Application of multivariate binary logistic regression grouped outlier statistics and geospatial logistic model to identify villages having unusual health-seeking habits for childhood malaria in Malawi.

BACKGROUND: Early diagnosis and prompt treatment of malaria in young children are crucial for preventing the serious stages of the disease. If delayed treatment-seeking habits are observed in certain areas, targeted campaigns and interventions can be implemented to improve the situation. METHODS: This study applied multivariate binary logistic regression model diagnostics and geospatial logistic model to identify traditional authorities in Malawi where caregivers have unusual health-seeking behaviour for childhood malaria. The data from the 2021 Malawi Malaria Indicator Survey were analysed using R software version 4.3.0 for regressions and STATA version 17 for data cleaning. RESULTS: Both models showed significant variability in treatment-seeking habits of caregivers between villages. The mixed-effects logit model residual identified Vuso Jere, Kampingo Sibande, Ngabu, and Dzoole as outliers in the model. Despite characteristics that promote late reporting of malaria at clinics, most mothers in these traditional authorities sought treatment within twenty-four hours of the onset of malaria symptoms in their children. On the other hand, the geospatial logit model showed that late seeking of malaria treatment was prevalent in most areas of the country, except a few traditional authorities such as Mwakaboko, Mwenemisuku, Mwabulambya, Mmbelwa, Mwadzama, Zulu, Amidu, Kasisi, and Mabuka. CONCLUSIONS: These findings suggest that using a combination of multivariate regression model residuals and geospatial statistics can help in identifying communities with distinct treatment-seeking patterns for childhood malaria within a population. Health policymakers could benefit from consulting traditional authorities who demonstrated early reporting for care in this study. This could help in understanding the best practices followed by mothers in those areas which can be replicated in regions where seeking care is delayed.

Impact of ignoring sampling design in the prediction of binary health outcomes through logistic regression: evidence from Malawi demographic and health survey under-five mortality data; 2000-2016.

The birth and death rates of a population are among the crucial vital statistics for socio-economic policy planning in any country. Since the under-five mortality rate is one of the indicators for monitoring the health of a population, it requires regular and accurate estimation. The national demographic and health survey data, that are readily available to the puplic, have become a means for answering most health-related questions among African populations, using relevant statistical methods. However, many of such applications tend to ignore survey design effect in the estimations, despite the availability of statistical tools that support the analyses. Little is known about the amount of inaccurate information that is generated when predicting under-five mortality rates. This study estimates and compares the bias encountered when applying unweighted and weighted logistic regression methods to predict under-five mortality rate in Malawi using nationwide survey data. The Malawi demographic and health survey data of 2004, 2010, and 2015-16 were used to determine the bias. The analyses were carried out in R software version 3.6.3 and Stata version 12.0. A logistic regression model that included various bio- and socio-demographic factors concerning the child, mother and households was used to estimate the under-five mortality rate. The results showed that accuracy of predicting the national under-five mortality rate hinges on cluster-weighting of the overall predicted probability of child-deaths, regardless of whether the model was weighted or not. Weighting the model caused small positive and negative changes in various fixed-effect estimates, which diffused the result of weighting in the fitted probabilities of deaths. In turn, there was no difference between the overall predicted mortality rate obtained using the weighted model and that obtained in the unweighted model. We recommend considering survey cluster-weights during the computation of overall predicted probability of events for a binary health outcome. This can be done without worrying about the weights during model fitting, whose aim is prediction of the population parameter.

Consultative meeting that examined alignment and discrepancies between health facility and household survey data in Malawi.

Malawi is midway through its current Malaria Strategic Plan 2017-2022, which aims to reduce malaria incidence and deaths by at least 50% by 2022. Malariometric data are available with health surveillance data housed in District Health Information Software 2 (DHIS2) and household survey data from two recent Malaria Indicator Surveys (MIS) and a Demographic and Health Survey (DHS). Strengths and weaknesses of the data were discussed during a consultative meeting in Lilongwe, Malawi in July 2019. The first 3 days included in-depth exploration and analysis of surveillance and survey data by 13 participants from the National Malaria Control Programme, district health offices, and partner organizations. Key indicators derived from both DHIS2 and MIS/DHS sources were analysed with three case studies, and presented to stakeholders on the fourth day of the meeting. Applications of the findings to programmatic decision-making and strategic plan evaluation were critiqued and discussed.

Bivariate logistic regression model diagnostics applied to analysis of outlier cancer patients with comorbid diabetes and hypertension in Malawi.

The joint occurrence of diabetes and hypertension conditions in a patient is common. The two diseases share a number of risk factors, and are hence usually modelled concurrently using bivariate logistic regression. However, the postestimation assessment for the model, such as analysis of outlier observations, is seldom carried out. In this article, we apply outlier detection methods for multivariate data models to study characteristics of cancer patients with joint outlying diabetes and hypertension outcomes observed from among 398 randomly selected cancer patients at Queen Elizabeth and Kamuzu Central Hospitals in Malawi. We used R software version 4.2.2 to perform the analyses and STATA version 12 for data cleaning. The results showed that one patient was an outlier to the bivariate diabetes and hypertension logit model. The patient had both diabetes and hypertension and was based in rural area of the study population, where it was observed that comorbidity of the two diseases was uncommon. We recommend thorough analysis of outlier patients to comorbid diabetes and hypertension before rolling out interventions for managing the two diseases in cancer patients to avoid misaligned interventions. Future research could perform the applied diagnostic assessments for the bivariate logit model on a wider and larger dataset of the two diseases.

