Journal of studies on alcohol and drugs · 2018
Investigating the Social Ecological Contexts of Opioid Use Disorder and Poisoning Hospitalizations in Pennsylvania.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Mair Christina, Sumetsky Natalie, Burke Jessica G, Gaidus Andrew
Аннотация
OBJECTIVE: Opioid use disorder (OUD) and overdose rates have been sharply on the rise in the United States. Although systematic patterns of geographic variation in OUD and opioid overdose have been identified, the factors that explain why opioid-related hospitalizations increase in certain areas are not well understood.
METHOD: We examined Pennsylvania Health Care Cost Containment Council (PHC4) hospital inpatient discharge data at the ZIP code level to measure the geographic growth and spread of OUD as measured by 44 quarters of inpatient hospitalization data (from 2004 through 2014) for the entire state of Pennsylvania (n = 16,275 ZIP codes). We assessed the relative contribution of specific attributes of areas (e.g., population density) to patterns of OUD, heroin poisonings, and non-heroin opioid poisonings. Unit misalignment and spatial autocorrelation were corrected for using Bayesian space-time conditional autoregressive models.
RESULTS: The associations between a greater density of manual labor establishments and all opioid-related hospitalizations were well supported and positive. A dose-response relationship between population density and opioid-related hospitalizations existed, with a stronger association for heroin poisonings (relative rate, densest quintile vs. least dense: 3.40 [95% credible interval 2.68, 4.39]).
CONCLUSIONS: Posterior distributions from these models enabled the identification of locations most vulnerable to problems related to the opioid epidemic in Pennsylvania. Understanding spatial patterns of OUD and poisonings can enhance the development and implementation of effective prevention programs.
METHOD: We examined Pennsylvania Health Care Cost Containment Council (PHC4) hospital inpatient discharge data at the ZIP code level to measure the geographic growth and spread of OUD as measured by 44 quarters of inpatient hospitalization data (from 2004 through 2014) for the entire state of Pennsylvania (n = 16,275 ZIP codes). We assessed the relative contribution of specific attributes of areas (e.g., population density) to patterns of OUD, heroin poisonings, and non-heroin opioid poisonings. Unit misalignment and spatial autocorrelation were corrected for using Bayesian space-time conditional autoregressive models.
RESULTS: The associations between a greater density of manual labor establishments and all opioid-related hospitalizations were well supported and positive. A dose-response relationship between population density and opioid-related hospitalizations existed, with a stronger association for heroin poisonings (relative rate, densest quintile vs. least dense: 3.40 [95% credible interval 2.68, 4.39]).
CONCLUSIONS: Posterior distributions from these models enabled the identification of locations most vulnerable to problems related to the opioid epidemic in Pennsylvania. Understanding spatial patterns of OUD and poisonings can enhance the development and implementation of effective prevention programs.