key: cord-0777135-36ekb8im authors: Écochard, René; Wimba, Patient; Bengehya, Justin; Bianga, Philippe Katchunga; Lugwarha, Séraphine; Oyimangirwe, Moise; Bazeboso, Jacques-Aimé; Tshilolo, Léon; Longo-Mbenza, Benjamin; Rabilloud, Muriel; Iwaz, Jean; Étard, Jean-François; Vanhems, Philippe title: The COVID-19 pandemic is deepening the health crisis in South Kivu date: 2021-03-17 journal: Int J Infect Dis DOI: 10.1016/j.ijid.2021.03.043 sha: 559af4de0ccba914ca64d40823d0b22fb4315831 doc_id: 777135 cord_uid: 36ekb8im Objective The outbreak of COVID-19 in South Kivu (DRC) raised the fear of added morbidity and mortality. Updating these indicators before a second wave seems essential to prepare for additional help. Methods From mid-May to mid-December 2020, weekly surveys in sampled streets from ten Health Areas quantified the application of barrier measures and analyzed questionnaires about sickness and death cases in interviewees’ households. Crude death rates (CDRs) were estimated. Results Little or no masking was observed in at least half of the streets. From May to December, the number of people presumably sick with Covid-19 increased sixfold (p < 0.05). Within 30 days before the interviews, 20% of deaths were presumably due to COVID-19. The monthly CDR at the beginning and end of the study were respectively close to 5 and 25 per 1000 people (p < 0.05); that is, annual CDRs of 60 and 260 per 1000, respectively. Thus, during the first epidemic wave, the estimated mortality rate increased by 50% vs. previous years and, by the end of 2020, it would have increased fourfold or more. Conclusion Despite possible overestimations, the excess mortality in South Kivu is extremely concerning. This profound crisis calls for rapid responses and increased humanitarian assistance. In South Kivu (nearly 6 million inhabitants), each 'Health Zone' is a well-defined geographical entity (maximum dimension: 150 kms) located within the limits of an administrative territory of at least 100,000 inhabitants with somewhat homogeneous socio-cultural communities. Each Health Zone is divided into well-delimited 'Health Areas' of nearly 10,000 inhabitants. This descriptive, weekly repeated, cross-sectional study was carried out in Bukavu and its surroundings (nearly 900,000 inhabitants); precisely, in three out of the 34 health zones of South Kivu. Within these three health zones, 10 representative health areas (total: 319,245 inhabitants) were chosen for the survey. Each week, from mid-May to mid-December 2020, a survey was conducted by 20 trained interviewers. May 14 to 21 was a pilot phase initiated by 'Université Officielle de Bukavu' whose results were published in August 2020 (Wimba et al., 2020) . From June 17 to July 10, that university continued data collection for four additional weeks then, from August 2 to December 15, the same methodology was applied by investigators supervised by a private partner (the Bureau Diocésain des OEuvres Médicales) (BDOM-SK, 2020). The questionnaire and survey method were described in the publication dedicated to the pilot study results (Wimba et al., 2020) .Briefly, three typical streets were chosen in each health area according to the pedestrian traffic: a busy, a medium-busy, and a quiet street. Once a week, the observers had to visit the streets for one hour, score the use of barrier measures, and interview randomly some pedestrians. The data were stored in a software application downloaded to the observers' smartphones. Examining the use of barrier measures included scoring three indicators: i) street population density; ii) physical distancing; and, iii) masking. Only masking will be considered thereafter. Each pedestrian interview was geolocated, dated, and saved on the server of the study. It included three parts: i) awareness of the barrier measures; ii) opinion about their usefulness; and, iii) the number and the health status of permanent residents in the interviewee's household. Only the health status will be considered thereafter. Masking was rated on a 4-point Likert scale: all people masked, nearly 2/3, nearly 1/3, very few or no mask wearing at the time of the observation. The pilot study has shown that mask wearing did not significantly change according to the time of the day. The health status of permanent residents in the interviewees' household (as reported by these interviewees) was estimated from the answers to the following questions: 1) the number of permanent residents in the household; 2) the number of sick persons in the household on the day of the interview; 3) the probable link between each sickness case and the Covid-19 pandemic (none, one, two or more); 4) the number of deaths that occurred in the household within the past 30 days; 5) the probable link between each death case and the Covid-19 pandemic (none, one, two or more). A suspected COVID-19 case was defined by the presence of fever, respiratory symptoms (cough, breathing difficulties, etc.) and intense fatigue. The frequency of mask wearing and its change over the study period was calculated and graphically represented. To calculate masking