Zip Code Level Research Articles

Uneven heat burden in the sunshine state: Spatial patterns and socio-economic disparities of heat-related illness in Florida.

Climate change has increased the frequency and severity of extreme heat events globally, adversely affecting socio-economic conditions and public health. However, extreme heat has disparate effects on different population groups and the socio-economic determinants of its health effects are not well understood. In this study, we analyzed the spatial patterns of heat-related illness (HRI) visit rates at the zip-code level in Florida and applied statistical methods to examine the relationships between HRIs and environmental and socio-economic variables. Hierarchical regression analysis was used to evaluate the socio-economic effects on HRI visit rates under the same heat conditions. This is a two-step approach: we first included heat indicators in the baseline model and then added the socio-economic variables to assess their unique contributions in predicting HRI visits. Our findings indicate that temperature can only explain a small fraction of the variance in HRI cases (R2=0.04, p<0.01), while socio-economic variables show stronger associations (R2=0.42, p<0.01 in urban areas and R2=0.20, p<0.01 in rural areas). Notably, marginalized and disadvantaged populations (e.g., individuals in poverty, those employed in construction, and those with disabilities) are positively associated with HRIs (p<0.01). These findings highlight the disproportionate impacts of heat-related health issues on disadvantaged groups, calling for climate justice policy interventions. Additionally, a comparative analysis between rural and urban areas revealed different determinants of HRIs. Our study enhances the understanding of the socio-economic determinants and disparities of HRIs in Florida, providing actionable insights for policymakers and health agencies to prioritize emergency services and heat resilience planning.

Distribution of air quality health benefits of medium and heavy-duty electrification policies in New York City

Abstract Increased electrification through the rapid transition in the transportation sector promises to both decrease emissions from the largest sector source of greenhouse gases (GHGs) as well as improve health through better air quality. The objective of this study is to evaluate the distribution of air quality health benefits of Medium- and Heavy-Duty Electric Vehicle (MHDEV) policies in New York City (NYC), quantifying differences across neighborhoods (census tracts) and population subgroups (race, ethnicity). We ran an integrated model for a 2040 baseline/business-as-usual scenario (BAU) scenario and for two policy scenarios simulating different rates of MHDEV adoption, also for 2040. Changes in air quality (fine particulate matter (PM2.5) and nitrogen dioxide (NO2)) and health and economic benefits were calculated for the two policy scenarios. We used a modified version of our previously developed ZIP Code-Level Air Pollution Policy Assessment (ZAPPA) tool which integrates two reduced form models: the Community Air Quality Tools (C-TOOLS) and the Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA). Our air quality modeling employed a novel receptor network to capture sharp gradients due to truck emissions in the densely population NYC region. We find that electrification of MHDV would have substantial air pollution and health benefits for NYC, with full on-road electrification of MHDV saving $2.4 billion in health costs in 2040, including the prevention of 248 deaths and 173 childhood asthma emergency departments visits. We find that NO2 benefits contributed between 85 – 97% of the total health benefits, emphasizing its importance for onroad health studies. We find that most health benefits would occur in neighborhoods with the highest percentage of historically marginalized populations, including substantial benefits to Hispanic populations.

Private Equity Ownership of US Opioid Treatment Programs

This cross-sectional study assesses acquisitions of US opioid treatment programs by private equity firms overall and at the zip-code level.

The role of the components of PM 2.5 in the incidence of Alzheimer's disease and related disorders.

The associations of PM 2.5 mass and various adverse health outcomes have been widely investigated. However, fewer studies focused on the potential health impacts of PM 2.5 components, especially for dementia and Alzheimer's diseases (AD). We constructed a nationwide population-based open cohort study among Medicare beneficiaries aged 65 or older during 2000-2018. This dataset was linked with the predicted levels of 15 PM 2.5 components, including 5 major mass contributors (EC, OC, NH 4 + , NO 3 - , SO 4 2- ) and 10 trace elements (Br, Ca, Cu, Fe, K, Ni, Pb, Si, V, Zn) across contiguous US territory. Data were aggregated by ZIP code, calendar year and individual level demographics. Two mixture analysis methods, weighted quantile sum regression (WQS) and quantile g-computation (qgcomp), were used with quasi-Poisson models to analyze the health effects of the total mixture of PM 2.5 components on dementia and AD, as well as the relative contribution of individual components. Exposure to PM 2.5 components over the previous 5 years was significantly associated with increased risks of both dementia and AD, with stronger associations observed for AD. SO 4 2- , OC, Cu were identified with large contributions to the combined positive association of the mixture from both WQS and qgcomp models. We found positive associations between the 15 PM 2.5 components and the incidence of dementia and AD. Our findings suggest that reducing PM 2.5 emissions from traffic and fossil fuel combustion could help mitigate the growing burden of dementia and Alzheimer's disease.

