Identifying Predictors for Pertussis Disease in Texas Infants Utilizing Surveillance and Birth Certificate Data from 1999-2003

Lucille PalenapaIDEW PresentationFri, Jun 27, 2008

Overview

• Review project– Objective– Methodology

• Results• Recommendations• Q&A

Pertussis Challenges

• Difficult to diagnose & distinguish• Often ruled out based on vaccine

history• Lab results often not reliable• Difficult to obtain sterile sample

**Need to facilitate earlier identification of disease– Establish risk factors for pertussis

disease

Texas Birth Certificate Data

• Utilized in past research to assess risk factors for infectious diseases

• Comprehensive source of infant, maternal and paternal characteristic data

Objective

• Identify significant risk factors for pertussis disease in infants <12 months of age in Texas utilizing surveillance and birth certificate data from 1999-2003

Methodology

• Case-infant defined: – Infant reported to DSHS as a

confirmed or probable pertussis case– <12 months at onset– Born in Texas from 1999-2003

• Control-infant defined:– Randomly selected by same date of

birth as case-infant – Not reported as pertussis case to

DSHS– Born in Texas from 1999-2003

Exclusion criteria

• Infants who were:

– Ruled out or lost to follow-up

– Not born in Texas

– >12 months at disease onset

– > 1 case of pertussis in 5-year study

(counted only once)

Methodology

• Retrospective case-control study

• 5 year data (1999-2003)

• DSHS pertussis surveillance data

• Control group– Texas birth certificate data

• DSHS IRB submission

Methodology (Data Collection)

• After IRB approval, collected:

– Surveillance Data:• Identified infants reported to DSHS as

(confirmed and probable) pertussis cases who were <12 months at onset for each respective year (1999-2003)

– Birth certificate data:• Received large birth data files (avg 300-

365K records w/ avg 200 fields /year)

Methodology (cont.)

• SAS Statistical Software Utilized:– Recode and reformat birth certificate

data into readable format– Match cases to birth certificate data:

• Matched on name and date of birth• Quality assurance done by hand to ensure

match of surveillance case to birth data– 451 cases

– 3 controls randomly selected for each case based on same date of birth

– 1353 controls

Methodology (cont.)

• 15 variables initially chosen for analysis– 2 variables (birth length & medicaid

participation) eliminated because data completeness <50%

• Variables chosen based on indications of biological feasibility and on previous epidemiological studies

Table 1. Variables Selected for Analysis from Texas Birth Certificate

Variable % missing Selected(Yes/No)

(Infant Charac.)

Birth length 51.2 No

Birth sequence 0.0 Yes Birth type 0.0 Yes Birth weight 0.1 Yes Gestational age 1.9 Yes No. siblings living 0.3 Yes Sex 0.0 Yes

Table 1. Variables Selected for Analysis from Texas Birth Certificate (cont.)

Variable % missing Selected(Yes/No)

(Maternal Charac.)

Age 0.0 Yes

Alcohol Use 0.1 Yes Education 2.2 Yes Hispanic Origin 0.4 Yes Marital Status 0.2 Yes Medicaid 64.9 No Prenatal Care 4.9 Yes Tobacco Use 0.7 Yes

Analysis

• Descriptive statistics

• Multivariate logistic regression model:

• Reducing confounders:– 10% odds ratio analyses, removal of

variables not causing at least 10% change in OR

Results

Table 2. Mean Values of Continuous Variables Describing Infant and Maternal Characteristics by Case Status

Variable CaseControl

n=451n=1353

Infant Charac.

Birth weight (gms) 3182.63306.7

Gestational age (wks) 40.1 39.4

No. siblings living 1.5 2.1

Maternal Charac.

Age (yrs) 24.8 26.4

Education (yrs) 13.7 14.1

Table 3. Distribution of Categorical Variables Describing Maternal Characteristics by Case Status

Variable CaseControln=451 n=1353

Does mother use alcohol?Yes 5 (1.1) 8 (0.6)No 445 (98.9) 1334 (99.4)

Education>12 yrs 188 (42.7) 414 (31.3)<12 252 (57.2) 911 (68.8)

Is mother of Hispanic origin?Yes 242 (54.0) 640 (47.4)No 206 (45.9) 709 (52.6)

Maternal Status (Mother married?)Yes 277 (61.4) 932 (69.0)No 174 (38.6) 418 (30.9)

Table 3. Distribution of Categorical Variables Describing Maternal Characteristics by Case Status (cont.)

