Social and economic determinants of drug overdose deaths: a systematic review of spatial relationships.

PURPOSE: To synthesize the available evidence on the extent to which area-level socioeconomic conditions are associated with drug overdose deaths in the United States. METHODS: We performed a systematic review (in MEDLINE, EMBASE, PsychINFO, Web of Science, EconLit) for papers published prior to July 2022. Eligible studies quantitatively estimated the association between an area-level measure of socioeconomic conditions and drug overdose deaths in the US, and were published in English. We assessed study quality using the Effective Public Health Practice Project Quality Assessment Tool. The protocol was preregistered at Prospero (CRD42019121317). RESULTS: We identified 28 studies that estimated area-level effects of socioeconomic conditions on drug overdose deaths in the US. Studies were scored as having moderate to serious risk of bias attributed to both confounding and in analysis. Socioeconomic conditions and drug overdose death rates were moderately associated, and this was a consistent finding across a large number of measures and differences in study designs (e.g., cross-sectional versus longitudinal), years of data analyzed, and primary unit of analysis (e.g., ZIP code, county, state). CONCLUSIONS: This review highlights the evidence for area-level socioeconomic conditions are an important factor underlying the geospatial distribution of drug overdose deaths in the US and the need to understand the mechanisms underlying these associations to inform future policy recommendations. The current evidence base suggests that, at least in the United States, employment, income, and poverty interventions may be effective targets for preventing drug overdose mortality rates.

Understanding the differential effect of local socio-economic conditions on the relation between prescription opioid supply and drug overdose deaths in US counties.

BACKGROUND AND AIMS: Both local socio-economic conditions and prescription opioid supply are associated with drug overdose deaths, which exhibit substantial geographical heterogeneity across the United States. We measured whether the associations of prescription opioid supply with drug overdose deaths vary by local socio-economic conditions. DESIGN: Ecological county-level study, including 3109 US counties between 2006 and 2019 (n = 43 526 county-years) using annual mortality data. SETTING: United States. CASES: A total of 711 447 drug overdose deaths. MEASUREMENTS: We modeled overdose counts using Bayesian hierarchical Poisson models, estimating associations between four types of drug overdose deaths (deaths involving any drugs, any opioid, prescription opioids only and heroin), prescription opioid supply and five socio-economic indicators: unemployment, poverty rate, income inequality, Rey index (components include mean household income, % high school graduates, % blue-collar workers and unemployment rate), and American human development index (HDI; an indicator of community wellbeing). FINDINGS: Drug overdose deaths and all substance-specific overdose deaths were higher in counties with higher income inequality [adjusted odds ratios (aORs) = 1.09-1.13], Rey index (aORs = 1.15-1.21) and prescription opioid supply (aORs = 1.14-1.21), and lower in counties with higher HDI scores (aORs = 0.75-0.92). Poverty rate, income inequality and HDI scores were found to modify the effect of prescription opioid supply on heroin overdose deaths. The plot of the interactions showed that when disadvantage is high, increasing prescription opioid supply does not increase heroin overdose deaths. The less disadvantage there is, indicated by lower poverty rates, higher HDI scores and lower income inequality, the greater the effect of increasing prescription opioid supply relative to population size on heroin overdose deaths in US counties. CONCLUSIONS: In the United States, prescription opioid supply is associated with higher drug overdose deaths; associations are stronger in counties with less disadvantage and less income inequality, but only for heroin overdose deaths.

Comparing mental and physical health of U.S. veterans by VA healthcare use: implications for generalizability of research in the VA electronic health records.

OBJECTIVE: The Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not. METHODS: We analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use. RESULTS: Among veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40). CONCLUSIONS: Heterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

Prescription Drug Monitoring Programs and Opioid Overdoses: Exploring Sources of Heterogeneity.

