ZIP Code-Level Estimates from a Local Health Survey: Added Value and Limitations.
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ABSTRACT:
SUBMITTER: Bi Q
PROVIDER: S-EPMC7392987 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThe objective of this study was to assess nonresponse error in telephone health survey data based on an address-based sample.Data sourcesTelephone and in-person interviews in Greater Boston.Study design/data collectionInterviewers attempted telephone interviews at addresses that were matched to telephone numbers using questions drawn from federal health surveys. In-person household interviews were carried out with telephone nonrespondents and at addresses without matching telephone numbers.Principal findingsAfter adjusting for demographic differences, only eight of 15 estimates based on the telephone interviews lay within two standard errors of the estimates when data from all three groups were included.ConclusionsFor health surveys of address-based samples, many estimates based on telephone respondents differ from the total population in ways that cannot be corrected with simple demographic adjustments.
| S-EPMC6505407 | biostudies-literature
Project description:Exposure to PM2.5 is associated with hundreds of premature mortalities every year in New York City (NYC). Current air quality and health impact assessment tools provide county-wide estimates but are inadequate for assessing health benefits at neighborhood scales, especially for evaluating policy options related to energy efficiency or climate goals. We developed a new ZIP Code-Level Air Pollution Policy Assessment (ZAPPA) tool for NYC by integrating two reduced form models─Community Air Quality Tools (C-TOOLS) and the Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA)─that propagate emissions changes to estimate air pollution exposures and health benefits. ZAPPA leverages custom higher resolution inputs for emissions, health incidences, and population. It, then, enables rapid policy evaluation with localized ZIP code tabulation area (ZCTA)-level analysis of potential health and monetary benefits stemming from air quality management decisions. We evaluated the modeled 2016 PM2.5 values against observed values at EPA and NYCCAS monitors, finding good model performance (FAC2, 1; NMSE, 0.05). We, then, applied ZAPPA to assess PM2.5 reduction-related health benefits from five illustrative policy scenarios in NYC focused on (1) commercial cooking, (2) residential and commercial building fuel regulations, (3) fleet electrification, (4) congestion pricing in Manhattan, and (5) these four combined as a "citywide sustainable policy implementation" scenario. The citywide scenario estimates an average reduction in PM2.5 of 0.9 μg/m3. This change translates to avoiding 210-475 deaths, 340 asthma emergency department visits, and monetized health benefits worth $2B to $5B annually, with significant variation across NYC's 192 ZCTAs. ZCTA-level assessments can help prioritize interventions in neighborhoods that would see the most health benefits from air pollution reduction. ZAPPA can provide quantitative insights on health and monetary benefits for future sustainability policy development in NYC.
| S-EPMC9178920 | biostudies-literature
Project description:BACKGROUND:Demographic and Health Survey (DHS) data are an important source of maternal, newborn, and child health as well as nutrition information for low- and middle-income countries. However, DHSs are often unavailable at the administrative unit that is most interesting or useful for program planning. In addition, the location of DHS survey clusters are geomasked within 10 km, and prior to 2009, may have crossed district boundaries. We aim to use DHS surveyed information with these geomasked coordinates to estimate district assignments for use in health program planning and evaluation. METHODS:We developed three methods to assign a district to a geomasked survey cluster in two DHS surveys from Malawi: 2000 and 2004. Method A assigns districts of origin in proportion to the likelihood that results from repeated simulated geomasking, allowing more than one possible district of origin. Method B assigns a single district of origin which contains the greatest proportion of simulated geomasked survey clusters. Method C maps the geomasked survey cluster's location to a district polygon. We used these method assignments to estimate a selection of commonly used coverage indicators for each district. We compared the district coverage estimates, confidence intervals, and concordance correlation coefficients, by each of the methods, to those which used validated district assignments in 2004, and we looked at coverage change from 2000 to 2004. RESULTS:The methods we tested each approximated the validated estimates in 2004 by confidence interval comparison and concordance correlation coefficient. Estimated agreement for method A was between .14 and .98, for method B the estimated agreement was between .97 and .99, and for method C the agreement ranged from .93 to .99 when compared with the validated district assignments. Therefore, we recommend the protocol which is the simplest to implement-method C-overlaying geomasked survey cluster within district polygon. CONCLUSIONS:Using geomasked survey clusters from DHSs to assign districts provided district level coverage rates similar to those using the validated surveyed locations. This method may be applied to data sources where survey cluster centroids are available and where district level estimates are needed for program implementation and evaluation in low- and middle-income settings. This method is of special interest to those using DHSs to study spatiotemporal trends as it allows for the utilization of historic DHS data where geomasking hinders the generation of reliable subnational estimates of health in areas smaller than the first-order administrative unit (ADM1).
