Harnessing digital technology for COVID-19 response in Uganda: lessons and implications for future public health emergencies.

COVID-19 was one of the greatest disruptors of the 21st century, causing significant morbidity and mortality globally. Countries around the world adopted digital technologies and innovations to support the containment of the pandemic. This study explored the use of digital technology and barriers to its utilisation in responding to COVID-19 and sustaining essential health services in Uganda to inform response to future public health emergencies in low-resource settings. We reviewed published and grey literature on the use of digital technology in Uganda's response from March 2020 to April 2021 and conducted interviews with key informants. We thematically synthesised and summarised information on digital technology use as well as related challenges. During the COVID-19 response, digital technology was used in testing, contact tracing and surveillance, risk communication, supportive supervision and training, and maintenance of essential health services. The challenges with technology use were the disparate digital tools and health information systems leading to duplication of effort; limited access and coverage of digital tools, poor data quality; inaccessibility of data and an inability to support data manipulation, analysis and visualisation. Moreover, the inherent inadequate technology support systems such as poor internet and electricity infrastructure in some areas posed challenges of inequity. The harnessing of technology was key in supporting the COVID-19 response in Uganda. However, gaps existed in access, adoption, harmonisation, evaluation, sustainability and scale up of technology options. These issues should be addressed in preparedness efforts to foster technology adoption and application in public health emergencies with a focus on equity.

Regional differences in the utilisation of antenatal care and skilled birth attendant services during the COVID-19 pandemic in Nigeria: an interrupted time series analysis.

INTRODUCTION: The COVID-19 pandemic has had a substantial negative impact on the utilisation of essential health services (EHS) globally, especially in resource-limited settings such as Nigeria. High maternal deaths associated with low access to and utilisation of EHS such as antenatal care (ANC) and skilled birth attendants (SBAs) remain a concern during the COVID-19 era. The study assessed the COVID-19 pandemic effects on ANC and SBA utilisation across regions in Nigeria. METHODS: Monthly data on ANC and SBA between January 2017 and July 2021 were obtained from the Federal Ministry of Health database. An interrupted time-series analysis, implemented using the Prophet model, was conducted to compare the regional variation of outcomes during the COVID-19 pandemic. Average percentage changes (PC) between the observed and predicted outcomes including their 95% CI were reported. RESULTS: From March 2020 to July 2021, the number of ANC visits was significantly lower than expected by a 16%-43% change in five of the six regions in Nigeria. The highest significant reduction was in North-West (PC=-43.4; 95% CI: -52.6 to -34.1) and the least in South-West (PC=-15.5; 95% CI: -24.8 to -6.1), with no significant change in the South-East. The number of deliveries by SBA was significantly lower than expected by a 18%-43% change in all the regions (p<0.01). North-East (PC=-43.3; 95% CI: -51.7 to -34.9) and South-West (PC=-18.3; 95% CI: -25.2 to -11.5), respectively, had the highest and the least decline in SBA utilisation. Overall, ANC and SBA patterns of change were relatively similar across the north-south divide though the change effect was considerably pronounced in the north. CONCLUSION: There was a substantial reduction in ANC and SBA utilisation due to the COVID-19 pandemic in Nigeria, especially in the northern regions. Targeted and contextually relevant interventions should be implemented to alleviate the impact of emergency response on access to EHS and promote access to care during the pandemic.

Spread and seasonality of COVID-19 pandemic confirmed cases in sub-Saharan Africa: experience from Democratic Republic of Congo, Nigeria, Senegal, and Uganda.

