California Counties Research Articles

A pilot study to improve provider adherence to NAEPP guidelines

The prevalence and morbidity of Asthma in the United States has increased since the 1991 National Asthma Education and Prevention Program (NAEPP) and updated Expert Panel Report −3 (EPR-3) guidelines in 2007 were published. To improve provider adherence to the NAEPP EPR-3 guidelines Children's Hospital of Orange County (CHOC) in California integrated the HealtheIntentSM Pediatric Asthma Registry (PAR) into the electronic medical record (EMR) in 2015. MethodsA serial cross-sectional design was used to compare provider management of CHOC MediCal asthma patients before 2014 (N = 6606) and after 2018 (N = 6945) integration of the Registry with NAEPP guidelines into the EMR. Four provider adherence measures (Asthma Control Test [ACT], Asthma Action Plan [AAP], inhaled corticosteroids [ICS] and spacers) were evaluated using General Linear Mixed Models and Chi square. FindingsIn 2018, patients were more likely to receive an ACT, (OR = 14.95, 95% CI 12.67, 17.65, p < .001), AAP (OR = 12.70, 95% CI 11.10, 14.54, p < .001), ICS (OR = 1.85, 95% CI 8.52, 14.54, p < .001) and spacer (OR = 1.45, 95% CI 1.31, 1.6, p < .001) compared to those in 2014. DiscussionThe pilot study showed integration of the Pediatric Asthma Registry into the EMR, as a computer decision support tool that was an effective intervention to increase provider adherence to NAEPP guidelines. Ongoing monitoring and education are needed to promote and sustain provider behavioral change. Additional research to include multi-sites and decreased time between evaluation years is recommended. Application to practiceCan be used for excellent health policy decision making as a direct impact on patient care and outcomes, by improving provider adherence to the NAEPP guidelines.

What is Suksdorf’s hawthorn? Revision of the Western North American 20-stamen black-fruited hawthorns (Crataegus series Douglasianae, Rosaceae subtribe Malinae)

An agamic complex of 20- and 10-stamen, black-fruited hawthorns (Crataegus subg. Sanguineae, sect. Douglasianae) occurs in western North America, with a 10-stamen taxon disjunct in the upper Great Lakes basin. Here, we recircumscribe the 20-stamen taxa at the core of this complex (C. ser. Douglasianae). This is needed in order to distinguish between a presumptively ancestral diploid and its allo- and autopolyploid derivatives, all differing in breeding system, distribution, morphology, and pattern of genetic variation. The earliest name for these 20-stamen taxa, Crataegus gaylussacia A. Heller, was given to distinctive plants of Marin and Sonoma counties in California that have recently been shown to be autotriploids. In Flora North America, however, this name was applied to all 20-stamen, black-fruited hawthorns. We recircumscribe C. gaylussacia, and also recircumscribe and typify a slightly younger name, C. suksdorfii (Sarg.) Kruschke, with a specimen from southern Washington with the diminished pollen fertility found in allopolyploid, apomictic black-fruited hawthorns occurring east of the Cascades, from northern California north to southernmost Alaska. Finally, we recognize the diploid, self-incompatible, sexual black-fruited hawthorns found west of the Cascades from northern California to southwestern Washington as Crataegus rhodamae-loveae sp. nov. Together with the 10-stamen, black-fruited hawthorns in predominantly tetraploid, apomictic, and self-fertile C. douglasii Lindl. and its segregates (discussed in detail in a companion paper), these species are linked by whole genome duplications associated in most cases with hybridization, with members of red-fruited C. subg. Americanae, and with each other. We suggest that this complex provides a model for understanding other such groups of Crataegus species that are related by whole genome duplications resulting from the fertilization of unreduced gametes (facilitated by but not necessarily dependent on occurrence of gametophytic apomixis), often together with hybridization. We note that an earlier effort at DNA barcoding these and other hawthorn species that provided limited support for our taxonomic decisions here also demonstrated some limited utility of the original plant DNA barcoding loci in groups like Crataegus. The taxonomic decisions we advocate will warrant consideration when other groups of hawthorns are revised in the light of data like those employed here.