| S-EPMC7011502 | biostudies-literature
Project description:BACKGROUND:Exome and genome sequencing is becoming the method of choice for rare disease diagnostics. One of the key challenges remaining is distinguishing the disease causing variants from the benign background variation. After analysis and annotation of the sequencing data there are typically thousands of candidate variants requiring further investigation. One of the most effective and least biased ways to reduce this number is to assess the rarity of a variant in any population. Currently, there are a number of reliable sources of information for major population frequencies when considering single nucleotide variants (SNVs) and small insertion and deletions (INDELs), with gnomAD as the most prominent public resource available. However, local variation or frequencies in sub-populations may be underrepresented in these public resources. In contrast, for structural variation (SV), the background frequency in the general population is more or less unknown mostly due to challenges in calling SVs in a consistent way. Keeping track of local variation is one way to overcome these problems and significantly reduce the number of potential disease causing variants retained for manual inspection, both for SNVs and SVs. RESULTS:Here, we present loqusdb, a tool to solve the challenge of keeping track of any type of variant observations from genome sequencing data. Loqusdb was designed to handle a large flow of samples and unlike other solutions, samples can be added continuously to the database without rebuilding it, facilitating improvements and additions. We assessed the added value of a local observations database using 98 samples annotated with information from a background of 888 unrelated individuals. CONCLUSIONS:We show both how powerful SV analysis can be when filtering for population frequencies and how the number of apparently rare SNVs/INDELs can be reduced by adding local population information even after annotating the data with other large frequency databases, such as gnomAD. In conclusion, we show that a local frequency database is an attractive, and a necessary addition to the publicly available databases that facilitate the analysis of exome and genome data in a clinical setting.
| S-EPMC7329469 | biostudies-literature
Project description:Regular health surveys can produce reliable estimates at higher geographic levels but not for small areas. Alternatives are to aggregate data over several years or use model-based methods. We created and evaluated model-based estimates for four health-related outcomes by gender, for 153 Local Government Areas using data from the New South Wales Population Health Survey. The evaluation examined evidence on bias and determined the covariates available and appropriate for each outcome variable. The evaluation considered the likely precision of the resulting estimates. The bias and precision of results for single years (2006-2008) for each outcome variable using six covariate specifications were compared with direct survey estimates based on a single year's data and those obtained by aggregating over seven years. A practical issue is how to choose covariates to include in the models as the best covariate specification varies between outcome variables. Model-based results had median root mean squared errors between 3.3% and 5.5% (max 5.2% and 11.3% respectively) and median relative root mean squared errors between 6.8% and 24.5% (max 11.7% and 41.5% respectively). The model-based estimates were unbiased compared with direct estimates based on one or seven years of data and when aggregated to a point where direct estimates were reliable. The bias and reliability assessment process provides a way for policymakers to have confidence in model-based estimates.
| S-EPMC7327397 | biostudies-literature
Project description:We created daily concentration estimates for fine particulate matter (PM2.5) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM2.5 measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data. Ten-fold spatial and random CV R2 were 0.66 and 0.73, respectively, for the 2008-2016 model and 0.58 and 0.72, respectively for the 2008-2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R2 of 0.70 for the 2008-2016 model and 0.58 for the 2008-2018 model, but we observed higher R2 (>0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to, and health impacts of PM2.5 in the western US, where PM2.5 levels have been heavily impacted by wildfire smoke over this time period.
