A cholera outbreak caused by drinking contaminated river water, Bulambuli District, Eastern Uganda, March 2016.

BACKGROUND: A cholera outbreak started on 29 February in Bwikhonge Sub-county, Bulambuli District in Eastern Uganda. Local public health authorities implemented initial control measures. However, in late March, cases sharply increased in Bwikhonge Sub-county. We investigated the outbreak to determine its scope and mode of transmission, and to inform control measures. METHODS: We defined a suspected case as sudden onset of watery diarrhea from 1 March 2016 onwards in a resident of Bulambuli District. A confirmed case was a suspected case with positive stool culture for V. cholerae. We conducted descriptive epidemiologic analysis of the cases to inform the hypothesis on mode of transmission. To test the hypothesis, we conducted a case-control study involving 100 suspected case-patients and 100 asymptomatic controls, individually-matched by residence village and age. We collected seven water samples for laboratory testing. RESULTS: We identified 108 suspected cases (attack rate: 1.3%, 108/8404), including 7 confirmed cases. The case-control study revealed that 78% (78/100) of case-patients compared with 51% (51/100) of control-persons usually collected drinking water from the nearby Cheptui River (ORMH = 7.8, 95% CI = 2.7-22); conversely, 35% (35/100) of case-patients compared with 54% (54/100) of control-persons usually collected drinking water from borehole pumps (ORMH = 0.31, 95% CI = 0.13-0.65). The index case in Bwikhonge Sub-county had onset on 29 February but the outbreak had been on-going in the neighbouring sub-counties in the previous 3 months. V. cholera was isolated in 2 of the 7 river water samples collected from different locations. CONCLUSIONS: We concluded that this cholera outbreak was caused by drinking contaminated water from Cheptui River. We recommended boiling and/or treating drinking water, improved sanitation, distribution of chlorine tablets to the affected villages, and as a long-term solution, construction of more borehole pumps. After implementing preventive measures, the number of cases declined and completely stopped after 6th April.

Factors contributing to measles transmission during an outbreak in Kamwenge District, Western Uganda, April to August 2015.

BACKGROUND: In April 2015, Kamwenge District, western Uganda reported a measles outbreak. We investigated the outbreak to identify potential exposures that facilitated measles transmission, assess vaccine effectiveness (VE) and vaccination coverage (VC), and recommend prevention and control measures. METHODS: For this investigation, a probable case was defined as onset of fever and generalized maculopapular rash, plus ≥1 of the following symptoms: Coryza, conjunctivitis, or cough. A confirmed case was defined as a probable case plus identification of measles-specific IgM in serum. For case-finding, we reviewed patients' medical records and conducted in-home patient examination. In a case-control study, we compared exposures of case-patients and controls matched by age and village of residence. For children aged 9 m-5y, we estimated VC using the percent of children among the controls who had been vaccinated against measles, and calculated VE using the formula, VE = 1 - ORM-H, where ORM-H was the Mantel-Haenszel odds ratio associated with having a measles vaccination history. RESULTS: We identified 213 probable cases with onset between April and August, 2015. Of 23 blood specimens collected, 78% were positive for measles-specific IgM. Measles attack rate was highest in the youngest age-group, 0-5y (13/10,000), and decreased as age increased. The epidemic curve indicated sustained propagation in the community. Of the 50 case-patients and 200 controls, 42% of case-patients and 12% of controls visited health centers during their likely exposure period (ORM-H = 6.1; 95% CI = 2.7-14). Among children aged 9 m-5y, VE was estimated at 70% (95% CI: 24-88%), and VC at 75% (95% CI: 67-83%). Excessive crowding was observed at all health centers; no patient triage-system existed. CONCLUSIONS: The spread of measles during this outbreak was facilitated by patient mixing at crowded health centers, suboptimal VE and inadequate VC. We recommended emergency immunization campaign targeting children <5y in the affected sub-counties, as well as triaging and isolation of febrile or rash patients visiting health centers.

Comparative epidemiologic analysis of COVID-19 patients during the first and second waves of COVID-19 in Uganda.

