Beyond national indicators: adapting the Demographic and Health Surveys’ sampling strategies and questions to better inform subnational malaria intervention policy

Ozodiegwu, Ifeoma D.; Ambrose, Monique; Battle, Katherine E.; Bever, Caitlin A.; Diallo, Ousmane; Galatas, Beatriz; Runge, Manuela; Gerardin, Jaline

doi:10.1186/s12936-021-03646-w

Cited by 45 publications

(24 citation statements)

References 11 publications

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“…The involvement of experts working on mathematical models in the decision-making may contribute to decision-makers’ understanding and help to apply the mathematical predictions in the decision making. As our participants explained their most useful data sources are the DHIS2 and the DHS, which resonates with work from Ozodiegwu and colleagues who confirm the usefulness of Demographic and Health Surveys for data concerning Malaria to inform Malaria transmission models in Nigeria 30 .…”

Section: Discussionsupporting

confidence: 67%

Exploring computer-aided health decision-making on cervical cancer interventions through deliberative interviews in Ethiopia

Berner-Rodoreda

Asnake

et al. 2023

npj Digit. Med.

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Cervical cancer is a significant disease burden in Ethiopia. Mathematical models and computer simulations on disease dynamics can support effective resource allocation. The objectives of this work are (i) to explore the perspectives of health decision-makers on computer-aided predictions supporting cervical cancer interventions, (ii) to identify their information needs from these predictions, and (iii) their willingness to apply the results in their work. We conducted deliberative interviews with 15 health decision-makers and advisors in Ethiopia in autumn 2019. We analyze the data using a five steps framework approach drawing on thematic analysis and find that Ethiopian health decision-makers are willing to use computer-aided predictions in their decisions. Data on HPV prevalence and the cervical cancer burden are scarce but valued highly and decision-makers are particularly interested in the identification of local HPV hotspots. Data-driven mathematical models and computer simulations may increasingly influence health decision-making in Ethiopia.

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Section: Discussionsupporting

confidence: 67%

Exploring computer-aided health decision-making on cervical cancer interventions through deliberative interviews in Ethiopia

Berner-Rodoreda

Asnake

et al. 2023

npj Digit. Med.

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“…Survey reports of fevers may include non-malarial fevers for which ACT use is not relevant. State-level estimates from the 2018 DHS, Nigeria’s most recent survey at the time of this study, suggest very low CM rates, in contrast with the view of the NMEP after consultation, and with 2015 ACTwatch data showing good availability of ACT [ 85 , 86 ]. Very low effective CM coverage in the model will skew prevalence and mortality levels higher and increase the potential impact of raising CM coverage to target levels.…”

Section: Discussionmentioning

confidence: 99%

“…The use of routine incidence data to depict seasonality patterns at the LGA and archetype level was challenged by data quality and representability, likely affected by data entry and reporting errors, preferential care-seeking in the private sector, or facility accessibility issues [ 87 ]. The probability sampling methods used by the DHS makes it an improved data source for estimating transmission intensity over routine data but since it is insufficiently representative at the LGA-level [ 86 ], archetype-level estimates was a reasonable alternative. However, archetype-level calibrations would not fully capture between-LGA variations in transmission indicators.…”

Section: Discussionmentioning

confidence: 99%

Application of mathematical modelling to inform national malaria intervention planning in Nigeria

