18 research outputs found
Cost analysis of adding hypertension and diabetes management into routine HIV care in Mbarara and Ibanda districts, Uganda
No full textAbstract Background In 2016, Uganda introduced services for hypertension and diabetes in selected HIV clinics. We evaluated the costs associated with scaling up these services in HIV clinics in Mbarara and Ibanda districts, Uganda. Methods We estimated the annual costs of providing hypertension and diabetes services using an activity-based costing approach from the health system perspective in ten randomly selected HIV clinics in Mbarara and Ibanda districts. Cost inputs included 2023 data on costs of medications, health provider time, salaries, training costs, and monitoring costs. We determined the average annual cost and medication costs for hypertension and diabetes treatment per enrolled adult patient, stratified by type of health facility. Results The total annual cost of hypertension and diabetes management services in ten selected HIV clinics was estimated to be 8,386 − 186,973). The annual average clinic-level cost per enrolled patient was estimated at 7 − 31). Of the total annual cost, the cost of provider time for initial and follow-up visits represented the largest cost component in 5/10 clinics (mean: 37%, range [13–58%]). In 4/10 clinics, the major cost components were the costs of medication, diagnostic tests, and related supplies (mean: 37%, range [10–75%]). The average cost per enrolled adult patient was 21 in private not-for-profit facilities. The average medication cost per patient for hypertension was 7 − 97) annually; 50 at private not-for-profit facilities. For diabetes treatment, the average annual medication cost per patient was estimated at 6 − 35); 22 at private not-for-profit facilities. Conclusion Adding hypertension and diabetes management to routine HIV care might be feasible based on the estimated annual cost per patient. Hypertension and diabetes treatment was more costly in private not-for-profit facility-based clinics than at public facilities. This variation was primarily driven by higher medication procurement prices at private facilities, revealing a potential area for optimizing costs through improved procurement practices
Prevalence of and factors associated with anxiety, depression and post-traumatic stress disorder among Sudan ebolavirus disease survivors and family members, Uganda, January 2023: a cross-sectional study
No full textAbstract Background Communities affected by Ebola disease (EBOD) may face resulting increases in mental health disorders. We evaluated the prevalence of and factors associated with mental health disorders among persons affected by the 2022 Sudan virus disease (SVD) outbreak in Uganda. Methods We conducted a cross-sectional study among SVD survivors and family members of survivors and fatal cases from 15–31 January 2023. We included only laboratory-confirmed SVD survivors and family members who lived with or cared for confirmed SVD patients during their illness. The Hospital Anxiety and Depression Scale was used to evaluate anxiety and depression. The post-traumatic stress disorder (PTSD) checklist for the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition was used to evaluate PTSD. Modified Poisson regression was used to determine factors associated with each mental health disorder. Results We enrolled 54 survivors and 82 family members; median age was 30 years (range, 15–73) and 54% were female. The prevalence of anxiety (55%) and depression (50%) was higher than PTSD (17%). The prevalence of all mental health disorders was similar between survivors and family members. Household size was associated with both anxiety and PTSD. Number of SVD deaths in the household was associated with depression. Conclusion Approximately two-thirds of SVD survivors and family members of patients in the 2022 outbreak in Uganda had ≥ 1 mental health disorders shortly after the outbreak ended. Strengthening mental health services during and after Ebola virus outbreaks for survivors and family members of patients may enhance the quality of outbreak response
Socio-demographic characteristics of cases and controls in Nyakabande Transit Centre, Kisoro District, Uganda, June–July 2022.