Introduction: Uganda was affected by two major waves of coronavirus disease 2019 (COVID-19). The first wave during late 2020 and the second wave in late April 2021. This study compared epidemiologic characteristics of hospitalized (HP) and non-hospitalized patients (NHP) with COVID-19 during the two waves of COVID-19 in Uganda. Methods: Wave 1 was defined as November-December 2020, and Wave 2 was defined as April-June 2021. In total, 800 patients were included in this study. Medical record data were collected for HP (200 for each wave). Contact information was retrieved for NHP who had polymerase-chain-reaction-confirmed COVID-19 (200 for each wave) from laboratory records; these patients were interviewed by telephone. Findings: A higher proportion of HP were male in Wave 1 compared with Wave 2 (73% vs 54%; P=0.0001). More HP had severe disease or died in Wave 2 compared with Wave 1 (65% vs 31%; P<0.0001). NHP in Wave 2 were younger than those in Wave 1, but this difference was not significant (mean age 29 vs 36 years; P=0.13). HP were significantly older than NHP in Wave 2 (mean age 48 vs 29 years; P<0.0001), but not Wave 1 (mean age 48 vs 43 years; P=0.31). Interpretation: Demographic and epidemiologic characteristics of HP and NHP differed between and within Waves 1 and 2 of COVID-19 in Uganda.

Factors that affect immunization data quality in Kabarole District, Uganda.

INTRODUCTION: Reliable and timely immunization data is vital at all levels of health care to inform decisions and improve program performance. Inadequate data quality may impair our understanding of the true vaccination coverage and also hinder our capability to meet the program objectives. It's therefore important to regularly assess immunization data quality to ensure good performance, sound decision making and efficient use of resources. METHODS: We conducted an immunization data quality audit between July and August 2016. The verification factor was estimated by dividing the recounted diphtheria, pertussis and tetanus third dose vaccination for children under 1 year (DPT3<1 year) by reported DPT3<1 year. The quality of data collection processes was measured using quality indices for the 3 different components: recording practices, storage/reporting, monitoring and evaluation. These indices were applied to the different levels of the health care service delivery system. Quality index score was estimated by dividing the total question or observation correctly answered by the total number of answers/ observations for a particular component. RESULTS: The mean health center verification factor was 87%. Sixty five percent (32/49) of the health centers had consistent data, 27% (13/49) over reported and 4% (2/49) under-reported. Health center 11s and 111s contributed to over-reporting and under-reporting. All the health centers' reports were complete and timely between January and June and from November to December. The mean quality indices for the 3 different componets assessed were; recording practices 66%, storing/reporting 75%, monitoring and evaluation 43%. There was a weak positive correlation between the health center verifaction factor and quality index though this was not statistically significant (r = 0.014; p = 0.92). CONCLUSION: Lower level health centers contributed significantly to the inconsistencies in immunization data; there were wide variation between the quality indices of recording practices, storage/reporting, monitoring and evaluation. We recommended that District Local Governments and Ministry of Health focus on improving data quality at lower levels of health service delivery.

Risk factors for hematemesis in Hoima and Buliisa Districts, Western Uganda, September-October 2015.

INTRODUCTION: On 17 September 2015, Buliisa District Health Office reported multiple deaths due to haemorrhage to the Uganda Ministry of Health. We conducted an investigation to verify the existence of an outbreak and to identify the disease nature, mode of transmission and risk factors. METHODS: We defined a suspected case as onset of hematemesis between 1 June 2015 and 15 October 2015 in a resident of Hoima, Buliisa or neighbouring districts. We identified cases by reviewing medical records and actively searching in the community. We interviewed case-patients and health-care workers and performed descriptive epidemiology to generate hypotheses on possible exposures. In a case-control study we compared exposures between 21 cases and 81 controls, matched by age (± 10 years), sex and village of residence. We collected 22 biological specimens from 19 case-patients to test for Viral Haemorrhagic Fevers (VHF). We analysed the data using the Mantel-Haenszel method to account for the matched study design. RESULTS: We identified 56 cases with onset from June to October (attack rate 15/100,000 in Buliisa District and 5.2/100,000 in Hoima District). The age-specific attack rate was highest in persons aged 31-60 years (15/100,000 in Hoima and 47/100,000 in Buliisa); no persons below 15 years of age had the illness. In the case-control study, 42% (5/12) of cases vs. 0.0% (0/77) of controls had liver disease (ORM-H = ∞; 95%CI = 3.7-∞); 71% (10/14) of cases vs. 35% (28/81) of controls had ulcer disease (ORM-H = 13; 95% CI = 1.6-98); 27% (3/11) of cases vs. 14% (11/81) of controls used indomethacin prior to disease onset (ORM-H = 6.0; 95% CI = 1.0-36). None of the blood samples were positive for any of the VHFs. CONCLUSION: This reported cluster of hematemesis illness was due to predisposing conditions and use of Non-Steroidal Anti-inflammatory Drugs (NSAID). Health education should be conducted on the danger of NSAIDs misuse, especially in persons with pre-disposing conditions.