COVID-19 epidemiology and changes in health service utilization in Uganda’s refugee settlements during the first year of the pandemic

BMC Public Health, Volume 22, No. 1, Year 2022

Interpretation

Background: The COVID-19 pandemic has been characterized by multiple waves with varying rates of transmission affecting countries at different times and magnitudes. Forced displacement settings were considered particularly at risk due to pre-existing vulnerabilities. Yet, the effects of COVID-19 in refugee settings are not well understood. In this study, we report on the epidemiology of COVID-19 cases in Uganda’s refugee settlement regions of West Nile, Center and South, and evaluate how health service utilization changed during the first year of the pandemic. Methods: We calculate descriptive statistics, testing rates, and incidence rates of COVID-19 cases in UNHCR’s line list and adjusted odds ratios for selected outcomes. We evaluate the changes in health services using monthly routine data from UNHCR’s health information system (January 2017 to March 2021) and apply interrupted time series analysis with a generalized additive model and negative binomial distribution, accounting for long-term trends and seasonality, reporting results as incidence rate ratios. Findings: The first COVID-19 case was registered in Uganda on March 20, 2020, and among refugees two months later on May 22, 2020 in Adjumani settlement. Incidence rates were higher at national level for the general population compared to refugees by region and overall. Testing capacity in the settlements was lower compared to the national level. Characteristics of COVID-19 cases among refugees in Uganda seem to align with the global epidemiology of COVID-19. Only hospitalization rate was higher than globally reported. The indirect effects of COVID-19 on routine health services and outcomes appear quite consistent across regions. Maternal and child routine and preventative health services seem to have been less affected by COVID-19 than consultations for acute conditions. All regions reported a decrease in consultations for respiratory tract infections. Interpretation: COVID-19 transmission seemed lower in settlement regions than the national average, but so was testing capacity. Disruptions to health services were limited, and mainly affected consultations for acute conditions. This study, focusing on the first year of the pandemic, warrants follow-up research to investigate how susceptibility evolved over time, and how and whether health services could be maintained. The majority of the 1.4 million refugees in Uganda live in 12 main settlements across the country, where refugees live alongside local communities: about 60% of the refugees are in Northern Uganda or West Nile, 25% in southwestern/ southern Uganda, 13% in central Uganda, and 6% in Kampala (Fig S1 Supplementary material) [24]. The majority (60%) of the refugee population is under the age of 18 years, and only 3% are 60 years old or more. All settlements were included in the analysis, while refugees living in Kampala were excluded. The leading causes of illness and death among the refugees are malaria, respiratory and diarrheal diseases. Mortality rates have, however, remained low since 2014 [25]. The refugee health and nutrition response is guided by the Uganda National Integrated Health Response Plan for Refugees & Host Communities [25] and the United Nations High Commissioner for Refugees (UNHCR) Public Health Strategic Plan 2018–2022 [26]. Health services are provided by the national authorities with support of both humanitarian and development actors and are accessible by both nationals and refugees. Health service delivery in the settlements focuses on disease prevention and community initiatives; sexual, reproductive, maternal, neonatal child and adolescent health; and prevention, management and control of communicable and non-communicable diseases. Primary health care facilities (levels II to IV) are located in the settlements, with most of the population residing within a 5 km radius to ensure all communities are served. Outreach activities are conducted by health facility personnel to reach remote areas, and community health workers are in charge of referrals and surveillance activities. Communities in the settlements rely on regional and national level referral hospitals (usually outside of the settlements) for tertiary level procedures. Ambulance services are provided in all settlements to transport patients to and from referral health facilities. Table Table11 provides key information about the settlements. Refugee settlements in Uganda regrouped by region Sources of data: i) UNHCR population data a SSD: South Sudan; DRC: Democratic Republic of the Congo; BDI: Burundi; RWA: Rwanda; SOM: Somalia b Adjumani settlements encompasses 18 smaller settlements c Kampala is not a settlement; rather this represents the estimated number of refugees living in the Uganda capital Kampala. Kampala was not included in the study The study used two primary sets of data by UNHCR: i) the COVID-19 line list, and ii) routine health data from UNHCR’s health information system (HIS). Data were initially recorded at settlement level and subsequently aggregated by region (West Nile, South and Center). This regional approach was chosen for several reasons: i) it allowed for mobility between settlements in the pre-COVID-19 period to be taken into account. Under Uganda’s refugee policy [27], refugees can move freely, have access to land in the settlement where they are registered, and can access services in other settlements. Especially in West Nile region where numerous proximate settlements have been created to host refugees in the same area, mobility cannot be excluded nor can it be tracked. Furthermore, not