Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic's death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number ( R 0 ) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R 0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia's age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic's death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number ( R 0 ) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R 0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia's age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

Estimating the obstetric co-morbidity burden using administrative data: The impact of the pregnancy-related assessment window

BACKGROUND

Despite increased research using large administrative databases to identify determinants of maternal morbidity and mortality, the extent to which these databases capture obstetric co-morbidities is unknown.

OBJECTIVE

To evaluate the impact that the time window used to assess obstetric co-morbidities has on the completeness of ascertainment of those co-morbidities.

METHODS

We conducted a five-year analysis of inpatient hospitalisations of pregnant women from 2010-2014 using the Nationwide Readmissions Database. For each woman, using discharge diagnoses, we identified 24 conditions used to create the Obstetric Comorbidity Index. Using various assessment windows for capturing obstetric co-morbidities, including the delivery hospitalisation only and all weekly windows from 7 to 280 days, we calculated the frequency and rate of each co-morbidity and the degree of underascertainment of the co-morbidity. Under each scenario, and for each co-morbidity, we also calculated the all-cause, 30-day readmission rate.

RESULTS

There were over 3 million delivery hospitalisations from 2010 to 2014 included in this analysis. Compared with a full 280-day window, assessment of obstetric co-morbidities using only diagnoses made during the delivery hospitalisation would result in failing to identify over 35% of cases of chronic renal disease, 28.5% cases in which alcohol abuse was documented during pregnancy, and 23.1% of women with pulmonary hypertension. For seven other co-morbidities, at least 1 in 20 women with that condition would have been missed with exclusive reliance on the delivery hospitalisation for co-morbidity diagnoses. Not only would reliance on delivery hospitalisations have resulted in missed cases of co-morbidities, but for many conditions, estimates of readmission rates for women with obstetric co-morbidities would have been underestimated.

CONCLUSIONS

An increasing proportion of maternal and child health research is based on large administrative databases. This study provides data that facilitate the assessment of the degree to which important obstetric co-morbidities may be underascertained when using these databases.