Need for Improved Timeliness of Reporting on Drug Overdose Fatalities: The HEALing Communities Study.

OBJECTIVE: Timely data on drug overdose deaths can help identify community needs, evaluate the effectiveness of interventions, and allocate resources. We identified variations in death investigation and reporting systems within and between states that affect the timeliness and accuracy of death certificate information. METHODS: The HEALing Communities Study (HCS) is a community-engaged, data-driven approach to combating the opioid crisis in 67 communities in 4 states: Kentucky, Massachusetts, New York, and Ohio. HCS conducted a survey of coroners and medical examiners to understand variability in drug overdose death data. We compared survey results in Massachusetts, New York, and Ohio with national data to investigate the completeness of provisional death counts by type of death investigation system. RESULTS: Communities in each HCS state had different ways of collecting and reporting mortality data. Completion of death certificates for drug overdoses ranged from <2 weeks in 23% (7 of 31) of those surveyed to more than 3 months in 10% (3 of 31) of those surveyed. Variabilities in the timeliness of reporting drug overdose deaths were not associated with type of coroner or medical examiner office in each state, urban versus rural setting, or specificity of drug information on the death certificate. CONCLUSION: Having specific drug information on the death certificate may increase death certificate quality, comparability, and accuracy. We recommend the following: (1) all coroners and medical examiners should be trained on conducting death investigations, interpreting toxicology reports, and completing death certificates; (2) 1 office in each state should oversee all coroners and medical examiners to increase data consistency; and (3) communities should identify and address barriers to timely death certification.

Spatial access to buprenorphine-waivered prescribers in the HEALing communities study: Enhanced 2-step floating catchment area analyses in Massachusetts, Ohio, and Kentucky.

INTRODUCTION: The opioid overdose epidemic continues to impact a large swath of the population in the US. Medications for opioid use disorders (MOUD) are an effective resource to combat the epidemic; however, there is limited research on MOUD treatment access that accounts for both supply of and demand for services. We aimed to examine access to buprenorphine prescribers in the HEALing Communities Study (HCS) Wave 2 communities in Massachusetts, Ohio, and Kentucky during 2021, and the association between buprenorphine access and opioid-related incidents, specifically fatal overdoses and opioid-related responses by emergency medical services (EMS). METHODS: We calculated Enhanced 2-Step Floating Catchment Area (E2SFCA) accessibility indices for each state, as well as Wave 2 communities in each state, based on the location of providers (buprenorphine-waivered clinicians from the US Drug Enforcement Agency Active Registrants database), population-weighted centroids at the census block group level, and catchment areas defined by the state or community's average commute time. In advance of intervention initiation, we quantified the opioid-related risk environment of communities. We assessed gaps in services by using bivariate Local Moran's I analysis, incorporating accessibility indices and opioid-related incident data. RESULTS: Massachusetts Wave 2 HCS communities had the highest rates of buprenorphine prescribers per 1000 patients (median: 165.8) compared to Kentucky (38.8) and Ohio (40.1). While urban centers in all three states had higher E2SFCA index scores compared to rural communities, we observed that suburban communities often had limited access. Through bivariate Local Moran's I analysis, we identified numerous locations with low buprenorphine access surrounded by high opioid-related incidents, particularly in communities that surrounded Boston, Massachusetts; Columbus, Ohio; and Louisville, Kentucky. CONCLUSION: Rural communities demonstrated a great need for additional access to buprenorphine prescribers. However, policymakers should also direct attention toward suburban communities that have experienced significant increases in opioid-related incidents.

Spatiotemporal Patterns of Deaths of Despair Across the U.S., 2000-2019.

