD. The significant interaction terms between age 7-12 months and RSV
season and between CLD and RSV season are reflected in the difference in point estimates with
59
non-overlapping confidence intervals. During season, the odds ratio for age 7-12 months was
2.88 (95% CI, 2.71 - 3.05) and 2.30 (95% CI, 2.09 - 2.53) off-season. CLD had an on-season
odds ratio of 2.46 (95% CI, 2.24 - 2.67) which increased to 3.79 (95% CI, 3.14 - 4.59) outside of
the RSV season. Finally, an important observation in figure 4-9 relates to the absolute extent of
RSV risk associated with each risk factor. Belonging to the youngest age category increased the
risk for RSV by more than 7 times compared to age 13-24 months while the other risk factors
had a much lower influence on the RSV risk.
Part V: Timing of Prophylaxis with Palivizumab vs. RSV Seasonality
Figures 4-10 and 4-11 illustrate patterns of palivizumab utilization contrasted to the
incidence rate of RSV hospitalizations for each of the states and for the regions of Florida. A
consistent utilization pattern could be observed on a state level with little variation between the
years. An exception was the first season in Texas which we excluded from further analysis. Its
distinct pattern seems to illustrate slow acceptance after market introduction of palivizumab and
was therefore not considered to be a valid comparator for the following seasons. The northwest
and north regions of Florida and to some extent the central region exhibited a similar utilization
pattern compared to the states. The southwest showed a less distinct pattern with some utilization
year-round, however seasons were still recognizable. In the southeast, utilization occurred year-
round in the later seasons and although the curves showed some degree of a seasonal pattern,
they didn’t allow the estimation of an onset or offset in utilization. Therefore, the southeast of
Florida was omitted from further utilization analysis. Tables 4-13 and 4-14 quantify the
relationship between utilization and RSV activity. The onset of utilization in the 4 states was
closest to a fixed date, namely the first week of October in Florida and Illinois and the first week
of November in California and Texas.
60
In all regions of Florida, onset of utilization was closest to early October. The direction of
the difference was very close to 0.0 suggesting a small random distribution around the first week
of October. Actual season onset according to RSV hospitalizations and onset estimates according
to surveillance data were further remote from onset of utilization compared to the fixed dates.
With the exception of the central and southwest regions of Florida, utilization started before the
onset of RSV hospitalizations. The results for offset were similar with the exception of Florida
and Illinois where utilization offset was closest to the offset of RSV hospitalizations.
Nevertheless, both estimates were very close to the fixed dates of the last week of March and the
last week of April. Utilization offset was observed in California and Illinois in the last week of
April. Offset in the regions of Florida coincided with the fixed dates of late March or April with
the exception of the northwest where offset occurred closest to the offset of RSV
hospitalizations. Yet again, the fixed date of last week of March was almost equally close.
Overall, RSV season offset preceded the offset of palivizumab utilization in all states with the
exception of the state of Florida and in Florida only in the southwest.
Part VI: Optimizing Timing of Prophylaxis
Incidence rates of RSV hospitalizations and corresponding NNTs by calendar month and
age category are displayed in figures 4-12 through 4-15 for each of the 4 states. Figure 4-16
illustrates regional RSV incidence rates and NNTs in Florida with age categories collapsed into a
single category. Color-coding of the figures was based on thresholds that facilitate orientation,
but no NNT thresholds have been established in the literature. Coding thresholds were consistent
between the figures to allow cross-state comparisons of burden of disease. Consistent with our
findings in part I, we observed a very distinct season in California (figure 4-12), and we found
the high seasonality index from part II confirmed in the high RSV hospitalization rates on-season
while almost no activity was measured outside of the season. We could further observe a clear
61
age pattern with the oldest age category being at much lower risk for infections compared to the
youngest age category. In Florida, the seasonal pattern was less distinct (figure 4-13), however
months with high activity were still distinguishable from months of lower activity, but unlike in
California, we dot not identify months with virtually no activity. The age difference in risk for
RSV infections was also pronounced in Florida with the oldest age category being at comparably
low risk. Illinois and Texas (figures 4-14 and 4-15) exhibited a similar pattern as California, with
distinct periods of high and low RSV activity. Comparing NNTs across states, we found that
immunoprophylaxis with palivizumab was less beneficial in Florida where NNTs were never
below 100 regardless of age or calendar month. NNTs below 200 were found even for the oldest
age category from 13-24 months in all states except Florida where the lowest NNT in this age
group was 252, followed by 320 as the second-lowest.
An examination of the regional RSV incidence rates and NNTs (figure 4-16) reveals that
the statewide picture in Florida was merely a blend of a very different burden of disease in the
regions. Although the northwest and north regions showed a pattern more similar to the other
states, even here we didn’t find months with virtually zero activity as we found in the other
states, most pronounced in California. The southwest and southeast regions exhibited a pattern of
prolonged viral activity, most obvious in the southeast. However, even in the southeast, we were
able to distinguish between months of relatively high and relatively low activity. April through
July in the southwest region and May and June in the southeast show NNTs exceeding 650 while
the winter months had a peak activity that was comparable to other regions at their peak months.
62
Table 4-1. Cohort characteristics
Variable California Florida Illinois Texas
Total subject-weeks*
Demographics
16,617,845 19,903,113 29,252,601 43,891,992
Sex, male
Race/Ethnicity
8,466,264 (50.9) 10,168,281 (51.1) 14,942,450 (51.1) 22,375,862 (51.0)
White
Black
Native American
Asian
Hispanic
Other
Age [months]
3,667,695 (22.1)
1,106,694 (6.66)
127,637 (0.77)
516,133 (3.17)
10,286,924 (61.9)
912,762 (5.50)
7.97 (6.56)
6,851,466 (34.4)
5,711,267 (28.7)
10,856 (0.05)
148,503 (0.75)
5,683,232 (28.6)
1,497,789 (7.53)
10.47 (6.66)
10,244,069 (35.0)
8,428,941 (28.8)
57,983 (0.20)
708,637 (2.42)
9,346,077 (32.0)
466,894 (1.59)
11.33 (6.66)
9,596,615 (21.9)
5,019,089 (11.4)
130,669 (0.30)
313,256 (0.71)
28,688,524 (65.4)
143,839 (0.33)
10.34 (6.57)
RSV risk factors
Prematurity I n/a 117,241 (0.59) n/a n/a
Prematurity II n/a 99,024 (0.50) n/a n/a
Prematurity III
Chronic lung disease
Congenital heart disease
Cystic fibrosis
Immunodeficiency
Down syndrome
Asthma
Transplant
Malignancy
Immunosuppression
n/a
41,894 (0.25)
72,759 (0.44)
5,396 (0.03)
9,179 (0.06)
28,814 (0.17)
530,733 (3.19)
2,126 (0.01)
46,988 (0.28)
1,135,201 (6.83)
297,633 (1.50)
127,706 (0.64)
234,441 (1.18)
15,782 (0.08)
45,534 (0.23)
38,633 (0.19)
1,334,749 (6.71)
3,243 (0.02)
179,291 (0.90)
2,878,774 (14.5)
n/a
118,490 (0.41)
303,589 (1.04)
17,571 (0.06)
17,891 (0.06)
43,114 (0.15)
1,328,029 (4.54)
4,606 (0.02)
282,293 (0.97)
3,186,507 (10.9)
n/a
234,136 (0.53)
409,400 (0.93)
12,273 (0.03)
22,445 (0.05)
78,312 (0.18)
