Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda
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 thei...
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Published in | PloS one Vol. 16; no. 8; p. e0253375 |
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06.08.2021
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Abstract | 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 ([beta].sub.1 = 0.66, r.sup.2 = 0.862), and correlation between area-level model and direct survey estimates was ([beta].sub.1 = 0.44, r.sup.2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level 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. |
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AbstractList | 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 ([beta].sub.1 = 0.66, r.sup.2 = 0.862), and correlation between area-level model and direct survey estimates was ([beta].sub.1 = 0.44, r.sup.2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level 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. Background 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. Methods 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. Results 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 ([beta].sub.1 = 0.66, r.sup.2 = 0.862), and correlation between area-level model and direct survey estimates was ([beta].sub.1 = 0.44, r.sup.2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level models, respectively, compared to the direct survey estimates. Conclusions 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. BackgroundModel-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.MethodsOur 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.ResultsEstimates 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 area-level models, respectively, compared to the direct survey estimates.ConclusionsAlthough 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. |
Audience | Academic |
Author | Awor, Colletar Anna Jeffery, Caroline Ouma, Joseph Biraro, Sam Wanyenze, Rhoda K Muruta, Allan Levin, Jonathan Musinguzi, Joshua Valadez, Joseph J |
AuthorAffiliation | 3 Data Science and Informatics Branch, Centers for Disease Control and Prevention, Uganda University of Salamanca, SPAIN 5 Department of Disease Control and Environmental Health, Makerere University School of Public Health, Kampala, Uganda 2 METRe Group, Department of International Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom 4 AIDS Control Program, Ministry of Health, Uganda 6 ICAP at Columbia University, Nakasero, Kampala, Uganda 1 Division of Epidemiology and Biostatistics, School of Public Health, University of Witwatersrand, Johannesburg, South Africa |
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CitedBy_id | crossref_primary_10_1186_s12885_024_11928_0 crossref_primary_10_1186_s12889_021_12095_8 crossref_primary_10_1515_spp_2022_0022 crossref_primary_10_1007_s10708_024_11133_3 crossref_primary_10_2174_0118749445304594240425112633 crossref_primary_10_18332_tid_169683 crossref_primary_10_2139_ssrn_4735326 crossref_primary_10_1111_hiv_13601 |
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Notes | Competing Interests: The authors have declared that no competing interests exist. |
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Snippet | Background Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes... Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not... BackgroundModel-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes... |
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SubjectTerms | Biology and Life Sciences Diagnosis Engineering and Technology Evaluation HIV infection Medicine and Health Sciences People and Places Prevalence studies (Epidemiology) Research and Analysis Methods Social Sciences |
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Title | Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda |
URI | https://pubmed.ncbi.nlm.nih.gov/PMC8345831 https://doaj.org/article/cc6f94ab738d4ed3921aee742feda9ad |
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