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Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda

Ouma, Joseph, Jeffery, Caroline ORCID:, Awor, Colletar Anna, Muruta, Allan, Musinguzi, Joshua, Wanyenze, Rhoda K., Biraro, Sam, Levin, Jonathan and Valadez, Joseph ORCID: (2021) 'Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda'. PLoS ONE, Vol 16, Issue 8, e0253375.

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Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda.
Our analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area level models, respectively.
Estimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β1 = 0.66, r2 = 0.862), and correlation between area-level model and direct survey estimates was (β1 = 0.44, r2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and arealevel
models, respectively, compared to the direct survey estimates.
Although the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available.

Item Type: Article
Subjects: WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries
WA Public Health > Statistics. Surveys > WA 900 Public health statistics
WC Communicable Diseases > Virus Diseases > Acquired Immunodeficiency Syndrome. HIV Infections > WC 503 Acquired immunodeficiency syndrome. HIV infections
Faculty: Department: Clinical Sciences & International Health > International Public Health Department
Digital Object Identifer (DOI):
Depositing User: Rachel Dominguez
Date Deposited: 24 Aug 2021 10:34
Last Modified: 24 Aug 2021 10:34


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