all camps opened at the same time and new camps’ health facilities were not operational right away; consequently, refugees used services offered in the other settlements. Finally, it is not uncommon for refugees to be registered in one camp and reside in another settlement or use another settlements’ services. Consequently, analyzing health care utilization at the regional level better captures population dynamics and utilization within the region; ii) it allows for more stable pre-COVID-19 trends, and therefore, for a better counterfactual in the interrupted times series analysis; and iii) it aligns with UNHCR’s operations, which facilitates the use of the findings for the agency’s programmatic and operational purposes. A COVID-19 line list (i.e., a table that contains key information about each case in an outbreak) was established by UNHCR in each settlement and included laboratory-confirmed COVID-19 cases between March 1, 2020, and March 31, 2021. While the variables included in the line lists varied by settlement (Table S3), the line list included some combination of patient demographics, SARS-CoV-2 test data, presence of comorbidities, isolation and hospitalization, exposure risks due to occupation, disease outcome, and a number of contacts followed by case. Only one settlement collected information on the epidemiological link of the cases (i.e., the possible source of infection). The number of cases from the line list was used to calculate monthly and overall incidence rates for refugees in settlements. In addition to the line list, aggregated number of conducted tests were obtained to calculate the overall testing rate and percent positive by settlement for the entire period. The number of tests conducted per month by the settlement is unavailable for the first wave (November 2020 to January 2021). National level (Uganda) data on COVID-19 cases for the study period and population estimates were obtained from the Johns Hopkins COVID-19 Resource Center [28]; national level testing data were obtained from Our World in Data [29]. Aggregated number of tests and confirmed COVID-19 cases in the districts where settlements are located were obtained from the district health offices; however, monthly data were unavailable. UNHCR’s HIS in Uganda includes monthly data from health facilities by settlement, which we added up to generate a monthly value for each settlement region. For this study, we extracted the following variables: number of outpatient consultations; first antenatal care (ANC1) visits; deliveries attended by skilled health workers; contraceptive prevalence; Diphtheria, Pertussis, Tetanus (DPT1) vaccination coverage; coverage of full vaccination; respiratory tract infections (RTIs; disaggregated by type: upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI) and influenza-like illnesses (ILI)); consultations for diarrheal diseases; consultations for malaria; and mortality. Complete definitions of indicators are provided in the Supplementary material (Table S1). The study covers the period from January 1, 2017, to March 31, 2021. Descriptive statistics were calculated to describe COVID-19 case epidemiology. Comparisons of categorical variables were made with chi-square tests or Fisher’s exact tests; comparisons of continuous variables used t-tests to detect differences in means between two categories (sex), and analysis of variance (ANOVA) tests to detect differences in means between multiple categories (age groups). Odds ratios for selected outcomes were calculated using generalized linear models (with the binomial family link) and controlling for covariates: sex, age, and displacement status. P-values less than 0.05 were considered statistically significant. Comparisons between regions and the host countries were explored. Analysis was conducted in R (Version 4.1.0) using RStudio v1.4.1106 [30]. We used Interrupted Time Series to evaluate changes in rates of consultations and other outcomes during the COVID-19 period. The generalized additive model with first-order autoregression was fit for each region as follows: where y is the outcome of interest, assumed to come from a negative binomial (NB) distribution with parameters μi and θ; Populationi is the number of people at risk or eligible to access relevant services at the time i; COVIDi is a binary variable (0 if month i is in the pre-COVID-19 period, and 1 if month i is in COVID-19 period); Month since COVIDi is time in months since the beginning of COVID-19 period (April 2020); s(Centered Monthi) is a smooth term, where Centered Monthi is the month number, centered at beginning of the COVID-19 period, which accounts for longer-term trend; and s(Month,bs=cc,k=12) is a smooth term with cubic splines to capture 12-month seasonality cycle, where Month is a calendar month (from 1 to 12). For services where seasonality was unlikely to be a factor, we used a model without seasonal dummy terms. For each indicator, we assessed possible lag using the autocorrelation function for up to 6 months; where a non-zero lag was observed, we ran the lagged model and presented those results in the main analysis; results comparing the model with and without lags are presented in Supplementary material for each indicator. The model was fit using mgcv function in R [31]. We report parameter estimates using incidence rate ratios (IRR) and related 95% CI. β1 estimates an immediate change in outcome at the beginning of the COVID-19 period (i.e., a change in level, or a step); β2 estimates a change in slope in the evolution of outcome over time. Counterfactual values are predicted by setting values of COVIDi and MonthsinceCOVIDi to 0, and forecasting the model for 12 months of the COVID-19 period. Model diagnostics for each indicator are presented in Supplementary material.