Malaria Burden Stratification in Malawi- A report of a consultative workshop to inform the 2023-2030 Malawi Malaria Strategic Plan.

Background: Malawi's National Malaria Control Programme (NMCP) is developing a new strategic plan for 2023-2030 to combat malaria and recognizes that a blanket approach to malaria interventions is no longer feasible. To inform this new strategy, the NMCP set up a task force comprising 18 members from various sectors, which convened a meeting to stratify the malaria burden in Malawi and recommend interventions for each stratum. Methods: The burden stratification workshop took place from November 29 to December 2, 2022, in Blantyre, Malawi, and collated essential data on malaria burden indicators, such as incidence, prevalence, and mortality. Workshop participants reviewed the malaria burden and intervention coverage data to describe the current status and identified the districts as a appropriate administrative level for stratification and action. Two scenarios were developed for the stratification, based on composites of three variables. Scenario 1 included incidence, prevalence, and under-five all-cause mortality, while Scenario 2 included total malaria cases, prevalence, and under-five all-cause mortality counts. The task force developed four burden strata (highest, high, moderate, and low) for each scenario, resulting in a final list of districts assigned to each stratum. Results: The task force concluded with 10 districts in the highest-burden stratum (Nkhotakota, Salima, Mchinji, Dowa, Ntchisi, Mwanza, Likoma, Lilongwe, Kasungu and Mangochi) 11 districts in the high burden stratum (Chitipa, Rumphi, Nkhata Bay, Dedza, Ntcheu, Neno, Thyolo, Nsanje, Zomba, Mzimba and Mulanje) and seven districts in the moderate burden stratum (Karonga, Chikwawa, Balaka, Machinga, Phalombe, Blantyre, and Chiradzulu). There were no districts in the low-burden stratum. Conclusion: The next steps for the NMCP are to review context-specific issues driving malaria transmission and recommend interventions for each stratum. Overall, this burden stratification workshop provides a critical foundation for developing a successful malaria strategic plan for Malawi.

Two decades of malaria control in Malawi: Geostatistical Analysis of the changing malaria prevalence from 2000-2022.

Background: Malaria remains a public health problem in Malawi and has a serious socio-economic impact on the population. In the past two decades, available malaria control measures have been substantially scaled up, such as insecticide-treated bed nets, artemisinin-based combination therapies, and, more recently, the introduction of the malaria vaccine, the RTS,S/AS01. In this paper, we describe the epidemiology of malaria for the last two decades to understand the past transmission and set the scene for the elimination agenda. Methods: A collation of parasite prevalence surveys conducted between the years 2000 and 2022 was done. A spatio-temporal geostatistical model was fitted to predict the yearly malaria risk for children aged 2-10 years (PfPR 2-10) at 1×1 km spatial resolutions. Parameter estimation was done using the Monte Carlo maximum likelihood method. District-level prevalence estimates adjusted for population are calculated for the years 2000 to 2022. Results: A total of 2,595 sampled unique locations from 2000 to 2022 were identified through the data collation exercise. This represents 70,565 individuals that were sampled in the period. In general, the PfPR2_10 declined over the 22 years. The mean modelled national PfPR2_10 in 2000 was 43.93 % (95% CI:17.9 to 73.8%) and declined to 19.2% (95%CI 7.49 to 37.0%) in 2022. The smoothened estimates of PfPR2_10 indicate that malaria prevalence is very heterogeneous with hotspot areas concentrated on the southern shores of Lake Malawi and the country's central region. Conclusions: The last two decades are associated with a decline in malaria prevalence, highly likely associated with the scale-up of control interventions. The country should move towards targeted malaria control approaches informed by surveillance data.

In Malawi, malaria continues to be a significant health issue, affecting people's well-being and the economy. Over the past twenty years, efforts to control malaria, such as using bed nets, specific medications, and introducing a malaria vaccine, have increased substantially. This paper explores malaria transmission patterns during this time to better understand the past situation and prepare for future efforts to eliminate the disease. We collected and analyzed data from various surveys conducted between 2000 and 2022, focusing on malaria risk for children aged 2­10 years. We used a detailed statistical model to predict yearly malaria risk. The results show a decline in malaria prevalence over the 22 years. The analysis also reveals variations in malaria prevalence, with hotspot areas particularly concentrated in the southern shores of Lake Malawi and the country's central region. This decline in malaria prevalence is likely linked to the increased implementation of control measures. The findings emphasize the importance of targeted approaches informed by ongoing surveillance data for continued progress in malaria control.