Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A.

Previous studies have reported associations between air pollution and COVID-19 morbidity and mortality, but most have limited their exposure assessment to a large area, have not used individual-level variables, nor studied infections. We examined 3.1 million SARS-CoV-2 infections and 49,691 COVID-19 deaths that occurred in California from February 2020 to February 2021 to evaluate risks associated with long-term neighborhood concentrations of particulate matter less than 2.5 µm in diameter (PM2.5). We obtained individual address data on SARS-CoV-2 infections and COVID-19 deaths and assigned 2000-2018 1km-1km gridded PM2.5 surfaces to census block groups. We included individual covariate data on age and sex, and census block data on race/ethnicity, air basin, Area Deprivation Index, and relevant comorbidities. Our analyses were based on generalized linear mixed models utilizing a Poisson distribution. Those living in the highest quintile of long-term PM2.5 exposure had risks of SARS-CoV-2 infections 20% higher and risks of COVID-19 mortality 51% higher, compared to those living in the lowest quintile of long-term PM2.5 exposure. Those living in the areas of highest long-term PM2.5 exposure were more likely to be Hispanic and more vulnerable, based on the Area Deprivation Index. The increased risks for SARS-CoV-2 Infections and COVID-19 mortality associated with highest long-term PM2.5 concentrations at the neighborhood-level in California were consistent with a growing body of literature from studies worldwide, and further highlight the importance of reducing levels of air pollution to protect public health.

Returning Individual Tap Water Testing Results to Research Study Participants after a Wildfire Disaster.

After the devastating wildfire that destroyed most of the town of Paradise, California in 2018, volatile organic compounds were found in water distribution pipes. Approximately 11 months after the fire, we collected tap water samples from 136 homes that were still standing and tested for over 100 chemicals. Each participant received a customized report showing the laboratory findings from their sample. Our goal was to communicate individual water results and chemical information rapidly in a way that was understandable, scientifically accurate, and useful to participants. On the basis of this process, we developed a framework to illustrate considerations and priorities that draw from best practices of previous environmental results return research and crisis communication, while also addressing challenges specific to the disaster context. We also conducted a follow-up survey on participants' perceptions of the results return process. In general, participants found the results return communications to be understandable, and they felt less worried about their drinking water quality after receiving the information. Over one-third of the participants reported taking some kind of action around their water usage habits after receiving their results. Communication with participants is a critical element of environmental disaster research, and it is important to have a strategy to communicate results that achieves the goals of timeliness, clarity, and scientific accuracy, ultimately empowering people toward actions that can reduce exposure.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA.

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Residential agricultural pesticide exposures and risks of preeclampsia.

We investigated risks of preeclampsia phenotypes from potential residential pesticide exposures, including 543 individual chemicals and 69 physicochemical groupings that were applied in the San Joaquin Valley of California during the study period, 1998-2011. The study population was derived from birth certificate data linked with Office of Statewide Health Planning and Development maternal and infant hospital discharge data. The following numbers of women with preeclampsia phenotypes were identified: 1045 with superimposed (pre-existing hypertension with preeclampsia) preeclampsia (265 with gestational weeks 20-31 and 780 with gestational weeks 32-36); 3471 with severe preeclampsia (824 with gestational weeks 20-31 and 2647 with gestational weeks 32-36); and 2780 with mild preeclampsia (207 with gestational weeks 20-31 and 2573 with gestational weeks 32-36). The reference population for these groups was 197,461 women who did not have diabetes (gestational or pre-existing), did not have any hypertensive disorder, and who delivered at 37 weeks or later. The frequency of any exposure was lower or about the same in each preeclampsia case group (further delineated by gestational age), and month time period, relative to the frequency in reference population controls. Nearly all odds ratios were below 1.0 for these any vs no exposure comparisons. This study showed a general lack of increased risks between a range of agriculture pesticide exposures near women's residences and various preeclampsia phenotypes.

Residential Agricultural Pesticide Exposures and Risks of Spontaneous Preterm Birth.