No full textSocio-demographic characteristics of cases and controls in Nyakabande Transit Centre, Kisoro District, Uganda, June–July 2022.</p
Performance and impact of contact tracing in the Sudan virus outbreak in Uganda, September 2022-January 2023
No full textBackground: Contact tracing (CT) is critical for ebolavirus outbreak response. Ideally, all new cases after the index case should be previously-known contacts (PKC) before their onset, and spend minimal time ill in the community. We assessed the impact of CT during the 2022 Sudan Virus Disease (SVD) outbreak in Uganda. Methods: We collated anonymized data from the SVD case and contacts database to obtain and analyze data on CT performance indicators, comparing confirmed cases that were PKC and were not PKC (NPKC) before onset. We assessed the effect of being PKC on the number of people infected using Poisson regression. Results: There were 3844 contacts of 142 confirmed cases (mean: 22 contacts/case). Forty-seven (33%) confirmed cases were PKC. PKCs had fewer median days from onset to isolation (4 vs 6; P<0.007) and laboratory confirmation (4 vs 7; P<0.001) than NPKC. Being a PKC vs NPKC reduced risk of transmitting infection by 84% (IRR=0.16, 95% CI 0.08-0.32). Conclusion: Contact identification was sub-optimal during the outbreak. However, CT reduced the time SVD cases spent in the community before isolation and the number of persons infected in Uganda. Approaches to improve contact tracing, especially contact listing, may improve control in future outbreaks
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No full textDue to conflict in the Democratic Republic of Congo (DRC), approximately 34,000 persons arrived at Nyakabande Transit Centre (NTC) between March and June 2022. On June 12, 2022, Kisoro District reported >330 cases of COVID-19 among NTC residents. We investigated the outbreak to assess its magnitude, identify risk factors, and recommend control measures. We defined a confirmed case as a positive SARS-CoV-2 antigen test in an NTC resident during March 1–June 30, 2022. We generated a line list through medical record reviews and interviews with residents and health workers. We assessed the setting to understand possible infection mechanisms. In a case-control study, we compared exposures between cases (persons staying ≥5 days at NTC between June 26 and July 16, 2022, with a negative COVID-19 test at NTC entry and a positive test at exit) and unmatched controls (persons with a negative COVID-19 test at both entry and exit who stayed ≥5 days at NTC during the same period). We used multivariable logistic regression to identify factors associated with contracting COVID-19. Among 380 case-persons, 206 (54.2%) were male, with a mean age of 19.3 years (SD = 12.6); none died. The attack rate was higher among exiting persons (3.8%) than entering persons (0.6%) (p</div
Distribution of COVID-19 cases by symptom onset date among refugees in Nyakabande Transit Centre, Kisoro District, Uganda, June–July 2022.
No full textDistribution of COVID-19 cases by symptom onset date among refugees in Nyakabande Transit Centre, Kisoro District, Uganda, June–July 2022.</p
Stigma among ebola disease survivors in Mubende and Kassanda districts, Central Uganda, 2022
No full textUnderstanding the delay in identifying Sudan Virus Disease: gaps in integrated disease surveillance and response and community-based surveillance to detect viral hemorrhagic fever outbreaks in Uganda, September 2022
No full textAbstract Background Early detection of outbreaks requires robust surveillance and reporting at both community and health facility levels. Uganda implements Integrated Disease Surveillance and Response (IDSR) for priority diseases and uses the national District Health Information System (DHIS2) for reporting. However, investigations after the first case in the 2022 Uganda Sudan virus outbreak was confirmed on September 20, 2022 revealed many community deaths among persons with Ebola-like symptoms as far back as August. Most had sought care at private facilities. We explored possible gaps in surveillance that may have resulted in late detection of the Sudan virus disease (SVD) outbreak in Uganda. Methods Using a standardized tool, we evaluated core surveillance capacities at public and private health facilities at the hospital level and below in three sub-counties reporting the earliest SVD cases in the outbreak. Key informant interviews (KIIs) were conducted with 12 purposively-selected participants from the district local government. Focus group discussions (FGDs) were conducted with community members from six villages where early probable SVD cases were identified. KIIs and FGDs focused on experiences with SVD and Viral Hemorrhagic Fever (VHF) surveillance in the district. Thematic data analysis was used for qualitative data. Results Forty-six (85%) of 54 health facilities surveyed were privately-owned, among which 42 (91%) did not report to DHIS2 and 39 (85%) had no health worker trained on IDSR; both metrics were 100% in the eight public facilities. Weak community-based surveillance, poor private facility engagement, low suspicion index for VHF among health workers, inability of facilities to analyze and utilize surveillance data, lack of knowledge about to whom to report, funding constraints for surveillance activities, lack of IDSR training, and lack of all-cause mortality surveillance were identified as gaps potentially contributing to delayed outbreak detection. Conclusion Both systemic and knowledge-related gaps in IDSR surveillance in SVD-affected districts contributed to the delayed detection of the 2022 Uganda SVD outbreak. Targeted interventions to address these gaps in both public and private facilities across Uganda could help avert similar situations in the future
