True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries
Abstract Background Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children u...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-02-01
|
| Series: | Malaria Journal |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12936-018-2211-y |
| _version_ | 1828392347216379904 |
|---|---|
| author | Elvire Mfueni Brecht Devleesschauwer Angel Rosas-Aguirre Carine Van Malderen Patrick T. Brandt Bernhards Ogutu Robert W. Snow Léon Tshilolo Dejan Zurovac Dieter Vanderelst Niko Speybroeck |
| author_facet | Elvire Mfueni Brecht Devleesschauwer Angel Rosas-Aguirre Carine Van Malderen Patrick T. Brandt Bernhards Ogutu Robert W. Snow Léon Tshilolo Dejan Zurovac Dieter Vanderelst Niko Speybroeck |
| author_sort | Elvire Mfueni |
| collection | DOAJ |
| description | Abstract Background Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys—i.e., rapid diagnostic tests and light microscopy. Methods Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013–2014), Uganda (MIS 2014–2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion. Results The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%–23%) in the Democratic Republic of the Congo, 22% (95% UI 9–32%) in Uganda and 1% (95% UI 0–3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic. Conclusions In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests. |
| first_indexed | 2024-12-10T07:19:24Z |
| format | Article |
| id | doaj.art-94925ce44311482fa451d8c99870cb26 |
| institution | Directory Open Access Journal |
| issn | 1475-2875 |
| language | English |
| last_indexed | 2024-12-10T07:19:24Z |
| publishDate | 2018-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Malaria Journal |
| spelling | doaj.art-94925ce44311482fa451d8c99870cb262022-12-22T01:57:52ZengBMCMalaria Journal1475-28752018-02-011711710.1186/s12936-018-2211-yTrue malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countriesElvire Mfueni0Brecht Devleesschauwer1Angel Rosas-Aguirre2Carine Van Malderen3Patrick T. Brandt4Bernhards Ogutu5Robert W. Snow6Léon Tshilolo7Dejan Zurovac8Dieter Vanderelst9Niko Speybroeck10Institute of Health and Society, Université Catholique de LouvainDepartment of Public Health and Surveillance, Scientific Institute of Public Health (WIV-ISP)Institute of Health and Society, Université Catholique de LouvainInstitute of Health and Society, Université Catholique de LouvainSchool of Economic, Political and Policy Sciences, The University of TexasKenya Medical Research InstitutePopulation & Health Theme, Kenya Medical Research Institute/Wellcome Trust Research ProgrammeCentre Hospitalier MonkolePopulation & Health Theme, Kenya Medical Research Institute/Wellcome Trust Research ProgrammeDepartment of Biology, University of CincinnatiInstitute of Health and Society, Université Catholique de LouvainAbstract Background Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys—i.e., rapid diagnostic tests and light microscopy. Methods Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013–2014), Uganda (MIS 2014–2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion. Results The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%–23%) in the Democratic Republic of the Congo, 22% (95% UI 9–32%) in Uganda and 1% (95% UI 0–3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic. Conclusions In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests.http://link.springer.com/article/10.1186/s12936-018-2211-yBayesian data analysisMalariaSub-Saharan AfricaTrue prevalence |
| spellingShingle | Elvire Mfueni Brecht Devleesschauwer Angel Rosas-Aguirre Carine Van Malderen Patrick T. Brandt Bernhards Ogutu Robert W. Snow Léon Tshilolo Dejan Zurovac Dieter Vanderelst Niko Speybroeck True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries Malaria Journal Bayesian data analysis Malaria Sub-Saharan Africa True prevalence |
| title | True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries |
| title_full | True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries |
| title_fullStr | True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries |
| title_full_unstemmed | True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries |
| title_short | True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries |
| title_sort | true malaria prevalence in children under five bayesian estimation using data of malaria household surveys from three sub saharan countries |
| topic | Bayesian data analysis Malaria Sub-Saharan Africa True prevalence |
| url | http://link.springer.com/article/10.1186/s12936-018-2211-y |
| work_keys_str_mv | AT elviremfueni truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT brechtdevleesschauwer truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT angelrosasaguirre truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT carinevanmalderen truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT patricktbrandt truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT bernhardsogutu truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT robertwsnow truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT leontshilolo truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT dejanzurovac truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT dietervanderelst truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries AT nikospeybroeck truemalariaprevalenceinchildrenunderfivebayesianestimationusingdataofmalariahouseholdsurveysfromthreesubsaharancountries |