The association between opioids, environmental, demographic, and socioeconomic indicators and COVID-19 mortality rates in the United States: an ecological study at the county level

Urban and Rural Disparities in COVID-19 Outcomes in the United States: A Systematic Review

This systematic review investigates disparities in COVID-19 outcomes (infections, hospitalizations, and deaths) between urban and rural populations in the United States. Of the 3,091 articles screened, 55 were selected. Most studies (n = 43) conducted national analyses, using 2020 data, with some extending into 2021. Findings show urban areas had higher COVID-19 cases and hospitalizations in 2020, while rural areas saw increased cases in 2021 and mixed hospitalization results. Urban areas also had higher mortality rates in 2020, with rural rates rising in 2021 and 2022. Most studies did not explore reasons for urban/rural differences. The few that did found that vulnerable groups, including racially and ethnically minoritized populations, older adults, and those with comorbidities and lower socioeconomic status and vaccination rates, experienced exacerbated disparities in rural regions. COVID-19 outcomes varied over time and by area due to population density, healthcare infrastructure, and socioeconomic factors. Tailored interventions are essential for health equity and effective policies.

The optimization of harm reduction services in Massachusetts through the use of GIS: Location-allocation analyses, 2019-2021

BACKGROUND

Fatal opioid-related overdoses (OOD) continue to be a leading cause of preventable death across the US. Opioid Overdose Education and Naloxone Distribution programs (OENDs) play a vital role in addressing morbidity and mortality associated with opioid use, but access to such services is often inequitable. We utilized a geographic information system (GIS) and spatial analytical methods to inform prioritized placement of OEND services in Massachusetts.

METHODS

We obtained addresses for OEND sites from the Massachusetts Department of Public Health and address-level fatal OOD data for January 2019 to December 2021 from the Massachusetts Registry of Vital Records and Statistics. Using location-allocation approaches in ArcGIS Pro, we created p-median models using locations of existing OEND sites and fatal OOD counts to identify areas that should be prioritized for future OEND placement. Variables included in our analysis were transportation mode, distance from public schools, race and ethnicity, and location feasibility.

RESULTS

Three Massachusetts communities - Athol, Dorchester, and Fitchburg - were identified as priority sites for new OEND locations using location-allocation models based on capacity to maximize OOD prevention. Communities identified by the models for OEND placement had similar demographics and overdose rates (42.8 per 100,000 vs 40.1 per 100,000 population) to communities with existing OEND programs but lower naloxone kit distribution rates (2589 doses per 100,000 vs 3704 doses per 100,000). Further models demonstrated differential access based on location and transportation.

CONCLUSION

Our analyses identified key areas of Massachusetts with greatest need for OEND services. Further, these results demonstrate the utility of using spatial epidemiological methods to inform public health recommendations.

A state-level analysis of macro-level factors associated with hospital readmissions

Investigation of the factors that contribute to hospital readmissions has focused largely on individual level factors. We extend the knowledge base by exploring macrolevel factors that may contribute to readmissions. We point to environmental, behavioral, and socioeconomic factors that are emerging as correlates to readmissions. Data were taken from publicly available reports provided by multiple agencies. Partial Least Squares-Structural Equation Modeling was used to test the association between economic stability and environmental factors on opioid use which was in turn tested for a direct association with hospital readmissions. We also tested whether hospital access as measured by the proportion of people per hospital moderates the relationship between opioid use and hospital readmissions. We found significant associations between Negative Economic Factors and Opioid Use, between Environmental Factors and Opioid Use, and between Opioid Use and Hospital Readmissions. We found that Hospital Access positively moderates the relationship between Opioid Use and Readmissions. A priori assumptions about factors that influence hospital readmissions must extend beyond just individualistic factors and must incorporate a holistic approach that also considers the impact of macrolevel environmental factors.

Trends in Fatal Opioid-Related Overdose in American Indian and Alaska Native Communities, 1999-2021

INTRODUCTION

Opioid-related overdose mortality rates have increased sharply in the U.S. over the past two decades, and inequities across racial and ethnic groups have been documented. Opioid-related overdose trends among American Indian and Alaska Natives require further quantification and assessment.

METHODS

Observational, U.S. population-based registry data on opioid-related overdose mortality between 1999 and 2021 were extracted in 2023 using ICD-10 codes from the U.S. Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research multiple cause of death file by race, Hispanic ethnicity, sex, and age. Segmented time series analyses were conducted to estimate opioid-related overdose mortality growth rates among the American Indian and Alaska Native population between 1999 and 2021. Analyses were performed in 2023.