2,983,022 (6.80)
3,669 (0.01)
61,948 (0.14)
6,844,538 (15.6)
*weeks are preceded by a 4-weeks ambulatory care period, table shows number of subject-weeks (percentages) for categorical
variables and mean (standard deviation) for age.
63
Table 4-2. Risk factors for RSV hospitalization in Florida
Factor Comparison OR [95% CI] Factor Comparison OR [95% CI]
Year
Calendar
month
Age [months]
Sex
Race
1999 vs 2004
2000 vs 2004
2001 vs 2004
2002 vs 2004
2003 vs 2004
1 vs 12
2 vs 12
3 vs 12
4 vs 12
5 vs 12
6 vs 12
7 vs 12
8 vs 12
9 vs 12
10 vs 12
11 vs 12
0-6 vs 13-24
7-12 vs 13-24
F vs M
Black vs White
Native American vs White
Asian vs White
Hispanic vs White
Unknown vs White
1.17 [1.07 - 1.30]
1.08 [1.01 - 1.17]
1.11 [1.03 - 1.20]
1.14 [1.06 - 1.22]
1.06 [0.99 - 1.14]
0.68 [0.63 - 0.74]
0.56 [0.51 - 0.61]
0.40 [0.36 - 0.44]
0.22 [0.19 - 0.25]
0.16 [0.13 - 0.18]
0.13 [0.11 - 0.15]
0.17 [0.15 - 0.20]
0.29 [0.26 - 0.33]
0.51 [0.47 - 0.57]
0.84 [0.77 - 0.91]
1.05 [0.97 - 1.13]
10.10 [9.32 - 10.94]
2.61 [2.39 - 2.84]
0.82 [0.78 - 0.85]
1.10 [1.04 - 1.16]
0.76 [0.25 - 2.37]
0.63 [0.45 - 0.90]
1.28 [1.21 - 1.35]
1.21 [1.10 - 1.32]
Birth month
Prematurity I
Prematurity II
Prematurity III
Chronic lung disease
Congenital heart disease
Cystic fibrosis
Immunodeficiency
Down syndrome
Asthma
Transplant
Malignancy
Immunosuppression
Current palivizumab
exposure
1 vs 12
2 vs 12
3 vs 12
4 vs 12
5 vs 12
6 vs 12
7 vs 12
8 vs 12
9 vs 12
10 vs 12
11 vs 12
0.92 [0.82 - 1.03]
0.81 [0.71 - 0.91]
0.70 [0.62 - 0.80]
0.62 [0.54 - 0.70]
0.65 [0.57 - 0.73]
0.61 [0.54 - 0.69]
0.71 [0.63 - 0.79]
0.79 [0.71 - 0.87]
0.87 [0.79 - 0.97]
0.99 [0.89 - 1.10]
1.06 [0.95 - 1.17]
1.44 [1.20 - 1.74]
1.79 [1.51 - 2.11]
1.73 [1.56 - 1.91]
2.70 [2.27 - 3.22]
2.16 [1.90 - 2.48]
2.07 [1.24 - 3.45]
1.17 [0.72 - 1.88]
2.76 [2.08 - 3.66]
1.94 [1.78 - 2.12]
1.97 [.063 - 6.15]
1.10 [0.86 - 1.14]
2.08 [1.93 - 2.24]
0.95 [0.81 - 1.11]
Abbreviations: OR: odds ratio, CI: confidence interval
64
Table 4-3. Palivizumab exposure and RSV hospitalizations by state and risk category
Risk categories California Florida Illinois Texas
Total subject-weeks*
High risk
Palivizumab exposure**
Palivizumab doses†
RSV- hospitalizations
Elevated risk
Palivizumab exposure
Palivizumab doses
RSV- hospitalizations
Low risk
Palivizumab exposure
Palivizumab doses
RSV- hospitalizations
16,617,845
105,113 (0.63)
9,889 (9.41)
2,298 (2.19)
108 (0.10)
1,347,922 (8.11)
5,848 (0.43)
1,288 (0.10)
550 (0.04)
15,164,810 (91.3)
28,944 (0.19)
7,556 (0.05)
5,506 (0.04)
19,903,113
500,060 (2.51)
89,043 (17.8)
21,149 (4.23)
607 (0.12)
3,576,688 (18.0)
42,469 (1.19)
10,006 (0.28)
1,762 (0.05)
15,826,365 (79.5)
36,381 (0.23)
8,334 (0.05)
5,727 (0.04)
29,252,601
388,337 (1.33)
25,797 (6.64)
5,826 (1.50)
367 (0.09)
3,698,822 (12.6)
9,846 (0.27)
2,111 (0.06)
1,498 (0.04)
25,165,442 (86.0)
42,790 (0.17)
9,724 (0.04)
6,802 (0.03)
43,891,992
592,460 (1.35)
50,995 (8.61)
11,599 (1.96)
695 (0.12)
7,607,583 (17.3)
29,076 (0.38)
6,418 (0.08)
4,042 (0.05)
35,691,949 (81.3)
142,725 (0.40)
33,711 (0.09)
15,493 (0.04)
*weeks are preceded by a 4-weeks ambulatory care period; table shows numbers (percentages)
**palivizumab exposure occurs when a dose was given between 28 days before and 3 days after the current week’s start date
†palivizumab doses represent doses administered during the current week
65
Table 4-4. Areas under the curve by state and region
State/region Area under the curve [95% CI]
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
0.98 [0.96 - 0.99]
0.92 [0.88 - 0.95]
0.88 [0.83 - 0.92]
0.92 [0.88 - 0.95]
0.88 [0.83 - 0.93]
0.80 [0.75 - 0.86]
0.88 [0.84 - 0.92]
0.83 [0.78 - 0.88]
0.76 [0.68 - 0.85]
Abbreviation: CI: confidence interval
66
Table 4-5. Test characteristics at the threshold of 10% median proportion positive laboratory tests*
State/region Threshold Sensitivity [95% CI] Specificity [95% CI] PPV [95% CI] NPV [95% CI]
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
0.81 [0.70 - 0.88]
0.88 [0.83 - 0.92]
0.83 [0.74 - 0.89]
0.91 [0.84 - 0.96]
0.88 [0.79 - 0.93]
0.88 [0.81 - 0.93]
0.96 [0.92 - 0.98]
0.75 [0.69 - 0.81]
0.81 [0.75 - 0.86]
0.95 [0.91 - 0.97]
0.71 [0.60 - 0.80]
0.82 [0.76 - 0.87]
0.80 [0.74 - 0.86]
0.79 [0.70 - 0.86]
0.56 [0.47 - 0.63]
0.58 [0.49 - 0.67]
0.67 [0.55 - 0.77]
0.51 [0.37 - 0.65]
0.87 [0.77 - 0.93]
0.88 [0.82 - 0.92]
0.72 [0.62 - 0.79]
0.72 [0.63 - 0.79]
0.77 [0.68 - 0.85]
0.58 [0.51 - 0.66]
0.75 [0.69 - 0.81]
0.86 [0.80 - 0.91]
0.89 [0.83 - 0.93]
0.93 [0.88 - 0.96]
0.72 [0.61 - 0.81]
0.90 [0.84 - 0.94]
0.94 [0.89 - 0.97]
0.89 [0.81 - 0.94]
0.87 [0.79 - 0.92]
0.93 [0.84 - 0.97]
0.51 [0.40 - 0.61]
0.37 [0.26 - 0.49]
*Highlighted are point estimates <0.7. Abbreviations: PPV: positive predictive value, NPV: negative predictive value,
CI: confidence interval
Table 4-6. Test characteristics at optimal thresholds of median proportion positive laboratory tests*
State/region Threshold Sensitivity [95% CI] Specificity [95% CI] PPV [95% CI] NPV [95% CI]
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
5.5
13.2
8.3
9.5
13.6
16.7
14.8
12.0
13.6
0.95 [0.87 - 0.98]
0.81 [0.74 - 0.86]
0.89 [0.81 - 0.94]
0.92 [0.85 - 0.96]
0.84 [0.75 - 0.90]
0.76 [0.67 - 0.84]
0.86 [0.79 - 0.91]
0.71 [0.64 - 0.77]
0.68 [0.62 - 0.74]
0.88 [0.82 - 0.92]
0.90 [0.82 - 0.95]
0.80 [0.74 - 0.86]
0.80 [0.74 - 0.86]
0.86 [0.78 - 0.91]
0.64 [0.79 - 0.85]
0.75 [0.66 - 0.83]
0.85 [0.75 - 0.92]
0.79 [0.65 - 0.89]
0.75 [0.65 - 0.83]
0.95 [0.91 - 0.98]
0.71 [0.62 - 0.79]
0.72 [0.64 - 0.80]
0.83 [0.74 - 0.90]
0.66 [0.57 - 0.74]
0.82 [0.75 - 0.88]
0.93 [0.87 - 0.96]
0.94 [0.89 - 0.97]
0.98 [0.94 - 0.99]
0.67 [0.57 - 0.75]
0.93 [0.88 - 0.96]
0.95 [0.90 - 0.98]
0.87 [0.79 - 0.92]
0.81 [0.74 - 0.87]
0.80 [0.71 - 0.87]
0.52 [0.43 - 0.61]
0.35 [0.26 - 0.45]
*Highlighted are point estimates <0.7. Abbreviations: PPV: positive predictive value, NPV: negative predictive value,
CI: confidence interval
67
Table 4-7. Mean of absolute differences and direction of difference between season onset according to clinical dataset and
surveillance dataset under different definitions for season onset
State/region 10% MPP Optimal MPP 10% MPP,
≥ 3 positive
Optimal MPP,
≥ 3 positive
10% MPP,
≥ 5 positive
Optimal MPP,
≥ 5 positive
p-
value
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
Mean states
Mean regions
3.2 [0.8 - 5.6]
+0.4
4.8 [0.0 - 12.1]
+1.8
4.2 [2.0 - 6.4]
-0.6
6.4 [0.8 - 12.0]
-4.0
3.7 [2.9 - 4.4]
+1.0
n/a*
9.3 [5.5 - 13.0]
-9.3
5.0 [2.7 - 7.3]
0.0
7.0 [0.0 - 18.0]
-7.0
4.6 [0.2 - 9.1]
-0.6
6.1 [0.3 - 11.8]
-4.6
3.2 [0.0 - 6.4]
-2.0
4.3 [0.0 - 11.5]
+3.8
4.4 [2.1 - 6.6]
-1.6
6.4 [0.8 - 12.0]
-4.0
3.7 [2.9 - 4.4]
+1.0
n/a*
5.3 [0.0 - 12.1]
-4.8
4.5 [2.3 - 6.6]
+4.5
5.8 [0.0 - 17.6]
-4.8
4.6 [0.1 - 9.1]
-1.0
4.8 [0.0 - 10.7]
-2.2
3.2 [0.8 - 5.6]
+0.4
4.8 [0.0 - 12.1]
+1.8
3.4 [0.2 - 6.6]
+0.2
5.0 [0.0 - 10.2]
-2.6
3.7 [2.9 - 4.4]
+1.0
1.8 [0.0 - 4.5]
-0.8
7.3 [3.3 - 11.2]
-7,3
4.8 [2.2 - 7.3]
+0.3
7.0 [0.0 - 18.0]
-7.0
4.1 [0.0 - 8.4]
-0.1
4.9 [0.0 - 10.5]
-3.8
3.2 [0.0 - 6.4]
-2.0
4.3 [0.0 - 11.5]
+3.8
2.8 [0.4 - 5.2]
0.0
5.0 [0.0 - 10.2]
-2.6
3.7 [2.9 - 4.4]
+1.0
1.5 [0.0 - 3.1]
+1.5
5.3 [0.0 - 12.1]
-4.8
4.5 [2.3 - 6.6]
+4.5
5.8 [0.0 - 18.6]
-4.8
3.8 [0.0 - 8.1]
-0.2
4.2 [0.0 - 10.0]
-1.5
3.0 [0.8 - 5.2]
+0.2
4.8 [0.0 - 12.1]
+1.8
3.0 [0.0 - 6.2]
+0.6
3.0 [0.0 - 6.2]
-0.6
4.7 [3.2 - 6.1]
+2.0
2.0 [0.0 - 6.2]
+2.0
4.5 [3.8 - 5.2]
-4.5
4.5 [1.5 - 7.5]
+0.5
7.0 [0.0 - 18.0]
-7.0
3.4 [0.0 - 7.2]
+0.5
4.5 [0.0 - 9.8]
-2.6
3.2 [0.0 - 6.4]
-2.0
4.3 [0.0 - 11.5]
+3.8
2.6 [0.3 - 4.9]
+0.2
3.0 [0.0 - 6.2]
-0.6
4.7 [3.2 - 6.1]
+2.0
3.0 [0.0 - 6.7]
+3.0
1.8 [0.0 - 4.5]
-0.8
4.5 [2.3 - 6.6]
+4.5
5.8 [0.0 - 17.6]
-5.3
3.2 [0.0 - 7.0]
+0.3
3.9 [0.0 - 9.3]
-0.5
0.999
0.999
0.681
0.567
0.366
0.853
0.158
0.998
0.999
0.730
0.797
Table 4-7 shows the absolute mean difference in weeks [95% confidence interval] and direction of difference, direction is
