Dataset Information

Spatial heterogeneity and socioeconomic determinants of opioid prescribing in England between 2015 and 2018.

ABSTRACT:

Background

Opioid overdoses have had a serious impact on the public health systems and socioeconomic welfare of several countries. Within this broader context, we focus our study on primary care opioid prescribing in England from 2015 to 2018, particularly the patterns of spatial variations at the community level and the socioeconomic and environmental factors that drive consumption.

Methods

Leveraging open data sources, we combine prescription records with aggregated data on patient provenance and build highly granular maps of Oral Morphine Equivalent (OME) prescribing rates for Lower Layer Super Output Areas (LSOA). We quantify the strength of spatial associations by means of the Empirical Bayes Index (EBI) that accounts for geographical variations in population density. We explore the interplay between socioeconomic and environmental determinants and prescribing rates by implementing a multivariate logistic regression model across different temporal snapshots and spatial scales.

Results

We observe, across time and geographical resolutions, a significant spatial association with the presence of localized hot and cold spots that group neighboring areas with homogeneous prescribing rates (e.g., EBI = 0.727 at LSOA level for 2018). Accounting for spatial dependency effects, we find that LSOA with both higher employment deprivation (OR = 62.6, CI 52.8-74.3) and a higher percentage of ethnically white (OR = 30.1, CI 25.4-35.7) inhabitants correspond to higher prescribing rates. Looking at educational attainment, we find LSOA with the prevalent degree of education being apprenticeship (OR = 2.33, CI 1.96-2.76) a risk factor and those with level 4+ (OR = 0.41, CI 0.35-0.48) a protective factor. Focusing on environmental determinants, housing (OR = 0.18, CI 0.15-0.21) and outdoor environment deprivation (OR = 0.62, CI 0.53-0.72) indices capture the bi-modal behavior observed in the literature concerning rural/urban areas. The results are consistent across time and spatial aggregations.

Conclusions

Failing to account for local variations in opioid prescribing rates smooths out spatial dependency effects that result in underestimating/overestimating the impact on public health policies at the community level. Our study suggests a novel approach to inform more targeted interventions toward the most vulnerable population strata.

SUBMITTER: Schifanella R

PROVIDER: S-EPMC7227089 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatial heterogeneity and socioeconomic determinants of opioid prescribing in England between 2015 and 2018.

Schifanella Rossano R Vedove Dario Delle DD Salomone Alberto A Bajardi Paolo P Paolotti Daniela D

BMC medicine 20200515 1

<h4>Background</h4>Opioid overdoses have had a serious impact on the public health systems and socioeconomic welfare of several countries. Within this broader context, we focus our study on primary care opioid prescribing in England from 2015 to 2018, particularly the patterns of spatial variations at the community level and the socioeconomic and environmental factors that drive consumption.<h4>Methods</h4>Leveraging open data sources, we combine prescription records with aggregated data on pati ...[more]

PMID: 32410615

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Project description:BackgroundLow socioeconomic position is consistently associated with increased risk of premature death. The aim of this study is to measure the aggregate scale of inequality in premature mortality for the whole population of England.MethodsWe used mortality records from the UK Office for National Statistics to study all 2 465 285 premature deaths (defined as those before age 75 years) in England between Jan 1, 2003, and Dec 31, 2018. Socioeconomic position was defined using deciles of the Index of Multiple Deprivation: a measure of neighbourhood income, employment, education levels, crime, health, availability of services, and local environment. We calculated the number of expected deaths by applying mortality in the least deprived decile to other deciles, within the strata of age, sex, and time. The mortality attributable to socioeconomic inequality was defined as the difference between the observed and expected deaths. We also used life table modelling to estimate years-of-life lost attributable to socioeconomic inequality.Findings35·6% (95% CI 35·3-35·9) of premature deaths were attributable to socioeconomic inequality, equating to 877 082 deaths, or one every 10 min. The biggest contributors were ischaemic heart disease (152 171 excess deaths), respiratory cancers (111 083) and chronic obstructive pulmonary disease (83 593). The most unequal causes of death were tuberculosis, opioid use, HIV, psychoactive drugs use, viral hepatitis, and obesity, each with more than two-thirds attributable to inequality. Inequality was greater among men and peaked in early childhood and at age 40-49 years. The proportion of deaths attributable to inequality increased during the study period, particularly for women, because mortality rates among the most deprived women (excluding cardiovascular diseases) plateaued, and for some diseases increased. A mean of 14·4 months of life before age 75 years are lost due to socioeconomic inequality.InterpretationOne in three premature deaths are attributable to socioeconomic inequality, making this our most important public health challenge. Interventions that address upstream determinants of health should be prioritised.FundingNational Institute of Health Research; Wellcome Trust.

Project description:BackgroundPotentially inappropriate prescribing (PIP) may contribute to opioid overdose.ObjectiveTo examine the association between PIP and adverse events.DesignCohort study.ParticipantsThree million seventy-eight thousand thirty-four individuals age ≥ 18, without disseminated cancer, who received prescription opioids between 2011 and 2015.Main measuresWe defined PIP as (a) morphine equivalent dose ≥ 100 mg/day in ≥ 3 months; (b) overlapping opioid and benzodiazepine prescriptions in ≥ 3 months; (c) ≥ 4 opioid prescribers in any quarter; (d) ≥ 4 opioid-dispensing pharmacies in any quarter; (e) cash purchase of prescription opioids on ≥ 3 occasions; and (f) receipt of opioids in 3 consecutive months without a documented pain diagnosis. We used Cox proportional hazards models to identify PIP practices associated with non-fatal opioid overdose, fatal opioid overdose, and all-cause mortality, controlling for covariates.Key resultsAll six types of PIP were associated with higher adjusted hazard for all-cause mortality, four of six with non-fatal overdose, and five of six with fatal overdose. Lacking a documented pain diagnosis was associated with non-fatal overdose (adjusted hazard ratio [AHR] 2.21, 95% confidence interval [CI] 2.02-2.41), as was high-dose opioids (AHR 1.68, 95% CI 1.59-1.76). Co-prescription of benzodiazepines was associated with fatal overdose (AHR 4.23, 95% CI 3.85-4.65). High-dose opioids were associated with all-cause mortality (AHR 2.18, 95% CI 2.14-2.23), as was lacking a documented pain diagnosis (AHR 2.05, 95% CI 2.01-2.09). Compared to those who received opioids without PIP, the hazard for fatal opioid overdose with one, two, three, and ≥ four PIP subtypes were 4.24, 7.05, 10.28, and 12.99 (test of linear trend, p < 0.001).ConclusionsPIP was associated with higher hazard for all-cause mortality, fatal overdose, and non-fatal overdose. Our study implies the possibility of creating a risk score incorporating multiple PIP subtypes, which could be displayed to prescribers in real time.

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Dataset Information

Spatial heterogeneity and socioeconomic determinants of opioid prescribing in England between 2015 and 2018.

Background

Methods

Results

Conclusions

Publications

Spatial heterogeneity and socioeconomic determinants of opioid prescribing in England between 2015 and 2018.

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