BACKGROUND: Pesticides exposures are aspects of the human exposome that have not been sufficiently studied for their contribution to risk for preterm birth. We investigated risks of spontaneous preterm birth from potential residential exposures to 543 individual chemicals and 69 physicochemical groupings that were applied in the San Joaquin Valley of California during the study period, 1998-2011. METHODS: The study population was derived from birth certificate data linked with Office of Statewide Health Planning and Development maternal and infant hospital discharge data. After exclusions, the analytic study base included 197,461 term control births and 27,913 preterm case births. Preterm cases were more narrowly defined as 20-23 weeks (n = 515), 24-27 weeks (n = 1,792), 28-31 weeks (n = 3,098), or 32-36 weeks (n = 22,508). RESULTS: The frequency of any (versus none) pesticide exposure was uniformly lower in each preterm case group relative to the frequency in term controls, irrespective of gestational month of exposure. All odds ratios were below 1.0 for these any versus no exposure comparisons. The majority of odds ratios were below 1.0, many of them statistically precise, for preterm birth and exposures to specific chemical groups or chemicals. CONCLUSIONS: This study showed a general lack of increased risk of preterm birth associated with a range of agriculture pesticide exposures near women's residences.

Traffic-Related Air Pollution and All-Cause Mortality during Tuberculosis Treatment in California.

BACKGROUND: Ambient air pollution and tuberculosis (TB) have an impact on public health worldwide, yet associations between the two remain uncertain. OBJECTIVE: We determined the impact of residential traffic on mortality during treatment of active TB. METHODS: From 2000-2012, we enrolled 32,875 patients in California with active TB and followed them throughout treatment. We obtained patient data from the California Tuberculosis Registry and calculated traffic volumes and traffic densities in 100- to 400-m radius buffers around residential addresses. We used Cox models to determine mortality hazard ratios, controlling for demographic, socioeconomic, and clinical potential confounders. We categorized traffic exposures as quintiles and determined trends using Wald tests. RESULTS: Participants contributed 22,576 person-years at risk. There were 2,305 deaths during treatment for a crude mortality rate of 1,021 deaths per 10,000 person-years. Traffic volumes and traffic densities in all buffers around patient residences were associated with increased mortality during TB treatment, although the findings were not statistically significant in all buffers. As the buffer size decreased, fifth-quintile mortality hazards increased, and trends across quintiles of traffic exposure became more statistically significant. Increasing quintiles of nearest-road traffic volumes in the 100-m buffer were associated with 3%, 14%, 19%, and 28% increased risk of death during TB treatment [first quintile, referent; second quintile hazard ratio (HR)=1.03 [95% confidence interval (CI): 0.86, 1.25]; third quintile HR=1.14 (95% CI: 0.95, 1.37); fourth quintile HR=1.19 (95% CI: 0.99, 1.43); fifth quintile HR=1.28 (95% CI: 1.07, 1.53), respectively; p-trend=0.002]. CONCLUSIONS: Residential proximity to road traffic volumes and traffic density were associated with increased all-cause mortality in patients undergoing treatment for active tuberculosis even after adjusting for multiple demographic, socioeconomic, and clinical factors, suggesting that TB patients are susceptible to the adverse health effects of traffic-related air pollution. https://doi.org/10.1289/EHP1699.

The Imperial County Community Air Monitoring Network: A Model for Community-based Environmental Monitoring for Public Health Action.

SUMMARY: The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA.

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM2.5 and PM10 (particulate matter with aerodynamic diameters <2.5 and <10 µm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R2 for converted hourly averaged Dylos mass measurements versus a PM2.5 BAM was 0.79 and that versus a PM10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R2 = 0.35-0.81). IMPLICATIONS: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.

Analysis of multiple-variable missing-not-at-random survey data for child lead surveillance using NHANES.