positive if onset according to definition occurred after onset of RSV hospitalizations; *season definition not applicable due to
indistinctive pattern; Abbreviation: MPP: median proportion of positive tests
68
Table 4-8. Mean of absolute differences and direction of difference between season offset according to clinical dataset and
surveillance dataset under different definitions for season offset
State/region 10% MPP Optimal MPP 10% MPP,
≥ 3 positive
Optimal MPP,
≥ 3 positive
10% MPP,
≥ 5 positive
Optimal MPP,
≥ 5 positive
p-
value
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
Mean states
Mean regions
1.8 [0.8 - 2.8]
+0.2
3.0 [0.0 - 6.9]
+1.0
2.8 [0.4 - 5.2]
+2.8
4.2 [1.1 - 7.3]
+4.2
2.3 [0.4 - 4.2]
+1.0
n/a*
2.0 [0.0 - 4.7]
+1.5
8.5 [1.0 - 16.0]
-4.0
8.8 [1.5 - 16.0]
-8.8
2.9 [0.2 - 5.7]
+2.1
6.2 [0.0 - 12.6]
+0.1
2.6 [0.0 - 6.0]
+2.2
2.0 [0.2 - 3.8]
-1.0
3.2 [0.8 - 5.6]
+3.2
4.2 [1.1 - 7.3]
+4.2
2.7 [0.1 - 5.3]
-2.0
n/a*
5.8 [0.5 - 11.0]
-5.8
7.8 [0.0 - 17.8]
-7.3
13.5 [2.8 - 24.1]
-13.5
3.1 [0.3 - 5.8]
+2.2
7.2 [0.0 - 15.7]
-6.2
1.8 [0.8 - 2.8]
+0.2
3.0 [0.0 - 6.9]
+1.0
2.0 [0.8 - 3.2]
+0.4
2.8 [0.8 - 4.8]
+2.8
2.3 [0.4 - 4.2]
+1.0
8.0 [3.6 - 12.4]
+1.0
2.0 [0.0 - 4.7]
+1.5
8.5 [1.0 - 16.0]
-4.0
8.75 [1.5 - 16.0]
-8.8
2.4 [0.2 - 4.5]
+1.1
6.1 [0.0 - 12.3]
-1.9
2.6 [0.0 - 6.0]
+2.2
2.0 [0.2 - 3.8]
-1.0
1.6 [0.5 - 2.7]
+0.4
2.8 [0.8 - 4.8]
+2.8
2.7 [0.1 - 5.3]
-2.0
5.3 [0.0 - 10.7]
-4.8
5.8 [0.5 - 11.0]
-5.8
7.8 [0.0 - 17.8]
-7.3
13.5 [2.8 - 24.1]
-13.5
2.3 [0.1 - 4.4]
+1.1
7.2 [0.0 - 15.4]
-6.6
1.8 [0.8 - 2.8]
+0.2
3.0 [0.0 - 6.9]
+1.0
1.8 [0.2 - 3.4]
-1.4
2.2 [0.4 - 4.0]
+2.2
2.7 [0.1 - 5.3]
-2.0
9.0 [0.0 - 23.2]
-5.5
1.5 [0.0 - 4.5]
+1.0
8.5 [1.0 - 16.0]
-4.0
8.75 [1.5 - 16.0]
-8.8
2.2 [0.0 - 4.3]
+0.5
6.3 [0.0 - 14.7]
-3.9
2.6 [0.0 - 6.0]
+2.2
2.0 [0.2 - 3.8]
-1.0
1.8 [0.2 - 3.4]
-1.4
2.2 [0.4 - 4.0]
+2.2
4.0 [1.8 - 6.2]
-4.0
9.0 [0.0 - 23.3]
-8.5
5.8 [0.5 - 11.0]
-5.8
7.8 [0.0 - 17.8]
-7.3
13.5 [2.8 - 24.1]
-13.5
2.2 [0.0 - 4.3]
+0.5
8.2 [0.0 - 17.8]
-7.8
0.941
0.888
0.444
0.367
0.882
0.914
0.158
0.999
0.771
0.507
0.904
Table 4-8 shows the absolute mean difference in weeks [95% confidence interval] and direction of difference, direction is
positive if offset according to definition occurred after offset of RSV hospitalizations; *season definition not applicable due to
indistinctive pattern; Abbreviation: MPP: median proportion of positive tests
69
Table 4-9. Extent of seasonality and seasonality index in each state and regions in Florida
State/region On-season RSV IR
[95% CI]
Off-season RSV IR
[95% CI]
Seasonality index
[95% CI]
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
0.118 [0.115 - 0.121]
0.060 [0.059 - 0.062]
0.089 [0.087 - 0.090]
0.131 [0.129 - 0.132]
0.072 [0.067 - 0.078]
0.060 [0.055 - 0.065]
0.076 [0.073 - 0.079]
0.080 [0.076 - 0.084]
0.055 [0.054 - 0.057]
0.009 [0.009 - 0.010]
0.017 [0.016 - 0.018]
0.007 [0.007 - 0.008]
0.013 [0.013 - 0.014]
0.008 [0.007 - 0.010]
0.009 [0.008 - 0.011]
0.013 [0.011 - 0.014]
0.016 [0.013 - 0.019]
0.023 [0.020 - 0.026]
12.6 [11.8 - 13.4]
3.6 [3.3 - 3.8]
12.1 [11.4 - 12.8]
9.7 [9.0 - 10.1]
9.0 [7.2 - 11.1]
6.4 [5.4 - 7.9]
6.0 [5.4 - 6.8]
5.1 [4.2 - 6.2]
2.5 [2.2 - 2.8]
Abbreviations: IR: Incidence rate [RSV-hosp./100 subject weeks], CI: confidence interval
70
Table 4-10. Variation in seasons within each state and regions in Florida
State/region Mean week (SD) Range (min-max) Upper limit, one-sided
95 % CI of range
Season onset
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
Peak week
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
Season offset
States
California
Florida
Illinois
Texas
Florida regions
Northwest
North
Central
Southwest
Southeast
51.0 (3.45)
32.2 (4.60)
48.6 (3.13)
45.6 (2.51)
45.2 (1.48)
40.8 (1.79)
37.4 (2.88)
36.6 (2.97)
28.6 (2.07)
6.2 (1.10)
49.0 (3.39)
5.0 (2.45)
4.2 (1.92)
52.0 (2.92)
50.2 (2.17)
48.0 (2.35)
48.4 (2.30)
39.6 (2.70)
12.4 (1.34)
15.2 (3.96)
13.8 (2.05)
11.0 (2.00)
14.2 (3.03)
8.2 (3.63)
13.6 (5.73)
20.4 (7.99)
18.2 (6.10)
9 (48-04)
12 (24-35)
7 (46-52)
7 (44-50)
5 (34-47)
5 (39-43)
7 (35-41)
9 (32-40)
6 (26-31)
4 (5-8)
9 (46-54)
8 (1-7)
7 (1-6)
8 (47-54)
6 (47-52)
6 (46-51)
7 (46-52)
8 (36-43)
4 (11-14)
11 (11-21)
5 (12-16)
6 (9-14)
9 (10-18)
9 (3-12)
17 (5-21)
21 (13-33)
15 (8-22)
25
33
23
18
11
13
21
22
15
8
25
18
14
21
16
17
17
20
10
29
15
15
22
26
41
57
44
71
Table 4-11. Comparison of season characteristics between regions in Florida
Dependent variable Model R2 p-value Region Mean [95% CI]
Week of onset
Week of offset
Season duration [weeks]
Peak week
Peak RSV incidence
[RSV-hosp.
/100 subject-weeks]
0.87
0.41
0.66
0.78
0.27
<0.001
0.025
<0.001
<0.001
0.156
Northwest
North
Central
Southwest
Southeast
Northwest
North
Central
Southwest
Southeast
Northwest
North
Central
Southwest
Southeast
Northwest
North
Central
Southwest
Southeast
Northwest
North
Central
Southwest
Southeast
45.2 [43.0 - 47.4]
40.8 [38.6 - 43.0]
37.4 [35.2 - 39.6]
36.6 [34.4 - 38.8]
28.6 [26.4 - 30.8]
14.2 [9.0 - 19.4]
8.2 [3.0 - 13.4]
13.6 [8.4 - 18.8]
20.4 [15.2 - 25.6]
18.2 [13.0 - 23.4]
24.0 [17.9 - 30.1]
20.6 [14.5 - 26.7]
29.4 [23.3 - 35.5]
36.8 [30.7 - 42.9]
42.8 [36.7 - 48.9]
52.0 [49.7 - 2.3]
50.2 [47.9 - 52.5]
48.0 [45.7 - 50.3]
48.4 [46.1 - 50.7]
39.6 [37.3 - 41.9]
0.166 [0.115 - 0.217]
0.132 [0.080 - 0.183]
0.158 [0.107 - 0.209]
0.210 [0.159 - 0.261]
0.126 [0.075 - 0.177]
Abbreviation: CI: confidence interval
Table 4-12. Linear regression analysis of the effects of latitude on season characteristics in
Florida
Dependent variable Model R2 p-value Estimated effect of latitude [95% CI]
Week of onset
Week of offset
Season duration
Peak week
Peak RSV incidence
0.76
0.23
0.63
0.58
0.00
<0.001
0.010
<0.001
<0.001
0.824
3.23 [2.49 - 3.97]
-2.10 [-3.55 – (-0.65)]
-5.03 [-6.61 – (-3.45)]
2.31 [1.50 - 3.12]
0.00 [-0.02 - 0.01]
Abbreviation: CI: confidence interval
72
Table 4-13. Mean of absolute differences and direction of difference between onset of palivizumab utilization and onset of RSV
season according to different determinants of RSV season
State/region Utilization
mean [95% CI]
RSV hosp.
[95% CI], direction
10% MPP
[95% CI], direction
1st week, October
[95% CI], direction
1st week, November
[95% CI], direction
p-value
States
California
Florida
Illinois
Texas
Florida regions*
Northwest
North
Central
Southwest
46.2 [44.2 - 48.2]
38.0 [36.0 - 40.0]
41.6 [40.2 - 43.0]
42.8 [40.2 - 45.3]
39.4 [36.3 - 42.5]
40.4 [38.5 - 42.3]
40.4 [37.4 - 43.4]
39.8 [36.5 - 43.1]
4.8 [0.0 - 9.8]
+4.8
5.8 [1.1 - 10.5]
-5.8
7.0 [2.8 - 11.2]
+7.0
3.8 [1.2 - 6.3]
+3.2
5.8 [1.5 - 10.0]
+5.8
2.0 [0.0 - 4.2]
+0.4
4.2 [0.5 - 7.9]
-3.0
4.0 [2.2 - 5.8]
-3.2
5.2 [1.1 - 9.3]
+5.2
4.8 [1.5 - 8.0]
-4.3
6.4 [1.0 - 11.8]
+6.4
5.8 [3.0 - 8.5]
-0.8
7.7 [0.0 - 15.7]
+7.7
7.0 [5.2 - 8.8]
-1.0
12.0 [6.9 - 17.0]
-12.0
7.0 [2.6 - 11.4]
-3.0
6.2 [4.2 - 8.2]
-6.2
2.0 [0.0 - 4.0]
+2.0
1.6 [0.2 - 3.0]
-1.6
2.8 [0.2 - 5.3]
-2.4
1.8 [0.0 - 3.8]
+0.6
1.2 [0.2 - 2.2]
-0.4
2.0 [0.8 - 3.2]
-0.4
2.2 [0.8 - 3.6]
+0.2
1.6 [0.0 - 3.9]
-1.6
6.6 [5.2 - 8.0]
+6.6
3.0 [1.2 - 4.8]
+3.0
2.3 [0.4 - 4.2]
+2.2
5.2 [2.4 - 8.0]
+5.2
4.2 [2.6 - 5.8]
+4.2
4.2 [1.2 - 7.2]
+4.2
4.8 [1.6 - 8.0]
+4.8
0.105
0.051
0.025
0.114
0.135
<0.001
<0.001
0.045
Table shows the absolute mean difference in weeks, the smallest difference is highlighted, direction is positive if onset occurred
after onset of utilization; *Florida southeast region: no onset/offset of utilization detectable; Abbreviations: MPP: median proportion
of positive tests, CI: confidence interval
73
Table 4-14. Mean of absolute differences and direction of difference between offset of palivizumab utilization and offset of RSV
season according to different determinants of RSV season
State/region Utilization
mean [95% CI]
RSV hosp.
