Covid-19 deaths in Africa: prospective systematic postmortem surveillance study

Abstract

Objective: To directly measure the fatal impact of coronavirus disease 2019 (covid-19) in an urban African population.

Design: Prospective systematic postmortem surveillance study.

Setting: Zambia's largest tertiary care referral hospital.

Participants: Deceased people of all ages at the University Teaching Hospital morgue in Lusaka, Zambia, enrolled within 48 hours of death.

Main outcome measure: Postmortem nasopharyngeal swabs were tested via reverse transcriptase quantitative polymerase chain reaction (PCR) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Deaths were stratified by covis-19 status, location, age, sex, and underlying risk factors.

Results: 372 participants were enrolled between June and September 2020; PCR results were available for 364 (97.8%). SARS-CoV-2 was detected in 58/364 (15.9%) according to the recommended cycle threshold value of <40 and in 70/364 (19.2%) when expanded to any level of PCR detection. The median age at death among people with a positive test for SARS-CoV-2 was 48 (interquartile range 36-72) years, and 69% (n=48) were male. Most deaths in people with covid-19 (51/70; 73%) occurred in the community; none had been tested for SARS-CoV-2 before death. Among the 19/70 people who died in hospital, six were tested before death. Among the 52/70 people with data on symptoms, 44/52 had typical symptoms of covid-19 (cough, fever, shortness of breath), of whom only five were tested before death. Covid-19 was identified in seven children, only one of whom had been tested before death. The proportion of deaths with covid-19 increased with age, but 76% (n=53) of people who died were aged under 60 years. The five most common comorbidities among people who died with covid-19 were tuberculosis (22; 31%), hypertension (19; 27%), HIV/AIDS (16; 23%), alcohol misuse (12; 17%), and diabetes (9; 13%).

Conclusions: Contrary to expectations, deaths with covid-19 were common in Lusaka. Most occurred in the community, where testing capacity is lacking. However, few people who died at facilities were tested, despite presenting with typical symptoms of covid-19. Therefore, cases of covid-19 were under-reported because testing was rarely done not because covid-19 was rare. If these data are generalizable, the impact of covid-19 in Africa has been vastly underestimated.

Fig 1

Biweekly detection of deaths with covid-19, June to September 2020. Cases positive for covid-19 are presented at two cycle thresholds: <40 (in yellow) and additional cases detected at cycle threshold (CT) value between 40 and 45 (in purple), against total deaths (in pink). For simplicity, these are presented with cases clustered in 2 week increments, from calendar weeks 24 through 39. Detection of covid-19 occurred throughout surveillance period, although with significant week to week variability. Owing to a high volume of deaths at the morgue, and the need to complete work on the ongoing postmortem infant study that continued through August 31, 2020, only a subset of deaths were enrolled on daily basis. In July, every fifth deceased person was enrolled, capping enrollments at around 5-6 participants per day; in August, this was reduced to every third person, with same daily cap; in September, when the full team could focus on covid-19 cases, it was expanded to 1:1 enrollment. For this reason, the total number of enrolled deaths in July and August represents only 10% of deaths that occurred during those periods. This has no bearing on the prevalence calculations but means that absolute number of deaths will be undercounted in this figure

Fig 2

Distribution of deaths with covid-19 by sex and facility versus community setting. Covid-19 was detected far more often among male participants than female ones. 73% of all deaths occurred in the community, and so were systematically excluded from antemortem covid-19 testing. This is important in explaining why covid-19 is being undercounted. However, covid-19 testing among facility deaths was also uncommon, although most participants had presented with typical symptoms suggestive of covid-19, such as cough, fever, and difficulty breathing

Fig 3

Distribution of deaths with covid-19 by 5 year age increments. Top panel: numbers of deaths by polymerase chain reaction (PCR) testing result. In pink are deaths without covid-19; in yellow are those in which covid-19 was detected at cycle threshold (CT) value <40; in purple are additional cases in which covid-19 was detected at CT value 40 and 45. Results falling in 40-45 range would be considered “indeterminate” results, but that does not mean that they are false positives. Given the high index of suspicion, a more parsimonious explanation is that they represent true positives, albeit at lower signal intensity. This could reflect natural biological variation (eg, waning signal intensity at end of arc of infection) or variations in sample collection, sample degradation over time, or laboratory processes. Bottom panel: same data shown as proportions of positive results, at both thresholds, by age strata. The highest proportions of deaths with covid-19/total deaths are seen clustered in older people. However, the highest number of cases is concentrated among those <60 years, with similar proportions of covid-19 detections across most age strata

Fig 4

Comparison of age distribution among total enrolled deaths versus registered deaths versus covid-19 deaths. The top panel shows age distribution of three populations assessed in this analysis, in 5 year age strata. In purple are all deaths in enrolled sample; in yellow are ages from burial registry, representing all deaths that occurred during sampling period; in pink are deaths within covid-19 positive sub-sample. For reference, the bottom panel shows the population age structure, by sex, for Zambia, based on the most recent Demographics and Health Survey for Zambia in 2018. This shows significant polarization to younger population age structure. Age distributions for the sampled and total populations are very similar, suggesting that the sample was representative of the larger population. By comparison, deaths with covid-19 show a relative increase in deaths among older people and a relative decrease in younger age groups. This tendency for deaths to be concentrated in older people is not unique, but the extent of the skew toward older people is less pronounced than that seen in the US, EU, and China. In those populations, the proportion of deaths is virtually nil in people under age 50 and almost entirely concentrated in those 65 and above, with deaths in children being almost undetectably few. By contrast, 10% of deaths in Lusaka were in children

Fig 5

Geographic distribution of deaths with covid-19 by ward in Lusaka, Zambia. Top panel: distribution of deaths with covid-19 by city ward. Figure was created using ArcGIS software, importing data on population by city ward from numbers available on Zambia data hub, which is managed by the Government of Zambia (https://zambia-open-data-nsdi-mlnr.hub.arcgis.com/). Each pip represents 5000 people. Heat map corresponds to number of covid-19 positive cases by referral clinic for 70 covid-19 positive deaths in study. The four wards with the highest case burdens were George, Chawama, Kanyama, and Emmasdale compounds. These are also four of the poorest areas of Lusaka with the highest population and population densities. Bottom panel: satellite view of Lusaka taken using Google Earth, highlighting Kanyama and Chawama wards. These are the two largest population centers in Lusaka and the most impoverished. Even at lower magnification, the increased population density in these wards is readily apparent, as are the sharp delineations between these and adjacent more affluent areas of the city. The congestion becomes more apparent in the street level inset figures