BACKGROUND: Although ongoing, multi-topic surveys form the basis of public health surveillance in many countries, their utility for specific subject matter areas can be limited by high proportions of missing data. For example, the National Health and Examination Survey is the main resource for surveillance of elevated blood lead levels (EBLLs) in US children, but key predictor variables are missing for as many as 35% of respondents. METHODS: Using a Bayesian framework, we formulate a t-distributed Heckman selection model applicable to the case of multiple missing-not-at-random variables in the context of a complex survey design. We demonstrate the utility of the results by calculating prevalence estimates for lead levels exceeding 2.5, 5.0, and 10.0 µg/dL among children 1 to 5 years of age for a variety of time points and geographies by applying the coefficients to data from the American Community Survey from the US Census. RESULTS: We present a protocol for estimating posterior distributions of parameters using Gibbs and grid sampling steps. Stark disparities in the prevalence of EBLL by race/ethnicity, age of housing, and poverty are readily quantified, and three- to five-fold differences in predicted prevalence across geographies within the US are presented. CONCLUSIONS: We are able to conduct multivariate analyses of EBLLs that incorporate the crucial variable age of housing, analyses that have not been previously available using these data. This represents an expansion of the utility of National Health and Examination Survey that is likely to be relevant to many similar ongoing, multi-topic health surveillance efforts. Copyright © 2016 John Wiley & Sons, Ltd.

Residential agricultural pesticide exposures and risks of selected birth defects among offspring in the San Joaquin Valley of California.

BACKGROUND: We examined associations of birth defects with residential proximity to commercial agricultural pesticide applications in California. Subjects included 367 cases representing five types of birth defects and 785 nonmalformed controls born 1997 to 2006. METHODS: Associations with any versus no exposure to physicochemical groups of pesticides and specific chemicals were assessed using logistic regression adjusted for covariates. Overall, 46% of cases and 38% of controls were classified as exposed to pesticides within a 500 m radius of mother's address during a 3-month periconceptional window. RESULTS: We estimated odds ratios (ORs) for 85 groups and 95 chemicals with five or more exposed cases and control mothers. Ninety-five percent confidence intervals (CI) excluded 1.0 for 11 ORs for groups and 22 ORs for chemicals, ranging from 1.9 to 3.1 for groups and 1.8 to 4.9 for chemicals except for two that were <1 (noted below). CONCLUSION: For groups, these ORs were for anotia/microtia (n = 95 cases) and dichlorophenoxy acids/esters and neonicotinoids; anorectal atresia/stenosis (n = 77) and alcohol/ethers and organophosphates (these ORs were < 1.0); transverse limb deficiencies (n = 59) and dichlorophenoxy acids/esters, petroleum derivatives, and triazines; and craniosynostosis (n = 79) and alcohol/ethers, avermectins, neonicotinoids, and organophosphates. For chemicals, ORs were: anotia/microtia and five pesticides from the groups dichlorophenoxy acids/esters, copper-containing compounds, neonicotinoids, organophosphates, and triazines; transverse limb deficiency and six pesticides - oxyfluorfen and pesticides from the groups copper-containing compounds, 2,6-dinitroanilines, neonicotinoids, petroleum derivatives and polyalkyloxy compounds; craniosynostosis and 10 pesticides - oxyfluorfen and pesticides from the groups alcohol/ethers, avermectins, n-methyl-carbamates, neonicotinoids, ogranophosphates (two chemicals), polyalkyloxy compounds (two chemicals), and pyrethroids; and congenital diaphragmatic hernia (n = 62) and a copper-containing compound.

Survival and Cause of Death among a Cohort of Confirmed Amyotrophic Lateral Sclerosis Cases.

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder. Estimates of survival from disease onset range from 20 to 48 months and have been generated using clinical populations or death records alone. METHODS: Data on a cohort of ALS cases diagnosed between 2009-2011 were collected as part of the Los Angeles and San Francisco Bay Area Metropolitan ALS Surveillance projects; death records 2009-2013 were linked to these confirmed cases to determine survival post diagnosis and factors associated with survival time. RESULTS: There were 618 cases identified and 283 of these died during the follow up time period. Median age at death was 64.3 years, and median survival time post-diagnosis was 2.6 years. Age at diagnosis and year of diagnosis were predictors of survival time in adjusted models; those diagnosed at age 80 or older had shorter survival than those diagnosed at age 50 or younger. Most (92%) had ALS noted as a cause of death. DISCUSSION: Survival post-diagnosis may be improved compared with previous reports. Age at diagnosis continues to be the strongest predictor of prognosis; recall case reporting bias may play a role in estimates of survival time.

Residential agricultural pesticide exposures and risk of selected congenital heart defects among offspring in the San Joaquin Valley of California.