BACKGROUND: The COVID-19 pandemic has impacted the world negatively with huge health and socioeconomic consequences. This study estimated the seasonality, trajectory, and projection of COVID-19 cases to understand the dynamics of the disease spread and inform response interventions. METHOD: Descriptive analysis of daily confirmed COVID-19 cases from January 2020 to 12th March 2022 was conducted in four purposefully selected sub-Saharan African countries (Nigeria, Democratic Republic of Congo (DRC), Senegal, and Uganda). We extrapolated the COVID-19 data from (2020 to 2022) to 2023 using a trigonometric time series model. A decomposition time series method was used to examine the seasonality in the data. RESULTS: Nigeria had the highest rate of spread (ß) of COVID-19 (ß = 381.2) while DRC had the least rate (ß = 119.4). DRC, Uganda, and Senegal had a similar pattern of COVID-19 spread from the onset through December 2020. The average doubling time in COVID-19 case count was highest in Uganda (148 days) and least in Nigeria (83 days). A seasonal variation was found in the COVID-19 data for all four countries but the timing of the cases showed some variations across countries. More cases are expected in the 1st (January-March) and 3rd (July-September) quarters of the year in Nigeria and Senegal, and in the 2nd (April-June) and 3rd (October-December) quarters in DRC and Uganda. CONCLUSION: Our findings show a seasonality that may warrant consideration for COVID-19 periodic interventions in the peak seasons in the preparedness and response strategies.

Uptake of COVID-19 vaccines and associated factors among adults in Uganda: a cross-sectional survey.

OBJECTIVE: COVID-19 pandemic remains one of the most significant public health challenges ever faced globally. Vaccines are key to ending the pandemic as well as minimise its consequences. This study determined the uptake of COVID-19 vaccines and associated factors among adults in Uganda. DESIGN, SETTING AND PARTICIPANTS: We conducted a cross-sectional mobile phone survey among adults in Uganda. MAIN OUTCOME VARIABLE: Participants reported their uptake of COVID-19 vaccines. RESULTS: Of the participants contacted, 94% (1173) completed the survey. Overall, 49.7% had received COVID-19 vaccines with 19.2% having obtained a full dose and 30.5% an incomplete dose. Among the unvaccinated, 91.0% indicated intention to vaccinate. Major reasons for vaccine uptake were protection of self from COVID-19 (86.8%) and a high perceived risk of getting the virus (19.6%). On the other hand, non-uptake was related to vaccine unavailability (42.4%), lack of time (24.1%) and perceived safety (12.5%) and effectiveness concerns (6.9%). The factors associated with receiving COVID-19 vaccines were older age (≥65 years) (Adjusted Prevalence Ratio (APR)=1.32 (95% CI: 1.08 to 1.61)), secondary (APR=1.36 (95% CI: 1.12 to 1.65)) or tertiary education (APR=1.62 (95% CI: 1.31 to 2.00)) and health workers as a source of information on COVID-19 (APR=1.26 (95% CI: 1.10 to 1.45)). Also, reporting a medium-income (APR=1.24 (95% CI: 1.02 to 1.52)) and residence in Northern (APR=1.55, 95% CI: 1.18 to 2.02) and Central regions (APR=1.48, 95% CI: 1.16 to 1.89) were associated with vaccine uptake. CONCLUSIONS: Uptake of COVID-19 vaccines was moderate in this sample and was associated with older age, secondary and tertiary education, medium-income, region of residence and health workers as a source of COVID-19 information. Efforts are needed to increase access to vaccines and should use health workers as champions to enhance uptake.

Quantifying the impact of COVID-19 on essential health services: a comparison of interrupted time series analysis using Prophet and Poisson regression models.

