Concepts Related ToAssessing Health Status/Outcome

Measures

2

Quantify Population-Based Need/Demand

for Primary Care

Adjust Demand for Population Variables?(Age/Gender, Health Status)

Quantify ProviderSupply/Capacity

Scale(s) of ProviderAdequacy/Shortage(Combined measure of

Supply vs Demand )

Set Threshold(s) forHPSA Designation

Assess Health Outcome Deficits/Disparities(Areas/Populations with

persistently and significantlynegative health indicators )

Assess Other Indicatorsof Med.Underservice

(Nature/Indicators TBD)

Scale(s) of MedicalUnderservice

(Assessed separately or Integrated into an index)

Set Threshold(s) forMUA/P Designation

or

or

or

3

Health Status/Outcome• Some communities demonstrate measurably

adverse primary-care related health statistics – May be associated with, or independent, of

provider workforce shortage/accessibility • May result from factors not easily observed/quantified

• Goal to Decide:– WHAT measures to use in identifying and

quantifying adverse health related statistics potentially indicative of primary care access or system issues in the community.

– HOW they should be measured and factored into the models of shortage and/or underservice.

4

Evidence of the health-promoting influence of primary care has been accumulating ever since researchers have been able to distinguish primary care from other aspects of the health services delivery system. This evidence shows that primary care helps prevent illness and death, regardless of whether the care is characterized by supply of primary care physicians, a relationship with a source of primary care, or the receipt of important features of primary care.

- Starfield B. et.al. 2005 The Milbank Quarterly Contribution of Primary Care to Health Systems and Health

Health Statistics Apart From Provider Supply

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STATUTORY REQUIREMENTS FOR INCLUSION OF HEALTH STATUS MEASURES

• MUA Statute:– “Criteria shall…. include factors indicative of the health

status of a population group or residents of an area”

• HPSA Statute:– “Secretary shall take into consideration… indicators of

need, notwithstanding the supply of health manpower, for health services for the individuals in an area or population group, or served or served by a medical facility or other public facility under consideration for designation”

6

Health Status/Outcome Measures in the Current Designations

• HPSA (geographic only): lower designation threshold if one of the following conditions is true:High Need Indicators:– High Birth Rate (> 100 births per 1000 women 15-44)– High IMR (>20 infant deaths / 1000 Live Births)

Insufficient Capacity Indicators:– Excessive use of ER for primary care (not defined)– Abnormally low utilization (<= 2 office visits per year)

• Infant mortality rate also a factor in HPSA scoring by NHSC

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Health Status/Outcome Measures in the Current Designations

• MUA/P– Infant Mortality Rate is 1 of 4 factors used

• Approximately ¼ of the total points in IMU

• Often requires aggregation over several years to achieve sufficient numbers

–Small areas often use rate for larger containing geography (ie. County rate applied to Census tracts)

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Selection and Application of

Health Status/Outcome Measures

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Considerations in Measure Selection• Evidence of relationship to primary care?

– See handout summarizing measures and literature

• Purpose of inclusion– What does each measure ‘say’ about the

community’s primary care system?– Single or multiple measures?

• Limitations of data availability/precision– Specificity / Flexibility – Currency / Reporting lag – Cost & technical accessibility– Frequency / Reliability of updates

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Potential Data Sources• Organizational Sources

– National/Federal Sources (CDC, AHRQ, etc.)– State Health Departments– Other (Local efforts, Non-profit orgs., etc.)

• Types of Data Systems– Vital Statistics (mandatory structured reporting)– Registries / Surveillance Systems– Sample/Survey Based Systems– Health Information Exchanges (evolving)

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Types of Health Statistics

• Health Status Measures

– Mortality

– Morbidity

• Utilization Pattern Measures

• Quality/Process Measures

• Measures of Health-Related Behavior

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Categories of Indicators Within Primary Care

• General system function

• Prevention/Education

• Screening/Detection

• Chronic disease management

• Perinatal care

One of Several Possible Conceptual Frameworks

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Health Statistic Examples by Category

• Indicators of General System Function– Age adjusted mortality rate, Standardized Mort Ratio

• Life expectancy, Years potential life lost, etc.