RESULTS

Two distinct time segments revealed significantly different opioid-related overdose mortality growth rates within the overall American Indian and Alaska Native population, from 0.36 per 100,000 (95% CI=0.32, 0.41) between 1999 and 2019 to 6.5 (95% CI=5.7, 7.31) between 2019 and 2021, with the most pronounced increase among those aged 24-44 years. Similar patterns were observed within the American Indian and Alaska Native population with Hispanic ethnicity, but the estimated growth rates were generally steeper across most age groups than across the overall American Indian and Alaska Native population. Patterns of opioid-related overdose mortality growth rates were similar between American Indian and Alaska Native females and males between 2019 and 2021.

CONCLUSIONS

Sharp increases in opioid-related overdose mortality rates among American Indian and Alaska Native communities are evident by age and Hispanic ethnicity, highlighting the need for culturally sensitive fatal opioid-related overdose prevention, opioid use disorder treatment, and harm-reduction efforts. Future research should aim to understand the underlying factors contributing to these high mortality rates and employ interventions that leverage the strengths of American Indian and Alaska Native culture, including the strong sense of community.

Predicting premature discontinuation of medication for opioid use disorder from electronic medical records

Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes.

Global association between air pollution and COVID-19 mortality: A systematic review and meta-analysis

BACKGROUND

The COVID-19 pandemic presents unprecedented challenge for global public health systems and exacerbates existing health disparities. Epidemiological evidence suggested a potential linkage between particulate and gaseous pollutants and COVID-19 mortality. We aimed to summarize the overall risk of COVID-19 mortality associated with ambient air pollutants over the short- and long-term.

METHODS

For the systematic review and meta-analysis, we searched five databases for studies evaluating the risk of COVID-19 mortality from exposure to air pollution. Inclusion of articles was assessed independently on the basis of research topic and availability of effect estimates. The risk estimates (relative risk) for each pollutant were pooled with a random-effect model. Potential heterogeneity was explored by subgroup analysis. Funnel plots and trim-and-fill methods were employed to assess and adjust for publication bias.

FINDINGS

The systematic review retrieved 2059 records, and finally included 43 original studies. PM2.5 (RR: 1.71, 95 % CI: 1.40-2.08, per 10 μg/m3 increase), NO2 (RR: 1.33, 1.07-1.65, per 10 ppb increase) and O3 (RR: 1.61, 1.00-2.57, per 10 ppb increase) were positively associated with COVID-19 mortality for long-term exposures. Accordingly, a higher risk of COVID-19 mortality was associated with PM2.5 (1.05, 1.02-1.08), PM10 (1.05, 1.01-1.08), and NO2 (1.40, 1.04-1.90) for short-term exposures. There was some heterogeneity across subgroups of income level and geographical areas.

CONCLUSION

Both long-term and short-term exposures to ambient air pollution may increase the risk of COVID-19 mortality. Future studies utilizing individual-level information on demographics, exposures, outcome ascertainment and confounders are warranted to improve the accuracy of estimates.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

Impacts of Built Environment on Risk of Women's Lung Cancer: A Case Study of China

Built environment factors such as air pollution are associated with the risk of respiratory disease, but few studies have carried out profound investigation. We aimed to evaluate the association between the built environment and Chinese women’s lung cancer incidence data from the China Cancer Registry Annual Report 2017, which covered 345,711,600 people and 449 qualified cancer registries in mainland China. The air quality indicator (PM2.5) and other built environment data are obtained from the China Statistical Yearbook and other official approved materials. An exploratory regression tool is applied by using Chinese women’s lung cancer incidence data (Segi population) as the dependent variable, PM2.5 index and other built environment factors as the independent variables. An apparent clustering region with a high incidence of women’s lung cancer was discovered, including regions surrounding Bohai bay and the three Chinese northeastern provinces, Heilongjiang, Liaoning and Inner Mongolia. Besides air quality, built environment factors were found to have a weak but clear impact on lung cancer incidence. Land-use intensity and the greening coverage ratio were positive, and the urbanization rate and population density were negatively correlated with lung cancer incidence. The role of green spaces in Chinese women’s lung cancer incidence has not been proven.

COVID ‐19 vaccination coverage and vaccine hesitancy among people with opioid use disorder in Barcelona, Spain

Introduction

People with substance use disorders are considered a priority group for SARS‐CoV‐2 vaccination as they are at elevated risk of COVID‐19 and its severe complications. However, data are scarce about vaccination coverage in a real‐world setting.

