How Expensive Are Hospitalizations by COVID-19? Evidence From Colombia.

OBJECTIVES: This study aimed to estimate the direct medical costs due to hospitalizations by COVID-19 in Colombia and to identify their cost drivers in Colombia. METHODS: This is a retrospective cost-of-illness study of COVID-19 in Colombia. We estimated direct medical costs using data from patients insured to a Benefit Plan Administrator Company, between March 15, 2020 and May 29, 2020. Absolute and relative frequencies, averages, medians, and interquartile ranges (IQRs) were used to characterize the population and estimate the costs of hospitalized patients with COVID-19. We stratified the cost analysis by sex, age groups, comorbidities, and type of hospitalization (general ward and intensive care unit [ICU]). Cost drivers were calculated from a generalized linear model. RESULTS: We studied 113 confirmed patients, 51.3% men. On average, the hospital length of stay was 7.3 (± 6.2) days. A person hospitalized with COVID-19 reported median costs of $1688 (IQR 788-2523). In women, this cost was $1328 (IQR 463-2098); in men, this was 1.4 times greater. The median cost for ICU was $4118 (IQR 2069-5455), 3 times higher than those hospitalized only in the general ward. Admission to the ICU, having 1 comorbidity, length of stay, high blood pressure, having 5 comorbidities, and being treated in the city of Cartagena were statistically significant with direct medical costs. CONCLUSIONS: Our study provides an idea of the magnitude of costs needed to hospitalize a COVID-19 case in Colombia. Other studies in Colombia have assessed the costs of hospitalization for infectious diseases such as influenza, costs significantly lower than those described here.

Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings.

Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVID-19 pandemic using logistic regression and a machine-learning approach, in Bogotá, Colombia. Participants from the CoVIDA project were included. A logistic regression using the model was chosen based on biological plausibility and the Akaike Information criterion. Also, we performed an analysis using machine learning with random forest, support vector machine, and extreme gradient boosting. The study included 58,577 participants with a positivity rate of 5.7%. The logistic regression showed that anosmia (aOR = 7.76, 95% CI [6.19, 9.73]), fever (aOR = 4.29, 95% CI [3.07, 6.02]), headache (aOR = 3.29, 95% CI [1.78, 6.07]), dry cough (aOR = 2.96, 95% CI [2.44, 3.58]), and fatigue (aOR = 1.93, 95% CI [1.57, 2.93]) were independently associated with SARS-CoV-2 infection. Our final model had an area under the curve of 0.73. The symptoms-based model correctly identified over 85% of participants. This model can be used to prioritize resource allocation related to COVID-19 diagnosis, to decide on early isolation, and contact-tracing strategies in individuals with a high probability of infection before receiving a confirmatory test result. This strategy has public health and clinical decision-making significance in low- and middle-income settings like Latin America.

SARS-CoV-2 Infection among School Population of One Developing Country. Do School Closures Protect Students and Teachers against SARS-CoV-2 Infection?

Evidence about the effectiveness of school closures as a measure to control the spread of COVID-19 is controversial. We posit that schools are not an important source of transmission; thus, we analyzed two surveillance methods: a web-based questionnaire and a telephone survey that monitored the impact of the pandemic due to COVID-19 cases in Bogotá, Colombia. We estimated the cumulative incidences for Acute Respiratory Infection (ARI) and COVID-19 for each population group. Then, we assessed the differences using the cumulative incidence ratio (CIR) and 95% confidence intervals (CI95%). The ARI incidence among students was 20.1 times higher when estimated from the telephone survey than from the online questionnaire (CIR: 20.1; CI95% 17.11-23.53). Likewise, the ARI incidence among schoolteachers was 10 times higher in the telephone survey (CIR: 9.8; CI95% 8.3-11.5). the incidence of COVID-19 among schoolteachers was 4.3 times higher than among students in the online questionnarie (CIR: 4.3, CI95%: 3.8-5.0) and 2.1 times higher in the telephone survey (CIR = 2.1, CI95%: 1.8-2.6), and this behavior was also observed in the general population data. Both methods showed a capacity to detect COVID-19 transmission among students and schoolteachers, but the telephone survey estimates were probably closer to the real incidence rate.

Impact of mobility restrictions on the dynamics of transmission of COVID-19 in Colombian cities.

