1. Geographic linkages between National Center for Health Statistics’ population health surveys and air quality measures
NSF-Census Research Network Meeting
September 11, 2014
Jennifer D. Parker
National Center for Health Statistics

2. Background
Air pollution has been found to be related to numerous acute and chronic health outcomes, including mortality, asthma exacerbation, emergency department visits, other cardiovascular and respiratory outcomes, etc
The Environmental Protection Agency regulates air quality, largely based on health impacts
Few national studies are available

3. National Center for Health Statistics
National Office of Vital Statistics and the National Health Survey merged to form National Center for Health Statistics
NCHS became part of the Centers for Disease Control and Prevention
Official Federal Health Statistics Agency and part of the Federal Statistical System
Data products
A principle source of health statistics for Department of Health and Human Services
Public use micro-data: individual records to enable data users to calculate estimates
Integrated Data Tools: reports and online tools with pre-tabulated indicators
Restricted use micro-data: individual records with information that could potentially be used to identify an individual
Geographic identifiers and dates
Genetic information, detailed race and ethnicity
Can be accessed in the NCHS Research Data Center (RDC)

4. Population Health Surveys National Health Interview Survey
Household interview survey – detailed demographic, health status, health insurance, access to care, economic etc.
Generally chronic health outcomes rather than acute
Approximately 35,000 – 40,000 households, 80,000 – 100,000 individuals per year
Data years can be combined to increase sample or study trends
Complex sample design (stratified, clustered, oversampling)
Collected since 1957
Interview dates and geocoded residential locations available on restricted use files
Locations can be used to examine characteristics of locations, not actual places

5. Population Health Surveys National Health and Nutrition Examination Survey
National examinations since 1960, since 1999 an ongoing survey
Physical measures, blood, urine, DNA
Household interview and 3-4 hr exam
Complex survey design
About 5,000 total survey respondents each year
About 15 locations each year, 30 location in a two-year data release; data can be combined across releases
Exam dates, interview dates and geocoded residential locations available on restricted use files
Locations can be used to examine characteristics of locations, not actual places
Data collected at each location over a few weeks

6. National Vital Statistics System (NVSS) Death and Birth Data
Vital statistics data can be used to assess events during specific time windows, at specific locations, aggregated locations, and/or for specific endpoints
Death data have been combined with daily pollution data at county-level to study acute effects of air pollution
Birth data have been combined with county level pollution data to study birth weight, infant mortality, preterm birth

7. National Health Care Surveys
Provider surveys designed to provide a comprehensive look at medical care provided in the U.S
Information generally obtained from administrative records (e.g. claims), not interview
Selected covariates: age, race, expected source of payment, procedures and diagnoses
Data can be combined across years to increase power
Restricted use files can contain dates, locations of hospitals, and zip codes of patients
Locations can be used to examine characteristics of locations, not actual places

8. EPA Air Quality System EPA is a regulatory agency
Selected pollution data
Criteria pollutants – ozone, sulfur dioxide, carbon monoxide, particulate matter, nitrogen dioxide, lead
Collected at monitors throughout the U.S. under local authority
Both raw numbers and annual summaries available
Air Quality System (AQS) is EPA’s repository for ambient air quality data.
Monitors, however, are not located everywhere.
Placement is not random, rather for regulatory and convenience purposes
Not all locations monitor for all pollutants, not all pollutants are monitored on the same periodicity, and the ability to extrapolate from the monitor to a distance away from the monitor varies by pollutant.
As the AQS has become more user-friendly, the number of studies using these data have greatly expanded.
More recently, EPA Community Multiscale Air Quality (CMAQ) model provides estimates for locations with no monitors

9. Hypothetical sampled segments and locations of monitors within a hypothetical NCHS sampled county
Not all monitors are near NCHS survey participants
Exposure estimates are highly clustered

10. Combining NCHS survey and EPA data
Complex sample survey designs
Not all counties in sample
While survey designed to be nationally representative, the merged survey may not be representative for pollution
Only ~ 30 counties sampled in a two-year NHANES release
Temporal correspondence between health measures and pollution measures may not align
Over sampling of subpopulation increases sample size for understudied groups