BACKGROUND: Pesticide exposures are ubiquitous and of substantial public concern. We examined the potential association of congenital heart defects with residential proximity to commercial agricultural pesticide applications in the San Joaquin Valley, California. METHODS: Study subjects included 569 heart defect cases and 785 non-malformed controls born from 1997 to 2006 whose mothers participated in a population-based case-control study. Associations with any versus no exposure to physicochemical groups of pesticides and specific chemicals were assessed using logistic regression adjusted for relevant covariates, for 8 heart defect phenotypes that included ≥ 50 cases and pesticide exposures with ≥ 5 exposed cases and controls, which resulted in 235 comparisons. RESULTS: 38% of cases and controls were classified as exposed to pesticides within a 500 m radius of mother's address during a 3-month periconceptional window. Adjusted odds ratios (AORs) with 95% CIs excluding 1.0 were observed for 18 comparisons; all were >1 and ranged from 1.9 to 7.1. They included tetralogy of Fallot (n=101 cases) and neonicotinoids; hypoplastic left heart syndrome (n=59) and strobins; coarctation of the aorta (n=74) and pyridazinones; pulmonary valve stenosis (n=53) and bipyridyliums and organophosphates; ventricular septal defects (n=93) and avermectins and pyrethroids; and atrial septal defects (n=132) and dichlorphenoxy acid or esters, organophosphates, organotins, and pyrethroids. No AORs met both of these criteria for d-transposition of the great arteries (n=58) or heterotaxia (n=53). CONCLUSIONS: Most pesticides were not associated with increased risk of specific heart defect phenotypes. For the few that were associated, results should be interpreted with caution until replicated in other study populations.

Early pregnancy agricultural pesticide exposures and risk of gastroschisis among offspring in the San Joaquin Valley of California.

BACKGROUND: Prevalence of gastroschisis has inexplicably been increasing over the past few decades. Our intent was to explore whether early gestational exposures to pesticides were associated with risk of gastroschisis. METHODS: We used population-based data, accompanied by detailed information from maternal interviews as well as information on residential proximity to a large number of commercial pesticide applications during early pregnancy. The study population derived from the San Joaquin Valley of California (). Cases were 156 infants/fetuses with gastroschisis and controls were 785 infants without birth defects. RESULTS: Among 22 chemical pesticide groups analyzed, none had an elevated odds ratio with an associated confidence interval that excluded 1.0, although exposure to the triazine group showed borderline significance. Among 36 specific pesticide chemicals analyzed, only exposure to petroleum distillates was associated with an elevated risk, odds ratio = 2.5 (1.1-5.6). In general, a substantially different inference was not derived when analyses were stratified by maternal age or when risk estimation included adjustment for race/ethnicity, body mass index, folic acid supplement use, and smoking. CONCLUSION: Our study rigorously adds to the scant literature on this topic. Our a priori expectation was that we would observe certain pesticide compounds to be particularly associated with young age owing to the disproportionate risk observed for young women to have offspring with gastroschisis. We did not observe an exposure profile unique to young women.

Residential agricultural pesticide exposures and risk of neural tube defects and orofacial clefts among offspring in the San Joaquin Valley of California.

We examined whether early gestational exposures to pesticides were associated with an increased risk of anencephaly, spina bifida, cleft lip with or without cleft palate (CLP), or cleft palate only. We used population-based data along with detailed information from maternal interviews. Exposure estimates were based on residential proximity to agricultural pesticide applications during early pregnancy. The study population derived from the San Joaquin Valley, California (1997-2006). Analyses included 73 cases with anencephaly, 123 with spina bifida, 277 with CLP, and 117 with cleft palate only in addition to 785 controls. A total of 38% of the subjects were exposed to 52 chemical groups and 257 specific chemicals. There were relatively few elevated odds ratios with 95% confidence intervals that excluded 1 after adjustment for relevant covariates. Those chemical groups included petroleum derivatives for anencephaly, hydroxybenzonitrile herbicides for spina bifida, and 2,6-dinitroaniline herbicides and dithiocarbamates-methyl isothiocyanate for CLP. The specific chemicals included 2,4-D dimethylamine salt, methomyl, imidacloprid, and α-(para-nonylphenyl)-ω-hydroxypoly(oxyethylene) phosphate ester for anencephaly; the herbicide bromoxynil octanoate for spina bifida; and trifluralin and maneb for CLP. Adjusted odds ratios ranged from 1.6 to 5.1. Given that such odds ratios might have arisen by chance because of the number of comparisons, our study showed a general lack of association between a range of agricultural pesticide exposures and risks of selected birth defects.

