Zip Code Level Data Research Articles

Improving Hospital Reporting of Patient Race and Ethnicity—Approaches to Data Auditing

To investigate new metrics to improve the reporting of patient race and ethnicity (R/E) by hospitals. California Patient Discharge Database (PDD) and birth registry, 2008-2009, Healthcare and Cost Utilization Project's State Inpatient Database, 2008-2011, cancer registry 2000-2008, and 2010 US Census Summary File 2. We examined agreement between hospital reported R/E versus self-report among mothers delivering babies and a cancer cohort in California. Metrics were created to measure root mean squared differences (RMSD) by hospital between reported R/E distribution and R/E estimates using R/E distribution within each patient's zip code of residence. RMSD comparisons were made to corresponding "gold standard" facility-level measures within the maternal cohort for California and six comparison states. Maternal birth hospitalization (linked to the state birth registry) and cancer cohort records linked to preceding and subsequent hospitalizations. Hospital discharges were linked to the corresponding Census zip code tabulation area using patient zip code. Overall agreement between the PDD and the gold standard for the maternal cohort was 86 percent for the combined R/E measure and 71 percent for race alone. The RMSD measure is modestly correlated with the summary level gold standard measure for R/E (r = 0.44). The RMSD metric revealed general improvement in data agreement and completeness across states. "Other" and "unknown" categories were inconsistently applied within inpatient databases. Comparison between reported R/E and R/E estimates using zip code level data may be a reasonable first approach to evaluate and track hospital R/E reporting. Further work should focus on using more granular geocoded data for estimates and tracking data to improve hospital collection of R/E data.

Neighborhood Contributions to Racial and Ethnic Disparities in Obesity Among New York City Adults

Objectives. We assessed neighborhood confounding on racial/ethnic obesity disparities among adults in New York City after accounting for complex sampling, and how much neighborhood factors (walkability, percentage Black or Hispanic, poverty) contributed to this effect. Methods. We combined New York City Community Health Survey 2002-2004 data with Census 2000 zip code-level data. We estimated odds ratios (ORs) for obesity with 2 sets of regression analyses. First, we used the method incorporating the conditional pseudolikelihood into complex sample adjustment. Second, we compared ORs for race/ethnicity from a conventional multilevel model for each neighborhood factor with those from a hybrid fixed-effect model. Results. The weighted estimate for obesity for Blacks versus Whites (OR = 1.8; 95% confidence interval = 1.6, 2.0) was attenuated when we controlled neighborhood confounding (OR = 1.4; 95% confidence interval = 1.2, 1.6; first analysis). Percentage of Blacks in the neighborhood made a large contribution whereas the walkability contribution was minimal (second analysis). Conclusions. Percentage of Blacks in New York City neighborhoods explained a large portion of the disparity in obesity between Blacks and Whites. The study highlights the importance of estimating valid neighborhood effects for public health surveillance and intervention.

Understanding Teen Mothers: A Zip Code Analysis

Researchers continue to analyze the determinants and consequences of teen motherhood. Here we explore this issue using ZIP code level data – to better identify the most vulnerable areas. Our results suggest that geographic location, poverty levels, rates of divorce (and separation), part-time unemployment and employment in finance related industries positively associate with teen motherhood rates, while employment in management related fields has a negative relationship. Our findings are believed to be most useful as supplementary analysis to teen birth studies conducted at larger geographic scales, as ZIP codes introduce ambiguity across the lines of separation from one community to the next.

What does SNAP benefit usage tell us about food access in low-income neighborhoods?

Current GIS based research on food access has focused primarily on the proximity of food sources to places of residence in low-income communities, with relatively little attention given to actual practices of food procurement. This project addresses this issue by using dasymetric mapping techniques to develop fine scale estimates of benefit usage for the Supplemental Nutrition Assistance Program (SNAP) in the Twin Cities of Minneapolis and St. Paul, Minnesota, drawing from existing zip code level data on benefit distribution and redemptions. Based on this data, this research shows that while supermarkets receive almost all SNAP benefits in suburban areas, these stores have a smaller share of all SNAP redemptions in low-income core neighborhoods. In these latter areas, both convenience stores and mid-sized grocers (e.g., discount grocers, food cooperatives, ethnic markets) play a much larger role in residents' food shopping, even when supermarkets are also present. In addition, these core neighborhoods have a net “outflow” of SNAP dollars, meaning that residents of these areas receive more in benefits than is spent at neighborhood food retailers. This finding confirms existing research showing that low-income residents often travel outside their neighborhoods to get food, regardless of the presence or absence of supermarkets. Rather than simply increasing the number of large food outlets in low-access areas, this research suggests that efforts to improve food access and community health must take into account the geographically complex ways residents interact with the food system.