Rental Market Risk and Radical Right Support

A growing literature examines how economic threat affects support for anti-establishment parties. While most existing work focuses on transforming labor markets as a source of anxiety, we advance the literature by studying changes in urban development and rent price appreciation. Our analysis examines the case of Germany, the country with the highest share of rental housing in the European Union. Combining individual-level geo-referenced panel data with a longitudinal data set on the cost of rental housing at the postcode level, we demonstrate that rising local rent levels increase support for radical right parties among long-term residents with lower household incomes, in particular in urban areas. Our results suggest that urban development, not unlike labor market transformation, represents an important and so far neglected source of economic insecurity and social concern with important political implications.

Drug Overdose Death among Residents of Urban Census Tracts: How Granular Geographical Analyses Uncover Socioenvironmental Correlates in Cuyahoga County, Ohio.

Geostatistical data aggregated at state, county, municipality, or ZIP code levels often are utilized for assessing drug overdose epidemic impact and planning resource distribution. Data aggregated at these levels may obscure critical disparities among populations experiencing high rates of drug-related mortality (DRM), especially in densely populated urban areas. Our research was centered on Cuyahoga County (Cleveland), OH, which ranks 15th in the USA for drug-related mortality. This study built on recent efforts that adopted a finer geographical lens by examining DRM rates at the census tract level. Our investigation used Cuyahoga County census tracts with high and low DRM rates and compared them with Cuyahoga County census tracts with high and low levels of opportunity as developed by a publicly available, statewide opportunity index. Analyzing DRM data from 2014 to 2022, we found that the odds of an individual experiencing DRM in low-opportunity areas were quadruple the odds for someone in high-opportunity zones. Our findings highlight the critical need for more granular geographic analysis in urban areas, where heterogenous socioenvironmental conditions appear to correlate with significant heterogeneity in the ways in which residents experience the risk of dying from a drug overdose. By focusing on smaller areas, this approach provides a clearer understanding of the DRM landscape that could facilitate the prioritization of more targeted, culturally centered, public health interventions.

Community alcohol sales and opioid poisoning deaths: Alcohol serving space as a harm reduction opportunity

The concurrent use of opioids and alcohol is particularly dangerous for individuals. Alcohol is commonly seen in opioid overdose death toxicology reports and, concurrent use of alcohol and opioids is often reported by individuals across a diverse range of opioid use profiles. This study investigates whether there is a community-level relationship between alcohol sales and opioid-related overdose deaths to inform the situating of harm reduction efforts in spaces most likely to reduce substance-related harms. Using an ecological design, zip code-level data for New Hampshire were combined from the US Census Bureau’s American Community Survey (sociodemographics), the National Alcohol Beverage Control Association (alcohol retail sales), and the NH Office of the Chief Medical Examiner (zip code level opioid poisoning deaths) to investigate the relationship between alcohol sales and opioid-related poisoning deaths at a community level in a state with the third highest rate of opioid poisoning deaths for the year the current study represents. Using a spatial error regression model approach, opioid-related poisoning deaths were higher in zip codes with greater population density and on-premise alcohol sales and were lower in zip codes with greater off-premise alcohol sales and area disadvantage. The findings here co-locate higher levels of on-premise alcohol sales and opioid-related poisoning deaths at a community-level, mirroring individual-level findings on the danger of mixing these two substances. Results inform harm reduction approaches by identifying substance use spaces where overdose prevention messaging or policy change may be most effective.

The Association of Where Patients with Prostate Cancer Live and Receive Care on Racial Treatment Inequities.