Methods

A descriptive study was conducted in people with opioid use disorder (OUD) from three public centres for outpatient drug addiction treatment in Barcelona, Spain, who received brief medical advice and were referred to vaccination clinic sites.

Results

Three hundred and sixty‐two individuals were included: 277 (77%) were men with a mean age of 48.1 ± 8.9 years and 77% were Spanish. Most (90%) participants engaged in polysubstance use and all individuals were on opioid agonist therapy. Psychiatric comorbidity was present in 56% subjects and 32% individuals had ≥1 chronic disease, 30% had HIV and 13% hepatitis C. There were 258 fully vaccinated individuals (71%; 95% confidence interval [CI] 67, 76). Age (odds ratio [OR] 1.04; 95% CI 1.01, 1.08; P < 0.01) and Charlson Comorbidity Index (OR 1.67; 95% CI 1.11, 2.5; P < 0.01) were associated with full vaccination. The vaccination hesitancy causes cited were complacency (53, 51%), convenience (40, 39%) and confidence (11, 10%).

Discussion and Conclusions

More than two‐thirds of our sample of people with OUD were fully vaccinated. Complacency and convenience represented a significant barrier to complete vaccination among people with OUD on opioid agonist therapy referred to vaccination clinic sites. Additional measures are necessary to increase vaccination, especially for younger individuals and those with less medical comorbidity. Integrating vaccination services in drug outpatient centres could be a useful alternative.

Similar COVID-19 incidence to the general population in people with opioid use disorder receiving integrated outpatient clinical care

Introduction

During the COVID-19 pandemic, limited access to health care augmented COVID-19 risk in subjects with opioid use disorder (OUD). The aim of the study was to compare COVID-19 incidence in individuals with OUD receiving continuous clinical care with that of the general population.

Methods

A prospective cohort study was carried out from March 2020 to March 2021 comparing COVID-19 cumulative incidence of individuals presenting an OUD receiving integrated clinical care with that of an age-reference general population, in three public outpatient treatment centers for addiction in Barcelona, Spain.

Results

Over the study period, 366 individuals received clinical care. Mean age: 48.2±8.9 years, 280 (76.5%) were men and 283 (77.3%) native Spanish. All subjects were on opioid agonist therapy. Prevalence of communicable diseases were: HIV infection in 109 (29.8%) and hepatitis C in 46 (12.6%). Psychiatric comorbidity was present in 207 (56.6%), and 119 (32.5%) had >1 chronic medical disease.

COVID-19 was diagnosed in 10 patients

a cumulative incidence of 2,732 casesx100,000 people/year (C.I.95%: 1,318–4,967). There were no differences compared to the age-general population: 2,856 casesx100,000 people/year (C.I.95%: 2,830–2,880) (p=0.81). In the bivariate analysis, hypertension (5[50.0%] vs. 53[14.9%], p=0.01) and cardiovascular chronic diseases (2 [20.0%] vs. 8 [2.2%], p=0.03) were more prevalent in patients with OUD and COVID-19.

Conclusions

Individuals with OUD who received integrated clinical care had a COVID-19 incidence comparable to the general population. Ensuring comprehensive healthcare is essential to prevent the clinical impact of COVID-19 on individuals with OUD.

Ambient air pollution and COVID-19 risk: Evidence from 35 observational studies

BACKGROUND AND AIMS

The coronavirus disease 2019 (COVID-19) pandemic is severely threatening and challenging public health worldwide. Epidemiological studies focused on the influence of outdoor air pollution (AP) on COVID-19 risk have produced inconsistent conclusions. We aimed to quantitatively explore this association using a meta-analysis.

METHODS

We searched for studies related to outdoor AP and COVID-19 risk in the Embase, PubMed, and Web of Science databases. No language restriction was utilized. The search date entries were up to August 13, 2021. Pooled estimates and 95% confidence intervals (CIs) were obtained with random-/fixed-effects models. PROSPERO registration number: CRD42021244656.