AI Digest

Study Justification:

– The COVID-19 pandemic has had varying impacts on different populations and settings, including refugee settlements.
– Understanding the epidemiology of COVID-19 in refugee settlements is crucial for effective response and resource allocation.
– This study aims to provide insights into the transmission of COVID-19 and changes in health service utilization in Uganda’s refugee settlements during the first year of the pandemic.

Study Highlights:

– The first COVID-19 case in Uganda’s refugee settlements was registered on May 22, 2020, two months after the national case.
– Incidence rates of COVID-19 were higher at the national level compared to refugee settlements.
– Testing capacity in the settlements was lower than the national level.
– COVID-19 cases among refugees in Uganda aligned with the global epidemiology, with higher hospitalization rates.
– Health services for acute conditions were more affected by COVID-19 compared to maternal and child routine and preventative health services.
– Disruptions to health services were consistent across regions.

Study Recommendations:

– Conduct follow-up research to investigate the evolution of susceptibility to COVID-19 over time in refugee settlements.
– Assess the maintenance of health services in refugee settlements during the pandemic.

Key Role Players:

– National authorities responsible for health service delivery in refugee settlements.
– Humanitarian and development actors providing support for health services.
– Health facility personnel conducting outreach activities and referrals.
– Community health workers involved in surveillance activities.
– Regional and national level referral hospitals providing tertiary level procedures.
– Ambulance services for transportation to and from referral health facilities.

Cost Items for Planning Recommendations:

– Funding for research activities, including data collection, analysis, and reporting.
– Resources for monitoring and evaluation of health service delivery in refugee settlements.
– Support for health facility personnel, community health workers, and referral hospitals.
– Budget for ambulance services and transportation of patients.
– Allocation of resources for maintaining and strengthening health services in refugee settlements during the pandemic.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are areas for improvement. The study provides descriptive statistics, testing rates, and incidence rates of COVID-19 cases in Uganda’s refugee settlement regions. It also applies interrupted time series analysis to evaluate changes in health services. However, the abstract does not provide details on the sample size, representativeness of the data, or potential limitations of the study. To improve the evidence, the authors could include information on the sample size and how it was determined, discuss the representativeness of the data, and acknowledge any limitations or potential biases in the study design. Additionally, providing more context on the generalizability of the findings and suggestions for future research would enhance the strength of the evidence.