11. Combining NCHS survey and EPA data, continued
Surveys currently geocoded to latitude and longitude of residence and/or administrative units
Air quality data
Latitude and longitude of monitor locations
Administrative units (county)
Modeled air pollution data available at lat/long and/or smaller administrative units
Basic krigging
EPA’s CMAQ – Community Multiscale Air Quality
Others, including Land Use Regression (LUR)
Geographically linked data cannot be used for maps or to identify sensitive locations, rather as a contextual variable for analysis (e.g., areas with higher compared to lower levels of air pollution)

12. Evolution of geographic linkages between NCHS survey data and EPA data
MSA-level
County-level
Distance measures
Krigging
Modeled data

13. NHIS linked to county level air pollution

14. Asthma attack past 12 months
Childhood asthma and county-level PM mo average air pollution NHIS
Current asthma
Asthma attack past 12 months
ER visit past 12 months

15. Evaluations of methods to link NCHS and EPA data

16. Percent NHIS respondents linked to pollutants by linkage method: 2000-2003 NHIS

17. Percent distribution race/ethnicity. NHIS linked to PM2
Percent distribution race/ethnicity. NHIS linked to PM2.5, by linkage method: NHIS

18. Percent with PM2.5 data by linkage method and time from examination: 1999-2004 NHANES
20 miles
County
5 miles
SOURCE: NHANES 99-04, unpublished data

19. Adjusted. odds of fair/poor health status by PM2
Adjusted* odds of fair/poor health status by PM2.5 (per 10 mg/m3) by geographic linkage method NHIS
*Adjusted for poverty, age, region, urban/rural, and race/ethnicity

20. Application of krigging used to obtain air measures for the NHIS

21. Relationship between asthma and PM2. 5 using krigged PM2. 5 estimates
Relationship between asthma and PM2.5 using krigged PM2.5 estimates. NHIS

22. What was learned comparing distance and county measures
The more tightly controlled the exposure window in terms of space and time, the fewer survey respondents have monitor data
Large literature on ‘exposure measurement error’
Growing interest in time-use patterns
The more pollutants examined simultaneously, the fewer survey respondents would be included
More urban and disadvantaged populations would be more likely to be included
Impact on policy inferences unknown for all outcomes, but may be small
Adjustment of sample weights may partially address this issue
Random effects and Bayesian statistical models may address these issues

23. Modeled Data from CDC/National Center for Environmental Health
Downscaler (DS) model based on Bayesian space-time methods
Combines monitored data from AQS with EPA CMAQ data
Daily, county, tract level
Available for ozone and PM2.5 for

24. Hazard ratios for association between heart disease mortality and PM2
Hazard ratios for association between heart disease mortality and PM2.5 (2001), black and white adults NHIS with mortality follow-up through 2006

25. What we’ve learned (so far) from DS model
Inferences for mortality and PM2.5 are similar for monitor data using AQS and the DS data
Confidence intervals narrower for DS models
Inferences for black adults similar to those for white adults
Availability of DS data in more locations may allow for inference by geographic indicators (e.g. region of the country, levels of urbanization)
Propagating model error into epidemiological models with complex survey data should be done but best method for doing so is unknown

26. Conclusion
Linkages between national health data and monitored and modeled air pollution data are resources for studying environmental health
Nationally representative data include population subgroups less often included in other studies
Wide range of health outcomes
Numerous methodological challenges
Temporal correspondence between exposure measures and health outcome
Geographic correspondence between exposure measures and health outcomes
Monitor locations
Residential and daily mobility
Incorporation of estimation and model errors into inference analyses

27. Thanks to my collaborators!
Nataliya Kravets
Ambarish Vaidyanathan
Lara Akinbami
Keeve Nachman
Tracey Woodruff

28. Contact:
Jennifer D. Parker Chief, Special Projects Branch Office of Analysis and Epidemiology National Center for Health Statistics (301)