BACKGROUND: Our aim was to study the association between case rates and reductions in urban mobility in state capitals of Colombia. METHODS: We designed an ecological time-series study to correlate the Colombian incidence rate with reductions in mobility trends of retail stores. RESULTS: The meta-analysis of ß coefficients describing the association between case rates and reductions in mobility trends of retail stores resulted in a mean estimate of 0.0637 (95% confidence interval 0.027 to 0.101; p<0.001) with nearly 100% heterogeneity. CONCLUSIONS: We recommend continuing to consider mobility restrictions when the number of cases starts to climb in each local jurisdiction.

Using serological studies to assess COVID-19 infection fatality rate in developing countries: A case study from one Colombian department.

This study describe the infection fatality rate (IFR) by COVID-19 by age groups in one department of Colombia. It used results from a serological survey to stablish a closer estimation of the true proportion of infected people. It found an overall IFR of 0.24% quite lower than the overall CFR (5.6%). We conclude that CFR severely overestimate the lethality of COVID-19 in developing areas.

Dynamical characteristics of the COVID-19 epidemic: Estimation from cases in Colombia.

OBJECTIVE: To characterize the dynamics of the coronavirus disease 2019 (COVID-19) epidemic, for modeling purposes. METHODS: Data from Colombian official case information were collated for a period of 5 months. Dynamical parameters of the disease spread were then estimated from the data. Probability distribution models were identified, representing the time from symptom onset to hospitalization, to intensive care unit (ICU) admission, and to death. Kaplan-Meier estimates were also computed for the probability of eventually requiring hospitalization, needing ICU attention, and dying from the disease (the case fatality ratio). RESULTS: Probability distributions of the times and probabilities were computed for the population and for groups based on age and sex. The results showed that for the times that characterize the course of the disease for a given patient (time to hospitalization, ICU admission, or death), the variation from one age group to another was very small (around 10% of the fixed effect intercept) and the effect of sex was even smaller (around 1%). The course of the disease appeared to be very similar for all patients. On the other hand, the probability that a patient would advance from one stage of the disease to another (to hospitalization, ICU admission, or death) was heavily influenced by sex and age. The relative risk of death for male individuals was 1.7 times that of female individuals (based on 22 924 deaths). CONCLUSIONS: The times from one stage of the disease to another were almost independent of the major patient variables (sex, age). This was in stark contrast to the probabilities of progressing from one stage to another, which showed a strong dependence on age and sex. Data also showed that the length of hospital and ICU stays were almost independent of sex and age. The only factor that affected this length was the eventual outcome of the disease (survival or death); the time was significantly longer for surviving patients.

Is Colombia an example of successful containment of the 2020 COVID-19 pandemic? A critical analysis of the epidemiological data, March to July 2020.

BACKGROUND: Colombia detected its first coronavirus disease 2019 (COVID-19) case on March 2, 2020. From March 22 to April 25, it implemented a national lockdown that, apparently, allowed the country to keep a low incidence and mortality rate up to mid-May. Forced by the economic losses, the government then opened many commercial activities, which was followed by an increase in cases and deaths. This paper presents a critical analysis of the Colombian surveillance data in order to identify strengths and pitfalls of the control measures. METHODS: A descriptive analysis of PCR-confirmed cases between March and July 25 was performed. Data were described according to the level of measurement. Incidence and mortality rates of COVID-19 were estimated by age, sex, and geographical area. Sampling rates for suspected cases were estimated by geographical area, and the potential for case underestimation was assessed using sampling differences. RESULTS: By July 25, Colombia (population 50 372 424) had reported 240 745 cases and 8269 deaths (case fatality rate of 3.4%). A total of 1 370 271 samples had been analyzed (27 405 samples per million people), with a positivity rate of 17%. Sampling rates per million varied by region from 2664 to 158 681 per million, and consequently the incidence and mortality rates also varied. Due to geographical variations in surveillance capacity, Colombia may have overlooked up to 82% of the actual cases. CONCLUSION: Colombia has a lower case and mortality incidence compared to other South American countries. This may be an effect of the lockdown, but may also be attributed, to some extent, to geographical differences in surveillance capacity. Indigenous populations with little health infrastructure have been hit the hardest.