Black individuals are less likely to be treated for prostate cancer even though they are more than twice as likely to die compared to White individuals. The complex causes of these inequities are influenced by social and structural factors, including racism, which contribute to the differential delivery of care. This study investigates how factors related to the location of where individuals live and receive care affect treatment inequities for prostate cancer between Black and White individuals. We hypothesize that both location and race independently influence treatment inequities. We used data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry linked to Medicare claims to estimate the treatment inequity, as defined by differences in radiation or radical prostatectomy. Fixed effects at the physician, hospital, and patient ZIP code levels were incorporated to adjust for all time-invariant factors at these levels. The results indicate that residential location-related factors explain only half of the treatment inequity, while provider- and hospital-level factors do not significantly account for disparities. Even after accounting for all time-invariant factors, significant differences in treatment rates persist. The study highlights the importance of understanding race as a social construct and racism as a systemic and structural phenomenon in addressing treatment inequities. These findings provide a necessary step toward understanding equitable care and designing interventions to solve this inequity.

Abstract 4141487: Social Determinants of Health and Outcomes in Patients with Hypertrophic Cardiomyopathy in the Sarcomeric Human Cardiomyopathy Registry (SHaRe)

Introduction: Area-based social determinants of health (SDOH) are associated with higher risk for acquired heart disease. However, the impact of area-based SDOH on clinical outcomes in patients with hypertrophic cardiomyopathy (HCM) has not been previously studied. Hypothesis: Residing in an area with lower median household income and a higher social deprivation index (SDI) are independently associated with adverse outcomes in patients with HCM. Methods: This study included 4,483 adult patients with HCM from five Sarcomeric Human Cardiomyopathy Registry (a multi-center prospective registry of patients with HCM) sites in the US followed for a median [IQR] of 4.0 [1.0-8.5] years. The patients’ residential address was geocoded at the zip code level to characterize median household income and SDI. SDI is based on various social factors (such as income and education), with a higher score reflecting more deprivation. Multivariate models, adjusting for sex, age, hypertension and body mass index were used to determine the independent association of household income and SDI on ventricular arrhythmias (VA), heart failure (HF), and an overall composite outcome (VA, HF, atrial fibrillation, death, and stroke). Results: Median age at diagnosis was 48.3 [32.0-60.4] years with 42.0% female. Median household income was $90,000 [60,000-110,000] and median SDI was 25 [10-55]. Adjusted hazard ratios per $10,000 higher income were 0.96 (95% CI 0.93-1.00, p=0.03) for VA, 0.91 (95% CI 0.90-0.93, p &lt;0.01) for HF, and 0.95 (95% CI 0.94-0.96, p &lt;0.01) for the composite outcome. Adjusted hazard ratios per 10 units higher SDI (more deprived) were 1.07 (95% CI 1.03-1.12, p&lt;0.01) for VA, 1.06 (95% CI 1.04-1.08, p &lt;0.01) for HF, and 1.05 (95% CI 1.03-1.06, p &lt;0.01) for the composite outcome. The associations of SDI and income on clinical outcomes were similar regardless of sarcomere gene variant status. Conclusions: Residing in an area with lower median household income and higher SDI were independently associated with adverse outcomes in patients with HCM. These findings extend observations from acquired cardiovascular disease and highlight the need to address adverse SDOH to reduce health disparities and improve outcomes in patients with HCM.

Assessing demographic vulnerability and weather impacts on utility disconnections in California.

When a household is disconnected from their electric utility service the consequences can be severe, including accumulation of debt, the inability to maintain comfortable temperatures, and in the most extreme cases, homelessness or mortality. While the survey-based literature on utility disconnections has yielded important findings about which households are most likely to experience a utility shutoff, only a few existing studies have used data from utility companies themselves. In this analysis, we utilize zip-code level data from four of California's largest utility providers to measure sociodemographic disparities in disconnections, in addition to the impact of adverse weather. We find that zip codes with a higher share of vulnerable households, especially Black and Hispanic households and households with young children, face a higher number and rate of utility disconnections, even after controlling for an extensive set of factors that are commonly thought to explain higher rates of energy insecurity, including income, housing characteristics, and energy costs. Our analysis also suggests that sociodemographic and weather disparities in disconnections differ between utility types. We conclude by discussing the implications of the findings for research and policy, including the impact of regulation, utility provider practices, and more extreme weather driven by climate change.