An Investigative Report on Homelessness, Its Correlation with Psychiatric Illness and Substance Abuse

Homelessness and substance abuse are prevalent, interconnected issues that present significant challenges for both social and medical communities. Despite significant efforts from various stakeholders, including supportive lawmakers, governing councils, and the medical community, these problems persist and are often refractory to interventions. This study aims to investigate the coexistence and severity of homelessness and substance abuse in Clinica Sierra Vista, a federally qualified health center serving Kern County and other counties in California’s Central Valley. By identifying potential points for early intervention, we hope to improve outcomes for individuals affected by homelessness and substance abuse. Evidence suggests that early intervention in disease processes can lead to better outcomes. We aim to explore whether this is true for the complex interplay between homelessness and substance abuse.

Epidemiology of Coccidioidomycosis in the Veterans Health Administration, 2013-2022.

We describe the epidemiology of coccidioidomycosis among a national cohort of Veterans. Using electronic health record data from adults tested for coccidioidomycosis between 1 January 2013 and 31 December 2022, we analyzed differences in baseline demographics (age, sex, race/ethnicity, birth country, comorbidities, residence, and Charlson Comorbidity Index score) between 4204 coccidioidomycosis-test-positive and 63,322 test-negative Veterans. Log-binomial regression models with adjusted risk ratios (aRRs) were used to evaluate risk factors associated with coccidioidomycosis including dissemination, hospitalization, and mortality. Case counts and incidence rates were highest in select counties in Arizona and California where Coccidioides is endemic. Coccidioidomycosis-positive Veterans were younger, more likely to be male, and Philippine-born. The risk factors most highly associated with being coccidioidomycosis-positive included Native Hawaiian/Pacific Islander (aRR 1.068 [95%CI: 1.039-1.098]), Asian (aRR 1.060 [95%CI: 1.037-1.083]), Black (aRR 1.029 [95%CI: 1.022-1.036]), American Indian/Alaska Native (aRR 1.026 [95%CI: 1.004-1.048]) race, and Hispanic/Latino ethnicity (aRR 1.021 [95%CI: 1.013-1.028]). Black race (aRR: 1.058 [95%CI: 1.037-1.081]) and Hispanic/Latino ethnicity (aRR 1.018 [95%CI: 1.0003-1.036]) were also associated with disseminated coccidioidomycosis, strengthening the evidence for the association of coccidioidomycosis, including severe infections, with specific racial and ethnic groups. There were no statistically significant differences in hospitalization within 45 days of testing or 30-day all-cause mortality. Improving our understanding of coccidioidomycosis risk factors is important for targeted prevention strategies and to reduce delays in diagnosis and ineffective treatment.

Programmatic Support for Peer Specialists that Serve Transition Age Youth Living with Serious Mental Illness: Perspectives of Program Managers from Two Southern California Counties

Peer Specialists (PS) often work in outpatient mental health programs serving transition age youth (TAY). This study examines program managers’ perspectives on efforts to strengthen PS’ professional development. In 2019, we interviewed program managers (n = 11) from two Southern California Counties employed by public outpatient mental health programs (n = 8) serving TAY and conducted thematic analyses. We present themes and illustrative quotes. PS’ roles are highly flexible; thus, PM support PS to strengthen skills to address organization-facing and client-facing responsibilities. PM addressed time management, documentation, PS integration into the organization, and workplace relationships. Trainings to better support clients included addressing cultural competency to serve LGBTQ TAY and racial/ethnic subgroups. Diverse supervision modalities address PS’ diverse needs. Supporting PS’ technical and administrative skills (e.g., planning, interpersonal communication skills) may aid their implementation of a complex role. Longitudinal research can examine the impact of organizational supports on PS’ job satisfaction, career trajectories, and TAY clients’ engagement with services.

Drinking and driving among Whites and Hispanics on and off the US/Mexico border in California

Objective To examine self-reported rates of driving under the influence (DUI) with and without arrest among border and non-border residents in California. Methods Data were obtained from 1,209 adults 18 to 39 years of age resident in four counties in California: Imperial on the U.S./Mexico border; and Kern, Tulare, and Madera in California’s Central Valley. Households were selected using a list assisted sample. Data were collected on the phone or online and analyzed with a heteroskedastic ordinal generalized linear model. Results Driving after drinking (11.1% vs. 6.5%; q = 0.04) and the lifetime DUI arrest rates were higher for men than women (10.7% vs. 4%; q = 0.001). In multivariable analysis driving after drinking and DUI arrests were not higher on the border, not higher among Hispanics than Whites, and among Hispanics, the rates were not higher among those located on the border. Income was positively associated with drinking and driving. Impulsivity was positively and significantly associated with both drinking and driving and lifetime DUI arrest. Conclusion The null results suggest that DUI related risk behaviors may not be higher on the border than in other areas of California. There may be health related risk behaviors of higher prevalence in the border population than in other areas, but DUI related behavior may not be one of them.