Abstract

The majority of the 1.4 million refugees in Uganda live in 12 main settlements across the country, where refugees live alongside local communities: about 60% of the refugees are in Northern Uganda or West Nile, 25% in southwestern/ southern Uganda, 13% in central Uganda, and 6% in Kampala (Fig S1 Supplementary material) [24]. The majority (60%) of the refugee population is under the age of 18 years, and only 3% are 60 years old or more. All settlements were included in the analysis, while refugees living in Kampala were excluded. The leading causes of illness and death among the refugees are malaria, respiratory and diarrheal diseases. Mortality rates have, however, remained low since 2014 [25]. The refugee health and nutrition response is guided by the Uganda National Integrated Health Response Plan for Refugees & Host Communities [25] and the United Nations High Commissioner for Refugees (UNHCR) Public Health Strategic Plan 2018–2022 [26]. Health services are provided by the national authorities with support of both humanitarian and development actors and are accessible by both nationals and refugees. Health service delivery in the settlements focuses on disease prevention and community initiatives; sexual, reproductive, maternal, neonatal child and adolescent health; and prevention, management and control of communicable and non-communicable diseases. Primary health care facilities (levels II to IV) are located in the settlements, with most of the population residing within a 5 km radius to ensure all communities are served. Outreach activities are conducted by health facility personnel to reach remote areas, and community health workers are in charge of referrals and surveillance activities. Communities in the settlements rely on regional and national level referral hospitals (usually outside of the settlements) for tertiary level procedures. Ambulance services are provided in all settlements to transport patients to and from referral health facilities. Table Table11 provides key information about the settlements. Refugee settlements in Uganda regrouped by region Sources of data: i) UNHCR population data a SSD: South Sudan; DRC: Democratic Republic of the Congo; BDI: Burundi; RWA: Rwanda; SOM: Somalia b Adjumani settlements encompasses 18 smaller settlements c Kampala is not a settlement; rather this represents the estimated number of refugees living in the Uganda capital Kampala. Kampala was not included in the study The study used two primary sets of data by UNHCR: i) the COVID-19 line list, and ii) routine health data from UNHCR’s health information system (HIS). Data were initially recorded at settlement level and subsequently aggregated by region (West Nile, South and Center). This regional approach was chosen for several reasons: i) it allowed for mobility between settlements in the pre-COVID-19 period to be taken into account. Under Uganda’s refugee policy [27], refugees can move freely, have access to land in the settlement where they are registered, and can access services in other settlements. Especially in West Nile region where numerous proximate settlements have been created to host refugees in the same area, mobility cannot be excluded nor can it be tracked. Furthermore, not all camps opened at the same time and new camps’ health facilities were not operational right away; consequently, refugees used services offered in the other settlements. Finally, it is not uncommon for refugees to be registered in one camp and reside in another settlement or use another settlements’ services. Consequently, analyzing health care utilization at the regional level better captures population dynamics and utilization within the region; ii) it allows for more stable pre-COVID-19 trends, and therefore, for a better counterfactual in the interrupted times series analysis; and iii) it aligns with UNHCR’s operations, which facilitates the use of the findings for the agency’s programmatic and operational purposes. A COVID-19 line list (i.e., a table that contains key information about each case in an outbreak) was established by UNHCR in each settlement and included laboratory-confirmed COVID-19 cases between March 1, 2020, and March 31, 2021. While the variables included in the line lists varied by settlement (Table S3), the line list included some combination of patient demographics, SARS-CoV-2 test data, presence of comorbidities, isolation and hospitalization, exposure risks due to occupation, disease outcome, and a number of contacts followed by