[95% CI] , direction
10% MPP
[95% CI] , direction
Last week, March
[95% CI] , direction
Last week, April
[95% CI] , direction
p-value
States
California
Florida
Illinois
Texas
Florida regions*
Northwest
North
Central
Southwest
16.0 [14.2 - 17.8]
14.6 [12.0 - 17.2]
14.8 [12.8 - 16.8]
15.3 [13.1 - 17.4]
14.2 [12.2 - 16.2]
14.0 [11.2 - 16.8]
14.4 [11.5 - 17.3]
16.2 [12.0 - 20.5]
3.6 [1.3 - 5.8]
-3.6
2.2 [0.0 - 4.9]
+0.6
1.4 [0.0 - 3.3]
-1.0
4.0 [0.3 - 7.7]
-4.0
1.2 [0.0 - 2.8]
0.0
5.8 [1.0 - 10.6]
-5.8
3.2 [0.4 - 6.0]
-0.8
5.4 [0.0 - 11.0]
+4.2
3.4 [0.0 - 6.9]
-3.4
3.3 [1.7 - 4.8]
+1.8
2.6 [0.0 - 5.8]
+1.8
3.3 [ 0.5 - 6.0]
+0.3
3.0 [1.8 - 4.2]
+1.6
6.0 [6.0 - 6.0]
+6.0
4.0 [1.0 - 7.0]
+2.0
5.3 [0.0 - 11.5]
+0.8
3.2 [1.2 - 5.2]
-3.2
2.6 [1.5 - 3.7]
-1.8
2.4 [1.0 - 3.8]
-2.0
2.5 [0.1 - 4.9]
-2.5
1.8 [0.2 - 3.4]
-1.4
2.0 [0.2 - 3.8]
-1.2
2.4 [0.5 - 4.3]
-1.6
4.2 [1.8 - 6.6]
-3.4
1.2 [0.0 - 2.8]
+1.2
2.6 [0.2 - 5.0]
+2.6
2.4 [0.3 - 4.5]
+2.4
2.0 [0.2 - 3.8]
+2.0
3.0 [1.0 - 5.0]
+3.0
3.2 [.08 - 5.6]
+3.2
2.8 [0.1 - 5.5]
+2.8
2.2 [0.0 - 5.0]
+1.0
0.230
0.828
0.725
0.606
0.162
0.104
0.714
0.500
Table shows the absolute mean difference in weeks, the smallest difference is highlighted, direction is positive if offset occurred
after offset of utilization; *Florida southeast region: no onset/offset of utilization detectable; Abbreviations: MPP: median proportion
of positive tests, CI: confidence interval
74
Figure 4-1. Flowchart of sample selection and resulting sample size
California Florida Illinois Texas
All Medicaid recipients born between 1999 and 2004
n= 2,017,445
subjects
n= 815,174
subjects,
16,617,845
subject-weeks
Eligible to fee-for service at birth
No hospitalization in previous 4 weeks
n= 455,311
subjects,
19,903,113
subject-weeks
n= 450,758
subjects,
29,252,601
subject-weeks
n= 933,404
subjects,
43,891,992
subject-weeks
n= 582,473
subjects
n= 1,506,355
subjects
n= 818,703
subjects
With matching
birth certificate
n= 816,255
subjects,
20,152,370
subject-weeks
n= 457,394
subjects,
22,150,318
subject-weeks
n= 451,502
subjects,
31,475,000
subject-weeks
n= 936,976
subjects,
48,651,406
subject-weeks
n= 647,985
subjects
75
A
B
C
D
Figure 4-2. RSV hospitalization rates and resulting seasons in A) California, B) Florida,
C) Illinois and D) Texas
-0.1
0.1
0.3
0.5
0.7
0.9
1.1
1.3
19
99
27
19
99
33
19
99
39
19
99
45
19
99
51
20
00
05
20
00
11
20
00
17
20
00
23
20
00
29
20
00
35
20
00
41
20
00
47
20
01
01
20
01
07
20
011
3
20
011
9
20
01
25
20
01
31
20
01
37
20
01
43
20
01
49
20
02
03
20
02
09
20
02
15
20
02
21
20
02
27
20
02
33
20
02
39
20
02
45
20
02
51
20
03
05
20
03
11
20
03
17
20
03
23
20
03
29
20
03
35
20
03
41
20
03
47
20
03
53
20
04
06
20
04
12
20
04
18
20
04
24
RS
V In
cid
ence
[ho
sp./
10
0 s
ub
ject
-wee
ks]
Week
RSV Season RSV Incidence high risk cohort RSV Incidence low risk cohort
0
0.2
0.4
0.6
0.8
1
19
99
27
19
99
33
19
99
39
19
99
45
19
99
51
20
00
05
20
00
11
20
00
17
20
00
23
20
00
29
20
00
35
20
00
41
20
00
47
20
01
01
20
01
07
20
011
3
20
011
9
20
01
25
20
01
31
20
01
37
20
01
43
20
01
49
20
02
03
20
02
09
20
02
15
20
02
21
20
02
27
20
02
33
20
02
39
20
02
45
20
02
51
20
03
05
20
03
11
20
03
17
20
03
23
20
03
29
20
03
35
20
03
41
20
03
47
20
03
53
20
04
06
20
04
12
20
04
18
20
04
24
RS
V In
cid
ence
[ho
sp./
10
0 s
ub
ject
-wee
ks]
Week
RSV Season RSV Incidence high risk cohort RSV Incidence low risk cohort
0
0.2
0.4
0.6
0.8
1
19
99
27
19
99
33
19
99
39
19
99
45
19
99
51
20
00
05
20
00
11
20
00
17
20
00
23
20
00
29
20
00
35
20
00
41
20
00
47
20
01
01
20
01
07
20
011
3
20
011
9
20
01
25
20
01
31
20
01
37
20
01
43
20
01
49
20
02
03
20
02
09
20
02
15
20
02
21
20
02
27
20
02
33
20
02
39
20
02
45
20
02
51
20
03
05
20
03
11
20
03
17
20
03
23
20
03
29
20
03
35
20
03
41
20
03
47
20
03
53
20
04
06
20
04
12
20
04
18
20
04
24
RS
V In
cid
ence
[ho
sp./
10
0 s
ub
ject
-wee
ks]
Week
RSV Season RSV Incidence high risk cohort RSV Incidence low risk cohort
0
0.2
0.4
0.6
0.8
1
19
99
27
19
99
33
19
99
39
19
99
45
19
99
51
20
00
05
20
00
11
20
00
17
20
00
23
20
00
29
20
00
35
20
00
41
20
00
47
20
01
01
20
01
07
20
011
3
20
011
9
20
01
25
20
01
31
20
01
37
20
01
43
20
01
49
20
02
03
20
02
09
20
02
15
20
02
21
20
02
27
20
02
33
20
02
39
20
02
45
20
02
51
20
03
05
20
03
11
20
03
17
20
03
23
20
03
29
20
03
35
20
03
41
20
03
47
20
03
53
20
04
06
20
04
12
20
04
18
20
04
24
RS
V In
cid
ence
[ho
sp./
10
0 s
ub
ject
-wee
ks]
Week
RSV Season RSV Incidence high risk cohort RSV Incidence low risk cohort
76
A
B
C
Figure 4-3. RSV hospitalization rates and resulting seasons in the regions of Florida.
A) Northwest, B) North, C) Central, D) Southwest and E) Southeast
0.000
0.050
0.100
0.150
0.200
0.250
0.300
19
99
27
19
99
34
19
99
41
19
99
48
20
00
03
20
00
10
20
00
17
20
00
24
20
00
31
20
00
38
20
00
45
20
00
52
20
01
07
20
011
4
20
01
21
20
01
28
20
01
35
20
01
42
20
01
49
20
02
04
20
02
11
20
02
18
20
02
25
20
02
32
20
02
39
20
02
46
20
03
01
20
03
08
20
03
15
20
03
22
20
03
29
20
03
36
20
03
43
20
03
50
20
04
04
20
04
11
20
04
18
20
04
25
RS
V I
nci
den
ce[h
osp
./1
00
su
bje
ct-w
eek
s]
Week
RSV Season RSV Incidence all children
0.000
0.050
0.100
0.150
0.200
0.250
0.300
19
99
27
19
99
34
19
99
41
19
99
48
20
00
03
20
00
10
20
00
17
20
00
24
20
00
31
20
00
38
20
00
45
20
00
52
20
01
07
20
011
4
20
01
21
20
01
28
20
01
35
20
01
42
20
01
49
20
02
04
20
02
11
20
02
18
20
02
25
20
02
32
20
02
39
20
02
46
20
03
01
20
03
08
20
03
15
20
03
22
20
03
29
20
03
36
20
03
43
20
03
50
20
04
04
20
04
11
20
04
18
20
04
25
RS
V I
nci
den
ce[h
osp
./1
00
su
bje
ct-w
eek
s]
Week
RSV Season RSV Incidence all children
0.000
0.050
0.100
0.150
0.200
0.250
0.300
19
99
27
19
99
34
19
99
41
19
99
48
20
00
03
20
00
10
20
00
17
20
00
24
20
00
31
20
00
38
20
00
45
20
00
52
20
01
07
20
011
4
20
01
21
20
01
28
20
01
35
20
01
42
20
01
49
20
02
04
20
02
11
20
02
18
20
02
25
20
02
32
20
02
39
20
02
46
20
03
01
20
03
08
20
03
15
20
03
22
20
03
29
20
03
36
20
03
43
20
03
50
20
04
04
20
04
11
20
04
18
20
04
25
RS
V I
nci
den
ce[h
osp
./100 s
ubje
ct-w
eeks]
Week
RSV Season RSV Incidence all children
77
E
F
Figure 4-3. Continued
0.000
0.050
0.100
0.150
0.200
0.250
0.300
19
99
27
19
99
34
19
99
41
19
99
48
20
00
03
20
00
10
20
00
17
20
00
24
20
00
31
20
00
38
20
00
45
20
00
52
20
01
07
20
011
4
20
01
21
20
01
28
20
01
35
20
01
42
20
01
49
20
02
04
20
02
11
20
02
18
20
02
25
20
02
32
20
02
39
20
02
46
20
03
01
20
03
08
20
03
15
20
03
22
20
03
29
20
03
36
20
03
43
20
03
50
20
04
04
20
04
11
20
04
18
20
04
25
RS
V I
nci
den
ce[h
osp
./100
subje
ct-w
eeks]
Week
RSV Season RSV Incidence all children
0.000
0.050
0.100
0.150
0.200
0.250
0.300
19
99
27
19
99
34
19
99
41
19
99
48
20
00
03
20
00
10
20
00
17
20
00
24
20
00
31
20
00
38
20
00
45
20
00
52
20
01
07
20
011
4
20
01
21
20
01
28
20
01
35
20
01
42
20
01
49
20
02
04
20
02
11
20
02
18
20
02
25
20
02
32
20
02
39
20
02
46
20
03
01
20
03
08
20
03
15
20
03
22
20
03
29
20
03
36
20
03
43
20
03
50
20
04
04
20
04
11
20
04
18
20
04
25
RS
V I
nci
den
ce[h
osp
./100
subje
ct-w
eeks]
Week
RSV Season RSV Incidence all children
78
A
B
C
D
Figure 4-4. NREVSS laboratory tests and resulting RSV season in A) California, B) Florida,
C) Illinois and D) Texas. The red line marks the 10% threshold and the green line
marks the optimal threshold. MPP: Median proportion of positive RSV laboratory
tests.
0
10
20
30
40
50
60
70
050
100150200250300350400450500
19
99
27
19
99
33
19
99
39
19
99
45
19
99
51
20
00
05
20
00
11
20
00
17
20
00
23
20
00
29
20
00
35
20
00
41
20
00
47
20
01
01
20
01
07
20
011
3
20
011
9
20
01
25
20
01
31
20
01
37
20
01
43
20
01
49
20
02
03
20
02
09
20
02
15
20
02
21
20
02
27
20
02
33
20
02
39
20
02
45
20
02
51
20
03
05
20
03
11
20
03
17
20
03
23
20
03
29
20
03
35
20
03
41
20
03
47
20
03
53
20
04
06
20
04
12
20
04
18
20
04
24
MP
P [%
]N
um
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
0
10
20
30
40
50
60
70
0100200300400500600700800900
20
00
27
20
00
33
20
00
39
20
00
45
20
00
51
20
01
05
20
011
1
20
011
7
20
01
23
20
01
29
20
01
35
20
01
41
20
01
47
20
01
53
20
02
06
20
02
12
20
02
18
20
02
24
20
02
30
20
02
36
20
02
42
20
02
48
20
03
02
20
03
08
20
03
14
20
03
20
20
03
26
20
03
32
20
03
38
20
03
44
20
03
50
20
04
03
20
04
09
20
04
15
20
04
21
MP
P [%
]
Num
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
0
10
20
30
40
50
60
70
020406080
100120140160180
19
99
27
19
99
33
19
99
39
19
99
45
19
99
51
20
00
05
200
011
20
00
17
20
00
23
20
00
29
20
00
35
20
00
41
20
00
47
20
01
01
20
01
07
20
011
3
20
011
9
20
01
25
20
01
31
20
01
37
20
01
43
20
01
49
20
02
03
20
02
09
20
02
15
20
02
21
20
02
27
20
02
33
20
02
39
20
02
45
20
02
51
20
03
05
200
311
20
03
17
20
03
23
20
03
29
20
03
35
20
03
41
20
03
47
20
03
53
20
04
06
20
04
12
20
04
18
20
04
24
MP
P [%
]N
um
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
0
10
20
30
40
50
60
70
0
100
200
300
400
500
600
19
99
27
19
99
33
19
99
39
19
99
45
19
99
51
20
00
05
200
011
20
00
17
20
00
23
20
00
29
20
00
35
20
00
41
20
00
47
20
01
01
20
01
07
20
011
3
20
011
9
20
01
25
20
01
31
20
01
37
20
01
43
20
01
49
20
02
03
20
02
09
20
02
15
20
02
22
20
02
30
20
02
36
20
02
42
20
02
48
20
03
02
20
03
08
20
03
14
20
03
20
20
03
26
20
03
32
20
03
38
20
03
44
20
03
50
20
04
03
20
04
09
20
04
15
20
04
21
MP
P [%
]N
um
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
79
A
B
C
Figure 4-5. NREVSS laboratory tests and resulting RSV season in the regions of Florida.
A) Northwest, B) North, C) Central, D) Southwest and E) Southeast. The red line
marks the 10% threshold and the green line marks the optimal threshold. MPP:
Median proportion of positive RSV laboratory tests.