Groundwater constituents and the incidence of kidney cancer.

Kidney cancer incidence demonstrates significant geographic variation suggesting a role for environmental risk factors. This study sought to evaluate associations between groundwater exposures and kidney cancer incidence. The authors identified constituents from 18,506 public groundwater wells in all 58 California counties measured in 1996-2010, and obtained county-level kidney cancer incidence data from the California Cancer Registry for 2003-2017. The authors developed a water-wide association study (WWAS) platform using XWAS methodology. Three cohorts were created with 5 years of groundwater measurements and 5-year kidney cancer incidence data. The authors fit Poisson regression models in each cohort to estimate the association between county-level average constituent concentrations and kidney cancer, adjusting for known risk factors: sex, obesity, smoking prevalence, and socioeconomic status at the county level. Thirteen groundwater constituents met stringent WWAS criteria (a false discovery rate<0.10 in the first cohort, followed by p values<.05 in subsequent cohorts) and were associated with kidney cancer incidence. The seven constituents directly related to kidney cancer incidence (and corresponding standardized incidence ratios) were chlordane (1.06; 95% confidence interval [CI], 1.02-1.10), dieldrin (1.04; 95% CI, 1.01-1.07), 1,2-dichloropropane (1.04; 95% CI, 1.02-1.05), 2,4,5-TP (1.03; 95% CI, 1.01-1.05), glyphosate (1.02; 95% CI, 1.01-1.04), endothall (1.02; 95% CI, 1.01-1.03), and carbaryl (1.02; 95% CI, 1.01-1.03). Among the six constituents inversely related to kidney cancer incidence, the standardized incidence ratio furthest from the null was for bromide (0.97; 95% CI, 0.94-0.99). This study identified several groundwater constituents associated with kidney cancer. Public health efforts to reduce the burden of kidney cancer should consider groundwater constituents as environmental exposures that may be associated with the incidence of kidney cancer.

Multilevel Federated Learning-Based Intelligent Traffic Flow Forecasting for Transportation Network Management

Accurate traffic flow forecasting is crucial to improving traffic safety and alleviating road congestion for intelligent transportation network management. Recently, spatial-temporal graph-based deep learning methods have achieving significant performance improvements in traffic flow forecasting. However, they only consider spatial-temporal correlation of traffic network but ignore a mass of semantic correlation. In addition, they need to centralize data for training models, leading to privacy leakage concern. To tackle these problems, we introduce a federated learning-based intelligent traffic flow forecasting model that integrates our proposed spatial-temporal graph-based deep learning model into the devised Multilevel Federated Learning framework(MFL), named MFVSTGNN. This MFL is used to allow data collaboration among different data owners to train an efficient model without sharing their private data, while achieving the trade-off between communication overhead and computation performance. The proposed spatial-temporal graph-based deep learning model is composed of two phases. The first phase utilizes Variational Graph Autoencoder (VGAE) to dynamically generate adjacency matrix that contains both the spatial and semantic dependencies, contributing to preserving valuable information for improving prediction accuracy, and the second phase employs general spatial-temporal graph neural network to conduct prediction. We evaluate the performance of MFVSTGNN with two large-scale traffic datasets from California and Los Angeles County. The experimental results demonstrate the superior performance of MFVSTGNN in reducing communication overhead, and improving prediction accuracy, validating the effectiveness of our proposed model.