Finance and Welfare: The Effect of Access to Credit on Family Structure

There is a large debate over the welfare effects of the early 2000s housing boom and bust. One potentially important welfare effect is the impact of mortgage credit expansion on family structure. Exploiting pre-housing boom variation on the distribution of old homeowners who live alone and are older than 65, I conduct within-county analysis with zip code level data to causally identify the effect of access to credit on fertility outcomes through a channel associated with a more efficient reallocation of the existing housing stock among households. I examine two other housing channels, house wealth gains and new construction, and show that the most relevant channel is the reallocation, which allows young households to access space by either moving to larger homes or achieving homeownership earlier in their life-cycle. A one standard deviation increase in reallocation leads to a 6.4% increase in fertility from 2000 to 2006. The same increase in house prices leads to only a 2.7% increase, and in new construction leads to a 1.5% decline in fertility from 2000 to 2006. I estimate that approximately 500,000 babies were born between 2000 and 2006 because of the reallocation channel.

Impact of spatial aggregation error on the spatial scan analysis: a case study of colorectal cancer

The paper aims to estimate the level and impact of spatial aggregation error for spatial scan statistics where disaggregated data below the zip code level are not available. Data on colorectal cancer cases in Cook county, Illinois, USA with a 5-year interval were used. An innovative procedure using SAS and Java was designed to make SaTScan auto-run. Characteristics of clusters at each reference level were compared to those at zip code level to observe differences related to spatial aggregation. The comparison reveals that spatial scan statistic at the zip code level can generate reliable clusters in areas with a large number of cases, but fail to detect clusters in areas where there are a sparse number of cases, since the spatial aggregation error is minimised in areas with sizeable numbers of cases. Without localised cancer data, zip code level data can be used effectively to identify dominant clusters. However, smaller clusters located in low-density areas may be missed.

International Real Estate Review

We examine the real estate owned (REO) discount by using ZIP code-level data on foreclosure rates, distressed and non- distressed sales in Chicago, Illinois. We find significant differences in price between distressed and non-distressed properties in high- versus low-foreclosure rate neighborhoods. We expand this analysis to determine if trends in the REO discount can be explained by trends in foreclosure rates and if this correlation can be used to identify a ¡¥tipping point¡¦ in foreclosure rates. We identify key relationships between trends in the REO discount, the proportion of REO sales to total sales, and sales volume.

Influence of local environmental, social, economic and political variables on the spatial distribution of residential solar PV arrays across the United States

This study used ZIP code level data from the 2000 US Census to investigate the influence of local environmental, social, economic and political variables on the distribution of residential solar PV arrays across the United States. Current locations of residential solar PVs were documented using data from the National Renewable Energy Laboratory's Open PV project. A zero-inflated negative binomial reression model was run to evaluate the influence of selected variables. Using the same model, predicted residential solar PV shares were generated and illustrated using GIS software. The results of the model indicate solar insolation, cost of electricity and amount of available financial incentives are important factors influencing adoption of residential solar PV arrays. Results also indicate the Southwestern region of the United States and the state of Florida are currently underperforming in terms of number of housing units with solar PV installations.

Residential Segregation and the Availability of Primary Care Physicians

To examine the association between residential segregation and geographic access to primary care physicians (PCPs) in metropolitan statistical areas (MSAs). We combined zip code level data on primary care physicians from the 2006 American Medical Association master file with demographic, socioeconomic, and segregation measures from the 2000 U.S. Census. Our sample consisted of 15,465 zip codes located completely or partially in an MSA. We defined PCP shortage areas as those zip codes with no PCP or a population to PCP ratio of >3,500. Using logistic regressions, we estimated the association between a zip code's odds of being a PCP shortage area and its minority composition and degree of segregation in its MSA. We found that odds of being a PCP shortage area were 67 percent higher for majority African American zip codes but 27 percent lower for majority Hispanic zip codes. The association varied with the degree of segregation. As the degree of segregation increased, the odds of being a PCP shortage area increased for majority African American zip codes; however, the converse was true for majority Hispanic and Asian zip codes. Efforts to address PCP shortages should target African American communities especially in segregated MSAs.

What role did piggyback lending play in the housing bubble and mortgage collapse?

We examine the use of simultaneous close junior lien lending (“piggybacks”) over the course of the recent housing bubble and subsequent mortgage market collapse. Using both state-level and Zip code-level data over the period 2001–2008, we find that the fraction of piggyback originations is related to higher foreclosure and default rates in subsequent years, and this relation is strongest for non-owner-occupied properties. The pattern, however, appears to be limited to the use of subprime piggybacks, rather than a more general phenomenon. Using a topology-based housing supply elasticity measure as an instrument for house price growth, we further confirm that the strong association of subprime piggyback origination with worse loan performance was not driven by the endogeneity of house price appreciation.