0
10
20
30
40
50
60
70
0
50
100
150
200
250
300
20
01
27
20
01
33
20
01
39
20
01
45
20
01
51
20
02
04
20
02
10
20
02
16
20
02
22
20
02
28
20
02
34
20
02
40
20
02
46
20
02
52
20
03
06
20
03
12
20
03
18
20
03
24
20
03
30
20
03
36
20
03
42
20
03
48
20
04
01
20
04
07
20
04
13
20
04
19
20
04
25
MP
P [%
]
Num
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
0
10
20
30
40
50
60
70
0
20
40
60
80
100
120
20
00
27
20
00
33
20
00
39
20
00
45
20
00
51
20
01
05
20
011
1
200
117
20
01
23
20
01
29
20
01
35
20
01
41
20
01
47
20
01
53
20
02
06
20
02
12
20
02
18
20
02
24
20
02
30
20
02
36
20
02
42
20
02
48
20
03
02
20
03
08
20
03
14
20
03
20
20
03
26
20
03
32
20
03
38
20
03
44
20
03
50
20
04
03
20
04
09
20
04
15
20
04
21
MP
P [%
]
Num
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
0
10
20
30
40
50
60
70
0
50
100
150
200
250
20
00
27
20
00
33
20
00
39
20
00
45
20
00
51
20
01
05
20
011
1
20
011
7
20
01
23
20
01
29
20
01
35
20
01
41
20
01
47
20
01
53
20
02
06
20
02
12
20
02
18
20
02
24
20
02
30
20
02
36
20
02
42
20
02
48
20
03
02
20
03
08
20
03
14
20
03
20
20
03
26
20
03
32
20
03
38
20
03
44
20
03
50
20
04
03
20
04
09
20
04
15
20
04
21
MP
P [%
]
Num
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
80
D
E
Figure 4-5. Continued
0
10
20
30
40
50
60
70
0
50
100
150
200
250
300
20
00
27
20
00
33
20
00
39
20
00
45
20
00
51
20
01
05
20
011
1
20
011
7
20
01
23
20
01
29
20
01
35
20
01
41
20
01
47
20
01
53
20
02
06
20
02
12
20
02
18
20
02
24
20
02
30
20
02
36
20
02
42
20
02
48
20
03
02
20
03
08
20
03
14
20
03
20
20
03
26
20
03
32
20
03
38
20
03
44
20
03
50
20
04
03
20
04
09
20
04
15
20
04
21
MP
P [%
]
Num
ber
of
test
s
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
0
10
20
30
40
50
60
70
0
50
100
150
200
250
300
20
00
27
20
00
33
20
00
39
20
00
45
20
00
51
20
01
05
20
011
1
20
011
7
20
01
23
20
01
29
20
01
35
20
01
41
20
01
47
20
01
53
20
02
06
20
02
12
20
02
18
20
02
24
20
02
30
20
02
36
20
02
42
20
02
48
20
03
02
20
03
08
20
03
14
20
03
20
20
03
26
20
03
32
20
03
38
20
03
44
20
03
50
20
04
03
20
04
09
20
04
15
20
04
21
MP
P [%
]
Nu
mb
er o
f te
sts
WeekRSV season based on 10% MPP # of tests # of positive tests MPP [%]
81
Figure 4-6. Receiver operating characteristics curves for each state
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
A. California
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
B. Florida
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
C. Illinois
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
D. Texas
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
82
Figure 4-7. Receiver operating characteristics curves for each region in Florida
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
A. Northwest
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
B. North
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
C. Central
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
D. Southwest
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Sen
siti
vit
y
1-Specificity
E. Southeast
Sensitivity/1-Specificitywith 10% threshold
with optimal threshold
83
A B
C
D
Figure 4-8. Linear effect of latitude on season characteristics in Florida. A) Week of season
onset, B) Week of season offset, C) Season duration and D) Peak week
25
30
35
40
45W
eek
of
On
set
26 26.5 27 27.5 28 28.5 29 29.5 30 30.5
Latitude [°N]
Regression Plot
0
5
10
15
20
25
30
35
Week
of
Off
set
26 26.5 27 27.5 28 28.5 29 29.5 30 30.5
Latitude [°N]
Regression Plot
10
15
20
25
30
35
40
45
50
55
Du
rati
on
(w
eek
s)
26 26.5 27 27.5 28 28.5 29 29.5 30 30.5
Latitude [°N]
Regression Plot
35
40
45
50
55
Peak
week
26 26.5 27 27.5 28 28.5 29 29.5 30 30.5
Latitude [°N]
Regression Plot
84
Odds ratio [95% confidence interval]
1 2 3 4 5 6 7 8 9
CHD on-season
CHD off-season
CLD on-season
CLD off-season
Age 7-12 months on-season
Age 7-12 months off-season
Age 0-6 months on-season
Age 0-6 months off-season
Figure 4-9. Heterogeneous effects of RSV risk factors on risk for RSV hospitalizations off-
season vs. on-season
85
A
B
C
D
Figure 4-10. Palivizumab utilization and RSV seasonality in A) California, B) Florida,
C) Illinois and D) Texas
0
0.1
0.2
0.3
0.4
0.5
00.050.1
0.150.2
0.250.3
19
99
27
19
99
35
19
99
43
19
99
51
20
00
07
20
00
15
20
00
23
20
00
31
20
00
39
20
00
47
20
01
03
20
011
1
20
011
9
20
01
27
20
01
35
20
01
43
20
01
51
20
02
07
20
02
15
20
02
23
20
02
31
20
02
39
20
02
47
20
03
03
20
03
11
20
03
19
20
03
27
20
03
35
20
03
43
20
03
51
20
04
06
20
04
14
20
04
22
Paliv
izum
ab d
oses
per 1
00
sub
ject week
s
RS
V in
cid
ence
[h
osp
./1
00
su
bje
ct-w
eek
s]
Week
Period of high utilization RSV incidence all children Palivizumab doses
0
0.1
0.2
0.3
0.4
0.5
00.050.1
0.150.2
0.250.3
19
99
27
19
99
35
19
99
43
19
99
51
20
00
07
20
00
15
20
00
23
20
00
31
20
00
39
20
00
47
20
01
03
200
111
200
119
20
01
27
20
01
35
20
01
43
20
01
51
20
02
07
20
02
15
20
02
23
20
02
31
20
02
39
20
02
47
20
03
03
200
311
20
03
19
20
03
27
20
03
35
20
03
43
20
03
51
20
04
06
20
04
14
20
04
22
Paliv
izum
ab d
oses
per 1
00 su
bject w
eeks
RS
V in
cid
ence
[h
osp
./100
subje
ct-w
eeks]
Week
Period of high utilization RSV incidence all children Palivizumab doses
0
0.1
0.2
0.3
0.4
0.5
00.050.1
0.150.2
0.250.3
19
99
27
19
99
35
19
99
43
19
99
51
20
00
07
20
00
15
20
00
23
20
00
31
20
00
39
20
00
47
20
01
03
20
011
1
20
011
9
20
01
27
20
01
35
20
01
43
20
01
51
20
02
07
20
02
15
20
02
23
20
02
31
20
02
39
20
02
47
20
03
03
20
03
11
20
03
19
20
03
27
20
03
35
20
03
43
20
03
51
20
04
06
20
04
14
20
04
22
Paliv
izum
ab d
oses
per 1
00
sub
ject week
s
RS
V in
cid
ence
[h
osp
./1
00
su
bje
ct-w
eek
s]
Week
Period of high utilization RSV incidence all children Palivizumab doses
0
0.1
0.2
0.3
0.4
0.5
00.050.1
0.150.2
0.250.3
19
99
27
19
99
35
19
99
43
19
99
51
20
00
07
20
00
15
20
00
23
20
00
31
20
00
39
20
00
47
20
01
03
20
011
1
20
011
9
20
01
27
20
01
35
20
01
43
20
01
51
20
02
07
20
02
15
20
02
23
20
02
31
20
02
39
20
02
47
20
03
03
20
03
11
20
03
19
20
03
27
20
03
35
20
03
43
20
03
51
20
04
06
20
04
14
20
04
22
Paliv
izum
ab d
oses
per 1
00
sub
ject week
s
RS
V in
cid
ence
[h
osp
./1
00
su
bje
ct-w
eek
s]
Week
Period of high utilization RSV incidence all children Palivizumab doses
86
A
B
C
Figure 4-11. Palivizumab utilization and RSV seasonality in the regions of Florida.
A) Northwest, B) North, C) Central, D) Southwest and E) Southeast
0
0.2
0.4
0.6
0.8
00.050.1
0.150.2
0.250.3
19
99
27
19
99
35
19
99
43
19
99
51
20
00
07
20
00
15
20
00
23
20
00
31
20
00
39
20
00
47
20
01
03
20
011
1
20
011
9
20
01
27
20
01
35
20
01
43
20
01
51
20
02
07
20
02
15
20
02
23
20
02
31
20
02
39
20
02
47
20
03
03
20
03
11
20
03
19
20
03
27
20
03
35
20
03
43
20
03
51
20
04
06
20
04
14
20
04
22
Paliv
izum
ab d
oses
per 1
00
sub
ject week
s
RS
V in
cid
ence
[h
osp
./1
00
su
bje
ct-w
eek
s]
Week
Period of high utilization RSV incidence all children Palivizumab doses
0
0.2
0.4
0.6
0.8
00.050.1
0.150.2
0.250.3
19
99
27
19
99
35
19
99
43
19
99
51
20
00
07
20
00
15
20
00
23
20
00
31
20
00
39
20
00
47
20
01
03
20
011
1
20
011
9
20
01
27
20
01
35
20
01
43
20
01
51
20
02
07
20
02
15
20
02
23
20
02
31
20
02
39
20
02
47
20
03
03
20
03
11
20
03
19
20
03
27
20
03
35
20
03
43
20
03
51
20
04
06
20
04
14
20
04
22
Paliv
izum
ab d
oses
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0
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Period of high utilization RSV incidence all children Palivizumab doses
87
D
E
Figure 4-11. Continued
0
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88
A B
Figure 4-12. A) Monthly RSV hospitalization incidence rates [per 100 subject-months], B) NNT
(numbers needed to treat) with palivizumab by age in the high risk cohort in
California
A B
Figure 4-13. A) Monthly RSV hospitalization incidence rates [per 100 subject-months], B) NNT
(numbers needed to treat) with palivizumab by age in the high risk cohort in Florida
RSV-
Incidence
>1.0
0.4-1.0
<0.4
RSV-
Incidence
>1.0
0.4-1.0
<0.4
Age [months]
Month 0--6 7--12 13--24
Jan 1.232 1.252 0.587
Feb 4.774 4.044 0.839
Mar 2.985 0.742 1.019
Apr 0.728 0.169 0.118
May 0.243 0.000 0.000
Jun 0.000 0.000 0.000
Jul 0.000 0.000 0.000
Aug 0.217 0.000 0.000
Sep 0.000 0.000 0.114
Oct 0.000 0.000 0.000
Nov 0.595 0.000 0.223
Dec 0.729 1.403 0.434
NNT
<200
200-500
>500
Age [months]
Month 0--6 7--12 13--24
Jan 162 160 341
Feb 42 49 238
Mar 67 270 196
Apr 275 1184 1690
May 824 99999 99999
Jun 99999 99999 99999
Jul 99999 99999 99999
Aug 922 99999 99999
Sep 99999 99999 1754
Oct 99999 99999 99999
Nov 336 99999 895
Dec 275 143 461
RSV-
Incidence
>1.0
0.4-1.0
<0.4
RSV-
Incidence
>1.0
0.4-1.0
<0.4
Age [months]
Month 0--6 7--12 13--24
Jan 1.279 1.037 0.394
Feb 1.150 0.716 0.339
Mar 0.723 0.622 0.297
Apr 0.159 0.552 0.183
May 0.228 0.297 0.072
Jun 0.153 0.251 0.104
Jul 0.367 0.286 0.101
Aug 0.580 0.142 0.266
Sep 0.952 0.761 0.570
Oct 1.256 1.216 0.625
Nov 1.398 0.753 0.792
Dec 1.596 0.788 0.525
NNT
<200
200-500
>500
Age [months]
Month 0--6 7--12 13--24
Jan 156 193 507
Feb 174 279 589
Mar 277 322 674
Apr 1256 362 1091
May 879 673 2762
Jun 1306 797 1924
Jul 544 698 1972
Aug 345 1406 751
Sep 210 263 351
Oct 159 164 320
Nov 143 265 252
Dec 125 254 381
89
A B
Figure 4-14. A) Monthly RSV hospitalization incidence rates [per 100 subject-months], B) NNT
(numbers needed to treat) with palivizumab by age in the high risk cohort in Illinois
A B
Figure 4-15. A) Monthly RSV hospitalization incidence rates [per 100 subject-months], B) NNT
(numbers needed to treat) with palivizumab by age in the high risk cohort in Texas
RSV-
Incidence
>1.0
0.4-1.0
<0.4
RSV-
Incidence
>1.0
0.4-1.0
<0.4
Age [months]
Month 0--6 7--12 13--24
Jan 2.564 1.563 1.033
Feb 2.489 1.888 1.097
Mar 1.552 1.245 0.734
Apr 0.703 0.411 0.227
May 0.153 0.086 0.083
Jun 0.145 0.214 0.027
Jul 0.000 0.085 0.000
Aug 0.000 0.000 0.053
Sep 0.000 0.087 0.027
Oct 0.137 0.130 0.027
Nov 0.396 0.260 0.132
Dec 0.832 0.553 0.409
NNT
<200
200-500
>500
Age [months]
Month 0--6 7--12 13--24
Jan 78 128 194
Feb 80 106 182
Mar 129 161 273
Apr 285 487 881
May 1304 2323 2407
Jun 1379 933 7408
Jul 99999 2348 99999
Aug 99999 99999 3757
Sep 99999 2292 7446
Oct 1455 1533 7404
Nov 505 770 1516
Dec 240 362 489
RSV-
Incidence
>1.0
0.4-1.0
<0.4
RSV-
Incidence
>1.0
0.4-1.0
<0.4
Age [months]
Month 0--6 7--12 13--24
Jan 2.788 2.126 1.180
Feb 2.774 1.184 0.981
Mar 1.116 0.752 0.421
Apr 0.305 0.330 0.165
May 0.211 0.076 0.159
Jun 0.124 0.051 0.000
Jul 0.000 0.052 0.020
Aug 0.043 0.129 0.020
Sep 0.167 0.082 0.020
Oct 0.279 0.248 0.139
Nov 1.478 1.070 0.448
Dec 1.837 1.307 0.646
NNT
<200
200-500
>500
Age [months]
Month 0--6 7--12 13--24
Jan 72 94 169
Feb 72 169 204
Mar 179 266 475
Apr 656 606 1216
May 946 2621 1261
Jun 1607 3913 99999
Jul 99999 3880 10142
Aug 4646 1554 10086
Sep 1198 2439 9992
Oct 717 806 1439
Nov 135 187 447
Dec 109 153 310
90
A B
Figure 4-16. A) Monthly RSV hospitalization incidence rates [per 100 subject-months], B) NNT
(numbers needed to treat) with palivizumab in the high-risk cohort for each region in
Florida
RSV-
Incidence
>1.0
0.4-1.0
<0.4
Month Northwest North Central Southwest Southeast
Jan 1.245 1.585 1.238 0.812 0.572
Feb 1.116 0.647 0.847 0.978 0.595
Mar 0.292 0.754 0.646 0.802 0.435
Apr 0.284 0.223 0.202 0.102 0.492
May 0.135 0.283 0.162 0.305 0.149
Jun 0.395 0.064 0.039 0.197 0.251
Jul 0.254 0.060 0.074 0.280 0.462
Aug 0.121 0.250 0.267 0.488 0.485
Sep 0.177 0.384 0.502 0.889 1.244
Oct 0.233 1.311 1.167 1.514 1.074
Nov 0.692 1.410 1.304 2.060 0.646