Impact of California Statute on Naloxone Availability and Opioid Overdose Rates

Ensuring that people at risk of overdosing on opioids have easy access to naloxone is an essential part of the fight against the opioid crisis. This study evaluates the impact of the 2016 California law (CA AB1535) permitting pharmacies to dispense this life-saving medication without a physician's prescription. California counties were categorized on the basis of population density (rural, suburban, urban), rate of opioid-related deaths by population density (high, medium, low), and rate of opioid prescriptions by population density (high, medium, low). Ten diverse pharmacies from each category were selected for inclusion. In a brief 1-minute interview conducted between July and August 2021, pharmacists from 146 California pharmacies were surveyed regarding their knowledge of CA AB1535, their practice of dispensing naloxone without a physician's prescription, and whether they normally stock naloxone. Chi-square tests were used to compare responses. Although almost all pharmacies interviewed (94%) were aware of the law and most of them (64%) dispensed naloxone without a physician's prescription, few statistically significant differences were found between surveyed categories. There were no significant relationships between naloxone availability at pharmacies and overdose death rates. Our results suggest that the number of California pharmacies dispensing naloxone without a physician's prescription has continued to increase since the implementation of CA AB1535. However, despite increased access to naloxone at pharmacies, opioid overdose rates have continued to rise since 2016, indicating the need for a multifaceted harm reduction approach.

Surveillance for Violent Deaths — National Violent Death Reporting System, 48 States, the District of Columbia, and Puerto Rico, 2020

Surveillance for Violent Deaths — National Violent Death Reporting System, 48 States, the District of Columbia, and Puerto Rico, 2020

The role of the California tier system in controlling population mobility during the COVID-19 pandemic

BackgroundPolicies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the first state in the U.S. to implement this novel approach; however, the effectiveness of California’s four-tier system on population mobility has not been quantified.MethodsUtilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each California county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels.ResultsWe found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, economic, social, and educational contexts, the prevalence of farms, and recent election results.ConclusionsThis analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that socio-political demographic indicators drive important variability in such patterns across counties.

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making

BackgroundThe COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future.MethodsUsing archived forecasts from the California Department of Public Health’s California COVID Assessment Tool (https://calcat.covid19.ca.gov/cacovidmodels/), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance.ResultsBased on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations.ConclusionsEnsemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.

Discrete-event simulation modeling for housing of homeless populations.

The San Francisco Bay Area has experienced a rapid rise in homelessness over the past decade. There is a critical need for quantitative analysis to help determine how to increase the amount of housing to meet the needs of people experiencing homelessness. Noting that the shortage of housing available through the homelessness response system can be modeled as a queue, we propose a discrete-event simulation to model the long-term flow of people through the homelessness response system. The model takes as input the rate of additional housing and shelter available each year and delivers as output the predicted number of people housed, sheltered, or unsheltered in the system. We worked with a team of stakeholders to analyze the data and processes for Alameda County in California and use this information to build and calibrate two simulation models. One model looks at aggregate need for housing, while the other differentiates the housing needs of the population into eight different types. The model suggests that a large investment in permanent housing and an initial ramp up of shelter is needed to solve unsheltered homelessness and accommodate future inflow to the system.

An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics.

Explanations for variations in hospital expenditures among four large California counties

ObjectiveTo investigate competing explanations for why Medicare Fee for Service (FFS) and private sector payments lead to hospital cost variations in Californian counties.Data sourcesRatios of private to Medicare hospital costs were obtained from state-based all-payer claims databases. Demographics were estimated from the U.S. Census Bureau and the California Health Interview Survey. Medicaid and Medicare spending was obtained from Kaiser Family Foundation. Medicare Advantage enrollment was obtained from the California Department of Health Care Services and market consolidation was estimated using the Herfindahl–Hirschman Index (HHI).Study designPer capita costs, demographics, Medicaid and Medicare spending, Medicare Advantage enrollment, and HHI scores were compared for San Francisco (SF), Sacramento, Los Angeles (LA), and San Diego (SD).Principal findingsLA hospitals had the lowest per capita private insurer costs, but the highest Medicare FFS costs. The findings might be explained by a lower HHI for LA, indicating a more competitive market, than SD, SF, and Sacramento.ConclusionsMedicare FFS hospital costs do not provide an accurate representation of health care spending in Californian counties. In more competitive markets, private insurance companies can negotiate lower prices, while oversupply may allow facilities to increase volume in Medicare FFS.

Financial Challenges of Family Child Care Providers During the COVID-19 Pandemic: A Phenomenological Study.

