Zip Code Caveat: Bias Due to Spatiotemporal Mismatches Between Zip Codes and US Census–Defined Geographic Areas—The Public Health Disparities Geocoding Project

Krieger, Nancy; Waterman, Pamela D.; Chen, Jarvis T.; Soobader, Mah-Jabeen; Subramanian, S. V.; Carson, Rosa

doi:10.2105/ajph.92.7.1100

Cited by 338 publications

(273 citation statements)

References 13 publications

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“…Merging both census-derived and zip code area data has been shown to leave potential for spatial-temporal mismatch. 44 In Massachusetts, there were 474 zip code areas listed in the 1990 census, but 10% of cancer cases occurred in 193 zip code areas not included in the 1990 census 44 that resulted because of 30 zip code areas that were changed after the 1990 census; in our data, however, we found that there were no deaths with a U.S. Postal Service zip code area that did not have a corresponding 2000 census ZCTA.…”

Section: Limitationscontrasting

confidence: 52%

Residential Segregation and Latino, Black and White Mortality in New York City

Inagami

Borrell²,

Wong³

et al. 2006

J Urban Health

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Section: Limitationscontrasting

confidence: 52%

Residential Segregation and Latino, Black and White Mortality in New York City

Inagami

Borrell²,

Wong³

et al. 2006

J Urban Health

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“…We were constrained to use ZIP code as the area of analysis in order to match the smallest indicator of geography available in the registry data. Although for some outcomes, ZIP code can lead to unstable associations due to heterogeneity of characteristics within a ZIP code, for most outcomes examined, results have been shown to be consistent with smaller census‐defined levels of geography 23. Neighborhood‐level socioeconomic variables at the ZIP‐code level included median rent, median household income, percentage living below the federal poverty line, percentage unemployed among those 16 years or older, and percentage with less than a high‐school education among those 25 years or older.…”

Section: Methodsmentioning

confidence: 93%

Associations of Glycemic Control With Cardiovascular Outcomes Among US Hemodialysis Patients With Diabetes Mellitus

Rhee

Zheng

Montez‐Rath

et al. 2017

JAHA

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BackgroundThere is a lack of data on the relationship between glycemic control and cardiovascular end points in hemodialysis patients with diabetes mellitus.Methods and ResultsWe included adult Medicare‐insured patients with diabetes mellitus who initiated in‐center hemodialysis treatment from 2006 to 2008 and survived for >90 days. Quarterly mean time‐averaged glycated hemoglobin (HbA1c) values were categorized into <48 mmol/mol (<6.5%) (reference), 48 to <58 mmol/mol (6.5% to <7.5%), 58 to <69 mmol/mol (7.5% to <8.5%), and ≥69 mmol/mol (≥8.5%). Medicare claims were used to identify outcomes of cardiovascular mortality, nonfatal myocardial infarction (MI), fatal or nonfatal MI, stroke, and peripheral arterial disease. We used Cox models as a function of time‐varying exposure to estimate multivariable adjusted hazard ratios and 95%CI for the associations between HbA1c and time to study outcomes in a cohort of 16 387 eligible patients. Patients with HbA1c 58 to <69 mmol/mol (7.5% to <8.5%) and ≥69 mmol/mol (≥8.5%) had 16% (CI, 2%, 32%) and 18% (CI, 1%, 37%) higher rates of cardiovascular mortality (P‐trend=0.01) and 16% (CI, 1%, 33%) and 15% (CI, 1%, 32%) higher rates of nonfatal MI (P‐trend=0.05), respectively, compared with those in the reference group. Patients with HbA1c ≥69 mmol/mol (≥8.5%) had a 20% (CI, 2%, 41%) higher rate of fatal or nonfatal MI (P‐trend=0.02), compared with those in the reference group. HbA1c was not associated with stroke, peripheral arterial disease, or all‐cause mortality.ConclusionsHigher HbA1c levels were significantly associated with higher rates of cardiovascular mortality and MI but not with stroke, peripheral arterial disease, or all‐cause mortality in this large cohort of hemodialysis patients with diabetes mellitus.

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“…We were unable to analyze the prevalence of obesity at the census tract level because respondents were only asked to report their ZIP code. Another challenge associated with the use of ZIP code areas is that they are designed to efficiently deliver mail, and the boundaries change subtly on a regular basis (Krieger, Waterman, Chen, Soobader, Subramanian & Carson, 2002).…”

Section: Discussionmentioning

confidence: 99%

Disparities in obesity rates: Analysis by ZIP code area

Drewnowski

Rehm

Solet

2007

Social Science & Medicine

138

103

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Obesity in the USA has been linked to individual income and education. Less is known about its geographic distribution. The goal of this study was to determine whether obesity rates in King County, Seattle, Washington state, at the ZIP code scale were associated with area-based measures of socioeconomic status and wealth. Data from the Behavioral Risk Factor Surveillance System were analyzed. At the ZIP code scale, crude obesity rates varied six-fold. In a model adjusting for covariates and spatial dependence, property values were the strongest predictor of the area-based smoothed obesity prevalence. Geocoding of health data provides new insights into the nature of social determinants of health. Disparities in obesity rates by ZIP code area were greater than disparities associated with individual income or race/ethnicity.

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Zip Code Caveat: Bias Due to Spatiotemporal Mismatches Between Zip Codes and US Census–Defined Geographic Areas—The Public Health Disparities Geocoding Project

Cited by 338 publications

References 13 publications

Residential Segregation and Latino, Black and White Mortality in New York City

Residential Segregation and Latino, Black and White Mortality in New York City

Associations of Glycemic Control With Cardiovascular Outcomes Among US Hemodialysis Patients With Diabetes Mellitus

Disparities in obesity rates: Analysis by ZIP code area

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