IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-40234-9.html
   My bibliography  Save this article

A small area model to assess temporal trends and sub-national disparities in healthcare quality

Author

Listed:
  • Adrien Allorant

    (McGill University
    University of Washington
    University of Washington)

  • Nancy Fullman

    (University of Washington
    University of Washington)

  • Hannah H. Leslie

    (University of California San Francisco)

  • Moussa Sarr

    (Institut de Recherche en Santé de Surveillance Epidémiologique et de Formation)

  • Daouda Gueye

    (Institut de Recherche en Santé de Surveillance Epidémiologique et de Formation)

  • Eliudi Eliakimu

    (Ministry of Health)

  • Jon Wakefield

    (University of Washington)

  • Joseph L. Dieleman

    (University of Washington
    University of Washington)

  • David Pigott

    (University of Washington
    University of Washington)

  • Nancy Puttkammer

    (University of Washington)

  • Robert C. Reiner

    (University of Washington
    University of Washington)

Abstract

Monitoring subnational healthcare quality is important for identifying and addressing geographic inequities. Yet, health facility surveys are rarely powered to support the generation of estimates at more local levels. With this study, we propose an analytical approach for estimating both temporal and subnational patterns of healthcare quality indicators from health facility survey data. This method uses random effects to account for differences between survey instruments; space-time processes to leverage correlations in space and time; and covariates to incorporate auxiliary information. We applied this method for three countries in which at least four health facility surveys had been conducted since 1999 – Kenya, Senegal, and Tanzania – and estimated measures of sick-child care quality per WHO Service Availability and Readiness Assessment (SARA) guidelines at programmatic subnational level, between 1999 and 2020. Model performance metrics indicated good out-of-sample predictive validity, illustrating the potential utility of geospatial statistical models for health facility data. This method offers a way to jointly estimate indicators of healthcare quality over space and time, which could then provide insights to decision-makers and health service program managers.

Suggested Citation

  • Adrien Allorant & Nancy Fullman & Hannah H. Leslie & Moussa Sarr & Daouda Gueye & Eliudi Eliakimu & Jon Wakefield & Joseph L. Dieleman & David Pigott & Nancy Puttkammer & Robert C. Reiner, 2023. "A small area model to assess temporal trends and sub-national disparities in healthcare quality," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40234-9
    DOI: 10.1038/s41467-023-40234-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-40234-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-40234-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Steve Gutreuter & Ehimario Igumbor & Njeri Wabiri & Mitesh Desai & Lizette Durand, 2019. "Improving estimates of district HIV prevalence and burden in South Africa using small area estimation techniques," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-14, February.
    2. Leonard, Kenneth L. & Masatu, Melkiory C., 2005. "The use of direct clinician observation and vignettes for health services quality evaluation in developing countries," Social Science & Medicine, Elsevier, vol. 61(9), pages 1944-1951, November.
    3. Lindsay Mallick & Gheda Temsah & Wenjuan Wang, 2019. "Comparing summary measures of quality of care for family planning in Haiti, Malawi, and Tanzania," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-18, June.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    5. Laura Di Giorgio & David K. Evans & Magnus Lindelow & Son Nam Nguyen & Jakob Svensson & Waly Wane & Anna Welander Tärneberg, 2020. "An Analysis of Clinical Knowledge, Absenteeism, and Availability of Resources for Maternal and Child Health: A Cross-Sectional Quality of Care Study in 10 African Countries," Working Papers 552, Center for Global Development.
    6. Lange, Siri & Mwisongo, Aziza & Mæstad, Ottar, 2014. "Why don't clinicians adhere more consistently to guidelines for the Integrated Management of Childhood Illness (IMCI)?," Social Science & Medicine, Elsevier, vol. 104(C), pages 56-63.
    7. Arman Bidarbakhtnia, 2020. "Measuring Sustainable Development Goals (SDGs): An Inclusive Approach," Global Policy, London School of Economics and Political Science, vol. 11(1), pages 56-67, February.
    8. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    9. Ramadhani Kigume & Stephen Maluka & Peter Kamuzora, 2018. "Decentralisation and health services delivery in Tanzania: Analysis of decision space in planning, allocation, and use of financial resources," International Journal of Health Planning and Management, Wiley Blackwell, vol. 33(2), pages 621-635, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cho, Daegon & Hwang, Youngdeok & Park, Jongwon, 2018. "More buzz, more vibes: Impact of social media on concert distribution," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 103-113.
    2. Anna D Gage & Margaret E Kruk & Tsinuel Girma & Ephrem T Lemango, 2018. "The know-do gap in sick child care in Ethiopia," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-10, December.
    3. Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.
    4. Johnson, Blair T. & Sisti, Anthony & Bernstein, Mary & Chen, Kun & Hennessy, Emily A. & Acabchuk, Rebecca L. & Matos, Michaela, 2021. "Community-level factors and incidence of gun violence in the United States, 2014–2017," Social Science & Medicine, Elsevier, vol. 280(C).
    5. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    6. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    7. John M. Humphreys & Robert B. Srygley & David H. Branson, 2022. "Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change," Geographies, MDPI, vol. 2(1), pages 1-19, January.
    8. Álvaro Briz-Redón, 2021. "Respondent Burden Effects on Item Non-Response and Careless Response Rates: An Analysis of Two Types of Surveys," Mathematics, MDPI, vol. 9(17), pages 1-16, August.
    9. Dong Liang & Genevieve Nesslage & Michael Wilberg & Thomas Miller, 2017. "Bayesian Calibration of Blue Crab (Callinectes sapidus) Abundance Indices Based on Probability Surveys," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 481-497, December.
    10. Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
    11. Humphreys, John M. & Srygley, Robert B. & Lawton, Douglas & Hudson, Amy R. & Branson, David H., 2022. "Grasshoppers exhibit asynchrony and spatial non-stationarity in response to the El Niño/Southern and Pacific Decadal Oscillations," Ecological Modelling, Elsevier, vol. 471(C).
    12. Carlos Díaz-Avalos & Pablo Juan & Somnath Chaudhuri & Marc Sáez & Laura Serra, 2020. "Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State," IJERPH, MDPI, vol. 17(23), pages 1-18, December.
    13. Beręsewicz Maciej, 2019. "Correlates of Representation Errors in Internet Data Sources for Real Estate Market," Journal of Official Statistics, Sciendo, vol. 35(3), pages 509-529, September.
    14. Deslatte, Aaron & Scott, Tyler A. & Carter, David P., 2019. "Specialized governance and regional land-use outcomes: A spatial analysis of Florida community development districts," Land Use Policy, Elsevier, vol. 83(C), pages 227-239.
    15. Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
    16. Ropo E. Ogunsakin & Themba G. Ginindza, 2022. "Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey," IJERPH, MDPI, vol. 19(15), pages 1-17, July.
    17. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    18. Jamie M. Madden & Simon More & Conor Teljeur & Justin Gleeson & Cathal Walsh & Guy McGrath, 2021. "Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland," IJERPH, MDPI, vol. 18(12), pages 1-16, June.
    19. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.
    20. Álvaro Briz‐Redón & Jorge Mateu & Francisco Montes, 2022. "Identifying crime generators and spatially overlapping high‐risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain)," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 97-120, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40234-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.