Dec 0.888 2.085 1.320 1.421 0.499
RSV-
Incidence
>1.0
0.4-1.0
<0.4
Month Northwest North Central Southwest Southeast
Jan 161 126 162 246 350
Feb 179 309 236 205 336
Mar 686 265 310 250 460
Apr 703 895 991 1958 406
May 1478 707 1232 656 1340
Jun 506 3102 5098 1017 798
Jul 788 3340 2688 714 433
Aug 1650 799 749 410 413
Sep 1130 521 399 225 161
Oct 859 153 171 132 186
Nov 289 142 153 97 310
Dec 225 96 152 141 401
NNT
<200
200-500
>500
91
CHAPTER 5
DISCUSSION
This study can be divided into two major sections, one section unrelated to the type of
immunoprophylaxis (parts I-IV) and a second one with content specific to palivizumab (parts V
and VI). In the first section, we used RSV hospitalization data to define season onset and offset
and validate the currently used surveillance system (part I). Next, we compared extent and
variability of seasonality between 4 states and 5 regions of Florida (part II). In part III, we
examined differences in seasonal characteristics as a factor of latitude in Florida. Part IV was
centered on the question whether patient characteristics play a role in timing of RSV infections
relative to the RSV season. This first section was not dependent on palivizumab as an agent for
immunoprophylaxis, and our conclusions about RSV seasonality and the validity of the
NREVSS should still be relevant when a new prophylactic agent or vaccine becomes available.
The subject of the second section was palivizumab; specifically, we examined whether
historically, palivizumab utilization was triggered by disease occurrence or followed a fixed
immunization schedule (part V). Finally, in part VI we provided monthly NNTs for palivizumab
to overcome limitations of a dichotomous season definition as a sole basis for RSV
immunoprophylaxis.
Part I: Validation of CDC’s Current RSV Season Definition
This analysis showed that the approach of using MPP of a sample of RSV laboratory tests
was able to detect seasons of RSV-hospitalizations for large US states. However, limitations to
this approach become apparent on the regional level in Florida with smaller AUCs and evidence
for poor specificity. Using the optimal MPP threshold as derived from the ROC analysis was
able to improve specificity on a regional level. Using the 10% threshold with the CDC’s
requirement that two consecutive weeks have to be above the threshold predicted a season onset
92
that was on average 4.6 weeks apart from the season onset according to hospitalizations in the
states and 6.1 weeks in the regions (table 4-7). Both measures were improved to 3.4 weeks and
4.5 weeks difference with the added requirement of at least 5 positive tests in a given week.
Using the optimal threshold and requiring that at least 5 tests be positive in each state or region
further reduced the difference to actual onset to 3.2 weeks in the states and 3.9 weeks in the
regions. Given that this is only a marginal improvement over the 10% MPP combined with 5
positive tests and given the distinct advantage of the 10% threshold that it is universal and does
not have to be predetermined for each geographic area as would be the case for the optimal
threshold, we recommend the continued use of the 10% MPP threshold but advocate for the
added requirement of 5 positive tests in a given week. This definition also optimizes the
prediction of season offset with only 2.2 weeks difference to the actual offset (table 4-8) on a
state level. However, on a regional level in Florida, the average absolute difference was 6.3
weeks and the direction -3.9, indicating that predicted offset would occur before the actual offset.
We hypothesize that the suboptimal performance of RSV surveillance on a regional level
in Florida was a consequence of two factors. First, if the number of laboratories in a geographic
area and the number of tests during a certain time period is small, outliers can skew the detection
of a season. This became especially apparent in the Florida north region (figure 4-5 B) where
single positive tests during periods of infrequent testing caused MPPs of up to 70% even outside
the season. In the north region, on average only 1.9 laboratories reported in a given week
compared to 4.8 in the southeast region. Yet, the number of laboratories in Illinois and Texas
was only slightly higher with an average of 2.1 and 3.0, respectively but we saw fewer outliers in
these states which may be a consequence of the more distinct RSV season with higher
seasonality indices in these states. Nevertheless, it is obvious that a small number of reported
93
tests can make the detection of a season difficult by either missing true cases or by providing a
small denominator, thus increasing the impact of single positive tests. The inclusion of
Surveillance Data, Inc. RSV data74
into NREVSS has the potential to overcome this limitation by
substantially increasing the number of participating laboratories. Second, the southwest and
especially southeast regions experienced a smaller degree of seasonality with seasonality indices
of only 5.2 and 2.5, respectively (table 4-9) and we observed extended periods where the MPP
was just above or just below the threshold in the southeast (figure 4-5 F). Hence, small variations
in the number of positive tests could have moved the MPP above the threshold causing a
different estimate of seasonality. Given the fact that seasonality was not very pronounced in the
southern part of Florida, it was expected and confirmed in our study that any season definition
has some degree of inaccuracy on a regional level in Florida. Therefore, it is not surprising that
even our recommended season definition has its poorest performance in Florida’s southeast
followed by the southwest. In fact, the remaining regions showed a level of accuracy comparable
to a state level.
Part II: RSV Epidemiology between Four US States and Five Regions in Florida
Implications of small seasonality indices in the southern regions of Florida were discussed
above. Pronounced differences between the states’ seasonality indices highlight the different
considerations that payers in these states are facing and will be discussed in part VI.
Our findings of seasonal variations within geographic areas over time deserve attention.
Season onset varied within states over 7 weeks in Illinois and Texas and 12 weeks in Florida.
Even the smallest variation of 7 weeks highlights the need for current surveillance information
on RSV activity. One may argue that using the center week of this range to predict the next
season has a similar accuracy as the NREVSS which is on average 3.4 weeks apart from the
actual RSV season onset (table 4-7). Nevertheless, our study period only included 5 seasons; it is
94
very possible that the range of season onset increases with more seasons observed which is
emphasized through the large confidence intervals of the range of season onset. In this case, a
fixed immunization recommendation based on previous seasons alone would not be able to
respond to changing seasons. Only current data can indicate current RSV activity in the light of
year-to year variation.
Part III: Latitude as a Factor in RSV Epidemiology in Florida
Florida Medicaid divides the state into 5 regions with regard to RSV seasonality and the
regional differences observed in our study confirm the need for this approach. Treating Florida
as a homogenous geographic area would overestimate season duration in the north and
underestimate it in the south, therefore not optimizing immunoprophylaxis. Our analysis showed
that latitude was a strong linear predictor for these regional differences as evidenced by large
coefficients of determination (R2) in the linear regression models (table 4-12). Moving 1 degree
south (111 km) was associated with 5 weeks longer season duration. Prior to this study, the
division was based on surveillance data only and we now can recommend the continued use of
the 5 regions based on actual hospitalization data. At this point, we cannot provide explanation
as to why latitude plays such a decisive role. It has been suggested that proximity to the coast can
influence RSV seasonality,8 yet coastal proximity characterizes most metropolitan areas in
Florida, independent of latitude. Climate differences may play a role since the climate becomes
more tropical and less seasonal towards the south but these influences deserve further research.
Regardless of the explanation, the presence of the regional differences in RSV seasonality needs
to be taken into account for decisions about the timing of immunoprophylaxis.
Part IV: Patient Factors and Seasonality
We found that the risk factor CLD was more important outside the season than during the
season. This observation would suggest that at periods of low viral activity, children at higher
95
risk for RSV were attacked first. However we could not confirm this association for the risk
factors CHD and young age (0-6 months). Conversely, belonging to the second age group (7-12
months) increased the RSV risk more during season than off-season, yet to a small extent. Taken
together, our observations do not provide conclusive evidence to support a hypothesis of a
different seasonal pattern relative to the risk status of a child.
Part V: Timing of Prophylaxis with Palivizumab vs. RSV Seasonality
Palivizumab utilization was consistently closest to a fixed date, not to the onset of RSV
hospitalizations or RSV season according to NREVSS. This observation suggests that
practitioners may not have based their timing of immunization on current surveillance, but rather
followed local PA requirements or the AAP guideline that recommends utilization between
November and April. The only PA requirement during our study period that restricted
immunoprophylaxis to fixed calendar months was used in California and allowed palivizumab
use between October and May, but only up to 6 doses. Here, we observed utilization between
November and April, a period that covered 6 doses. Texas Medicaid adjusted their PA period to
current NREVSS data, yet utilization seemed not to have been triggered by surveillance data but
rather followed a fixed schedule, namely from November through April.
The AAP guideline recommends that local surveillance data be taken into account, but we
cannot confirm that this was common practice. Since 3 out of 4 study states did not connect prior
authorization to RSV surveillance data from NREVSS and prescribers seem to have followed the
AAP guideline rather than current surveillance, research into the acceptance of RSV surveillance
data would be helpful. After our study period, Medicaid agencies in Florida and Illinois have
updated their PA requirements to include that palivizumab not be given outside of the local
season. This suggests that more states started to consider data from NREVSS in their
reimbursement decisions; however, our sample of only 4 states cannot give a representative
96
picture of PA throughout the nation. A survey of both third party payers and practitioners could
help understand the basis of timing of immunoprophylaxis in clinical practice. From our limited
sample it seems that practitioners used NREVSS data only indirectly since their prescribing
decisions were restricted by PA requirements. The predominant users of NREVSS are potentially
third party payers who have an incentive to minimize palivizumab utilization outside of the RSV
season.
