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Background: Malaria continues to be a major public health problem in Malawi and the greatest load of mortality and morbidity occurs in children five years and under. However, there is no information yet regarding trends and predictions of malaria incidence in children five years and under at district hospital level, particularly at Nsanje district hospital. Aim: Therefore, this study aimed at investigating the trends of malaria morbidity and mortality in order to design appropriate interventions on the best approach to contain the disease in the near future. Methodology: Trend analysis of malaria morbidity and mortality together with time series analysis using the SARIMA (Seasonal Autoregressive Integrated Moving Average) model was used to predict malaria incidence in Nsanje district. Results: The SARIMA model used malaria cases from 2015 to 2019 and created the best model to forecast the malaria cases in Nsanje from 2020 to 2022. An SARIMA (0, 1, 2) (0,1,1)12 was suitable for forecasting the incidence of malaria for Nsanje. Conclusion: The mortality and morbidity trend showed that malaria cases were growing at a fluctuating rate at Nsanje district hospital. The relative errors between the actual values and predicted values indicated that the predicted values matched the actual values well. Therefore, the model proved that it was adequate to forecast monthly malaria cases and it had a good fit, hence, was appropriate for this study

ObjectiveAccurate reporting of birth outcomes in low-income and middle-income countries (LMICs) is essential. Mobile health (mHealth) tools have been proposed as a replacement for conventional paper-based registers. mHealth could provide timely data for individual facilities and health departments, as well as capture deliveries outside facilities. This scoping review evaluates which mHealth tools have been reported to birth outcomes in the delivering room in LMICs and documents their reported advantages and drawbacks.DesignA scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Joanna Briggs Institute guidelines for scoping reviews and the mHealth evidence reporting and assessment checklist for evaluating mHealth interventions.Data sourcesPubMed, CINAHL and Global Health were searched for records until 3 February 2022 with no earliest date limit.Eligibility criteriaStudies were included where healthcare workers used mHealth tools in LMICs to record birth outcomes. Exclusion criteria included mHealth not being used at the point of delivery, non-peer reviewed literature and studies not written in English.Data extraction and synthesisTwo independent reviewers screened studies and extracted data. Common themes among studies were identified.Results640 records were screened, 21 of which met the inclusion criteria, describing 15 different mHealth tools. We identified six themes: (1) digital tools for labour monitoring (8 studies); (2) digital data collection of specific birth outcomes (3 studies); (3) digital technologies used in community settings (6 studies); (4) attitudes of healthcare workers (10 studies); (5) paper versus electronic data collection (3 studies) and (6) infrastructure, interoperability and sustainability (8 studies).ConclusionSeveral mHealth technologies are reported to have the capability to record birth outcomes at delivery, but none were identified that were designed solely for that purpose. Use of digital delivery registers appears feasible and acceptable to healthcare workers, but definitive evaluations are lacking. Further assessment of the sustainability of technologies and their ability to integrate with existing health information systems is needed.