– Self reported health status (age adj. % fair/poor)– Preventable/A.C.S. hospitalization or ER rates– Medicare % with no annual visit

• Indicators of Chronic Disease Management– Asthma hospital admission rate– Diabetic Sequelae (Medicare)

• Amputation, blindness, kidney failure

– Diabetic HbA1c testing rate (Medicare)

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Health Statistic Examples by Category (2)

• Indicators of Prevention/Education– Immunization rates (childhood)– Teen pregnancy rate– Behavioral Risks

• Smoking rate, Obesity rate, Diet (fruits/vegetables), Exercise

• Indicators of Screening/Detection– Disease Specific Mortality Rates

• Colorectal, Cervical, Breast Cancer, etc. • Heart Disease

– Age Group Screening Rates• Blood Pressure, Cholesterol, Diabetes, Colorectal• Pap, Mammogram

– Cancer Stage of Diagnosis

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Health Statistic Examples by Category (3)

• Indicators of Perinatal Care– Infant Mortality:

• Infant mortality rate • Post-neonatal mortality rate

– Low, Very Low birth weight rate– Maternal Tobacco/Alcohol use %– Late/No prenatal care %– Gestational Age (Premature Delivery) %

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Considerations for Applying Health Statistic Measures

• Avoiding false comparisons– Age/Gender standardization or stratification– Tests of statistical significance

• Making small area, sub-population estimates

• Potential methods for incorporating health statistics into a rule

• How can the impact of existing programs on health status be accounted for?

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Age/Gender Standardization

Issue: Many health statistics are highly influenced by the age and gender mix of the population

• Age (or age & gender) adjustment corrects the measure to a standardized population – Many health statistic reporting systems will provide age

adjusted values directly• Can be done manually when age-specific values are available.

• Age stratification limits a measure to a narrow age range

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Testing Statistical Significance• Issue: Many health statistics lack sufficient numbers

to describe small populations with precision• Confidence Interval calculations assess the ‘strength’

of an observed difference in values +

– Provided directly by many health statistic reporting systems– Easily calculated (using std. error) for population rates

Note: Aggregating

years or geography

can narrow the C.I.

by increasing events

Age-adjusted Mortality Rates and 95% Confidence Intervals

760 (95% CI: 759-761) 646

(95% CI: 620-672)

1245 (95% CI: 1113-1377)

818 (95% CI: 647-988)

600

700

800

900

1000

1100

1200

1300

1400

1500

U.S. National Barnstable County,MA

Boone County, WV

Bon Homme County,SD

All

caus

e m

orta

lity

rate

per

100

,000

Source: 2007 CDC Wonder

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Small-Area, Sub-Population Estimation

• If a statistic is not available for a specific small area or sub-population, it may be estimated from larger populations using several techniques:

– Direct attribution

– Proportional allocation

– Extrapolation

• See BPHC NFA Resource Guide for some examples

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Potential Options for Application of Health Status/Outcome Measures

• Comparison to Benchmark/Target – Geographic (national, state) or population-

specific average– NNth percentile (of counties, PCSAs, etc.)– Established Goal (e.g. Healthy People 2020)

• Factors measured individually or a composite index of multiple health factors

(e.g. RWJF County Health Rankings)

• Continuous scale with relative scoring – Only used in combination with other measures

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Example: Age Adjusted Mortality Rate• Source: CDC Wonder, State Vital Records• Pros:

– Relatively large numbers, Uniform reporting/ease of access to age standardized data, Whole population measure

• Cons:– Lagging indicator, Somewhat influenced by secondary/

tertiary care care system, Many premature deaths not related to primary care (homicide, MVA, etc.)

• Evidence– Many studies have shown an inverse association between

primary care access and mortality rates (Shi, Mancinco, Farmer)

• Shi L. 2005 “…primary care is likely to be independently associated with lower population mortality. An increase of one primary care doctor is independently associated, on average, with a reduction of 14.4 deaths, per 100,000”

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Example: Infant Mortality Rate• Source: CDC Wonder, State Vital Records• Pros:

– Uniform data collection, Widely understood measure, No need for age/gender standardization, Historical precedent

• Cons:– Narrowly focused by age/gender/provider, Rare/declining

event, Not amenable to small populations, Significantly influenced by secondary/tertiary care (NICU)

• Evidence:– Shi L. 2004: “Primary care is a statistically significant

predictor of lower “low birth weight” and infant mortality, and the magnitude of this relation was stable over time. “

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Example: Ambulatory Care Sensitive Admissions

• Source: HCUP (AHRQ), State Inpatient Databases• Pros:

– Developed specifically to assess primary care systems, Uniform reporting for 44 states - accessible in others, conditions representative across life cycles, significant numbers of events

• Cons:– Relies on administrative data, Complex definition, Not directly

reported at local levels, Requires expert analysis

• Evidence:– Widely used indicator of access to primary health care and of the

overall effectiveness of the primary health care system (Laditka 2009)• Probst J. 2009: “Our results suggest that CHCs and RHCs may play a

useful role in providing access to primary health care. Their presence in a county may help to limit the county's rate of hospitalization for ACS diagnoses, particularly among older people. “

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Decisions for Health Statistic Measures• Measure(s) to be examined/included?• Acceptable data sources?• Standards/techniques for comparability?• Acceptable methods for small areas and sub-

populations?• Identification of health deficits/disparities?• How to apply and relate to other factors?• Use of predictive models where direct

measurement not possible or confounded by current programs?