BACKGROUND: Prescription drug monitoring program are designed to reduce harms from prescription opioids; however, little is known about what populations benefit the most from these programs. We investigated how the relation between implementation of online prescription drug monitoring programs and rates of hospitalizations related to prescription opioids and heroin overdose changed over time, and varied across county levels of poverty and unemployment, and levels of medical access to opioids. METHODS: Ecologic county-level, spatiotemporal study, including 990 counties within 16 states, in 2001-2014. We modeled overdose counts using Bayesian hierarchical Poisson models. We defined medical access to opioids as the county-level rate of hospital discharges for noncancer pain conditions. RESULTS: In 2010-2014, online prescription drug monitoring programs were associated with lower rates of prescription opioid-related hospitalizations (rate ratio 2014 = 0.74; 95% credible interval = 0.69, 0.80). The association between online prescription drug monitoring programs and heroin-related hospitalization was also negative but tended to increase in later years. Counties with lower rates of noncancer pain conditions experienced a lower decrease in prescription opioid overdose and a faster increase in heroin overdoses. No differences were observed across different county levels of poverty and unemployment. CONCLUSIONS: Areas with lower levels of noncancer pain conditions experienced the smallest decrease in prescription opioid overdose and the faster increase in heroin overdose following implementation of online prescription drug monitoring programs. Our results are consistent with the hypothesis that prescription drug monitoring programs are most effective in areas where people are likely to access opioids through medical providers.

Urban-rural variation in the socioeconomic determinants of opioid overdose.

BACKGROUND: Prescription opioid overdose (POD) and heroin overdose (HOD) rates have quadrupled since 1999. Community-level socioeconomic characteristics are associated with opioid overdoses, but whether this varies by urbanicity is unknown. METHODS: In this serial cross-sectional study of zip codes in 17 states, 2002-2014 (n = 145,241 space-time units), we used hierarchical Bayesian Poisson space-time models to analyze the association between zip code-level socioeconomic features (poverty, unemployment, educational attainment, and income) and counts of POD or HOD hospital discharges. We tested multiplicative interactions between each socioeconomic feature and zip code urbanicity measured with Rural-Urban Commuting Area codes. RESULTS: Percent in poverty and of adults with ≤ high school education were associated with higher POD rates (Rate Ratio [RR], 5% poverty: 1.07 [95% credible interval: 1.06-1.07]; 5% low education: 1.02 [1.02-1.03]), while median household income was associated with lower rates (RR, $10,000: 0.88 [0.87-0.89]). Urbanicity modified the association between socioeconomic features and HOD. Poverty and unemployment were associated with increased HOD in metropolitan areas (RR, 5% poverty: 1.12 [1.11-1.13]; 5% unemployment: 1.04 [1.02-1.05]), and median household income was associated with decreased HOD (RR, $10,000: 0.88 [0.87-0.90]). In rural areas, low educational attainment alone was associated with HOD (RR, 5%: 1.09 [1.02-1.16]). CONCLUSIONS: Regardless of urbanicity, elevated rates of POD were found in more economically disadvantaged zip codes. Economic disadvantage played a larger role in HOD in urban than rural areas, suggesting rural HOD rates may have alternative drivers. Identifying social determinants of opioid overdoses is particularly important for creating effective population-level interventions.

America's Declining Well-Being, Health, and Life Expectancy: Not Just a White Problem.

Although recent declines in life expectancy among non-Hispanic Whites, coined "deaths of despair," grabbed the headlines of most major media outlets, this is neither a recent problem nor is it confined to Whites. The decline in America's health has been described in the public health literature for decades and has long been hypothesized to be attributable to an array of worsening psychosocial problems that are not specific to Whites. To test some of the dominant hypotheses, we show how various measures of despair have been increasing in the United States since 1980 and how these trends relate to changes in health and longevity. We show that mortality increases among Whites caused by the opioid epidemic come on the heels of the crack and HIV syndemic among Blacks. Both occurred on top of already higher mortality rates among all Americans relative to people in other nations, and both occurred among declines in measures of well-being. We believe that the attention given to Whites is distracting researchers and policymakers from much more serious, longer-term structural problems that affect all Americans.

Social Determinants of Population Health: A Systems Sciences Approach.

Population distributions of health emerge from the complex interplay of health-related factors at multiple levels, from the biological to the societal level. Individuals are aggregated within social networks, affected by their locations, and influenced differently across time. From aggregations of individuals, group properties can emerge, including some exposures that are ubiquitous within populations but variant across populations. By combining a focus on social determinants of health with a conceptual framework for understanding how genetics, biology, behavior, psychology, society, and environment interact, a systems science approach can inform our understanding of the underlying causes of the unequal distribution of health across generations and populations, and can help us identify promising approaches to reduce such inequalities. In this paper, we discuss how systems science approaches have already made several substantive and methodological contributions to the study of population health from a social epidemiology perspective.

Health insurance, alcohol and tobacco use among pregnant and non-pregnant women of reproductive age.