Changes in Emergency Department Access between 2001 and 2005 among General and Vulnerable Populations

This retrospective review sought to analyze how geographic access to emergency departments (EDs) changed between 2001 and 2005 in the overall population and in vulnerable communities. Communities nationwide were divided based on zip code-level data from the 2000 Census, and hospital data were extracted from the American Hospital Association Annual Surveys from 2001 and 2005. The number of hospital EDs decreased from 4176 to 3195 between 1995 and 2005, whereas the number of annual ED visits increased from 96.5 to 115.3 million during this period.

Spatial Panel Econometric Analysis of Economic Impacts of Bypasses

This study took a regional approach to the economic impacts of bypasses. The study considered not only how the bypassed communities were affected but also how areas surrounding these communities were affected and whether these impacts differed. Zip code–level data were collected for three states in the Midwest: Illinois, Indiana, and Ohio. This area contained more than 60 bypasses of variably sized communities. The study focused on the manufacturing sector, which made up a major portion of the economy in this region. With a synthesis of economic theory and transportation systems analysis, spatial panel econometric models were estimated to determine whether these communities were significantly affected with respect to employment, establishment, and payroll levels. The findings of this study provided insight into the magnitude of the impacts of bypasses as well as how these impacts varied over space.

Possible Geographical Barriers to Trauma Center Access for Vulnerable Patients in the United States

To study whether traditionally vulnerable populations have worse geographic access to trauma centers. A cross-sectional analysis using data from the American Hospital Association Annual Survey from 2005 linked with zip code-level data from the US Census. We used a multinomial logit model to examine the odds of having difficult as opposed to easy access to trauma centers for a given subgroup of vulnerable populations. Population in rural and urban communities as defined by zip codes in the United States. Each community's distance to the nearest trauma center (levels I-III). In urban areas, 67% of the population had easy access to trauma centers and 12% had difficult access compared with 24% and 31% in rural areas, respectively. Areas with higher shares of the following vulnerable population groups had higher risks (odds ratios) of facing difficult access to trauma center services in 2005: foreign born in urban areas (1.65 for a medium share and 2.18 for a high share [both P < .01]); African American in urban and rural areas (1.25 for a medium share and 1.35 for a high share, respectively [both P < .05]); and near-poor in urban and rural areas (1.52 [P < .05] and 1.69 [P < .01] for a high share, respectively). A significant segment of the US population (representing 38.4 million people) does not have access to trauma care within 1 hour of driving time. Moreover, certain vulnerable groups are at higher risk than others for worse access to trauma centers. Stakeholders and health care planners should consider these factors in the development of trauma systems because a mismatch of potential need and access could signal inefficiencies in the delivery of care.

Poverty, Place or Race: Causes of the Retail Gap in Smaller U.S. Cities

In this study we explore the possibility that any or all of three dimensions of the U.S. inner city, central location, relatively low income, and a relatively high percentage of minority residents, correlate with a relative lack of neighborhood retail services, the so-called retail gap. Our empirical analysis is based on zip code level data for 39 U.S. cities with populations from 50 to 225 thousand people. After specifying an empirical model based on hypotheses drawn from the urban economics, urban studies, and urban development literature, we test access to retail services using both geographical density and per capita retail measures. While several results from this study are worthy of discussion, our primary empirical finding is that neighborhoods with high percentages of African-Americans are systematically under-served by retail, all else equal, while Latino, low income, or otherwise centrally located neighborhoods are not. We then test a selection of product categories for retail density, finding some differing results by product category but verifying our more general conclusions for most products. Finally, we find that grocery stores have significantly smaller scale in African-American communities, a finding that provides partial support for the urban “food desert” hypothesis. These findings suggest that the inner city retail gap may be more of a racial than geographic issue, and that increased emphasis on racial composition is warranted in the retail development policies applied to smaller U.S. cities.

The Neighborhood Food Environment and Adult Weight Status: Estimates From Longitudinal Data

I used longitudinal data to consider the relationship between the neighborhood food environment and adult weight status. I combined individual-level data on adults from the 1998 through 2004 survey years of the National Longitudinal Survey of Youth 1979 with zip code-level data on the neighborhood food environment. I estimated ordinary least squares models of obesity, body mass index (BMI), and change in BMI. For residents of urban areas, the neighborhood density of small grocery stores was positively and significantly related to obesity and BMI. For individuals who moved from a rural area to an urban area over a 2-year period, changes in neighborhood supermarket density, small grocery store density, and full-service restaurant density were significantly related to the change in BMI over that period. Residents of urban neighborhoods with a higher concentration of small grocery stores may be more likely to patronize these stores and consume more calories because small grocery stores tend to offer more unhealthy food options than healthy food options. Moving to an urban area may expose movers to a wider variety of food options that may influence calorie consumption.