INTRODUCTION: Deaths of despair (i.e., suicide, drug/alcohol overdose, and chronic liver disease and cirrhosis) have been increasing over the past 2 decades. However, no large-scale studies have examined geographic patterns of deaths of despair in the U.S. This ecologic study identifies geographic and temporal patterns of individual and co-occurring clusters of deaths of despair. METHODS: All individuals aged ≥10 years who died in the U.S. between 2000 and 2019 and resided within the 48 contiguous states and Washington, District of Columbia were included (N=2,171,105). Causes of death were limited to deaths of despair, namely suicide, drug/alcohol overdose, and chronic liver disease and cirrhosis. Univariate and multivariate space-time scan statistics were used to identify individual and co-occurring clusters with excess risk of deaths of despair. County-level RRs account for heterogeneity within each cluster. Analyses were conducted from late 2021 to early 2022. RESULTS: Six suicide clusters, four overdose clusters, nine liver disease clusters, and three co-occurring clusters of all three types of deaths were identified. A large portion of the western U.S., southeastern U.S., and Appalachia/rust belt were contained within the co-occurring clusters. The co-occurring clusters had average county RRs ranging from 1.17 (p<0.001) in the southeastern U.S. to 4.90 (p<0.001) in the western U.S. CONCLUSIONS: Findings support identifying and targeting risk factors common to all types of deaths of despair when planning public health interventions. Resources and policies that address all deaths of despair simultaneously may be beneficial for the areas contained within the co-occurring high-risk clusters.

Prevalence of current and past COVID-19 in Ohio adults.

Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.

Investigating the association of room features with healthcare-facility-onset Clostridioides difficile: An exploratory study.

OBJECTIVE: To investigate hospital room and patient-level risk factors associated with increased risk of healthcare-facility-onset Clostridioides difficile infection (HO-CDI). DESIGN: The study used a retrospective cohort design that included patient data from the institution's electronic health record, existing surveillance data on HO-CDI, and a walk-through survey of hospital rooms to identify potential room-level risk factors. The primary outcome was HO-CDI diagnosis. SETTING: A large academic medical center. PATIENTS AND PARTICIPANTS: All adult patients admitted between January 1, 2015, and December 31, 2016 were eligible for inclusion. Prisoners were excluded. Patients who only stayed in rooms that were not surveyed were excluded. RESULTS: The hospital room survey collected room-level data on 806 rooms. Included in the study were 17,034 patients without HO-CDI and 251 with HO-CDI nested within 535 unique rooms. In this exploratory study, room-level risk factors associated with the outcome in the multivariate model included wear on furniture and flooring and antibiotic use by the prior room occupant. Hand hygiene devices and fixed in-room computers were associated with reduced odds of a HO-CDI. Differences between hospital buildings were also detected. The only individual patient factors that were associated with increased odds of HO-CDI were antibiotic use and comorbidity score. CONCLUSION: Combining a hospital-room walk-through data collection survey, EHR data, and CDI surveillance data, we were able to develop a model to investigate room and patient-level risks for HO-CDI.

Network analysis of intra-hospital transfers and hospital onset clostridium difficile infection.

OBJECTIVES: To explore how social network analysis (SNA) can be used to analyse intra-hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment. METHODS: A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS. RESULTS: The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor. DISCUSSION: This study confirmed the importance that intra-hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals. CONCLUSION: Utilising SNA and GIS analysis in conjunction with one another provided a data-rich environment in which the risk inherent in intra-hospital transfer networks was quantified, visualised and interpreted for potential interventions.

Social and Environmental Neighborhood Typologies and Lung Function in a Low-Income, Urban Population.

Consensus is growing on the need to investigate the joint impact of neighborhood-level social factors and environmental hazards on respiratory health. This study used latent profile analysis (LPA) to empirically identify distinct neighborhood subtypes according to a clustering of social factors and environmental hazards, and to examine whether those subtypes are associated with lung function. The study included 182 low-income participants who were enrolled in the Colorado Home Energy Efficiency and Respiratory Health (CHEER) study during the years 2015⁻2017. Distinct neighborhood typologies were identified based on analyses of 632 census tracts in the Denver-Metro and Front Range area of Colorado; neighborhood characteristics used to identify typologies included green space, traffic-related air pollution, violent and property crime, racial/ethnic composition, and socioeconomic status (SES). Generalized estimating equations were used to examine the association between neighborhood typology and lung function. We found four distinct neighborhood typologies and provide evidence that these social and environmental aspects of neighborhoods cluster along lines of advantage/disadvantage. We provide suggestive evidence of a double jeopardy situation where low-income populations living in disadvantaged neighborhoods may have decreased lung function. Using LPA with social and environmental characteristics may help to identify meaningful neighborhood subtypes and inform research on the mechanisms by which neighborhoods influence health.