case. Only one settlement collected information on the epidemiological link of the cases (i.e., the possible source of infection). The number of cases from the line list was used to calculate monthly and overall incidence rates for refugees in settlements. In addition to the line list, aggregated number of conducted tests were obtained to calculate the overall testing rate and percent positive by settlement for the entire period. The number of tests conducted per month by the settlement is unavailable for the first wave (November 2020 to January 2021). National level (Uganda) data on COVID-19 cases for the study period and population estimates were obtained from the Johns Hopkins COVID-19 Resource Center [28]; national level testing data were obtained from Our World in Data [29]. Aggregated number of tests and confirmed COVID-19 cases in the districts where settlements are located were obtained from the district health offices; however, monthly data were unavailable. UNHCR’s HIS in Uganda includes monthly data from health facilities by settlement, which we added up to generate a monthly value for each settlement region. For this study, we extracted the following variables: number of outpatient consultations; first antenatal care (ANC1) visits; deliveries attended by skilled health workers; contraceptive prevalence; Diphtheria, Pertussis, Tetanus (DPT1) vaccination coverage; coverage of full vaccination; respiratory tract infections (RTIs; disaggregated by type: upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI) and influenza-like illnesses (ILI)); consultations for diarrheal diseases; consultations for malaria; and mortality. Complete definitions of indicators are provided in the Supplementary material (Table S1). The study covers the period from January 1, 2017, to March 31, 2021. Descriptive statistics were calculated to describe COVID-19 case epidemiology. Comparisons of categorical variables were made with chi-square tests or Fisher’s exact tests; comparisons of continuous variables used t-tests to detect differences in means between two categories (sex), and analysis of variance (ANOVA) tests to detect differences in means between multiple categories (age groups). Odds ratios for selected outcomes were calculated using generalized linear models (with the binomial family link) and controlling for covariates: sex, age, and displacement status. P-values less than 0.05 were considered statistically significant. Comparisons between regions and the host countries were explored. Analysis was conducted in R (Version 4.1.0) using RStudio v1.4.1106 [30]. We used Interrupted Time Series to evaluate changes in rates of consultations and other outcomes during the COVID-19 period. The generalized additive model with first-order autoregression was fit for each region as follows: where y is the outcome of interest, assumed to come from a negative binomial (NB) distribution with parameters μi and θ; Populationi is the number of people at risk or eligible to access relevant services at the time i; COVIDi is a binary variable (0 if month i is in the pre-COVID-19 period, and 1 if month i is in COVID-19 period); Month since COVIDi is time in months since the beginning of COVID-19 period (April 2020); s(Centered Monthi) is a smooth term, where Centered Monthi is the month number, centered at beginning of the COVID-19 period, which accounts for longer-term trend; and s(Month,bs=cc,k=12) is a smooth term with cubic splines to capture 12-month seasonality cycle, where Month is a calendar month (from 1 to 12). For services where seasonality was unlikely to be a factor, we used a model without seasonal dummy terms. For each indicator, we assessed possible lag using the autocorrelation function for up to 6 months; where a non-zero lag was observed, we ran the lagged model and presented those results in the main analysis; results comparing the model with and without lags are presented in Supplementary material for each indicator. The model was fit using mgcv function in R [31]. We report parameter estimates using incidence rate ratios (IRR) and related 95% CI. β1 estimates an immediate change in outcome at the beginning of the COVID-19 period (i.e., a change in level, or a step); β2 estimates a change in slope in the evolution of outcome over time. Counterfactual values are predicted by setting values of COVIDi and MonthsinceCOVIDi to 0, and forecasting the model for 12 months of the COVID-19 period. Model diagnostics for each indicator are presented in Supplementary material.