Variable CaseControl

n=451 n=1353

Number of prenatal visits>1 420 (97.9) 1264 (98.2)0 9 (2.1) 23 (1.8)

Mother’s RaceWhite* 399 (88.5) 1160 (85.8)Black 41 (9.1) 146 (10.8)Native-American 1 (0.2) 1 (0.1)Asian 9 (2.0) 39 (2.9)Pacific Islander 0 (0.0) 4 (0.3)Unknown 1 (0.2) 3 (0.2)

Does mother smoke cigarettes?Yes 42 (9.3) 70 (5.2)No 408 (90.7) 1271 (94.8)

Table 4. Crude Odds Ratios and 95% Confidence Intervals for Infant Pertussis According to Maternal Characteristics

Variable Case Control Effect

n=451 n=1353OR (95% CI)

Maternal age (yrs)

<19 103 178 2.3 (1.6-3.1)

20-29 238 749 1.2 (0.9-1.6)30-39 103 401 Referent

>40 7 25 1.1 (0.5-2.6)

Maternal education (yrs)

<8 48 125 2.1 (1.3-3.3)

>8 and <12 280 698 2.2 (1.6-3.0)

>12 and <15 60 219 1.5 (0.9-2.2)

>16 52 283 Referent

Is mother of Hispanic origin?

Yes 242 640 1.3 (1.1-1.6)No 206 709 Referent

Table 4. Crude Odds Ratios and 95% Confidence Intervals for Infant Pertussis According to Maternal Characteristics (cont.)

Variable Case Control Effect

n=451 n=1353OR (95% CI)

Mother marriedYes 277 932 ReferentNo 174 418 1.4 (1.1-1.7)

Does mother smokecigarettes?

Yes 42 70 1.9 (1.3-2.8)No 408 1341 Referent

Table 5. Crude Odds Ratios and 95% Confidence Intervals for Infant Characteristics

Variable Case Control Effect

n=451 n=1353OR (95% CI)

Infant birth sequence1st 426 1320 Referent>2nd 25 33 2.3 (1.4-4.0)

Infant birth typeSingle 426 1320 ReferentMultiple 25 33 2.4 (1.4-4.0)

Infant birth weight (gms) <1499 8 22 1.2 (0.5-2.7)1500-2499 50 61 2.7 (1.8-3.9)>2500 393 1270 Referent

Table 5. Crude Odds Ratios and 95% Confidence Intervals for Infant Characteristics (cont.)

Variable Case Control Effect

n=451 n=1353OR (95% CI)

Infant gestational age (wks)<37 110 245 1.5 (1.2-1.9)>38 327 1087 Referent

Infant sexMale 218 710 ReferentFemale 233 643 1.2 (0.9-1.9)

No. of siblings living0 146 526 Referent1-4 290 787 1.3 (1.1-1.7)>5 14 23 2.2 (1.4-4.4)

Final Data Analyses

• Multivariate logistic regression:– 6 variables selected as significant

predictors of pertussis disease

• 10% adjusted odds ratio:– No variable eliminated– 6 variables still remained

Table 6. Crude Odds and Adjusted Ratios and 95% Confidence Intervals of Significant Predictors for Pertussis

Variable Case Control Effect Adj. Effect n=451 n=1353 OR (95% CI) OR (95% CI)

Infant birth typeSingle 422 1290 Referent ReferentMultiple 25 33 2.4 (1.4-4.0) 1.9 (1.0-3.4)

Infant birth weight<1499 8 20 1.2 (0.5-2.7) 1.4 (0.6-3.4)1500-2499 48 60 2.7 (1.8-3.9) 2.1 (1.4-3.3)>2500 391 1243 Referent Referent

No. of siblings living0 144 520 Referent

Referent1-4 289 780 1.3 (1.1-1.7) 1.8 (1.4-2.3)>5 14 23 2.2 (1.4-4.4) 3.1 (1.5-6.5)

Table 6. Crude Odds and Adjusted Ratios and 95% Confidence Intervals of Significant Predictors for Pertussis (cont.)