Bayesian modeling of time-dependent vulnerability to environmental hazards: an example using autism and pesticide data.

BACKGROUND: Flexible modeling of time-dependent effects is required when vulnerability to hazards can be expected to vary over time, but the nature of this temporal dependency cannot be specified in advance. We present an analytic approach requiring minimal a priori assumptions about temporal parameters and producing measures of uncertainty for these parameters. METHODS: As a demonstration, we employ data describing autism spectrum disorders and applications of organochlorine pesticides in proximity to maternal residence before, during, and after pregnancy. We formulate a Bayesian model specifying temporal vulnerability as a flexible step function and constrain the dose-response relationship to be linear. We separately pooled information regarding hazard frequency and magnitude among cases and controls and used it as inputs for a Metropolis-within-Gibbs algorithm. To assess statistical significance, we conduct Monte Carlo simulations based on parameters calculated in the Gibbs portion of the algorithm. RESULTS: This method delineated two discrete periods of association between hazard and outcome. The first corresponded to a previously noted period of vulnerability with the added information of wide credible intervals, suggesting a high degree of uncertainty with respect to timing. Parameters for the second, previously unobserved period displayed slightly higher precision. Assessment of model fit favored the simultaneous inclusion of both these periods, and both periods appeared statistically significant on the basis of posterior distributions of specific parameters using Monte Carlo simulations. CONCLUSIONS: This method enabled a fuller accounting of time-dependent associations between hazards and outcomes without specifying temporal structure in advance.

Evaluation of a heat vulnerability index on abnormally hot days: an environmental public health tracking study.

BACKGROUND: Extreme hot weather conditions have been associated with increased morbidity and mortality, but risks are not evenly distributed throughout the population. Previously, a heat vulnerability index (HVI) was created to geographically locate populations with increased vulnerability to heat in metropolitan areas throughout the United States. OBJECTIVES: We sought to determine whether areas with higher heat vulnerability, as characterized by the HVI, experienced higher rates of morbidity and mortality on abnormally hot days. METHODS: We used Poisson regression to model the interaction of HVI and deviant days (days whose deviation of maximum temperature from the 30-year normal maximum temperature is at or above the 95th percentile) on hospitalization and mortality counts in five states participating in the Environmental Public Health Tracking Network for the years 2000 through 2007. RESULTS: The HVI was associated with higher hospitalization and mortality rates in all states on both normal days and deviant days. However, associations were significantly stronger (interaction p-value < 0.05) on deviant days for heat-related illness, acute renal failure, electrolyte imbalance, and nephritis in California, heat-related illness in Washington, all-cause mortality in New Mexico, and respiratory hospitalizations in Massachusetts. CONCLUSION: Our results suggest that the HVI may be a marker of health vulnerability in general, although it may indicate greater vulnerability to heat in some cases.

Environmental health indicators of climate change for the United States: findings from the State Environmental Health Indicator Collaborative.

OBJECTIVE: To develop public health adaptation strategies and to project the impacts of climate change on human health, indicators of vulnerability and preparedness along with accurate surveillance data on climate-sensitive health outcomes are needed. We researched and developed environmental health indicators for inputs into human health vulnerability assessments for climate change and to propose public health preventative actions. DATA SOURCES: We conducted a review of the scientific literature to identify outcomes and actions that were related to climate change. Data sources included governmental and nongovernmental agencies and the published literature. DATA EXTRACTION: Sources were identified and assessed for completeness, usability, and accuracy. Priority was then given to identifying longitudinal data sets that were applicable at the state and community level. DATA SYNTHESIS: We present a list of surveillance indicators for practitioners and policy makers that include climate-sensitive health outcomes and environmental and vulnerability indicators, as well as mitigation, adaptation, and policy indicators of climate change. CONCLUSIONS: A review of environmental health indicators for climate change shows that data exist for many of these measures, but more evaluation of their sensitivity and usefulness is needed. Further attention is necessary to increase data quality and availability and to develop new surveillance databases, especially for climate-sensitive morbidity.