Increasing trends of antibiotic resistance in Uganda: analysis of the national antimicrobial resistance surveillance data, 2018–2021
No full textAbstract Background Continuous monitoring of antimicrobial resistance (AMR) in Uganda involves testing bacterial isolates from clinical samples at national and regional hospitals. Although the National Microbiology Reference Laboratory (NMRL) analyzes these isolates for official AMR surveillance data, there's limited integration into public health planning. To enhance the utilization of NMRL data to better inform drug selection and public health strategies in combating antibiotic resistance, we evaluated the trends and spatial distribution of AMR to common antibiotics used in Uganda. Methods We analyzed data from pathogenic bacterial isolates from blood, cerebrospinal, peritoneal, and pleural fluid from AMR surveillance data for 2018–2021. We calculated the proportions of isolates that were resistant to common antimicrobial classes. We used the chi-square test for trends to evaluate changes in AMR resistance over the study period. Results Out of 537 isolates with 15 pathogenic bacteria, 478 (89%) were from blood, 34 (6.3%) were from pleural fluid, 21 (4%) were from cerebrospinal fluid, and 4 (0.7%) were from peritoneal fluid. The most common pathogen was Staphylococcus aureus (20.1%), followed by Salmonella species (18.8%). The overall change in resistance over the four years was 63–84% for sulfonamides, fluoroquinolones macrolides (46–76%), phenicols (48–71%), penicillins (42–97%), β-lactamase inhibitors (20–92%), aminoglycosides (17–53%), cephalosporins (8.3–90%), carbapenems (5.3–26%), and glycopeptides (0–20%). There was a fluctuation in resistance of Staphylococcus aureus to methicillin (60%-45%) (using cefoxitin resistance as a surrogate for oxacillin resistance) Among gram-negative organisms, there were increases in resistance to tetracycline (29–78% p < 0.001), ciprofloxacin (17–43%, p = 0.004), ceftriaxone (8–72%, p = 0.003), imipenem (6–26%, p = 0.004), and meropenem (7–18%, p = 0.03). Conclusion The study highlights a concerning increase in antibiotic resistance rates over four years, with significant increase in resistance observed across different classes of antibiotics for both gram-positive and gram-negative organisms. This increased antibiotic resistance, particularly to commonly used antibiotics like ceftriaxone and ciprofloxacin, makes adhering to the WHO's Access, Watch, and Reserve (AWaRe) category even more critical. It also emphasizes how important it is to guard against the growing threat of antibiotic resistance by appropriately using medicines, especially those that are marked for "Watch" or "Reserve.
The Role of Community Beliefs and Practices on the Spread of Ebola in Uganda, September 2022
No full textAbstract Background On September 20, 2022, Uganda declared an Sudan Virus Disease (SVD) outbreak in Mubende District. Another eight districts were infected September–November 2022. We examined how Ugandan community beliefs and practices spread Sudan Ebola Virus (SUDV) in 2022. Methods A qualitative study was conducted in Mubende, Kassanda, and Kyegegwa districts in February 2023. Nine focus group discussions and six key informant interviews were held. We investigated whether community beliefs and practices contributed to spreading Sudan Ebola Virus (SUDV). Interviews were recorded, translated, transcribed, and thematically analyzed. Results The community deaths, later found to be due to Sudan Virus Disease(SVD), were often attributed to witchcraft or poisoning. Key informants reported that SVD patients often sought traditional healers or spiritual leaders before or after formal healthcare failed. They also found that traditional healers treated SVD patients without precautions. Religious leaders praying for SVD patients and their symptomatic contacts, SVD patients hiding in friends’ homes, and exhuming SVD patients from safe and dignified burials to allow traditional burials were other themes. Conclusion Diversity in community beliefs and culture likely contributed to spreading the 2022 Ugandan SVD outbreak. Public health systems, traditional healers, and religious leaders can help Uganda control ebolavirus outbreaks by identifying socially acceptable and scientifically supported infection control methods