In all states and in most regions of Florida, utilization was started before the onset of RSV
hospitalizations and ceased after the offset of viral activity, indicating sufficient temporal
coverage. Nevertheless, immunoprophylaxis was initiated early, as much as 7 weeks on average
before RSV onset in Illinois. While this practice can be beneficial from a pure clinical
perspective, it may need to be reviewed in the light of resource optimization. Once again, the
incorporation of current surveillance data can help target immunoprophylaxis to periods of high
RSV activity.
Part VI: Optimizing Timing of Prophylaxis
The analysis of monthly RSV incidences and corresponding NNTs deserves interpretation.
An NNT of 200 translates into 200 children who have to receive immunoprophylaxis in a given
month to prevent one case of RSV hospitalization; hence a small NNT generally indicates a more
beneficial intervention. Since palivizumab is a costly drug, third party payers interpret NNTs
with regard to related expenses. Our own research has shown that a single dose costs on average
$1,338 in the age category 0-6 months, $1,750 from 7-12 months and $2,087 for children from
13-24 months of age from a Medicaid perspective.5 For a NNT of 200, this means that for the
youngest age group at high-risk for RSV and therefore with indication for palivizumab, $267,600
have to be spent on immunoprophylaxis to prevent one hospitalization (table 5-1). Older age
categories are associated with lower RSV risk and, due to higher body weight, with higher cost
97
for prophylaxis. A NNT of 500 for the category of children between one and two years of age
requires $1,043,500 to prevent a single hospitalization. The same study found that the actual cost
for RSV hospitalizations averages well below $10,000. For subjects with indication for
immunoprophylaxis, the average hospitalization cost was $7,198.01. Hospital claims ranged
from $927.65 to $54,689.31 and 90% of paid claims were below $16,714.47. Half of the
hospitalizations were associated with claims below $4,551.38. We compared these estimates
with cost estimates from the Healthcare Cost and Utilization Project (HCUP) which uses data
based on all-payer information. A search for RSV hospitalizations in the year 2006 found 66,266
RSV-related hospitalizations nationwide for infants up to one year of age. The average charge
was $11,745 with an average length of stay of 3.4 days and a total of 45 in-hospital deaths.91
Of
note, HCUP does not provide cost estimates and, because of discrepancies, charges should not be
used to approximate actual cost.92
To overcome this limitation, we applied a cost-to charge ratio
(CCR) of 0.53, which yielded an average cost estimate of $6,225 for one RSV-hospitalization for
all children, regardless of their risk status. This CCR has been used with HCUP data from
2002,93
but we were unable to obtain a more recent estimate. The consistency between Medicaid
and HCUP cost estimates strengthens the generalizability of our findings, namely the distinct
difference between immunization and hospitalization cost.
Statewide RSV incidence rates decreased with increasing age which is consistent with the
RSV literature24, 94
and confirms results of our multivariate model from part I. The model
revealed that after adjusting for clinical risk factors, the youngest age category (0-6 months) had
a 10.1 fold increased RSV risk compared to the oldest age category (13-24 months) while the
middle category only has a 2.6 fold higher risk (table 4-2). Since the NNT is inversely related to
RSV incidence, we found a distinct increase in NNT associated with the older age groups. Both
98
factors, higher cost for prophylaxis and lower RSV incidence in older children should be taken
into account in the evaluation of the appropriateness of immunoprophylaxis in the second year of
life.
The analysis of their own monthly RSV hospitalization data should be considered by each
state or third party payer. It offers several advantages over only using surveillance data to guide
RSV immunoprophylaxis. First, while surveillance data only provide information about presence
or absence of viral activity, hospitalization data can inform about the extent of the RSV burden.
Payers may find that in some months, the burden is too low to justify prophylaxis even though
the surveillance system would indicate RSV season. Second, hospitalization data can provide
insight about RSV risk factors such as age. Different third party payers may have different
willingness-to pay thresholds and can determine which groups of patients have acceptable NNTs
using their own criteria. Similarly, since the burden of disease differs between the states as
evidenced in part II, states may use hospitalization data to determine whether lower local RSV
activity justifies prophylaxis at all.
External Validity
Generalizability of our study findings needs to be evaluated on two different levels: first,
whether our results derived from a Medicaid sample are generalizable to the general pediatric
population and second, whether our sample of 4 states is nationally representative. A strength of
the selection of Medicaid data for our study is that a large proportion of all young children are
enrolled in Medicaid at a given point in time. During the year 2000, one of our study years,
532,610 infants were born in California.95
Of these, 212,361 (39.9%) were enrolled in the
Medicaid program at some point during the year 2000. After applying our eligibility criteria, our
final sample retained 111,622 (21.0%) infants with Medicaid fee-for service eligibility at birth.
In Florida, of 204,305 live births, 50.4% were enrolled in Medicaid in 2000 and 32.1% were
99
included in our final sample. Our study sample further included 42.2% of 181,984 live births in
Illinois and 46.2% were enrolled in Medicaid at some point in 2000. Finally, out of 368,019 live
births in Texas in the year 2000, 53.6% were enrolled in Medicaid and we included 39.4% of all
newborns in our final study sample. These data confirm that Medicaid is the single largest
healthcare program for infants. Our final sample included as many as 42.2% of all newborns in
Illinois, however significantly fewer in California due to the large managed care population in
the latter state.
Although our sample covered a large proportion of the pediatric population,
representativeness is not automatically guaranteed, especially if the sample differs from the
general population with regard to relevant characteristics. Medicaid enrollees are often reported
to be at a worse health state compared to the rest of the population which suggests that the
Medicaid dataset may over represent children at higher risk for RSV hospitalization. This is
confirmed by the Palivizumab Outcomes Registry reporting an unadjusted odds ratio of 1.76
(p<0.0001) for RSV hospitalization between Medicaid recipients and non-recipients.96
This
concern can be addressed. Our study included a division of children into high-risk and low-risk
for RSV hospitalization based on the current AAP guideline. According to our definition for
seasonality based on claims data, seasons were periods where the RSV incidence rate among
high-risk infants was above the maximum incidence rate for low-risk children. Not relevant to
generalizability is the relative size of these groups, which would differ between the Medicaid
sample and the general population due to the over representation of high-risk infants. It is
irrelevant simply because the incidence rate is not a factor of sample size.
Our choice of 4 large geographically diverse states allows for geographic generalizations.
The fact that the AUCs together with sensitivity and specificity were consistently high for the
100
statewide application of RSV surveillance data makes us confident that the validity of the
NREVSS and the RSV season definition are applicable on the level of large states or even larger
surveillance regions as used by the CDC, independent of their geographic locations. While the
season definition is universal, the burden of RSV and the extent of seasonality differ widely
between geographic areas. As a consequence, our calculation of monthly incidences and
corresponding NNTs are only applicable to the study states. As discussed before, third party
payers who are interested in the burden of RSV relevant to their population are served best by
consulting their own hospitalization data.
Study Limitations
Several limitations should be considered when interpreting the results of this study. First,
we faced unforeseen difficulties in obtaining social security numbers from state Vital Statistics
departments as a linkage variable between birth certificates and CMS claims data. Alternative
variables for linking records were not available either. At this point, the study only includes birth
certificates and therefore gestational age estimates for Florida, not for the other states. As a
consequence, the high-risk cohorts in the other states only included CLD and CHD, not
prematurity I and II, and the low-risk cohorts did not exclude prematurity III. In a sensitivity
analysis based on the Florida dataset, we tested how the absence of information on prematurity
may have affected our results. Ignoring prematurity in Florida, we estimated a season onset that
was on average 1.6 weeks apart from estimates with prematurity. Season offset was on average
2.4 weeks apart from estimates based on the complete dataset. This small difference supports the
accuracy of our study results even in the absence of gestational age information in California,
Illinois and Texas. Nevertheless, to maximize accuracy and ultimately strengthen our study, we
are still pursuing ways to access birth certificates in all study states and we will update all
analyses upon successfully defining prematurity.
101
Second, a limitation to our study is the possibility of misclassified RSV infections; i.e.
bronchiolitis and pneumonia hospitalizations that were due to RSV but not coded as such. We
aimed to quantify the potential for misclassification. For this, we divided cases of bronchiolitis
and pneumonia hospitalizations into three categories: RSV-related, specific to other organisms,
and unspecific (see appendix for diagnostic codes). For Florida, we plotted the incidence rate of
hospitalizations falling into each of these categories in each calendar month (figure 5-1). We
considered the number of unspecified cases as the maximum of potentially misclassified RSV
cases. It is conceivable that some cases with specific codes for other agents were also
misclassified RSV cases, however, potentially with a lower likelihood of misclassification since
an alternative causal agent was identified. The results show that in case all unspecific
bronchiolitis and pneumonia hospitalizations were in fact RSV cases, the RSV incidence rate
would be underestimated by approximately 40%. Furthermore, given that the incidence rates of
specified and un-specified non-RSV cases have a strikingly parallel course; it is very likely that
many of the unspecified cases are in fact non-RSV-related bronchiolitis or pneumonia
admissions, thus not affecting our study. This misclassification would not change conclusions
about RSV seasonality under the assumption that it occurred to an equal amount in the high-risk
and low-risk cohort. Misclassification of RSV cases would mainly affect part VI, resulting in an
underestimate of the monthly RSV incidence rates and in an overestimate of NNTs.
Nevertheless, given the limited amount of misclassification, NNTs would remain high even after
accounting for missed RSV cases and conclusions about the need for careful consideration of
immunoprophylaxis would remain unchanged.
Lastly, the accuracy of palivizumab claims in Medicaid claims data is limited as a study
found using data from the North Carolina Medicaid program.97
Briefly, this study compared
102
dates and frequencies of palivizumab claims with abstracted ambulatory and inpatient medical
records. The investigators found 87.0% agreement of infants who received any palivizumab
injection in both datasets, but agreement was only reached in 46.1% of infants about the number
of injections. Although not mentioned in the study, the second finding may be related to
injections during inpatient stays that would have been identified in the medical records but not in
the Medicaid claims dataset due to the aggregate nature of a Medicaid hospitalization claim. We
tried to increase accuracy of exposure information in our study by requiring 4 weeks of
ambulatory care preceding the current week to ensure that palivizumab administration could
have created an outpatient/pharmacy claim. Furthermore, we required that a palivizumab
recipient had a physician office visit (or nurse practitioner, physician assistant…, see appendix)
within 10 days of the claim. Although this requirement is not very specific, it can exclude
palivizumab claims with questionable validity. Inaccuracy in detecting palivizumab exposure has
the theoretical potential to bias RSV incidence estimates since they were adjusted to account for
palivizumab exposure. Since palivizumab was targeted to high-risk children (table 4-3), only
their RSV incidence estimates would be affected. However, since less than 20% of subject-
weeks in the high-risk cohort were associated with palivizumab prophylaxis, our incidence
estimates were mostly driven by observed hospitalizations and only to a small amount affected
by adjustment for immunoprophylaxis. Thus, we feel confident that our results regarding RSV
seasonality are robust despite the potential for exposure misclassification.
Future Research
With the inclusion of more RSV laboratories provided by Surveillance Data, Inc.,
NREVSS is expected to offer more accurate estimates on a smaller geographic level. Future
studies should investigate whether regional limitations of RSV surveillance which we identified
in Florida could be overcome with the added number of laboratories. Since the inclusion of the
103
additional laboratories occurred only from the 2006-07 RSV season, more years of observation
are necessary to determine beneficial effects of the larger sample.
As we outlined before, third party payers are strongly encouraged to use their own
inpatient data to determine the absolute burden of RSV relevant to their population. This would
enable them to select a population and timing of immunoprophylaxis that is consistent with their
own willingness-to pay thresholds.
Finally, our utilization analysis showed that historically, immunoprophylaxis seemed not
primarily triggered by season detections based on RSV surveillance. Surveying third party
payers and practitioners about the basis of their decisions about timing of prophylaxis can help
understand and potentially increase the acceptance of NREVSS. Moreover, this research could
provide a synopsis of current PA programs and share the experience of their success and
limitations.