Effects of fine particulate matter from wildfire and non-wildfire sources on emergency-department visits in people who were housed and unhoused in San Diego County (CA, USA) during 2012–20: a time-stratified case–crossover study

Being unhoused can increase vulnerability to adverse health effects due to air pollution. We aimed to quantify changes in emergency-department visits during and after exposure to wildfire-specific and non-wildfire particulate matter 2·5 μm or less in diameter (PM2·5) in San Diego County (CA, USA) in people who were both unhoused and housed. For this time-stratified case-crossover study, we used data on exposure to wildfire-specific PM2·5 in California and individual-level data for people admitted to the emergency departments of two hospitals (UC San Diego Health emergency departments at La Jolla and Hillcrest, San Diego) in San Diego County between July 1, 2012, and Dec 31, 2020. People with a postcode outside of San Diego County were excluded. Demographic information was age group, race or ethnicity, and transport to the emergency department. Wildfire-specific PM2·5 concentration at the postcode level was previously estimated using an ensemble model that combined multiple machine-learning algorithms and explanatory variables obtained via data on 24-h mean PM2·5 concentrations from the US Environmental Protection Agency Air Quality System. Conditional logistic regression models were applied, adjusting for specific humidity, wind velocity, and maximum temperature extracted from the US Gridded Surface Meteorological Dataset. Housing status was established by registration staff or triage nurses on arrival at the emergency department. For people who were unhoused, exposure was defined based on the weighted mean PM2·5 concentration at the city level proportional to the number of people who were unhoused in each specific city across urban centres in San Diego County. For people who were housed, we used residence postcode to measure exposure. We assessed the association between PM2·5 from wildfire and non-wildfire sources and emergency-department visits in people who were housed and unhoused. There were 587 562 emergency-department visits at the two hospitals, 76 407 (13·0%) of which were by people who were unhoused. People who were housed had a higher exposure to overall PM2·5 (24-h mean over the study period of 9·904 mg/m3, SD 3·445) and non-wildfire PM2·5 (9·663, 2·977) than people who were unhoused (9·863, 3·221; 9·557, 2·599). However, people who were unhoused had a higher exposure to wildfire-specific PM2·5 (0·305, 1·797) than people who were housed (0·240, 1·690). Overall PM2·5 exposure was associated with increased odds of emergency-department visits for both people who were housed (odds ratio 1·003, 95% CI 1·001-1·004 per 1 μg/m3 PM2·5 for 0-3 days after exposure) and people who were unhoused (1·004, 1·000-1·008 for 0-3 days after exposure). We found that non-wildfire PM2·5 was associated with emergency-department visits among people who were housed (1·003, 1·002-1·005 for 0-3 days after exposure) and wildfire-specific PM2·5 was associated with emergency-department visits in people who were unhoused (1·006, 1·001-1·011 for 0-3 days after exposure). People who were unhoused in San Diego County were more likely to visit emergency departments after exposure to increased wildfire-specific PM2·5. As the intensity and frequency of wildfires increase, understanding risk factors for vulnerable populations, such as people who are unhoused, is crucial to develop effective adaptation strategies. US National Institutes of Health, National Institute on Aging.

Estimation of HIV prevalence at the ZIP code-level in Atlanta, Georgia: Bayesian prediction modeling using passive surveillance data and social determinants of disease spreading

ObjectiveThis study aims to predict the number of undiagnosed HIV cases at the ZIP Code-level in Atlanta, Georgia, based on publicly available information. Study designStatistical modeling. MethodsWe fitted a Bayesian hierarchical Binomial model to county-level estimates of the passive-surveillance-system. The denominator was the true total HIV cases arising from a Negative Binomial distribution. The trial probability, known as ascertainment probability, depended on socio-economic determinants of HIV, retained via feature-selection algorithms. Data were obtained from CDC's HIV report for End of the HIV Epidemic and the American Community Survey. The prediction model was assessed out-of-sample in Georgia counties. We combined socio-economic data with the posterior predictive distribution of the coefficients to predict the mean ascertainment probability and total HIV cases at the ZIP Code-level. These estimates were spatially smoothed and aggregated at the county-level for secondary validations. ResultsThe county-level model showed good mixing properties and predictive accuracy. The mean ascertainment probability calibrated to the ZIP Code-level varied from 78.4% (95% credible interval: 24.4%–99.3%) to 93.8% (95%CI: 80.6%–99.8%). Further, the predicted undiagnosed HIV cases ranged between 12 (95%CI: 6–19; ZIP Code 30322) to 1603 (95%CI 1209–1968; ZIP Code 30318). ConclusionsOur findings provide a more complete picture of the relative burden of HIV across ZIP codes. Such information can be used by Local Health Departments to identify underserved areas and allocate resources accordingly. Furthermore, our methodological approach can be applied to complement the information obtained from passive surveillance, especially when more resource-intensive approaches are not available or are unfeasible to employ.