The impact of COVID-19, both in quality and quantity, on the field of early childhood education, ECE is immeasurable. However, as research shows, its impact on family child care (FCC), has been worse than other sectors of ECE. FCC providers worldwide have always perceived their work as a service to families and children, yet FCC homes have not received much attention and recognition from researchers and policymakers compared to center-based ECE programs. This phenomenological inquiry with 20 FCC providers in a large urban county in California highlights the financial challenges that FCC providers faced during the early part of the pandemic before they received financial support from the state in the spring of 2021. The cost of running the program was quite high because of reduced enrollment and the regular purchase of sanitary materials. To keep their programs afloat, some participants had to lay off their staff, others kept them without salary, others had to exhaust their savings, and most incurred credit card debt. Most of them also experienced psychosocial stress. Their financial hardships during the pandemic would have been far worse without the emergency funding received from the state. However, as experts warn, the field of ECE needs a permanent solution and the situation could be even worse after the emergency funds run out in 2024. The nation has witnessed the dedicated service of FCC providers during the pandemic, especially to families of essential workers. Much work is needed at empirical and policy levels to support and celebrate the service of FCC providers.

An interactive web-based solar energy prediction system using machine learning techniques

Solar energy being one of the most inexpensive renewable energy sources, has shown to be a viable alternative to traditional fossil-fuel and wood-based electricity generation. For the purpose of creating a more trustworthy and successful energy planning strategy, accurate projections of sun irradiation, solar energy generation, and revenues are crucial. Hence, in this work we have proposed web-based optimal prediction system that estimates solar radiation based on location and meteorological data using Machine Learning techniques. Furthermore, an interactive dashboard solar digital map has been developed that enables real-time investigation of solar energy consumption, production, solar radiation, and investment potential for a specific county in California. The model's performance has been measured using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Average Error (MAE), and Mean Absolute Percentage Error (MAPE) scores. Experimental results demonstrate that stacking model outperformed all the models with the lowest RMSE, MSE, and MAE.

Socioeconomic impacts of COVID-19 pandemic on foodborne illnesses in the United States

Foodborne diseases continue to impact human health and the economy. The COVID-19 pandemic has dramatically affected the food system from production to consumption. This project aims to determine the impact of the COVID-19 pandemic on the spread of foodborne diseases and the factors that may have contributed, including environmental, behavioral, political, and socioeconomic. Data for this study were collected from The Foodborne Diseases Active Surveillance Network (FoodNet) for 2015-2020. FoodNet personnel located at state health departments regularly contact the clinical laboratories in Connecticut (CT), Georgia (GA), Maryland (MD), Minnesota (MN), New Mexico (NM), Oregon (OR), Tennessee (TN), and selected counties in California (CA), Colorado (CO), and New York (NY). Data were analyzed using SAS to determine the changes in rates of foodborne pathogens reported in FoodNet before and during the COVID-19 pandemic in the ten reporting states. Results of the study showed a significant decline in the incidences of foodborne diseases ranging between 25% and 60%. A geographical variation was also observed between California and states with the highest decline rate of foodborne illnesses. Policies and restrictions, in addition to environmental and behavioral changes during the COVID-19 pandemic, may have reduced rates of foodborne diseases.