frequencies, the denominator was the number of street visits during the month and the numerator the number of streets with either all people masked, nearly 2/3 masked, nearly 1/3 masked, very few or no mask wearing at all. Table 1 shows that the three health zones were truly monthly surveyed. Table 2 shows the monthly estimates of the prevalence of sickness of any origin over 2020; i.e., the proportion of permanent residents in the interviewees' households with any undesirable change in health status as well as estimates of CDRs. Table 3 presents the results considering the presence of at least one sick person or the occurrence of at least one death in a household (i.e., the statistical unit is the household and not each resident). Table 3 Comparing the estimated mortality with the mortality of the previous years required estimating an annual probability of death. For this, the daily rate of death was simply calculated as the number of deaths over the previous 30 days divided by 30 and by the number of permanent residents in the households. An annual death rate was then calculated according to the usual formula: Annual crude probability of death = 1 -exp(-365*daily death rate). The changes in these indicators over time were quantified and tested using regression models; precisely, a mixed-effect logistic regression model for the prevalence of sickness and a mixedeffect Poisson regression model for the mortality rate. Using mixed-effects models allowed taking into account the repeated measurements in the health areas. However, in June and August, 40% of street visits showed that masks were worn by nearly 1/3 of the pedestrians. At the beginning of the study period, the overall or crude monthly death rate per 1,000 inhabitants was close to 5; it increased to around 25 in December (Table 2 ). This corresponds respectively to 1.6 and 8.3 daily crude death rate per 10,000 persons and to 60 and 260 annual crude death rates per 1,000 persons. The mixed-effect logistic regression confirmed the increase in the proportion of sick people in the interviewees' households (p <0.05) and the Poisson regression confirmed the increase in the incidence of mortality in the interviewees' households (p <0.05). From mid-May to mid-December 2020, the number of potentially COVID-1-related sick people increased sixfold (Table 3) . Covid-19 was deemed responsible for nearly one out of four deaths that occurred within 30 days before the interviews (46 out of 210 over the whole study period and 9 out of 34 in December). With very limited resources, that study allowed collecting information on adherence to barrier measures in the streets and on the health statuses in the interviewees' households. The overall impression from the analysis results was very impressing and concerning. The masks were worn by less than a third of the population, a large number of people appeared to be in poor health status, and death rates were very high. The situation deteriorated dramatically over the rest of 2020 reaching very alarming levels; the latter exceeded the international reference defining a 'humanitarian crisis'. According to the interviewees, Covid-19 was deemed directly responsible for part of this deterioration in terms of morbidity and mortality; probably one fifth of reported events. increased morbidity and mortality most probably linked with the outbreak of COVID-19 in the area without providing a causal or strong association between the epidemiological indicators and the disease. Indeed, at the time of the study and within its timeframe, several data (e.g., lab tests, hospital admissions and causes, hospital deaths and causes, funeral data) could not be reliably collected to document various conclusions or impressions. Anyway, in a highly deprived context, the access to lab analyses, hospital care, or even funerals might be seriously hampered by poverty and even stigma. Besides, other types of data were not collected to avoid adding bulk to the interview grid (age, sex, histories of persons who died; other declared plausible causes of death). The use of population-reported data is obviously subject to various causes of bias (overreporting as a call for help or under-reporting as a protection against stigma). Nevertheless, in some difficult conditions, these data are the only ones that are immediately available and able to provide an instant picture on the basis of which corrective measures can be promptly taken. All these limitations call for some degree of caution in the interpretation of the findings and should be adequately addressed in any future work on the subject. 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Rev Esp Aneste-siol Reanim COVID-19 in sub-Saharan Africa: impacts on vulnerable populations and sustaining home-grown solutions COVID-19 on the African continent A dashboard for monitoring preventive measures in response to COVID-19 outbreak in the Democratic Republic of Congo The authors thank Caritas International for its financial support. The agreement for the conduction of the study was obtained on May 9, 2020 from the provincial health authority of Bukavu.