Can we identify the prevalence of perinatal mental health using routinely collected health data?: A review of publicly available perinatal mental health data sources in England

Sarah Masefield; Kathryn Willan; Zoe Darwin; Sarah Blower; Chandani Nekitsing; Josie Dickerson

doi:10.1002/lrh2.10374

Can we identify the prevalence of perinatal mental health using routinely collected health data? A review of publicly available perinatal mental health data sources in England

Sarah Masefield, Kathryn Willan, Zoe Darwin, Sarah Blower, Chandani Nekitsing, Josie Dickerson^*

^*Corresponding author for this work

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

Introduction: Perinatal mental health (PMH) conditions affect around one in four women, and may be even higher in women from some ethnic minority groups and those living in low socioeconomic circumstances. Poor PMH causes significant distress and can have lifelong adverse impacts for some children. In England, current prevalence rates are estimated using mental health data of the general population and do not take sociodemographic variance of geographical areas into account. Services cannot plan their capacity and ensure appropriate and timely support using these estimates. Our aim was to see if PMH prevalence rates could be identified using existing publicly available sources of routine health data. Methods: A review of data sources was completed by searching NHS Digital (now NHS England), Public Health England and other national PMH resources, performing keyword searches online, and research team knowledge of the field. The sources were screened for routine data that could be used to produce prevalence of PMH conditions by sociodemographic variation. Included sources were reviewed for their utility in accessibility, data relevance and technical specification relating to PMH and sociodemographic data items. Results: We found a PMH data ‘blind spot’ with significant inadequacies in the utility of all identified data sources, making it impossible to provide information on the prevalence of PMH in England and understand variation by sociodemographic differences. Conclusions: To enhance the utility of publicly available routine data to provide PMH prevalence rates requires improved mandatory PMH data capture in universal services, available publicly via one platform and including assessment outcomes and sociodemographic data.

Original language	English
Article number	e10374
Number of pages	9
Journal	Learning Health Systems
Early online date	19 Jun 2023
DOIs	https://doi.org/10.1002/lrh2.10374
Publication status	E-pub ahead of print - 19 Jun 2023

Bibliographical note

Funding Information:
West Yorkshire and Harrogate Health and Care Partnership, NHS Wakefield Clinical Commissioning Group, £225,000. Sarah Blower and Josie Dickerson are also supported by National Institute for Health Research; Applied Research Collaboration – Yorkshire and Humber (NIHR; ARC‐YH; Ref: NIHR200166). The funding body was not involved in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.

Publisher Copyright:
© 2023 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.

Keywords

health data systems
inequalities
NHS data
open data
perinatal mental health
system change

Access to Document

10.1002/lrh2.10374Licence: CC BY

Learning Health Systems - 2023 - Masefield
© 2023 The Authors.
Final published version, 222 KBLicence: CC BY

Cite this

Masefield, S., Willan, K., Darwin, Z., Blower, S., Nekitsing, C., & Dickerson, J. (2023). Can we identify the prevalence of perinatal mental health using routinely collected health data? A review of publicly available perinatal mental health data sources in England. Learning Health Systems, Article e10374. Advance online publication. https://doi.org/10.1002/lrh2.10374

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title = "Can we identify the prevalence of perinatal mental health using routinely collected health data?: A review of publicly available perinatal mental health data sources in England",

abstract = "Introduction: Perinatal mental health (PMH) conditions affect around one in four women, and may be even higher in women from some ethnic minority groups and those living in low socioeconomic circumstances. Poor PMH causes significant distress and can have lifelong adverse impacts for some children. In England, current prevalence rates are estimated using mental health data of the general population and do not take sociodemographic variance of geographical areas into account. Services cannot plan their capacity and ensure appropriate and timely support using these estimates. Our aim was to see if PMH prevalence rates could be identified using existing publicly available sources of routine health data. Methods: A review of data sources was completed by searching NHS Digital (now NHS England), Public Health England and other national PMH resources, performing keyword searches online, and research team knowledge of the field. The sources were screened for routine data that could be used to produce prevalence of PMH conditions by sociodemographic variation. Included sources were reviewed for their utility in accessibility, data relevance and technical specification relating to PMH and sociodemographic data items. Results: We found a PMH data {\textquoteleft}blind spot{\textquoteright} with significant inadequacies in the utility of all identified data sources, making it impossible to provide information on the prevalence of PMH in England and understand variation by sociodemographic differences. Conclusions: To enhance the utility of publicly available routine data to provide PMH prevalence rates requires improved mandatory PMH data capture in universal services, available publicly via one platform and including assessment outcomes and sociodemographic data.",