MP64-01 GROUNDWATER CONSTITUENTS AND THE INCIDENCE OF KIDNEY CANCER

You have accessJournal of UrologyCME1 Apr 2023MP64-01 GROUNDWATER CONSTITUENTS AND THE INCIDENCE OF KIDNEY CANCER Simon John Christoph Soerensen, Maria E. Montez-Rath, Iona Cheng, Scarlett Lin Gomez, Debora L. Oh, Christian Jackson, David Rehkopf, Glenn M. Chertow, Calyani Ganesan, Alan C. Pao, John T. Leppert, and Benjamin I. Chung Simon John Christoph SoerensenSimon John Christoph Soerensen More articles by this author , Maria E. Montez-RathMaria E. Montez-Rath More articles by this author , Iona ChengIona Cheng More articles by this author , Scarlett Lin GomezScarlett Lin Gomez More articles by this author , Debora L. OhDebora L. Oh More articles by this author , Christian JacksonChristian Jackson More articles by this author , David RehkopfDavid Rehkopf More articles by this author , Glenn M. ChertowGlenn M. Chertow More articles by this author , Calyani GanesanCalyani Ganesan More articles by this author , Alan C. PaoAlan C. Pao More articles by this author , John T. LeppertJohn T. Leppert More articles by this author , and Benjamin I. ChungBenjamin I. Chung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003322.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The incidence of kidney cancer demonstrates significant geographic variation that suggests a role for environmental risk factors, in addition to genetic and clinical risk factors. We sought to evaluate associations between groundwater environmental exposures and kidney cancer incidence. METHODS: We identified 180 constituents from 18,506 public groundwater wells in all 58 counties in California from 1996-2010. We obtained county-level kidney cancer incidence data from the California Cancer Registry from 2003-2017. Using an XWAS framework, which is the application of statistical approaches popularized in GWAS studies, we created a water-wide ecological association study (WWAS). We fit Poisson regression models to estimate the association between each constituent and county-level kidney cancer standardized incidence ratios, adjusting for known risk factors including sex, obesity, prevalence of smoking, and socioeconomic status at the county-level. RESULTS: Eleven groundwater constituents met stringent WWAS criteria and were associated with kidney cancer incidence. The seven constituents that had a positive association with kidney cancer standardized incidence ratios were chlordane (relative risk (RR)=1.06, 95% CI: 1.02–1.10), dieldrin (RR=1.04, 95% CI: 1.01–1.07), 1,2-dichloropropane (RR=1.04, 95% CI: 1.02–1.05), 2,4,5-TP (Silvex) (RR=1.03, 95% CI: 1.01–1.05), glyphosate (RR=1.02 95% CI: 1.01–1.04), endothall (RR=1.02, 95% CI: 1.01–1.03), and carbaryl (RR=1.02, 95% CI: 1.01–1.03). The constituent with the strongest inverse association was bromide (RR=0.96, 95% CI: 0.94–0.99). CONCLUSIONS: Eleven groundwater constituents met stringent WWAS criteria and were associated with kidney cancer incidence. The seven constituents that had a positive association with kidney cancer standardized incidence ratios were chlordane (relative risk (RR)=1.06, 95% CI: 1.02–1.10), dieldrin (RR=1.04, 95% CI: 1.01–1.07), 1,2-dichloropropane (RR=1.04, 95% CI: 1.02–1.05), 2,4,5-TP (Silvex) (RR=1.03, 95% CI: 1.01–1.05), glyphosate (RR=1.02 95% CI: 1.01–1.04), endothall (RR=1.02, 95% CI: 1.01–1.03), and carbaryl (RR=1.02, 95% CI: 1.01–1.03). The constituent with the strongest inverse association was bromide (RR=0.96, 95% CI: 0.94–0.99). Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e880 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Simon John Christoph Soerensen More articles by this author Maria E. Montez-Rath More articles by this author Iona Cheng More articles by this author Scarlett Lin Gomez More articles by this author Debora L. Oh More articles by this author Christian Jackson More articles by this author David Rehkopf More articles by this author Glenn M. Chertow More articles by this author Calyani Ganesan More articles by this author Alan C. Pao More articles by this author John T. Leppert More articles by this author Benjamin I. Chung More articles by this author Expand All Advertisement PDF downloadLoading ...

The Effect of the Drug Medi-Cal Organized Delivery System 1115 Demonstration Waiver on Substance Use Disorder Treatment Access: Evidence from California

BackgroundIn August 2015, the California Department of Health Care Services created the Drug Medi-Cal Organized Delivery System 1115 demonstration waiver (DMC-ODS waiver) to improve service delivery to Medi-Cal-eligible individuals with a substance use disorder (SUD). We examine if implementing the DMC-ODS waiver across California counties improved patient access to SUD treatment services. MethodsWe use administrative data from 2016 to 2020 from a reporting system for all publicly-funded SUD treatment services delivered in California and employ difference-in-differences and event study empirical strategies exploiting the differential timing of DMC-ODS waiver adoption across counties. ResultsEvent study analyses show that eleven or more months after the introduction of the DMC-ODS waiver, the number of unique patient admissions significantly increase by nearly 20%. Residential treatment admissions significantly increase by roughly 25% in all months post-waiver introduction. ConclusionsThis study provides valuable information for policymakers about implementing 1115 waivers, and the important public health implications. California’s DMC-ODS waiver has demonstrated that 1115 waivers similar to it can likely increase access to SUD treatment.