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Unravelling the dynamics of mental health inequalities in England: a 12-year nationwide longitudinal spatial analysis of recorded depression prevalence

Unravelling the dynamics of mental health inequalities in England: a 12-year nationwide longitudinal spatial analysis of recorded depression prevalence
Unravelling the dynamics of mental health inequalities in England: a 12-year nationwide longitudinal spatial analysis of recorded depression prevalence

Background: depression is one of the most significant public health issues, but evidence of geographic patterns and trends of depression is limited. We aimed to examine the spatio-temporal patterns and trends of depression prevalence among adults in a nationwide longitudinal spatial study in England and evaluate the influence of neighbourhood socioeconomic deprivation in explaining patterns. 

Methods: information on recorded depression prevalence was obtained from the indicator Quality and Outcomes Framework: Depression prevalence that measured the annual percentage of adults diagnosed with depression for Lower Super Output Areas (LSOA) from 2011 to 2022. We applied Cluster and Outlier Analysis using the Local Moran's I algorithm. Local effects of deprivation on depression in 2020 examined with Geographically Weighted Regression (GWR). Inequalities in recorded prevalence were presented using Prevalence Rate Ratios (PRR). 

Results: the North West Region of England had the highest concentration of High-High clusters of depression, with 17.4% of the area having high values surrounded by high values in both space and time and the greatest percentage of areas with a high rate of increase (43.1%). Inequalities widened among areas with a high rate of increase in prevalence compared to those with a lower rate of increase, with the PRR increasing from 1.66 (99% CI 1.61–1.70) in 2011 to 1.81 (99% CI 1.76–1.85) by 2022. Deprivation explained 3%–39% of the variance in depression in 2020 across the country. 

Conclusions: it is crucial to monitor depression's spatial patterns and trends and investigate mechanisms of mental health inequalities. Our findings can help identify priority areas and target prevention and intervention strategies in England. Evaluating mental health interventions in different geographic contexts can provide valuable insights to policymakers on the most effective and context-sensitive strategies, enabling them to allocate resources towards preventing the worsening of mental health inequalities.

Depression, Geographically weighted regression, Inequalities, Mental health, Morans' I, Spatial statistics
2352-8273
Tsimpida, Dialechti
2fff4517-3c8e-445b-8646-7f645fa36b0a
Tsakiridi, Anastasia
af331b61-1d67-40de-bd17-fd6c2ebde67e
Daras, Konstantinos
11e002e4-3421-487c-9abe-0612fd6516e0
Corcoran, Rhiannon
9bd5f7b2-85d9-454b-baae-d239ea43811d
Gabbay, Mark
841e8b5a-5d9d-40fd-94eb-734b398baf2a
Tsimpida, Dialechti
2fff4517-3c8e-445b-8646-7f645fa36b0a
Tsakiridi, Anastasia
af331b61-1d67-40de-bd17-fd6c2ebde67e
Daras, Konstantinos
11e002e4-3421-487c-9abe-0612fd6516e0
Corcoran, Rhiannon
9bd5f7b2-85d9-454b-baae-d239ea43811d
Gabbay, Mark
841e8b5a-5d9d-40fd-94eb-734b398baf2a

Tsimpida, Dialechti, Tsakiridi, Anastasia, Daras, Konstantinos, Corcoran, Rhiannon and Gabbay, Mark (2024) Unravelling the dynamics of mental health inequalities in England: a 12-year nationwide longitudinal spatial analysis of recorded depression prevalence. SSM - Population Health, 26, [101669]. (doi:10.1016/j.ssmph.2024.101669).

Record type: Article

Abstract

Background: depression is one of the most significant public health issues, but evidence of geographic patterns and trends of depression is limited. We aimed to examine the spatio-temporal patterns and trends of depression prevalence among adults in a nationwide longitudinal spatial study in England and evaluate the influence of neighbourhood socioeconomic deprivation in explaining patterns. 

Methods: information on recorded depression prevalence was obtained from the indicator Quality and Outcomes Framework: Depression prevalence that measured the annual percentage of adults diagnosed with depression for Lower Super Output Areas (LSOA) from 2011 to 2022. We applied Cluster and Outlier Analysis using the Local Moran's I algorithm. Local effects of deprivation on depression in 2020 examined with Geographically Weighted Regression (GWR). Inequalities in recorded prevalence were presented using Prevalence Rate Ratios (PRR). 

Results: the North West Region of England had the highest concentration of High-High clusters of depression, with 17.4% of the area having high values surrounded by high values in both space and time and the greatest percentage of areas with a high rate of increase (43.1%). Inequalities widened among areas with a high rate of increase in prevalence compared to those with a lower rate of increase, with the PRR increasing from 1.66 (99% CI 1.61–1.70) in 2011 to 1.81 (99% CI 1.76–1.85) by 2022. Deprivation explained 3%–39% of the variance in depression in 2020 across the country. 

Conclusions: it is crucial to monitor depression's spatial patterns and trends and investigate mechanisms of mental health inequalities. Our findings can help identify priority areas and target prevention and intervention strategies in England. Evaluating mental health interventions in different geographic contexts can provide valuable insights to policymakers on the most effective and context-sensitive strategies, enabling them to allocate resources towards preventing the worsening of mental health inequalities.

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Accepted/In Press date: 3 April 2024
e-pub ahead of print date: 15 April 2024
Published date: 26 April 2024
Keywords: Depression, Geographically weighted regression, Inequalities, Mental health, Morans' I, Spatial statistics

Identifiers

Local EPrints ID: 490059
URI: http://eprints.soton.ac.uk/id/eprint/490059
ISSN: 2352-8273
PURE UUID: 3723a7fb-8193-404f-97f1-8b5ab7f11db0
ORCID for Dialechti Tsimpida: ORCID iD orcid.org/0000-0002-3709-5651

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Date deposited: 14 May 2024 16:37
Last modified: 15 May 2024 02:08

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Contributors

Author: Dialechti Tsimpida ORCID iD
Author: Anastasia Tsakiridi
Author: Konstantinos Daras
Author: Rhiannon Corcoran
Author: Mark Gabbay

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