keywords = "health data systems, inequalities, NHS data, open data, perinatal mental health, system change",

author = "Sarah Masefield and Kathryn Willan and Zoe Darwin and Sarah Blower and Chandani Nekitsing and Josie Dickerson",

note = "Funding Information: West Yorkshire and Harrogate Health and Care Partnership, NHS Wakefield Clinical Commissioning Group, £225,000. Sarah Blower and Josie Dickerson are also supported by National Institute for Health Research; Applied Research Collaboration – Yorkshire and Humber (NIHR; ARC‐YH; Ref: NIHR200166). The funding body was not involved in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript. Publisher Copyright: {\textcopyright} 2023 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.",

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AU - Blower, Sarah

AU - Nekitsing, Chandani

AU - Dickerson, Josie

N1 - Funding Information: West Yorkshire and Harrogate Health and Care Partnership, NHS Wakefield Clinical Commissioning Group, £225,000. Sarah Blower and Josie Dickerson are also supported by National Institute for Health Research; Applied Research Collaboration – Yorkshire and Humber (NIHR; ARC‐YH; Ref: NIHR200166). The funding body was not involved in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript. Publisher Copyright: © 2023 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.

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N2 - Introduction: Perinatal mental health (PMH) conditions affect around one in four women, and may be even higher in women from some ethnic minority groups and those living in low socioeconomic circumstances. Poor PMH causes significant distress and can have lifelong adverse impacts for some children. In England, current prevalence rates are estimated using mental health data of the general population and do not take sociodemographic variance of geographical areas into account. Services cannot plan their capacity and ensure appropriate and timely support using these estimates. Our aim was to see if PMH prevalence rates could be identified using existing publicly available sources of routine health data. Methods: A review of data sources was completed by searching NHS Digital (now NHS England), Public Health England and other national PMH resources, performing keyword searches online, and research team knowledge of the field. The sources were screened for routine data that could be used to produce prevalence of PMH conditions by sociodemographic variation. Included sources were reviewed for their utility in accessibility, data relevance and technical specification relating to PMH and sociodemographic data items. Results: We found a PMH data ‘blind spot’ with significant inadequacies in the utility of all identified data sources, making it impossible to provide information on the prevalence of PMH in England and understand variation by sociodemographic differences. Conclusions: To enhance the utility of publicly available routine data to provide PMH prevalence rates requires improved mandatory PMH data capture in universal services, available publicly via one platform and including assessment outcomes and sociodemographic data.

AB - Introduction: Perinatal mental health (PMH) conditions affect around one in four women, and may be even higher in women from some ethnic minority groups and those living in low socioeconomic circumstances. Poor PMH causes significant distress and can have lifelong adverse impacts for some children. In England, current prevalence rates are estimated using mental health data of the general population and do not take sociodemographic variance of geographical areas into account. Services cannot plan their capacity and ensure appropriate and timely support using these estimates. Our aim was to see if PMH prevalence rates could be identified using existing publicly available sources of routine health data. Methods: A review of data sources was completed by searching NHS Digital (now NHS England), Public Health England and other national PMH resources, performing keyword searches online, and research team knowledge of the field. The sources were screened for routine data that could be used to produce prevalence of PMH conditions by sociodemographic variation. Included sources were reviewed for their utility in accessibility, data relevance and technical specification relating to PMH and sociodemographic data items. Results: We found a PMH data ‘blind spot’ with significant inadequacies in the utility of all identified data sources, making it impossible to provide information on the prevalence of PMH in England and understand variation by sociodemographic differences. Conclusions: To enhance the utility of publicly available routine data to provide PMH prevalence rates requires improved mandatory PMH data capture in universal services, available publicly via one platform and including assessment outcomes and sociodemographic data.

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