Assessing local chlamydia screening performance by combining survey and administrative data to account for differences in local population characteristics

Nathan Green; Ellie Sherrard-Smith; Clare Tanton; Pam Sonnenberg; Catherine H. Mercer; Peter J. White

doi:10.1038/s41598-019-43521-y

Assessing local chlamydia screening performance by combining survey and administrative data to account for differences in local population characteristics

Nathan Green^*, Ellie Sherrard-Smith, Clare Tanton, Pam Sonnenberg, Catherine H. Mercer, Peter J. White

^*Corresponding author for this work

Modelling Economics Unit

Research output: Contribution to journal › Article › peer-review

Abstract

Reducing health inequalities requires improved understanding of the causes of variation. Local-level variation reflects differences in local population characteristics and health system performance. Identifying low- and high-performing localities allows investigation into these differences. We used Multilevel Regression with Post-stratification (MRP) to synthesise data from multiple sources, using chlamydia testing as our example. We used national probability survey data to identify individual-level characteristics associated with chlamydia testing and combined this with local-level census data to calculate expected levels of testing in each local authority (LA) in England, allowing us to identify LAs where observed chlamydia testing rates were lower or higher than expected, given population characteristics. Taking account of multiple covariates, including age, sex, ethnicity, student and cohabiting status, 5.4% and 3.5% of LAs had testing rates higher than expected for 95% and 99% posterior credible intervals, respectively; 60.9% and 50.8% had rates lower than expected. Residual differences between observed and MRP expected values were smallest for LAs with large proportions of non-white ethnic populations. London boroughs that were markedly different from expected MRP values (≥90% posterior exceedance probability) had actively targeted risk groups. This type of synthesis allows more refined inferences to be made at small-area levels than previously feasible.

Original language	English
Article number	7070
Journal	Scientific Reports
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41598-019-43521-y
Publication status	Published - 1 Dec 2019

Bibliographical note

Funding Information:
This work by N.G. and P.J.W. was supported by the UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) (HPRU-2012-10080). P.J.W. also acknowledges joint Centre funding from the UK Medical Research Council and Department for International Development (MR/R015600/1). Natsal-3 was supported by grants from the Medical Research Council [G0701757]; and the Wellcome Trust [084840]; with contributions from the Economic and Social Research Council and Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed in this paper are those of the authors and not necessarily those of the Department of Health and Social Care (DHSC), Department for International Development (DfID), MRC, NHS, NIHR, or Public Health England.

Publisher Copyright:
© 2019, The Author(s).

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Cite this

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abstract = "Reducing health inequalities requires improved understanding of the causes of variation. Local-level variation reflects differences in local population characteristics and health system performance. Identifying low- and high-performing localities allows investigation into these differences. We used Multilevel Regression with Post-stratification (MRP) to synthesise data from multiple sources, using chlamydia testing as our example. We used national probability survey data to identify individual-level characteristics associated with chlamydia testing and combined this with local-level census data to calculate expected levels of testing in each local authority (LA) in England, allowing us to identify LAs where observed chlamydia testing rates were lower or higher than expected, given population characteristics. Taking account of multiple covariates, including age, sex, ethnicity, student and cohabiting status, 5.4% and 3.5% of LAs had testing rates higher than expected for 95% and 99% posterior credible intervals, respectively; 60.9% and 50.8% had rates lower than expected. Residual differences between observed and MRP expected values were smallest for LAs with large proportions of non-white ethnic populations. London boroughs that were markedly different from expected MRP values (≥90% posterior exceedance probability) had actively targeted risk groups. This type of synthesis allows more refined inferences to be made at small-area levels than previously feasible.",

author = "Nathan Green and Ellie Sherrard-Smith and Clare Tanton and Pam Sonnenberg and Mercer, {Catherine H.} and White, {Peter J.}",

note = "Funding Information: This work by N.G. and P.J.W. was supported by the UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) (HPRU-2012-10080). P.J.W. also acknowledges joint Centre funding from the UK Medical Research Council and Department for International Development (MR/R015600/1). Natsal-3 was supported by grants from the Medical Research Council [G0701757]; and the Wellcome Trust [084840]; with contributions from the Economic and Social Research Council and Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed in this paper are those of the authors and not necessarily those of the Department of Health and Social Care (DHSC), Department for International Development (DfID), MRC, NHS, NIHR, or Public Health England. Publisher Copyright: {\textcopyright} 2019, The Author(s).",

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Assessing local chlamydia screening performance by combining survey and administrative data to account for differences in local population characteristics

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