Materials

https://efashare.b-cdn.net/materials/b8f3e2701477c05c5e856b09ee5c54e0c88be884cf391d0d32e2b9eb9dc447d7/12889_2022_14305_MOESM1_ESM.docx

Innovations

Based on the provided information, it is difficult to identify specific innovations for improving access to maternal health. The study focuses on the epidemiology of COVID-19 cases in Uganda’s refugee settlement regions and the changes in health service utilization during the first year of the pandemic. It does not directly address innovations for maternal health. To identify innovations for improving access to maternal health, it would be necessary to review studies, reports, or initiatives specifically focused on maternal health in refugee settings or Uganda.

AI Innovations Description

Based on the provided description, the following recommendation can be developed into an innovation to improve access to maternal health in refugee settlements:

1. Strengthen Testing Capacity: Increase testing capacity for COVID-19 in refugee settlements to ensure early detection and containment of the virus. This can be done by providing necessary resources and equipment to health facilities in the settlements, training healthcare workers on testing procedures, and establishing testing centers within the settlements.

2. Improve Health Service Utilization: Enhance access to maternal health services by implementing strategies to increase utilization. This can include conducting awareness campaigns to educate refugees about the importance of maternal health, addressing cultural barriers that may prevent women from seeking care, and providing incentives for pregnant women to attend antenatal care visits.

3. Enhance Telemedicine Services: Implement telemedicine services to provide remote access to maternal health consultations and follow-up care. This can involve setting up telemedicine centers within the settlements, training healthcare providers on telemedicine practices, and ensuring access to reliable internet connectivity.

4. Strengthen Referral Systems: Improve the referral system between primary healthcare facilities in the settlements and regional/national hospitals for specialized maternal care. This can be achieved by establishing clear protocols and communication channels for referrals, providing transportation services for pregnant women in need of higher-level care, and ensuring coordination between healthcare providers at different levels.

5. Increase Availability of Skilled Health Workers: Address the shortage of skilled health workers in refugee settlements by recruiting and training more healthcare professionals, particularly midwives and obstetricians. This can be done through partnerships with local and international organizations, offering incentives for healthcare professionals to work in the settlements, and providing ongoing training and support.

6. Strengthen Community Health Programs: Enhance community-based maternal health programs by engaging and empowering community health workers. This can involve training community health workers on maternal health topics, equipping them with necessary resources and tools, and involving them in the planning and implementation of maternal health initiatives.

7. Improve Data Collection and Monitoring: Establish a robust data collection and monitoring system to track maternal health indicators and identify areas for improvement. This can include implementing electronic health records in the settlements, conducting regular data analysis and reporting, and using the data to inform decision-making and resource allocation.

By implementing these recommendations, access to maternal health services in refugee settlements can be improved, leading to better health outcomes for pregnant women and their babies.

AI Innovations Methodology

To improve access to maternal health in refugee settlements during the COVID-19 pandemic, here are some potential recommendations:

1. Mobile Clinics: Implement mobile clinics that can travel to different settlements to provide maternal health services. This can help overcome transportation barriers and reach women who may have difficulty accessing healthcare facilities.

2. Telemedicine: Establish telemedicine services to provide remote consultations and support for pregnant women. This can help reduce the need for in-person visits and ensure that women receive timely and appropriate care.

3. Community Health Workers: Train and deploy community health workers within the settlements to provide education, support, and basic maternal health services. These workers can act as a bridge between the healthcare system and the refugee community, improving access and awareness of available services.

4. Strengthening Referral Systems: Enhance the referral systems between primary healthcare facilities in the settlements and regional/national hospitals. This can ensure that pregnant women with complications receive timely and appropriate care at higher-level facilities.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Baseline Data Collection: Gather data on the current state of maternal health access in the refugee settlements, including the number of pregnant women, utilization of maternal health services, and any existing barriers.

2. Define Key Indicators: Identify key indicators that reflect access to maternal health, such as the number of antenatal care visits, deliveries attended by skilled health workers, and maternal mortality rates.

3. Simulation Model Development: Develop a simulation model that incorporates the baseline data and the potential impact of the recommendations. This model should consider factors such as population size, service utilization rates, and the effectiveness of the proposed interventions.

4. Data Analysis: Run the simulation model using different scenarios, varying the implementation and effectiveness of the recommendations. Analyze the outputs to assess the potential impact on the key indicators of maternal health access.

5. Sensitivity Analysis: Conduct sensitivity analysis to test the robustness of the simulation model and assess the impact of uncertainties or variations in the input parameters.

6. Interpretation and Recommendations: Interpret the results of the simulation analysis and provide recommendations based on the findings. This can help inform decision-making and prioritize interventions that are likely to have the greatest impact on improving access to maternal health in the refugee settlements.

It is important to note that the methodology described above is a general framework, and the specific details and data requirements may vary depending on the context and available resources.

Author

Dima Health

Identifiers:

DOI: 10.1186/s12889-022-14305-3

Research Areas:

Community Interventions, COVID, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Burundi, Congo, Multi-Countries, Rwanda, Somalia, South Sudan, Sudan, Uganda

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative