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

被引:1
作者
Masefield, Sarah [1 ,2 ]
Willan, Kathryn [2 ]
Darwin, Zoe [3 ]
Blower, Sarah [1 ]
Nekitsing, Chandani [1 ]
Dickerson, Josie [2 ]
机构
[1] Univ York, Fac Sci, Dept Hlth Sci, York, England
[2] Bradford Teaching Hosp NHS Fdn Trust, Bradford Inst Hlth Res, Bradford, England
[3] Univ Huddersfield, Sch Human & Hlth Sci, Dept Allied Hlth Profess Sport & Exercise, Huddersfield, England
关键词
health data systems; inequalities; NHS data; open data; perinatal mental health; system change; DEPRESSION; CARE; INNOVATION; ANXIETY;
D O I
10.1002/lrh2.10374
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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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页数:9
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共 44 条
[1]   Prevalence of maternal mental illness among children and adolescents in the UK between 2005 and 2017: a national retrospective cohort analysis [J].
Abel, Kathryn M. ;
Hope, Holly ;
Swift, Eleanor ;
Parisi, Rosa ;
Ashcroft, Darren M. ;
Kosidou, Kyriaki ;
Osam, Cemre Su ;
Dalman, Christina ;
Pierce, Matthias .
LANCET PUBLIC HEALTH, 2019, 4 (06) :E291-E300
[2]   Symptoms of Depression Postpartum and 12 years Later-Associations to Child Mental Health at 12 years of Age [J].
Agnafors, Sara ;
Sydsjo, Gunilla ;
deKeyser, Linda ;
Svedin, Carl Goran .
MATERNAL AND CHILD HEALTH JOURNAL, 2013, 17 (03) :405-414
[3]  
[Anonymous], 2022, Guide for Integration of Perinatal Mental Health in Maternal and Child Health Services
[4]   Barriers to Working With National Health Service England's Open Data [J].
Bacon, Seb ;
Goldacre, Ben .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2020, 22 (01)
[5]  
Bauer A., 2014, The costs of perinatal mental health problems
[6]   Open innovation in health care: Analysis of an open health platform [J].
Bullinger, Angelika C. ;
Rass, Matthias ;
Adamczyk, Sabrina ;
Moeslein, Kathrin M. ;
Sohn, Stefan .
HEALTH POLICY, 2012, 105 (2-3) :165-175
[7]  
Community and Mental Health team and Health and Social Care Information Centre, 2015, COMP MAT STAT ENGL A
[8]   DETECTION OF POSTNATAL DEPRESSION - DEVELOPMENT OF THE 10-ITEM EDINBURGH POSTNATAL DEPRESSION SCALE [J].
COX, JL ;
HOLDEN, JM ;
SAGOVSKY, R .
BRITISH JOURNAL OF PSYCHIATRY, 1987, 150 :782-786
[9]  
Department of Health, 2023, DOH STAT RES
[10]  
Department of Health and Social Care, 2022, Better, broader, safer: using health data for research and analysis