Variable Case Control Effect Adj. Effect n=451 n=1353 OR (95% CI) OR (95% CI)

Maternal cigarette useYes 42 70 1.9 (1.3-2.8) 2.1 (1.3-3.5)No 405 1253 ReferentReferent

Maternal age<19 101 175 2.3 (1.6-3.1) 3.0 (2.1-4.4)

20-29 237 734 1.2 (0.9-1.6) 1.3 (1.0-1.7)30-39 102 390 Referent Referent>40 7 24 1.1 (0.5-2.6) 1.0 (0.4-2.5)

Maternal Hispanic OriginYes 242 628 1.3 (1.1-1.6) 1.3 (1.0-1.5)

No 205 695 ReferentReferent

Significant Predictor Variables

Variable Adj. OR (95% CI)

-Number of siblings >5 3.1 (1.5-6.5)-Maternal age <19 yrs 3.0 (2.1-4.4)-Infant low birth weight

(1500-2499 gms) 2.1 (1.4-3.3)-Maternal cigarette use

(Yes) 2.1 (1.3-3.5)

Discussion & Recommendations

Number of siblings living

• No current literature w/ exact findings• Similar findings:

– As no. of older siblings increased, delay in immunization increased for household infants and younger siblings (Reading, Surridge, & Adamson, 2004)

– Infants from larger household size less likely to be fully immunized, more likely to have delayed immunization (Li & Taylor, 1993; Peckham, Bedford, Senturia, & Ades, 1989)

– Later born siblings more likely to have delayed immunization than firstborn children (Higgins, 1990; Kaplan, Macie-Taylor, & Boldsen, 1992; Schaffer & Szilagyi, 1995)

– Later born children more prone to infectious disease (Kaplan, 1990)

Significant Maternal Variables

• Maternal Age– Similar findings for young maternal

age (Izurieta et al., 1996)– Adolescent aged mothers found to

have significantly lower levels of antibodies for pertussis than older mothers (Gonik, 2005 & Healy, 2006)

Significant Infant Predictors

• Low birth weight (LBW) infant– Biologically feasible– Similar findings, LBW infants more

likely to develop pertussis than normal birth weight infants (Langkamp & Davis, 1996)

Maternal Cigarette Use

• Similar findings established:– Maternal smoking increases the

likelihood of respiratory infections in infants (Ahmer et al., 1999; Ahmer, et al., 1998; Geng, Savage, Razani-Boroujerdi & Sopori, 1996; Saadi, et al., 1996; Stocks & Dezateux, 2003)

• Smoking during pregnancy assoc w/ several adverse outcomes:– Premature delivery– Spontaneous abortion– Growth restriction– Increased risk of SIDS

Limitations of Study

• Difficult to gauge the effects of many covariates with the statistical procedures used

• Problem of multiple comparisons present, acceptance criterion may have been satisfied purely by chance

• Use of birth certificate data to predict health outcomes has had mixed reviews

• Method for reporting disease

Recommendations

• Increase awareness and knowledge of serious dangers of pertussis

• Specially targeted education campaigns should include a focus on:– Infant households with large number

of siblings– Teen mothers – Low birth weight infants– Pregnant mothers and mothers with

infants who smoke

Recommendations (cont.)

• “Cocooning” strategy– “Immunization of family members and

close contacts of the newborn”– Post-partum

• Keep infants up-to-date on vaccinations

• Tdap booster vaccine for adolescents and adults

Benefits of Study

• Aid clinicians by establishing risk factors to facilitate earlier recognition of disease and earlier consequent prophylaxis treatment of patient and close contacts

• Reduce health care costs associated with pertussis

• Reduce lost productivity

Benefits of Study (cont.)

• Protect those most vulnerable, the future of Texas…

Study Contributors

• Rita Espinoza• Marilyn Felkner• Richard Taylor• Eric Miller

THANK YOU!Lucille Palenapa

(512) 458-7111 x.6611lucille.palenapa@dshs.state.tx.us