Study design and data source<\/p>\n
Longitudinal study was conducted using secondary data drawn from two survey datasets: \u2018Premise Malaria COVID-19 Health Services Disruption Survey 2020\u2019 and a follow-up \u2018Premise Malaria COVID-19 Health Services Disruption Survey 2021\u2019. These surveys were designed to evaluate the change in levels of preventive measures for malaria and malaria service delivery throughout a specified period of the COVID-19 global pandemic. The surveys were designed by Institute for Health Metrics and Evaluation (IHME) in collaboration with Bill and Melinda Gates Foundation (BMGF), and implemented by Premise Data Corporation [19<\/a>].<\/p>\n The surveys were conducted in 20 African countries were where malaria is endemic: Benin; Burkina Faso, C\u00f4te d’Ivoire, Democratic Republic of the Congo, Ethiopia, Ghana, Kenya, Liberia, Mali, Mozambique, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Uganda, Zambia, and Zimbabwe. As the survey aimed to assess disruptions to malaria-related health services among the general population, no additional inclusion criteria applied beyond age (16\u00a0years and older) and country of residence in one of the 20 malaria-endemic African countries. Premise Data Corporation distributed the survey via its smartphone-based data collection platform. Premise maintains a global network of users with basic demographic data, including age range, gender, and country of residence. The survey was made available to all users within the Premise network, who were associated with any of the 20 target countries. Users were invited to participate through in-app notifications or prompts, and participation was voluntary. A smartphone-based Premise data collection platform was used for the data collection. Inclusion criteria were members of the general population in the 20 African countries who were aged 16 and above. Weights were not calculated for these surveys [19<\/a>].<\/p>\n The \u2018Premise Malaria COVID-19 Health Services Disruption Survey 2020\u2019 was collected throughout July 2020 which provides information about malaria healthcare utilization for two time periods: from December 2019 to February 2020 (before COVID-19 outbreak in the various countries) and from March 2020 to June 2020 (early onset of COVID-19 outbreak in the various countries). In the follow-up survey, \u2018Premise Malaria COVID-19 Health Services Disruption Survey 2021\u2019, data were collected throughout May\u2013June 2021 to give further insight into the changes in malaria healthcare utilization for February 2021\u2013May 2021 (1 year after onset of COVID-19 pandemic in the various countries) [19<\/a>].<\/p>\n Study variables and its definition<\/p>\n The outcome variable for this study was malaria healthcare service utilization, which included three specific measures: being tested for malaria, visiting a healthcare provider (HCP) for malaria symptoms, and receiving treatment for malaria symptoms. Survey questions related to malaria service utilization were organized into three distinct time periods: before COVID-19 (December 2019\u2013February 2020), during COVID-19 onset (March 2020\u2013June 2020), and 1 year after the pandemic onset (February 2021\u2013May 2021). Explanatory variables included in this study were age, gender, education status, employment status, wealth status, geographical location, and region. Age was divided into four categories: 16\u201325\u00a0years, 26\u201335, 36\u201345, and over 45\u00a0years. The gender variable was coded as male and female. Education status was categorized into primary and lower, secondary and college, and above college. Work status included employed, students, and the unemployed. Financial situation was a dichotomous variable of whether participants were able to afford basic requirements or not. Geographic location was based on type of residence and included urban, rural, and suburban areas. Regional classification was done by grouping the 20 malaria-endemic countries into four sub-Saharan African regions. East Africa included Ethiopia, Kenya, Rwanda, Somalia, Tanzania, and Uganda; West Africa included Benin, Burkina Faso, C\u00f4te d’Ivoire, Ghana, Liberia, Mali, Niger, Nigeria, Senegal, and Sierra Leone; Central Africa included the Democratic Republic of the Congo; and Southern Africa included Mozambique, Zambia, and Zimbabwe. These variables were selected based on their theoretical relevance and prior research linking sociodemographic and regional factors to healthcare access and malaria outcomes.<\/p>\n Statistical analysis and model building<\/p>\n The data analysis was conducted using STATA version 18. Descriptive statistics were used to summarize the baseline characteristics of the study population and to present the distribution of malaria healthcare utilization across different categories of explanatory variables. The 2021 Premise Malaria COVID-19 Health Services Disruption Survey did not include questions regarding the reasons for not visiting a health provider or not taking malaria medication in response to symptoms. As the primary objective of this study was to assess trends in malaria-related service utilization over time, the absence of this information in the 2021 survey does not affect the core analysis or findings presented in the main findings. To account for the complex nature of the data, associations between repeated measures of the outcome variables and various factors were evaluated for statistical significance by applying regression models to each outcome variable using a Generalized Estimating Equation (GEE) approach. The GEE model incorporated a logit link function with a binomial family and an exchangeable working correlation structure. This method combined repeated measures for each explanatory and outcome variable across three phases \u2018Pre-COVID-19 Phase,\u2019 \u2018Initial Phase of the COVID-19 Outbreak,\u2019 and \u20181 Year After the COVID-19 Outbreak\u2019 to estimate the population-averaged association between these time periods and the outcome variables. Since the outcome variable of this study is binary, a logistic regression model applied with the following structure:<\/p>\n $${\\text{Logit}}\\left( {{\\text{Yit}}} \\right)\\, = \\,{\\text{B}}_{0} \\, + \\,{\\text{B}}_{{1}} {\\text{X}}_{{{\\text{it}}}}$$<\/p>\n