We conducted a cross-sectional survey using the US Centers for Disease Control and Prevention (CDC) Updated Guidelines for Evaluating Public Health Surveillance Systems (9) in four refugee settlements in Uganda i.e., Bidibidi, Adjumani, Kiryandongo and Rhino Camp (Fig. 1). Bidibidi Refugee Settlement which covers an area of 250 sq. km is located in Yumbe District, Northern Uganda which borders South Sudan to the north and Moyo District along the western bank of River Kochi. The settlement has a capacity of 180,000, but by March 2020 there were over 270,000 refugees mainly from South Sudan.
Adjumani Refugee Settlement which comprises 17 camps, is located in Adjumani District which is bordered by Moyo District to the north, South Sudan to the northeast, Amuru District to the east and south, Arua District to the southwest and Yumbe District to the northwest. The total refugee population for Adjumani District stood at 209,048 by March 2020. The resettlement areas are organized in clusters, blocks and zones. Adjumani district is prone to refugee influxes due to its location at the border with South Sudan; as such the district has frequently registered several disease outbreaks, the most recent being measles and cholera outbreaks in Pagirinya Refugee Camp.
Kiryandongo Refugee Settlement which has three major divisions called Ranches, is located in Kiryandongo District which is bordered by Nwoya District to the north, Oyam District to the northeast, Apac District to the east, and Masindi District to the south and west. In 2020, the population of Kiryandongo District was estimated at about 317,500.
Rhino Camp Refugee Settlement is located in Arua District and is spread over 3 sub-counties (Rigbo, Odupi and Uriama). Arua District is located in North western Uganda and is bordered by Yumbe District to the north, Adjumani District to the northeast, Amuru District to the east, Nebbi District to the southeast, Zombo District to the southwest, the Democratic Republic of the Congo (DRC) to the west, and Maracha District to the northwest. In 2020, the population of Arua District was estimated at about 862,700.
The study units were the stakeholders utilizing the public health surveillance system in the refugee settlements. These include: health facility in-charges and surveillance focal persons, District Health Teams (DHTs), District Rapid Response Teams (DRRTs), District Epidemic Preparedness and Response Committees (DEPRCs) and Village Health Teams (VHTs). Six health facilities were picked using simple random sampling in Adjumani, 16 in Bidibidi, 24 in Kiryandongo and 7 in Rhino Camp.
We used a single stage cluster sampling method to select villages. The refugee settlements are zoned and each zone is divided into villages. Since the number of villages in each zone vary from one zone to another, sampling proportionate to size was used to determine the number of clusters in each zone to be included in the study. The primary sampling units (clusters) were selected using systematic random sampling. Sample size was calculated using sample size calculator software (vSphere) (10), inter-cluster correlation was estimated to be 0.3, and a baseline compliance rate assumed to be 50%. Accordingly, 24 villages were each selected from Bidibidi and Kiryandongo, and 32 villages were each selected from Adjumani and Rhino Camp Refugee Settlements. From each village, 2 VHT members were selected for interviews on a first found first picked basis. Interviews with VHTs was used to generate data on community surveillance.
Face to face interviews were conducted to collect information regarding the surveillance attributes using a semi structured questionnaire. Simplicity, acceptability and flexibility were assessed using questions concerning compliance, ease of use, and number of steps in the system alongside users’ opinions on the appropriateness of IDSR in detecting, recording and reporting of priority diseases. Completeness was assessed by looking at the filling of the registers and the reporting forms which translates into quality. Other relevant information was collected by document reviews and key informant interviews. Key informant interviews were conducted with key District Health Team members including the District Health Officer, District Surveillance Focal Person, and some members of the DRRT and DEPRC. Important to note is that the United Nations High Commissioner for Refugees (UNHCR) and Implementing Partners (IPs) staff are part and parcel of the DEPRC. Data was collected electronically using an Open Data Kit Software (Kobo Collect for Humanitarian Emergencies) using Tablet PCs.
Observation method was used to assess health facility registers and reporting tools based on the following attributes: Simplicity, Flexibility, Acceptability, Sensitivity, Representativeness, and Timeliness.
The capacity of the refugee settlements in performing surveillance functions (Table 1) and all the attributes (Table 2) of a public health surveillance system were assessed in the four refugee settlements of Adjumani, Bidibidi, Kiryandongo and Rhino Camp (Fig. 1).
Table 1
Capacity of refugee settlements in performing surveillance functions
| Average % score by Refugee Settlement |
Surveillance function | Bidibidi | Rhino Camp | Adjumani | Kiryandongo |
Detection | 55 | 56 | 53 | 57 |
Recording | 77 | 90 | 83 | 67 |
Reporting | 75 | 95 | 85 | 67 |
Data analysis and interpretation | 19 | 95 | 50 | 33 |
Confirmation of outbreaks and events | 50 | 85 | 86 | 83 |
Preparedness | 72 | 68 | 65 | 83 |
Response | 11 | 25 | 50 | 50 |
Feedback | 75 | 75 | 79 | 100 |
Evaluate and improve system | 55 | 70 | 60 | 83 |
Table 2
Surveillance attributes of refugee settlements as per evaluation assessment
| Description of a surveillance attribute by Refugee Settlement |
Attribute | Bidibidi | Rhino Camp | Adjumani | Kiryandongo |
Simplicity | Moderate | Moderate | Moderate | Moderate |
Flexibility | Low | Low | Low | Low |
Data quality | Moderate | Moderate | Moderate | Moderate |
Acceptability | Moderate | Moderate | Moderate | Moderate |
Sensitivity | 76% | 77% | 78% | 83% |
Predictive value positive | 50% | 70% | 66% | 70% |
Representativeness | Low | Low | Low | Low |
Timeliness | 52% | 78% | 76% | 79% |
Stability | Low | Low | Low | Low |
NB: Adapted From Unhcr Archives
We analyzed qualitative data using the content analysis model. We sorted and coded the data into categories, in order to bring together related terms. We edited and summarized useful information and developed conclusions without changing the meaning of what the respondents said. We used Epi-data to capture quantitative data and exported to Stata version 16 for analysis. We used descriptive statistics to summarize variables in form of rates and proportions. We calculated the positive predictive value of the system using cholera data by comparing the proportion of persons identified as having cholera to those who actually had the condition under surveillance.