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An analysis of antidepressant prescribing trends in England 2015-2019.

ABSTRACT: Background Growing concerns about the impact of coronavirus disease 2019 (COVID-19) will likely lead to increased mental health diagnoses and treatment. To provide a pre-COVID-19 baseline, we have examined antidepressant prescribing trends for 5 years preceding COVID-19. Methods A retrospective analysis of anonymised data on medicines prescribed by GPs in England from the Open-Prescribing Database (January 2015 to December 2019) identified the 10 most prescribed antidepressant and, for comparison, cardiovascular medicines. Results Prescription items for the 10 most prescribed antidepressants rose 25% from 58 million (2015) to 72 million (2019). Citalopram was the most prescribed antidepressant; prescriptions for sertraline rose fastest at 2 million items year on year. Over the same period, costs for antidepressant prescribing fell 27.8%. Across all Clinical Commissioning Groups (CCGs) in England, antidepressant prescribing levels, adjusted for population were positively correlated with the index of multiple deprivation (IMD) score. In comparison, prescribing for the top 10 most prescribed cardiovascular medicines increased by 2.75% from 207 million (2015) to 213 million (2019) items. Limitations Anonymised data in the Open-Prescribing Database means no patient diagnoses or treatment plans are linked to this data. Conclusion Antidepressant prescribing, particularly sertraline, is increasing. Prescribing is higher in more deprived regions, but costs are falling to < 2% of all items prescribed. Absolute numbers of prescriptions for cardiovascular medicines are higher, likely reflecting the greater prevalence of cardiovascular disease, and are rising more slowly. This study will enable future work to look at the impact of COVID-19 on prescribing for mental health.

SUBMITTER: Lalji HM

PROVIDER: S-EPMC8684293 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOpioid 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.MethodsLeveraging 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.ResultsWe 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.ConclusionsFailing 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.

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An analysis of antidepressant prescribing trends in England 2015-2019.

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