Quantifying post-disaster community well-being: A case study of Hurricane Harvey

Natural hazards such as hurricanes affect various aspects of the community members’ lives and their post-disaster well-being by causing significant disruptions in the key community activities in the immediate recovery phase. Furthermore, natural hazards leave behind short-term socio-economic impacts such as stress, anxiety, huge recovery expense, and lack of affordable housing. There is a need for incorporating both immediate and short-term impacts of natural hazards when measuring disaster recovery. This study aims to address this need by introducing community well-being as the metric for the recovery of communities from natural disasters. From this perspective, community resilience is defined as the capability of community to reach its pre-disaster state of well-being, in a timely and efficient manner. The study leverages Bottom-Up Spillover Theory and the existing literature to introduce a community well-being model. This model quantifies how the functionality of infrastructure systems can affect various aspects of community well-being based on 6 domains, 17 sub-domains, and 51 indicators. The indicators were quantified using survey data and 211-call data for the City of Houston, and data on the impact of Hurricane Harvey at the zip code level. The results showed that various dimensions of well-being were affected heterogeneously and followed different recovery patterns. The proposed framework can serve decision makers as a dashboard for identifying the well-being domains and sub-domains that should be addressed to enhance post-disaster well-being in the immediate-to short-term. Furthermore, the study introduces the phone call data as an inexpensive and timely replacement for multiple rounds of survey questionnaires for quantifying community well-being.

Understanding the zero-emission vehicle market spatial diffusion and its determinants from 2019 to 2022 using spatial econometric models

Due to the rapid rise in the acceptance of zero-emission vehicles (ZEVs), much research has been conducted on the market evolution of alternative fuel technology. The research reported in this paper builds on prior studies by incorporating several new directions. Firstly, the spatial distribution of ZEV purchases in California is explored, then seven models (two nonspatial models and five spatially explicit models) are developed to predict regional ZEV sales. The predictions are based on fundamental and widely accessible characteristics such as infrastructure and demographic statistics. Next, a spatial econometric model known as the “spatial error model” is chosen to distribute ZEV sales from their original postal code (ZIP) level to the more often utilized US census tract level. This allows for carrying out a comprehensive analysis that includes studying the correlation of ZEV sales with local incentives and disadvantaged communities. Results reveal that ZEV sales in California increased significantly between 2019 and 2022, as spatial autocorrelation decreased. Vulnerable groups were less likely to have access to ZEVs, particularly during the initial stage of the ZEV market penetration in 2019. As expected, regions with considerable ZEV sales tended to also experience substantial purchase rebates and lower disadvantaged population, especially in 2019.

Syndemic geographic patterns of cancer risk in a health-deprived area of England

ObjectivesThis study aims to analyse the geographical co-occurrence of cancers and their individual and shared risk factors in a highly deprived area of the North West of England to aid the identification of potential interventions. Study designAn ecological study design was employed and applied at postcode sector level in the Morecambe Bay region. MethodsA novel spatial joint modelling framework designed to account for large frequencies of left-censored cancer data was employed. Nine cancer types (breast, colorectal, gynaecology, haematology, head and neck, lung, skin, upper gastrointestinal, urology) alongside demographic, behavioural factors and socio-economic variables were included in the model. Explanatory factors were selected by employing an accelerated failure model with lognormal distribution. Post-processing included principal components analysis and hierarchical clustering to delineate geographic areas with similar spatial risk patterns of different cancer types. Results15,506 cancers were diagnosed from 2017 to 2022, with the highest incidence in skin, breast and urology cancers. Factors such as age, ethnicity, frailty and comorbidities were associated with cancer risk for most of the cancer types. A positive geographical association was found mostly between the colorectal, haematology, upper GI, urology and head and neck cancer types. That is, these cancers had their largest risk in the same areas, similarly to their lowest risk values. The spatial distribution of the risk and cumulative risk of the cancer types revealed regional variations, with five clusters identified based on cancer type risk, demographic and socio-economic characteristics. Rural areas were the least affected by cancer and the urban area of Barrow-in-Furness was the area with the highest cancer risk, three times greater than the risk in the surrounding rural areas. ConclusionsThis study emphasizes the utility of joint disease mapping by geographically identifying common or shared factors that, if targeted, could lead to reduced risk of multiple cancers simultaneously. The findings suggest the need for tailored public health interventions, considering specific risk factors and socio-economic disparities. Policymakers can utilize the spatial patterns identified to allocate resources effectively and implement targeted cancer prevention programmes.