BACKGROUND: Understanding the relationship between health insurance coverage and tobacco and alcohol use among reproductive age women can provide important insight into the role of access to care in preventing tobacco and alcohol use among pregnant women and women planning to become pregnant. METHODS: We examined the association between health insurance coverage and both past month alcohol use and past month tobacco use in a nationally representative sample of women age 12-44 years old, by pregnancy status. The women (n=97,788) were participants in the National Survey of Drug Use and Health (NSDUH) in 2010-2013. Logistic regression models assessed the association between health insurance (insured versus uninsured), past month tobacco and alcohol use, and whether this was modified by pregnancy status. RESULTS: Pregnancy status significantly moderated the relationship between health insurance and tobacco use (p-value≤0.01) and alcohol use (p-value≤0.01). Among pregnant women, being insured was associated with lower odds of alcohol use (adjusted odds ratio [AOR]=0.47; 95% confidence interval [CI]=0.27-0.82), but not associated with tobacco use (AOR=1.14; 95% CI=0.73-1.76). Among non-pregnant women, being insured was associated with lower odds of tobacco use (AOR=0.67; 95% CI=0.63-0.72), but higher odds of alcohol use (AOR=1.23; 95% CI=1.15-1.32). CONCLUSION: Access to health care, via health insurance coverage is a promising method to help reduce alcohol use during pregnancy. However, despite health insurance coverage, tobacco use persists during pregnancy, suggesting missed opportunities for prevention during prenatal visits.

Racial discrimination, socioeconomic position, and illicit drug use among US Blacks.

PURPOSE: We assessed the relationship of self-reported racial discrimination with illicit drug use among US Blacks, and whether this differed by socioeconomic position (SEP). METHODS: Among 6587 Black participants in Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (2004-2005), we used multiple logistic regression models to test the association between racial discrimination (measured on the 6-item Experiences of Discrimination scale) and past-year illicit drug use, and whether this differed by SEP. RESULTS: Racial discrimination was associated with past-year drug use [adjusted odds ratio (aOR) 2.32; 95 % confidence interval (CI) 1.70, 3.16] and with frequent drug use (aOR 1.91; 95 % CI 1.22, 2.99). For frequent illicit drug use, this relationship was stronger among higher SEP participants (aOR 3.55; 95 % CI 2.09, 6.02; p interaction < 0.01). CONCLUSIONS: The stronger association between racial discrimination and frequent illicit drug use among higher SEP Blacks suggests a complex interplay between disadvantaged and privileged statuses that merits further investigation. The finding of a significant difference by SEP highlights the importance of considering differences within heterogeneous race/ethnic groups when investigating health disparities.

An epidemiological assessment of reintegration and behavioral health risk at Joint Base Lewis-McCord, Washington.

Soldiers from a brigade at Joint Base Lewis-McChord, Washington, were alleged to have committed numerous crimes, including murder of civilians, during a recent deployment. This study was done to assist the command with (1) analyzing the climate and challenges facing redeploying Soldiers; (2) assessing behavioral risk at both individual and unit levels through targeted reintegration screening; and (3) recommending mitigating strategies to enhance current reintegration processes and reduce the level of high-risk behavior among Soldiers following deployment. The findings from this public health investigation suggest levels of risk and major areas of concern during the redeployment period varied across battalions within the brigade and that risk stratification postdeployment was not correlated with discernible differences in predeployment indicators. Acts of violence were limited to the deployment and immediate postdeployment periods and were allegedly perpetrated by a very small number of Soldiers.

Assessment of subthreshold and developing behavioral health concerns among U.S. army soldiers.

This study aimed to merge existing methodologies of identifying high-risk soldiers to create a comprehensive testable model to assist leaders in the identification, mitigation, and prevention of negative behavioral health (BH) issues. In 2011, a total of 2,664 active duty U.S. Army soldiers completed a survey of demographic and military characteristics, combat exposures, and BH diagnoses and symptoms. Multivariable linear and logistic regression models were fit to examine the relationship between demographic and military characteristics, subthreshold behavioral and social health issues, and positive screening for BH symptoms. The "recent loss of someone close" and self-reporting a history of BH issues were the strongest and most consistent predictors of subthreshold behavioral and social health issues. This study found that individual and occupational factors were associated with subthreshold behavioral and social health issues, which were in turn, associated with screening positive for BH symptoms. The recent loss of someone close (an indicator of grief and loss) was not the study's primary research question, but warrants further investigation to determine its impact on the mental well-being of soldiers.