Summary and Conclusions
This study validated the CDC’s approach to detect seasons of RSV activity based on
laboratory surveillance. We recommend the continued use of the 10% MPP threshold, however
with the added requirement of 5 positive tests in a given week. The performance of the
laboratory-based approach was suboptimal in the southwest and southeast regions in Florida and
should be applied carefully in areas with less distinct seasonality. Next, our study identified
differences in seasonality over time and a different extent of seasonality between the 4 study
states and even within Florida, therefore confirming the importance of a surveillance system that
offers current and local information on RSV activity. Based on RSV hospitalization data, we
confirmed the need to subdivide the state of Florida into 5 regions to appropriately account for
differences in RSV epidemiology. We found that historically, palivizumab utilization seemed not
primarily triggered by seasons detected with laboratory surveillance; therefore further research is
104
necessary to help understand the acceptance of the NREVSS. Finally, we provided monthly RSV
incidence rates for each state and for each region in Florida. The corresponding NNT estimates
can provide further detail on the burden of disease to guide reimbursement practices and thus,
overcome limitations of a dichotomous RSV season definition. Higher NNTs for older children
as a result of a lower RSV incidence combined with the need for higher and more costly doses of
palivizumab after infancy highlight the reduced benefit of immunoprophylaxis in the second year
of life.
105
Table 5-1. Cost of prophylaxis per avoided RSV hospitalization
Age [months]
0-6 7-12 13-24 0-24
avg. cost per dose $1,338 $1,750 $2,087 $1,688
NNT cost per avoided hospitalization
100 $133,800 $175,000 $208,700 $168,800
200 $267,600 $350,000 $417,400 $337,600
300 $401,400 $525,000 $626,100 $506,400
400 $535,200 $700,000 $834,800 $675,200
500 $669,000 $875,000 $1,043,500 $844,000
Abbreviation: NNT: number needed to treat
Figure 5-1. Distribution of diagnostic codes for bronchiolitis and pneumonia related
hospitalizations
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Iin
cid
en
ce ra
te [h
osp
./1
00
su
bje
ct-
week
s]
Incidence of unspecific non-
RSV hospitalizations
Incidence of RSV
hospitalizations
Incidence of specific non-RSV
hospitalizations
Incidence if all unspecific cases
were due to RSV
106
APPENDIX
A. Operational Definitions
The Medicaid prescription drug dataset did not include generic class codes, only NDC. We
ordered a crosswalk file from NDC codes to drug names, generic class codes and American
Hospital Formulary Service (AHFS) class codes. We were able to match 99.56 % of all
prescriptions from the Medicaid dataset.
Palivizumab Exposure
Any of (NDC codes for palivizumab: 60574411101, 60574411201, 60574411301,
60574411401 or procedure code for palivizumab: 90387, (additionally in California: C9003,
X7439; Texas: 1086X, 1095X)) in conjunction with an outpatient visit to a physician, other
practitioner, outpatient hospital, clinic, home health, other services, nurse practitioner or private
duty nurse (Medicaid statistical information system (MSIS) type of service codes 08, 10, 11, 12,
13, 19, 37 or 38) within 10 days before or after the palivizumab claim.
Risk Factors for RSV
Chronic lung disease
Children younger than 2 years with CLD who received medication for CLD (Steroids,
bronchodilators, oxygen, diuretics) within 6 months of the current week. They need to have at
least one (ICD-9:770.7, 496x) claim at any time before the current week and either
A) At least one of the following CLD medications based on AHFS codes:
121208 BETA-ADRENERGIC AGONISTS
680400 ADRENALS
861600 RESPIRATORY SMOOTH MUSCLE RELAXANTS
481024 LEUKOTRIENE MODIFIERS
402820 THIAZIDE DIURETICS
107
402824 THIAZIDE-LIKE DIURETICS
402808 LOOP DIURETICS
402816 POTASSIUM-SPARING DIURETIC
or B) ICD-9 code: V46.2
or C) Oxygen Procedure Code: 93.96, E1390, E1392, E1400, E1401, E1402, E1403,
E1404, E1405, E1406, E0424, E0431, E0434, E0439, E0441, E0442, E0443,
E0444, E0450
within the previous 6 months of the current week
Prematurity
Prematurity I: Infants with a gestational age of less than 28 weeks if they are not more than 12
months at the beginning of the current week
Prematurity II: Infants with gestational age of 29-32 (32 weeks and 0 days) weeks if they are not
more than 6 months old in at the beginning of the current week
Prematurity III: Infants with gestational age of 32-35 (from 32 weeks and 1 day) weeks if they
are not more than 6 months old in at the beginning of the current week
Congenital heart disease
Children younger than 2 years with hemodynamically significant cyanotic and acyanotic
CHD: Acyanotic CHD is only considered significant if medication (ACE-Inhibitor, Digoxin,
Diuretics, or Oxygen) is necessary to treat the disease.
They need to have at least one of the following ICD-9 codes: 745.10, 747.41, 745.0,
745.11, 745.2, 747.42, 745.3, 746.1, 746.7, 745.1, 745.12, 745.19, 746.2, 747.3, 747.4, 747.40,
747.49 (cyanotic heart disease) any time before the current week
OR [at least one of the ICD-9 codes: 746.86, 747.11, 747.22, 745.4, 745.5, 745.6,
745.60, 745.61, 745.69, 745.7, 745.8, 745.9, 746.0, 746.00, 746.01, 746.02, 746.09,746.3, 746.4,
108
746.5, 746.6, 746.8, 746.81, 746.82, 746.83, 746.84, 746.85, 746.87, 746.89, 747.0, 747.1,
747.10, 746.9, 747, 747.2, 747.20, 747.21,747.29, 747.5, 747.6, 747.60, 747.61, 747.62, 747.63,
747.64, 747.69, 747.8, 747.81, 747.82, 747.83, 747.89, 747.9 (acyanotic CHD) any time before
the current week AND [at least one of the following CHD medications based on AHFS codes
within 6 months before the current week:
243204 ANGIOTENSIN-CONVERTING ENZYME INHIBITORS
402808 LOOP DIURETICS
402816 POTASSIUM-SPARING DIURETICS
402820 THIAZIDE DIURETICS
402824 THIAZIDE-LIKE DIURETICS
240408 CARDIOTONIC AGENTS (Digoxin)
OR Oxygen Procedure Codes: 93.96, E1390, E1392, E1400, E1401, E1402, E1403,
E1404, E1405, E1406, E0424, E0431, E0434, E0439, E0441, E0442, E0443, E0444, E0450]]
Cystic fibrosis
At any time before the current week: ICD-9 code: 277.0x
Severe combined or acquired immunodeficiency
At any time before the current week: ICD-9 code: 042xx, 279.2, 279.11 or 758.32
Down syndrome
At any time before the current week: ICD-9 code: 758.0
Asthma
At any time before 2 weeks before the current week: ICD-9 code 493xx AND asthma
medication between 120 and 14 days before the beginning of the current week:
AHFS code:
121208 BETA-ADRENERGIC AGONISTS
109
481024 LEUKOTRIENE MODIFIERS
120808 ANTIMUSCARINICS/ANTISPASMODICS only Ipratropium
(not nasal)
680400 ADRENALS
861600 RESPIRATORY SMOOTH MUSCLE RELAXANTS
Transplant
Transplant (ICD-9: V42.x) or awaiting transplant (ICD-9: V49.83) at any time before the
current week
Malignancy
At any time before the current week: ICD-9 codes: 140-209, 230-234
Immunosuppression or antineoplastic agents
Any time before the current week:
AHFS code:
100000 ANTINEOPLASTIC AGENTS
680400 ADRENALS (oral only)
OR Drug names: INFLIXIMAB, GOLD, AURANOFIN, AUROTHIOGLUCOSE,
LEFLUNOMIDE, ETANERCEPT, ADALIMUMAB, ANAKINRA, LETROZOLE,
AZATHIOPRINE, MYCOPHENOLATE, CYCLOSPORINE, TACROLIMUS ANHYDROUS
(excludes topical form), SIROLIMUS, DACLIZUMAB, INTERFERON, LETROZOLE,
ALDESLEUKIN, OMALIZUMAB, THALIDOMIDE
Hospitalizations
RSV hospitalization
ICD-9 codes: 079.6, 466.11 or 480.1 in the inpatient dataset.
110
Specific non-RSV bronchiolitis or pneumonia
ICD-9 codes: 480.0, 480.2, 480.3, 480.8, 481, 482.xx, 483.xx, 484.xx or 466.19 in the
inpatient dataset.
Unspecific bronchiolitis or pneumonia
ICD-9 codes: 480.9 or 486 in the inpatient dataset.
111
B. Supplemental Tables
Table B-1. List of counties in Florida
County FIPS code Region County FIPS code Region
ALACHUA 1 N LAKE 69 C
BAKER 3 N LEE 71 SW
BAY 5 NW LEON 73 NW
BRADFORD 7 N LEVY 75 N
BREVARD 9 C LIBERTY 77 NW
BROWARD 11 SE MADISON 79 N
CALHOUN 13 NW MANATEE 81 SW
CHARLOTTE 15 SW MARION 83 C
CITRUS 17 C MARTIN 85 SE
CLAY 19 N MONROE 87 SE
COLLIER 21 SW NASSAU 89 N
COLUMBIA 23 N OKALOOSA 91 NW
DADE 25 SE OKEECHOBEE 93 SW
DESOTO 27 SW ORANGE 95 C
DIXIE 29 N OSCEOLA 97 C
DUVAL 31 N PALM BEACH 99 SE
ESCAMBIA 33 NW PASCO 101 C
FLAGLER 35 C PINELLAS 103 C
FRANKLIN 37 NW POLK 105 SW
GADSDEN 39 NW PUTNAM 107 N
GILCHRIST 41 N SANTA ROSA 113 NW
GLADES 43 SW SARASOTA 115 SW
GULF 45 NW SEMINOLE 117 C
HAMILTON 47 N ST. JOHNS 109 N
HARDEE 49 SW ST. LUCIE 111 SE
HENDRY 51 SW SUMTER 119 C
HERNANDO 53 C SUWANNEE 121 N
HIGHLANDS 55 SW TAYLOR 123 N
HILLSBOROUGH 57 C UNION 125 N
HOLMES 59 NW VOLUSIA 127 C
INDIAN RIVER 61 SE WAKULLA 129 NW
JACKSON 63 NW WALTON 131 NW
JEFFERSON 65 NW WASHINGTON 133 NW
LAFAYETTE 67 N
Abbreviation: FIPS: Federal information processing standard
112
Table B-2. Coordinates of Florida regions
Florida Region Centroid latitude
[degrees North]
Centroid longitude
[degrees West]
Northwest 30.293 85.597
North 29.909 82.607
Central 28.622 81.629
Southwest 27.083 81.712
Southeast 26.197 81.087
Table B-3. Week numbers and corresponding calendar months, shown for the year 2000.
Week Month Week Month
1*
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
January
January
January
January
February
February
February
February
March
March
March
March
March
April
April
April
April
May
May
May
May
June
June
June
June
June
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
July
July
July
July
August
August
August
August
August
September
September
September
September
October
October
October
October
November
November
November
November
November
December
December
December
December
*week 1 is the first week that ended in 2000.
113
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BIOGRAPHICAL SKETCH
Christian Hampp was born in Neunkirchen/Saar and raised in St. Ingbert, Germany. He
received his bachelor’s degree in pharmaceutical sciences from Saarland University in
Saarbrücken in 2003. In 2004, after he became a registered pharmacist in Germany he joined the
department of Pharmaceutical Outcomes & Policy at the University of Florida where he was
named the first honorary recipient of the DuBow Family Fellowship for Pharmaceutical
Outcomes and Policy Research.
Christian has authored and coauthored several peer-reviewed publications and presented at
national and international conferences. His research interests focus on drug safety and
effectiveness, cost-effectiveness and the evaluation and prevention of inappropriate drug use. He
is further interested in infectious disease epidemiology and methods to describe temporal and
geographic patterns of disease occurrence.