The rise in America’s drug poisoning rates has been described as a public health crisis and has long been attributed to the rapid rise in opioid supply due to increased volumes of medical prescribing in the United States that began in the mid-1990s and peaked in 2012. In 2016, the introduction of the “deaths of despair” hypothesis provided a more nuanced explanation for the rising rates of drug poisoning deaths: increasing income inequality and stagnation of middle-class worker wages, driven by long-term shifts in the labor market, reduced employment opportunities and overall life prospects for persons with a high school degree or less, driving increases in “deaths of despair” (i.e., deaths from suicide, cirrhosis of the liver, and drug poisonings). This focus on economic and social conditions as capable of shaping geospatial differences in drug demand and attendant drug-related harms (e.g., drug poisonings) provides a larger context to factors potentially underlying the heterogeneous distribution of prescription opioid supply across the United States. However, despite the likelihood that economic and social conditions may be important demand-side factors that also interact with supply-side factors to produce the rates of fatal drug poisonings, little information exists about the effect of area-level socioeconomic conditions on fatal drug poisoning rates, and no study has investigated whether socioeconomic conditions interact with prescription opioid supply to affect area-level rates of fatal drug poisonings. The overarching goal of this dissertation was to test the independent and joint effects of supply- and demand-side factors, operationalized as prescription opioid supply and socioeconomic conditions, on fatal drug poisoning in the U.S. First, a systematic review of the literature was conducted to critically evaluate the evidence on the ecological relationship of prescription opioid supply and socioeconomic conditions on rates of drug poisoning deaths. The systematic review provides robust evidence of the independent effect of each prescription opioid supply and socioeconomic conditions on rates of drug poisoning deaths. The gap in the literature on the joint effects of prescription opioid supply and socioeconomic conditions was clear, with no study examining the interaction between supply- and demand-side factors on rates of fatal drug poisonings. Moreover, although greater prescription opioid supply was associated with higher rates of fatal drug poisonings in most of the studies, two studies presented contradictory findings, with one study showing no effect of supply on drug poisoning deaths and the other showing locations with higher levels of prescription opioid supply were associated with fewer drug-related deaths. Three limitations were also identified in the reviewed studies that could partially explain the observed associations. First, although studies aggregated data on drug poisoning deaths to a range of administrative spatial levels, including census tract, 5-digit ZIP code, county, 3-digit ZIP code, and state, no study investigated the sensitivity of findings to the level of geographic aggregation. Second, spatial modeling requires the assessment of spatial autocorrelation in both the unadjusted and adjusted data, but few studies even assessed spatial autocorrelation in the data, and fewer still incorporated spatial dependencies in the model. This is important because when spatial autocorrelation is present, the independence assumption in standard statistical regression models is violated, potentially causing bias and loss of efficiency. Third, studies operationalized prescription opioid supply and socioeconomic conditions using a variety of different measures, and no study assessed the sensitivity of findings to the different measures of supply and socioeconomic conditions.
Second, the ecological relationship between prescription opioid supply and fatal drug poisonings was examined. For this, pooled cross-sectional time series data from 3,109 U.S. counties in 49 states (2006-2016) were used in Bayesian Poisson conditional autoregressive models to estimate the effect of county prescription opioid supply on four types of drug poisoning deaths: any drug (drug-related death), any opioid (opioid-related death), any prescription opioid but not heroin (prescription opioid-related death), and heroin (heroin-related death), adjusting for compositional and contextual differences across counties.
Comparisons were made by type of drug poisoning (any drug, any opioid, prescription opioids only, heroin), level of geographic aggregation (county versus state), and measure of prescription opioid supply (rate of opioid-prescribing per 100 persons and morphine milligram equivalents per-capita). Results indicated a positive association between prescription opioid supply and rates of fatal drug poisonings consistent across changes in type of drug poisoning, level of aggregation, and measure of prescription opioid supply. However, removing confounders from the model caused the direction of the effect estimate to reverse for drug poisoning deaths from any drug, any opioid, and heroin. These results suggested that differences in adjustment for confounding could explain most of the inconsistent findings in the literature.
Finally, a rigorous test of the hypothesis that worse socioeconomic conditions increase risk of fatal drug poisonings at the county level, and interact with prescription opioid supply was conducted. This analysis used the same pooled cross-sectional time series data from 3,109 U.S. counties in 49 states (2006-2016). The analysis modeled the effect of five key socioeconomic variables, including three single socioeconomic variables (unemployment, poverty rate, income inequality) and two index variables (Rey index, American Human Development Index [HDI]) on four types of drug poisoning deaths: any drug (drug-related death), any opioid (opioid-related death), any prescription opioid but not heroin (prescription opioid-related death), and heroin (heroin-related death).
Using a hierarchical Bayesian modeling approach to account for spatial dependence and the variability of fatal drug poisoning rates due to the small number of events, the independent effect of socioeconomic conditions on rates of drug poisoning deaths and their joint multiplicative and additive effect with prescription opioid supply were estimated. Results showed that rates of fatal drug poisonings were higher in more economically and socially disadvantaged counties; the five key indicator variables were differentially associated with drug poisoning rates; and the American Human Development Index (HDI) and income inequality were most strongly associated with fatal drug poisoning rates. Finally, the results indicate that both HDI and income inequality interact with county-level prescription opioid supply to affect drug poisoning rates. Specifically, the effect of higher prescription opioid supply on rates of fatal drug poisonings was greater in counties with higher HDI and more equal income distributions than counties with lower HDI and less equal income distributions. Overall, this dissertation increased knowledge about the separate and conjoint roles of supply- and demand-side factors in the geospatial distribution of fatal drug poisonings in the U.S. The idea that area-level prescription opioid supply are key drivers of prescription drug use, misuse, and addiction and the attendant consequences, including nonfatal and fatal drug poisonings, has been in the literature for well over a decade. However, no study to date has shown that area-level socioeconomic conditions modify the effect of prescription opioid supply on fatal drug poisonings. By identifying important contextual factors capable of modifying the effect of prescription opioid supply reductions on mortality, high-risk geographic areas can be prioritized for interventions to counter any unintended effects of reducing the prescription opioid supply in an area. As federal and state policies continue to target the rising rates of fatal drug poisonings, these findings show that area-level socioeconomic conditions may represent an important target for policy intervention during the current drug poisoning crisis and a critical piece of information necessary for predicting any future drug-related crises.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-4851-zb84 |
| Date | January 2020 |
| Creators | Fink, David S. |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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