Ozodiegwu

Ambrose

Galatas

et al. 2023

Malar J

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Background For their 2021–2025 National Malaria Strategic Plan (NMSP), Nigeria’s National Malaria Elimination Programme (NMEP), in partnership with the World Health Organization (WHO), developed a targeted approach to intervention deployment at the local government area (LGA) level as part of the High Burden to High Impact response. Mathematical models of malaria transmission were used to predict the impact of proposed intervention strategies on malaria burden. Methods An agent-based model of Plasmodium falciparum transmission was used to simulate malaria morbidity and mortality in Nigeria’s 774 LGAs under four possible intervention strategies from 2020 to 2030. The scenarios represented the previously implemented plan (business-as-usual), the NMSP at an 80% or higher coverage level and two prioritized plans according to the resources available to Nigeria. LGAs were clustered into 22 epidemiological archetypes using monthly rainfall, temperature suitability index, vector abundance, pre-2010 parasite prevalence, and pre-2010 vector control coverage. Routine incidence data were used to parameterize seasonality in each archetype. Each LGA’s baseline malaria transmission intensity was calibrated to parasite prevalence in children under the age of five years measured in the 2010 Malaria Indicator Survey (MIS). Intervention coverage in the 2010–2019 period was obtained from the Demographic and Health Survey, MIS, the NMEP, and post-campaign surveys. Results Pursuing a business-as-usual strategy was projected to result in a 5% and 9% increase in malaria incidence in 2025 and 2030 compared with 2020, while deaths were projected to remain unchanged by 2030. The greatest intervention impact was associated with the NMSP scenario with 80% or greater coverage of standard interventions coupled with intermittent preventive treatment in infants and extension of seasonal malaria chemoprevention (SMC) to 404 LGAs, compared to 80 LGAs in 2019. The budget-prioritized scenario with SMC expansion to 310 LGAs, high bed net coverage with new formulations, and increase in effective case management rate at the same pace as historical levels was adopted as an adequate alternative for the resources available. Conclusions Dynamical models can be applied for relative assessment of the impact of intervention scenarios but improved subnational data collection systems are required to allow increased confidence in predictions at sub-national level.

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“…Georeferenced survey data, such as the Malaria Indicator Surveys (MIS) and Demographic and Health surveys (DHS) and modeled geospatial data can be utilized to understand urban malaria transmission risk. Unlike routine surveillance systems that collect solely information on malaria infection status and are biased towards individuals that live close to health facilities or seek care in public institutions [21,22], the MIS and DHS uses a cluster sampling methodology to collect data on individual level infection status and risk factors that can be aggregated for urban clusters, and when supplemented by geospatial rasters facilitate examination of risk factor associations. Moreover, de nitions of urban areas within the DHS and MIS survey are aligned with those of local administrators increasing the likelihood that analysis results will be accepted by policymakers.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic, demographic and environmental factors inform malaria intervention prioritization in urban Nigeria

Chiziba¹,

Diallo²,

Bertozzi‐Villa

et al. 2023

Preprint

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Background Nigeria is one of three countries projected to have the largest absolute increase in the size of its urban population and this could intensify malaria transmission in cities. Accelerated urban population growth is outpacing the availability of affordable housing and basic services and resulting in living conditions that foster vector breeding and heterogeneous malaria transmission. Understanding community determinants of malaria transmission in urban areas informs the targeting of interventions to populations at greatest risk. Methods In this study, we analyzed cluster-level data from the Demographic and Health Surveys (DHS) and the Malaria Indicator Survey (MIS) as well as geospatial covariates to describe malaria burden and its determinants in areas administratively defined as urban in Nigeria. Univariate and multivariable models were respectively used to identify potential intervention prioritization thresholds and likely casual impact of individual variables. Results Overall, we found low malaria test positivity across urban areas. We observed declines in test positivity rate over time and identified the percentage of individuals with post-primary education, the percentage of individuals in the rich wealth quintiles, the percentage of individuals living in improved housing in 2015, all age population density, median age, the percentage of children under the age of five that sought medical treatment for fever, total precipitation, and enhanced vegetation index as key community predictors of malaria transmission intensity. Conclusions The unrepresentativeness of the DHS and MIS in urban settings at the state and geopolitical zonal level, regional differences in malaria seasonality across Nigeria, and information detection bias were among likely factors that limited our ability to compare malaria burden across geographic space and ultimately drove model uncertainty. Nevertheless, study findings provide a starting point for informing decisions on intervention prioritization within urban spaces and underscore the need for improved regionally focused surveillance systems in Nigeria.

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Beyond national indicators: adapting the Demographic and Health Surveys’ sampling strategies and questions to better inform subnational malaria intervention policy

Cited by 45 publications

References 11 publications

Exploring computer-aided health decision-making on cervical cancer interventions through deliberative interviews in Ethiopia

Exploring computer-aided health decision-making on cervical cancer interventions through deliberative interviews in Ethiopia

Application of mathematical modelling to inform national malaria intervention planning in Nigeria

Socioeconomic, demographic and environmental factors inform malaria intervention prioritization in urban Nigeria

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