Spatially continuous local rate modeling for communication in public health: a practical approach.

A primary function of Environmental Public Health Tracking is the communication of spatial trends of diseases. Traditional (choropleth) approaches to disease mapping}?> have difficulty conveying intuitive understandings of the spatial continuity of disease risk, rate calculations in rural areas, and degrees of statistical significance. A spatial loess function can be utilized to depict continuous variations in preterm birth risk for the state of California on the basis of a 3-year birth cohort. Results from this function were graphically depicted and incorporated into a Web mapping service to maximize public accessibility. The function was evaluated as a tool for communication by considering its intuitive interpretation and comparing information derived from the function with that, which would be derived from a choropleth map using the same data. In general, the loess function was able to generate risk information for a variety of both urban and rural settings. Although richer in detail, this information was mostly consistent with that which would come from choropleth maps. Occasionally, information from the loess function stood in contradiction to the choropleth mapping procedure; however; we enumerate these occasions and discuss ways to maximize the consistency of the loess function with intuitive understandings of disease risk.

Maternal residence near agricultural pesticide applications and autism spectrum disorders among children in the California Central Valley.

BACKGROUND: Ambient levels of pesticides ("pesticide drift") are detectable at residences near agricultural field sites. OBJECTIVE: Our goal was to evaluate the hypothesis that maternal residence near agricultural pesticide applications during key periods of gestation could be associated with the development of autism spectrum disorders (ASD) in children. METHODS: We identified 465 children with ASD born during 1996-1998 using the California Department of Developmental Services electronic files, and matched them by maternal date of last menstrual period to 6,975 live-born, normal-birth-weight, term infants as controls. We determined proximity to pesticide applications using California Department of Pesticide Regulation records refined using Department of Water Resources land use polygons. A staged analytic design applying a priori criteria to the results of conditional logistic regressions was employed to exclude associations likely due to multiple testing error. RESULTS: Of 249 unique hypotheses, four that described organochlorine pesticide applications--specifically those of dicofol and endosulfan--occurring during the period immediately before and concurrent with central nervous system embryogenesis (clinical weeks 1 through 8) met a priori criteria and were unlikely to be a result of multiple testing. Multivariate a posteriori models comparing children of mothers living within 500 m of field sites with the highest nonzero quartile of organochlorine poundage to those with mothers not living near field sites suggested an odds ratio for ASD of 6.1 (95% confidence interval, 2.4-15.3). ASD risk increased with the poundage of organochlorine applied and decreased with distance from field sites. CONCLUSIONS: The association between residential proximity to organochlorine pesticide applications during gestation and ASD among children should be further studied.

Progress in pediatric asthma surveillance I: the application of health care use data in Alameda County, California.

INTRODUCTION: The ability to conduct community-level asthma surveillance is increasingly crucial for public health programming and child health advocacy. We explored the potential and limitations of health care use records from both public and private sources for asthma surveillance in a California county. METHODS: We combined administrative patient record data from Kaiser Permanente of Northern California and Medi-Cal (the California Medicaid program) for Alameda County residents during 2001. We assessed the resulting data set for completeness, population representation, consistency with external data, and internal indicator consistency. RESULTS: Our resulting data set included records for 226,383 children younger than 18 years. Completeness of Medicaid data was affected by managed care market share, reducing our usable data set size to 176,789, approximately equal to one of every two children in the county or one of every 3 person-months. External data documenting hospitalization rates due to asthma were poorly correlated with hospitalization rates (r = 0.2120, P = .20) but highly correlated with emergency department visits (r = 0.8607, P <.001) in the resulting data set. High internal consistency of indicators suggested that the data set represented a broad spectrum of health care access and quality of care congruent with clinical aspects of the disease. CONCLUSION: The utility of these data is affected by logistical and administrative factors, including the health care payment structure and the market shares of care providers. These factors can be expected to similarly affect the utility of this approach in other counties. Our ability to generate county-level health statistics for comparison with other locations was limited, although the data set appeared well suited for within-county geographic analysis. In light of these findings, these data have the potential to expand the local health surveillance capacity of communities.