Prescription opioid poisoning across urban and rural areas: identifying vulnerable groups and geographic areas

Abstract

Aims: To determine (1) whether prescription opioid poisoning (PO) hospital discharges spread across space over time, (2) the locations of 'hot-spots' of PO-related hospital discharges, (3) how features of the local environment contribute to the growth in PO-related hospital discharges and (4) where each environmental feature makes the strongest contribution.

Design: Hierarchical Bayesian Poisson space-time analysis to relate annual discharges from community hospitals to postal code characteristics over 10 years.

Setting: California, USA.

Participants: Residents of 18 517 postal codes in California, 2001-11.

Measurements: Annual postal code-level counts of hospital discharges due to PO poisoning were related to postal code pharmacy density, measures of medical need for POs (i.e. rates of cancer and arthritis-related hospital discharges), economic stressors (i.e. median household income, percentage of families in poverty and the unemployment rate) and concentration of manual labor industries.

Findings: PO-related hospital discharges spread from rural and suburban/exurban 'hot-spots' to urban areas. They increased more in postal codes with greater pharmacy density [rate ratio (RR) = 1.03; 95% credible interval (CI) = 1.01, 1.05], more arthritis-related hospital discharges (RR = 1.08; 95% CI = 1.06, 1.11), lower income (RR = 0.85; 95% CI = 0.83, 0.87) and more manual labor industries (RR = 1.15; 95% CI = 1.10, 1.19 for construction; RR = 1.12; 95% CI = 1.04, 1.20 for manufacturing industries). Changes in pharmacy density primarily affected PO-related discharges in urban areas, while changes in income and manual labor industries especially affected PO-related discharges in suburban/exurban and rural areas.

Conclusions: Hospital discharge rates for prescription opioid (PO) poisoning spread from rural and suburban/exurban hot-spots to urban areas, suggesting spatial contagion. The distribution of age-related and work-place-related sources of medical need for POs in rural areas and, to a lesser extent, the availability of POs through pharmacies in urban areas, partly explain the growth of PO poisoning across California, USA.

Keywords: Bayesian space-time models; drug overdose; geography; hospital discharges; prescription opioids; rural and urban.

Figure 1

Prescription opioid hospital discharges per 10 000 people in California zip codes, 2001–11

Figure 2

Estimated growth in relative rate per year by county and the distribution of the population in rural and urban zip codes, California, 2001–11. Note that the growth in relative rate is estimated after controlling for all of the variables in our model (model 1)

Figure 3

Posterior estimated growth of relative incidence rates of prescription opioid poisoning hospital discharges by zip code for selected years, California (model 1). Relative rate values are symbolized by quantiles across all 11 years

Figure 4

Posterior estimated relative incidence rates of prescription opioid poisoning hospital discharges in California in 2011 contributed by four combinations of model covariates: % 65+ and arthritis rate; median household income; construction industries per capita and manufacturing industries per capita (blue collar); and pharmacies per square mile (model 2)