BACKGROUND: Coronavirus disease 2019 (COVID-19) altered healthcare utilization patterns. However, there is a dearth of literature comparing methods for quantifying the extent to which the pandemic disrupted healthcare service provision in sub-Saharan African countries. OBJECTIVE: To compare interrupted time series analysis using Prophet and Poisson regression models in evaluating the impact of COVID-19 on essential health services. METHODS: We used reported data from Uganda's Health Management Information System from February 2018 to December 2020. We compared Prophet and Poisson models in evaluating the impact of COVID-19 on new clinic visits, diabetes clinic visits, and in-hospital deliveries between March 2020 to December 2020 and across the Central, Eastern, Northern, and Western regions of Uganda. RESULTS: The models generated similar estimates of the impact of COVID-19 in 10 of the 12 outcome-region pairs evaluated. Both models estimated declines in new clinic visits in the Central, Northern, and Western regions, and an increase in the Eastern Region. Both models estimated declines in diabetes clinic visits in the Central and Western regions, with no significant changes in the Eastern and Northern regions. For in-hospital deliveries, the models estimated a decline in the Western Region, no changes in the Central Region, and had different estimates in the Eastern and Northern regions. CONCLUSIONS: The Prophet and Poisson models are useful in quantifying the impact of interruptions on essential health services during pandemics but may result in different measures of effect. Rigor and multimethod triangulation are necessary to study the true effect of pandemics on essential health services.

Deploying machine learning with messy, real world data in low- and middle-income countries: Developing a global health use case.

The rapid emergence of machine learning in the form of large-scale computational statistics and accumulation of data offers global health implementing partners an opportunity to adopt, adapt, and apply these techniques and technologies to low- and middle-income country (LMIC) contexts where we work. These benefits reside just out of the reach of many implementing partners because they lack the experience and specific skills to use them. Yet the growth of available analytical systems and exponential growth of data require the global digital health community to become conversant in this technology to continue to make contributions to help fulfill our missions. In this community case study, we describe the approach we took at IntraHealth International to inform the use case for machine learning in global health and development. We found that the data needed to take advantage of machine learning were plentiful and that an international, interdisciplinary team can be formed to collect, clean, and analyze the data at hand using cloud-based (e.g., Dropbox, Google Drive) and open source tools (e.g., R). We organized our work as a "sprint" lasting roughly 10 weeks in length so that we could rapidly prototype these approaches in order to achieve institutional buy in. Our initial sprint resulted in two requests in subsequent workplans for analytics using the data we compiled and directly impacted program implementation.

Feasibility of using a mobile App to monitor and report COVID-19 related symptoms and people's movements in Uganda.

BACKGROUND: Feasibility of mobile Apps to monitor diseases has not been well documented particularly in developing countries. We developed and studied the feasibility of using a mobile App to collect daily data on COVID-19 symptoms and people's movements. METHODS: We used an open source software "KoBo Toolbox" to develop the App and installed it on low cost smart mobile phones. We named this App "Wetaase" ("protect yourself"). The App was tested on 30 selected households from 3 densely populated areas of Kampala, Uganda, and followed them for 3 months. One trained member per household captured the data in the App for each enrolled member and uploaded it to a virtual server on a daily basis. The App is embedded with an algorithm that flags participants who report fever and any other COVID-19 related symptom. RESULTS: A total of 101 participants were enrolled; 61% female; median age 23 (interquartile range (IQR): 17-36) years. Usage of the App was 78% (95% confidence interval (CI): 77.0%-78.8%). It increased from 40% on day 1 to a peak of 81% on day 45 and then declined to 59% on day 90. Usage of the App did not significantly vary by site, sex or age. Only 57/6617 (0.86%) records included a report of at least one of the 17 listed COVID-19 related symptoms. The most reported symptom was flu/runny nose (21%) followed by sneezing (15%), with the rest ranging between 2% and 7%. Reports on movements away from home were 45% with 74% going to markets or shops. The participants liked the "Wetaase" App and recommended it for use as an alert system for COVID-19. CONCLUSION: Usage of the "Wetaase" App was high (78%) and it was similar across the three study sites, sex and age groups. Reporting of symptoms related to COVID-19 was low. Movements were mainly to markets and shops. Users reported that the App was easy to use and recommended its scale up. We recommend that this App be assessed at a large scale for feasibility, usability and acceptability as an additional tool for increasing alerts on COVID-19 in Uganda and similar settings.