Concepts Related To Assessing Health Status/Outcome Measures

1. Concepts Related ToAssessing Health Status/Outcome Measures

2. Conceptual Outline for Designation Component Integration Quantify Population- Based Need/Demand for Primary Care Assess Health Outcome Deficits/Disparities (Areas/Populations with persistently and significantly negative health indicators ) Quantify Provider Supply/Capacity Adjust Demand for Population Variables? (Age/Gender, Health Status) Assess Other Indicators of Med.Underservice (Nature/Indicators TBD) or Scale(s) of Provider Adequacy/Shortage (Combined measure of Supply vs Demand ) Scale(s) of Medical Underservice (Assessed separately or Integrated into an index) or or Set Threshold(s) for HPSA Designation Set Threshold(s) for MUA/P Designation HPSA MUA/P

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. Health Statistics Apart From Provider Supply 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

5. 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

7. 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)

8. Selection and Application of Health Status/Outcome Measures

9. 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

10. 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)

11. Types of Health Statistics • Health Status Measures • Mortality • Morbidity • Utilization Pattern Measures • Quality/Process Measures • Measures of Health-Related Behavior

12. Categories of Indicators Within Primary Care • General system function • Prevention/Education • Screening/Detection • Chronic disease management • Perinatal care One of Several Possible Conceptual Frameworks

13. 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)

14. 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

15. 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) %

16. 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?

17. 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

18. 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

19. 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

20. 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

21. 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”

22. 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. “

23. 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. “

24. 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?