Social Determinants of Health and Timing of Tracheostomy for Severe Bronchopulmonary Dysplasia

To estimate the association of ZIP code-level social determinants of health (SDoH), specifically household income, education level, and unemployment rate, with post-menstrual age (PMA) at tracheostomy placement in patients with severe bronchopulmonary dysplasia (BPD). This was a retrospective observational study of infants born <32 weeks' gestation and discharged from a Children's Hospitals Neonatal Consortium newborn intensive care unit. Patients were diagnosed with severe BPD and received tracheostomies before discharge. Maternal ZIP code at admission was linked to that ZIP code's SDoH via the 2021 US Census Bureau 5-year data. Unadjusted and adjusted analyses were completed with separate models fit for each SDoH marker. 877 patients received tracheostomies at median (interquartile) 48 (44-53) weeks' PMA. In multivariable models, patients in the highest education groups received tracheostomies earlier (OR: 95% CI = 0.972: 0.947-0.997, p=0.031), and non-Hispanic Black patients received tracheostomies later compared with non-Hispanic White patients (OR: 1.026; 95% CI =1.005-1.048, p=0.017). For household income and unemployment, the PMA at tracheostomy did not differ by SDoH or race. For all three models, male sex, small for gestation status, and later PMA at admission were associated with later PMA at tracheostomy. For each SDoH marker, significant inter-center variation was noted as several centers had independently increased PMA at tracheostomy. Education at the ZIP code-level influenced PMA at tracheostomy after adjusting for patient and clinical factors. Adjusted for each SDoH studied, significant differences were noted among centers. Factors leading to the decision and timing of neonatal tracheostomy need further evaluation.

The carbon footprints of consumption of goods and services in Sweden at municipal and postcode level and policy interventions

Carbon footprints show the greenhouse gas emissions associated with consumption of goods and services. Many countries now calculate carbon footprints at the national level, published as part of national environmental accounts. However, carbon footprint estimates at the local or regional levels are less common. Municipalities call for consistent and comparable carbon footprint data to support their low carbon transitions.This study presents estimates of households' carbon footprints for all Swedish municipalities, down to the postcode level. It uses a downscaling approach to disaggregate the Swedish national carbon footprint from the national level to Swedish postcode level, using the best available local data on expenditure, greenhouse gas emissions or other data such as energy use, at the smallest geographical scale. The variation in carbon footprints at municipal and postcode levels is analysed and key contributing consumption categories identified. The study then discusses how these data can and have been used to support policymaking.The results show considerable variations in average carbon footprints at the postcode level in Sweden, between about 3.7 and 17.8 t CO2eq per capita. Footprints of items such as flights and personal vehicles have high variation, whereas the consumption categories of food, electricity, and clothing are more similar. A strong positive correlation exists between incomes and the total carbon footprint. Carbon footprints from personal vehicles have a negative correlation to population density, while the footprint of clothing, restaurants and flights have a positive correlation to population density.In terms of policy, several municipalities in Sweden were found to have carbon footprint reduction goals and related policies. A number of municipalities also demonstrated the application of preliminary data from the study for raising public awareness, policymaking, and monitoring progress towards reduction targets. The recommendations for future development and use of these data for policy and decision-making include: (1) ensuring data access, learning and stakeholder engagement with the data; (2) using the data to develop policy visions and goals and associated reduction targets and; (3) designing policy interventions that will reduce carbon footprints, taking into account factors such as variation at the local scale and between consumption categories, geography, socio-economic circumstances, inequalities, and systemic change.