What Role Did Piggyback Lending Play in the Housing Bubble and Mortgage Collapse?

We examine the use of simultaneous close junior lien lending (“piggybacks”) over the course of the recent housing bubble and subsequent mortgage market collapse. Using both state-level and zip code-level data over the period 2001-2008, we find that the fraction of piggyback originations is related to higher foreclosure and default rates in subsequent years, and this relation is strongest for non-owner occupied properties. The pattern, however, appears to be limited to the use of subprime piggybacks, rather than a more general phenomenon. Using a topology-based housing supply elasticity measure as an instrument for house price growth, we further confirm that the strong association of subprime piggyback origination with worse loan performance was not driven by the endogeneity of house price appreciation.

Foreclosure Contagion and REO Versus Non-REO Sales

Using ZIP code-level data on foreclosure rates, distressed and non-distressed sales in Chicago, Illinois we examine the REO discount. We find significant differences in the difference in price between distressed and non-distressed properties in high- versus low-foreclosure rate neighborhoods. We expand this analysis to determine if trends in the REO discount can be explained by trends in foreclosure rates and if this correlation can be used to identify a ‘tipping point’ in foreclosure rates. We identify key relationships between trends in the REO discount, the proportion of REO sales to total sales and sales volume.

The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis*

We conduct a within-county analysis using detailed ZIP code—level data to document new findings regarding the origins of the biggest financial crisis since the Great Depression. The sharp increase in mortgage defaults in 2007 is significantly amplified in subprime ZIP codes, or ZIP codes with a disproportionately large share of subprime borrowers as of 1996. Prior to the default crisis, these subprime ZIP codes experience an unprecedented relative growth in mortgage credit. The expansion in mortgage credit from 2002 to 2005 to subprime ZIP codes occurs despite sharply declining relative (and in some cases absolute) income growth in these neighborhoods. In fact, 2002 to 2005 is the only period in the past eighteen years in which income and mortgage credit growth are negatively correlated. We show that the expansion in mortgage credit to subprime ZIP codes and its dissociation from income growth is closely correlated with the increase in securitization of subprime mortgages.

Evaluating geographic imputation approaches for zip code level data: an application to a study of pediatric diabetes.

BackgroundThere is increasing interest in the study of place effects on health, facilitated in part by geographic information systems. Incomplete or missing address information reduces geocoding success. Several geographic imputation methods have been suggested to overcome this limitation. Accuracy evaluation of these methods can be focused at the level of individuals and at higher group-levels (e.g., spatial distribution).MethodsWe evaluated the accuracy of eight geo-imputation methods for address allocation from ZIP codes to census tracts at the individual and group level. The spatial apportioning approaches underlying the imputation methods included four fixed (deterministic) and four random (stochastic) allocation methods using land area, total population, population under age 20, and race/ethnicity as weighting factors. Data included more than 2,000 geocoded cases of diabetes mellitus among youth aged 0-19 in four U.S. regions. The imputed distribution of cases across tracts was compared to the true distribution using a chi-squared statistic.ResultsAt the individual level, population-weighted (total or under age 20) fixed allocation showed the greatest level of accuracy, with correct census tract assignments averaging 30.01% across all regions, followed by the race/ethnicity-weighted random method (23.83%). The true distribution of cases across census tracts was that 58.2% of tracts exhibited no cases, 26.2% had one case, 9.5% had two cases, and less than 3% had three or more. This distribution was best captured by random allocation methods, with no significant differences (p-value > 0.90). However, significant differences in distributions based on fixed allocation methods were found (p-value < 0.0003).ConclusionFixed imputation methods seemed to yield greatest accuracy at the individual level, suggesting use for studies on area-level environmental exposures. Fixed methods result in artificial clusters in single census tracts. For studies focusing on spatial distribution of disease, random methods seemed superior, as they most closely replicated the true spatial distribution. When selecting an imputation approach, researchers should consider carefully the study aims.

Understanding Spatial Variation in Tax Sheltering: The Role of Demographics, Ideology, and Taxes

Taxpayers shelter income from taxation both through illegal evasion and legal avoidance. This tax sheltering creates a difference between a household's actual income and what it reports to the tax authorities. While tax sheltering is a central concern for designing a tax system, the private nature of this behavior complicates evaluating the magnitude and determinants of such behavior. In this article, we combine zip-code level data on reported income from the Internal Revenue Service and the Census Bureau to examine three types of determinants of tax sheltering: (1) tax policy variables, including tax rates (2) political attitudes toward taxation; and (3) demographics. Our estimates suggest that higher tax rates increase the amount of tax sheltering. In terms of political support, our results suggest that places with voters who are either more conservative or less supportive of tax increases actually shelter less income.