RESULTS

A total of 35 articles were eligible for the meta-analysis. For long-term exposure to AP, COVID-19 incidence was positively associated with 1 μg/m3 increase in nitrogen dioxide (NO2; effect size = 1.042, 95% CI 1.017-1.068), particulate matter with diameter <2.5 μm (PM2.5; effect size = 1.056, 95% CI 1.039-1.072), and sulfur dioxide (SO2; effect size = 1.071, 95% CI 1.002-1.145). The COVID-19 mortality was positively associated with 1 μg/m3 increase in nitrogen dioxide (NO2; effect size = 1.034, 95% CI 1.006-1.063), PM2.5 (effect size = 1.047, 95% CI 1.025-1.1071). For short-term exposure to air pollutants, COVID-19 incidence was positively associated with 1 unit increase in air quality index (effect size = 1.001, 95% CI 1.001-1.002), 1 μg/m3 increase NO2 (effect size = 1.014, 95% CI 1.011-1.016), particulate matter with diameter <10 μm (PM10; effect size = 1.005, 95% CI 1.003-1.008), PM2.5 (effect size = 1.003, 95% CI 1.002-1.004), and SO2 (effect size = 1.015, 95% CI 1.007-1.023).

CONCLUSIONS

Outdoor air pollutants are detrimental factors to COVID-19 outcomes. Measurements beneficial to reducing pollutant levels might also reduce the burden of the pandemic.

Ecological studies of COVID-19 and air pollution: How useful are they?

Background:

Results from ecological studies have suggested that air pollution increases the risk of developing and dying from COVID-19. Drawing causal inferences from the measures of association reported in ecological studies is fraught with challenges given biases arising from an outcome whose ascertainment is incomplete, varies by region, time, and across sociodemographic characteristics, and cannot account for clustering or within-area heterogeneity. Through a series of analyses, we illustrate the dangers of using ecological studies to assess whether ambient air pollution increases the risk of dying from, or transmitting, COVID-19.

Methods:

We performed an ecological analysis in the continental United States using county-level ambient concentrations of fine particulate matter (PM_2.5) between 2000 and 2016 and cumulative COVID-19 mortality counts through June 2020, December 2020, and April 2021. To show that spurious associations can be obtained in ecological data, we modeled the association between PM_2.5 and the prevalence of human immunodeficiency virus (HIV). We fitted negative binomial models, with a logarithmic offset for county-specific population, to these data. Natural cubic splines were used to describe the shape of the exposure-response curves.

Results:

Our analyses revealed that the shape of the exposure-response curve between PM_2.5 and COVID-19 changed substantially over time. Analyses of COVID-19 mortality through June 30, 2021, suggested a positive linear relationship. In contrast, an inverse pattern was observed using county-level concentrations of PM_2.5 and the prevalence of HIV.

Conclusions:

Our analyses indicated that ecological analyses are prone to showing spurious relationships between ambient air pollution and mortality from COVID-19 as well as the prevalence of HIV. We discuss the many potential biases inherent in any ecological-based analysis of air pollution and COVID-19.

Risk of COVID-19 infection and the associated hospitalization, ICU admission and mortality in opioid use disorder: a systematic review and meta-analysis

BACKGROUND

Opioid use disorder (OUD) as a common drug use disorder can affect public health issues, including the COVID-19 pandemic, in which patients with OUD may have higher risk of infection and severe disease. This systematic review and meta-analysis was conducted to investigate the risk of COVID-19 and the associated hospitalization, intensive care unit (ICU) admission, and mortality in patients with OUD.

MATERIALS AND METHODS

A comprehensive systematic search was performed on PubMed, Scopus, Embase, and Web of Science to find studies which compared the infection rate and outcomes of COVID-19 in OUD patients in comparison with the normal population. A random effects meta-analysis model was developed to estimate odd ratios (OR) and 95% confidence interval (CI) between the outcomes of COVID-19 and OUD.

RESULTS

Out of 2647 articles identified through the systematic search, eight were included in the systematic review and five in the meta-analysis. Among 73,345,758 participants with a mean age of 57.90 ± 13.4 years, 45.67% were male. The findings suggested no significant statistical relationship between COVID-19 infection and OUD (OR (95% CI): 1.18 (0.47-2.96), p-value: 0.73). Additionally, patients with OUD had higher rate of hospitalization (OR (95% CI) 5.98 (5.02-7.13), p-value<0.01), ICU admission (OR (95% CI): 3.47 (2.24-5.39), p-value<0.01), and mortality by COVID-19) OR (95% CI): 1.52(1.27-1.82), pvalue< 0.01).

CONCLUSION

The present findings suggested that OUD is a major risk factor for mortality and the need for hospitalization and ICU admission in patients with COVID-19. It is recommended that policymakers and healthcare providers adopt targeted methods to prevent and manage clinical outcomes and decrease the burden of COVID-19, especially in specific populations such as OUD patients.