Wildland Fire-Related Smoke PM 2.5 and Cardiovascular Disease ED Visits in the Western United States.

The impact of short-term exposure to fine particulate matter (PM 2.5 ) due to wildland fire smoke on the risk of cardiovascular disease (CVD) remains unclear. We investigated the association between short-term exposure to wildfire smoke PM 2.5 and Emergency Department (ED) visits for acute CVD in the Western United States from 2007 to 2018. ED visits for primary or secondary diagnoses of atrial fibrillation (AF), acute myocardial infarction (AMI), heart failure (HF), stroke, and total CVD were obtained from hospital associations or state health departments in California, Arizona, Nevada, Oregon, and Utah. ED visits included those that were subsequently hospitalized. Daily smoke, non-smoke, and total PM 2.5 were estimated using a satellite-driven multi-stage model with a high resolution of 1 km. The data were aggregated to the zip code level and a case-crossover study design was employed. Temperature, relative humidity, and day of the year were included as covariates. We analyzed 49,759,958 ED visits for primary or secondary CVD diagnoses, which included 6,808,839 (13.7%) AFs, 1,222,053 (2.5%) AMIs, 7,194,474 (14.5%) HFs, and 808,396 (1.6%) strokes. Over the study period from 2007-01-01 to 2018-12-31, the mean smoke PM 2.5 was 1.27 (Q1: 0, Q3: 1.29) µg/m 3 . A 10 µg/m 3 increase in smoke PM 2.5 was associated with a minuscule decreased risk for AF (OR 0.994, 95% CI 0.991-0.997), HF (OR 0.995, 95% CI 0.992-0.998), and CVD (OR 0.9997, 95% CI 0.996-0.998), but not for AMI and stroke. Adjusting for non-smoke PM 2.5 did not alter these associations. A 10 µg/m 3 increase in total PM 2.5 was linked to a small increased risk for all outcomes except stroke (OR for CVD 1.006, 95% CI 1.006-1.007). Associations were similar across sex and age groups. We identified an unexpected slight lower risk of CVD ED visits associated with short-term wildfire smoke PM 2.5 exposure. Whether these findings are due to methodological issues, behavioral changes, or other factors requires further investigation.

Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis

As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude of the crisis, especially in at-risk rural areas. We analyzed sets of ZIP code level electronic health records for emergency department visits from 6 hospitals for two rural regions of Maryland with higher opioid-related overdose rates. Analysis of the demographics of visits found Black or African American emergency department visits in both rural regions were higher than the proportion of their population per region. We trained random forest models with socio-demographic factors and health risk factors on the visits data to understand drivers and risk factors for opioid misuse. The models ranked factors relating to opioid prescribing rates, race, housing, and poor mental health as highly important. Factors associated with opioid-related overdose emergency department visits were found to vary by race, gender, and location and may provide useful insights for designing mitigation initiatives.

Young adults, particularly young women, account for an increasingly large share of Dutch mental healthcare expenditure over the period between 2015 and 2021.

There is increasing concern over the mental distress of youth in recent years, which may impact mental healthcare utilisation. Here we aim to examine temporal patterns of mental healthcare expenditures in the Netherlands by age and sex in the period between 2015 and 2021. Comprehensive data from health insurers in the Netherlands at the 3-number postal code level were used for cluster weighted linear regressions to examine temporal patterns of mental healthcare expenditure by age group (18-34 vs 35-65). The same was done for medical specialist and general practitioner costs. Additionally, we examined interactions with gender, by adding the interaction between age, year and sex to the model. Mental healthcare costs for younger adults (18-34) were higher than those for older adults (35-65) at all time points (β = 0.22, 95%-CI = 0.19; 0.25). Furthermore there was an increase in the strength of the association between younger age and mental healthcare costs from β = 0.22 (95%-CI = 0.19; 0.25) in 2015 to β = 0.37 (95%-CI = 0.35; 0.40) in 2021 (p < 0.0001) and this was most evident in women (p < 0.0001). Younger age was associated with lower general practitioner costs at all time points, but this association weakened over time. Younger age was also associated with lower medical specialist costs, which did not weaken over time. Young adults, particularly young women, account for an increasing share of mental healthcare expenditure in the Netherlands. This suggests that mental distress in young people is increasingly met by a response from the medical system. To mitigate this trend a public mental health approach is needed.