| S-EPMC8055869 | biostudies-literature
Project description:Although the recent rise and uptake of COVID-19 vaccines in the United States has been encouraging, there continues to be significant vaccine hesitancy in various geographic and demographic clusters of the adult population. Surveys, such as the one conducted by Gallup over the past year, can be useful in determining vaccine hesitancy, but can be expensive to conduct and do not provide real-time data. At the same time, the advent of social media suggests that it may be possible to get vaccine hesitancy signals at an aggregate level, such as at the level of zip codes. Theoretically, machine learning models can be learned using socioeconomic (and other) features from publicly available sources. Experimentally, it remains an open question whether such an endeavor is feasible, and how it would compare to non-adaptive baselines. In this article, we present a proper methodology and experimental study for addressing this question. We use publicly available Twitter data collected over the previous year. Our goal is not to devise novel machine learning algorithms, but to rigorously evaluate and compare established models. Here we show that the best models significantly outperform non-learning baselines. They can also be set up using open-source tools and software.
| S-EPMC9931269 | biostudies-literature
Project description:Wildfires constitute a growing source of extremely high levels of particulate matter that is less than 2.5 microns in diameter (PM2.5). Recently, toxicologic and epidemiologic studies have shown that PM2.5 generated from wildfires may have a greater health burden than PM2.5 generated from other pollutant sources. This study examined the impact of PM2.5 on hospitalizations for respiratory diseases in California between 2006 and 2019 using a health impact assessment approach that considers differential concentration-response functions (CRF) for PM2.5 from wildfire and non-wildfire sources of emissions. We quantified the burden of respiratory hospitalizations related to PM2.5 exposure at the zip code level through two different approaches: (a) naïve (considering the same CRF for all PM2.5 emissions) and (b) nuanced (considering different CRFs for PM2.5 from wildfires and from other sources). We conducted a Geographically Weighted Regression to analyze spatially varying relationships between the delta (i.e., the difference between the naïve and nuanced approaches) and the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). A higher attributable number of respiratory hospitalizations was found when accounting for the larger health burden of wildfire PM2.5. We found that, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13% as a result of not accounting for the higher CRF of wildfire-related PM2.5 throughout California. This underestimation was higher in northern California and areas with higher SVI rankings. The relationship between delta and SVI varied spatially across California. These findings can be useful for updating future air pollution guideline recommendations.
| S-EPMC10586090 | biostudies-literature
Project description:There 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).We 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.At 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).Fixed 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.
| S-EPMC2763852 | biostudies-other
Project description:To illustrate methods for assessing environmental exposures associated with lung cancer risk, we investigated anthropogenic based air pollutant data in a major metropolitan area using United States-Environmental Protection Agency (US-EPA) Toxic Release Inventory (TRI) (1987-2017), and PM2.5 (1998-2016) and NO2 (1996-2012) concentrations from NASA satellite data. We studied chemicals reported according to the following five exposome features: (1) International Agency for Research on Cancer (IARC) cancer grouping; (2) priority EPA polycyclic aromatic hydrocarbons (PAHs); (3) component of diesel exhaust; (4) status as a volatile organic compound (VOC); and (5) evidence of lung carcinogenesis. Published articles from PubChem were tallied for occurrences of 10 key characteristics of cancer-causing agents on those chemicals. Zone Improvement Plan (ZIP) codes with higher exposures were identified in two ways: (1) combined mean exposure from all features, and (2) hazard index derived through a multi-step multi-criteria decision analysis (MMCDA) process. VOCs, IARC Group 1 carcinogens consisted 82.3% and 11.5% of the reported TRI emissions, respectively. ZIP codes along major highways tended to have greater exposure. The MMCDA approach yielded hazard indices based on imputed toxicity, occurrence, and persistence for risk assessment. Despite many studies describing environmental exposures and lung cancer risk, this study develops a method to integrate these exposures into population-based exposure estimates that could be incorporated into future lung cancer screening trials and benefit public health surveillance of lung cancer incidence. Our methodology may be applied to probe other hazardous exposures for other cancers.
| S-EPMC8238722 | biostudies-literature