Background: Routine health data can guide health systems improvements, but poor quality of these data hinders use. To address concerns about data quality in Malawi, the Ministry of Health and National Statistical Office conducted a data quality assessment (DQA) in July 2016 to identify systems-level factors that could be improved. Methods: We used 2-stage stratified random sampling methods to select health centers and hospitals under Ministry of Health auspices, included those managed by faith-based entities, for this DQA. Dispensaries, village clinics, police and military facilities, tertiary-level hospitals, and private facilities were excluded. We reviewed client registers and monthly reports to verify availability, completeness, and accuracy of data in 4 service areas: antenatal care (ANC), family planning, HIV testing and counseling, and acute respiratory infection (ARI). We also conducted interviews with facility and district personnel to assess health management information system (HMIS) functioning and systems-level factors that may be associated with data quality. We compared systems and quality factors by facility characteristics using 2-sample t tests with Welch9s approximation, and calculated verification ratios comparing total entries in registers to totals from summarized reports. Results: We selected 16 hospitals (of 113 total in Malawi), 90 health centers (of 466), and 16 district health offices (of 28) in 16 of Malawi9s 28 districts. Nearly all registers were available and complete in health centers and district hospitals, but data quality varied across service areas; median verification ratios comparing register and report totals at health centers ranged from 0.78 (interquartile range [IQR]: 0.25, 1.07) for ARI and 0.99 (IQR: 0.82, 1.36) for family planning to 1.00 (IQR: 0.96, 1.00) for HIV testing and counseling and 1.00 (IQR: 0.80, 1.23) for ANC. More than half (60%) of facilities reported receiving a documented supervisory visit for HMIS in the prior 6 months. A recent supervision visit was associated with better availability of data ( P =.05), but regular district- or central-level supervision was not. Use of data by the facility to track performance toward targets was associated with both improved availability ( P =.04) and completeness of data ( P =.02). Half of facilities had a full-time statistical clerk, but their presence did not improve the availability or completeness of data ( P =.39 and P =.69, respectively). Conclusion: Findings indicate both strengths and weaknesses in Malawi9s HMIS performance, with key weaknesses including infrequent data quality checks and unreliable supervision. Efforts to strengthen HMIS in low- and middle-income countries should be informed by similar assessments.

Health facility data are a critical source of local and continuous health statistics. Countries have introduced web-based information systems that facilitate data management, analysis, use and visualisation of health facility data. Working with teams of Ministry of Health and country public health institutions analysts from 14 countries in Eastern and Southern Africa, we explored data quality using national-level and subnational-level (mostly district) data for the period 2013–2017. The focus was on endline analysis where reported health facility and other data are compiled, assessed and adjusted for data quality, primarily to inform planning and assessments of progress and performance. The analyses showed that although completeness of reporting was generally high, there were persistent data quality issues that were common across the 14 countries, especially at the subnational level. These included the presence of extreme outliers, lack of consistency of the reported data over time and between indicators (such as vaccination and antenatal care), and challenges related to projected target populations, which are used as denominators in the computation of coverage statistics. Continuous efforts to improve recording and reporting of events by health facilities, systematic examination and reporting of data quality issues, feedback and communication mechanisms between programme managers, care providers and data officers, and transparent corrections and adjustments will be critical to improve the quality of health statistics generated from health facility data.

Introduction: Most people in Africa die without appearing in official vital statistics records. To improve this situation, Malawi has introduced solar-powered electronic village registers (EVR), managed by village headmen, to record birth and death information for production of vital statistics. The EVR is deployed in 83 villages in Traditional Authority Mtema, Lilongwe, which is an area without electricity. In 17 villages, village headmen were also trained to use a simple verbal autopsy (VA) tool adapted from one developed by the World Health Organization (WHO). Study objectives were to (i) document numbers and causes of death occurring in 17 villages between April 2016 and September 2017, and (ii) assess percentage measures of agreement on causes of death as recorded by village headmen using a simple VA tool and by a team of health surveillance assistant (HSA)/medical doctor using the WHO VA tool.Methods: The study was in two-parts: (i) a cross-sectional study using secondary data from the EVR; (ii) primary data collection study comparing causes of death obtained by village headmen using a simple VA tool and by HSA/medical doctor using the WHO VA tool.Results: Over 18 months, 120 deaths were recorded by EVR in 14,264 residents - crude annual death rate 5.6/1,000 population. Median age at death was 43 years with 69 (58%) deaths being in males. Death occurred at home (75%) and at health facility (25%). Malaria, diarrhoeal disease, pulmonary tuberculosis, acute respiratory infection, and stroke accounted for 56% of deaths recorded by village headmen using the simple VA tool. Causes of death between village headmen and the HSA/medical doctor team were compared for 107 deaths. There was full agreement in causes of death in 33 (31%) deaths, mostly for malaria, severe anemia, intentional self-harm, cancer, and epilepsy. Unknown-sudden death and sepsis recorded by the HSA/medical doctor team were responsible for most disagreements.Conclusion: It is feasible for village headmen in rural Malawi to use an EVR and simple VA tool to document numbers and causes of deaths. More work is needed to improve accuracy of causes of death by village headmen.