Increased mortality attributed to Chagas disease: a systematic review and meta-analysis

Background: The clinical outcomes associated with Chagas disease remain poorly understood. In addition to the burden of morbidity, the burden of mortality due to Trypanosoma cruzi infection can be substantial, yet its quantification has eluded rigorous scrutiny. This is partly due to considerable heterogeneity between studies, which can influence the resulting estimates. There is a pressing need for accurate estimates of mortality due to Chagas disease that can be used to improve mathematical modelling, burden of disease evaluations, and cost-effectiveness studies. Methods: A systematic literature review was conducted to select observational studies comparing mortality in populations with and without a diagnosis of Chagas disease using the PubMed, MEDLINE, EMBASE, Web of Science and LILACS databases, without restrictions on language or date of publication. The primary outcome of interest was mortality (as all-cause mortality, sudden cardiac death, heart transplant or cardiovascular deaths). Data were analysed using a random-effects model to obtain the relative risk (RR) of mortality, the attributable risk percent (ARP), and the annual mortality rates (AMR). The statistic I-2 (proportion of variance in the meta-analysis due to study heterogeneity) was calculated. Sensitivity analyses and publication bias test were also conducted. Results: Twenty five studies were selected for quantitative analysis, providing data on 10,638 patients, 53,346 patient-years of follow-up, and 2739 events. Pooled estimates revealed that Chagas disease patients have significantly higher AMR compared with non-Chagas disease patients (0.18 versus 0.10; RR = 1.74, 95 % CI 1.49-2.03). Substantial heterogeneity was found among studies (I-2 = 67.3 %). The ARP above background mortality was 42.5 %. Through a sub-analysis patients were classified by clinical group (severe, moderate, asymptomatic). While RR did not differ significantly between clinical groups, important differences in AMR were found: AMR = 0.43 in Chagas vs. 0.29 in non-Chagas patients (RR = 1.40, 95 % CI 1.21-1.62) in the severe group; AMR = 0.16 (Chagas) vs. 0.08 (nonChagas) (RR = 2.10, 95 % CI 1.52-2.91) in the moderate group, and AMR = 0.02 vs. 0.01 (RR = 1.42, 95 % CI 1.14-1.77) in the asymptomatic group. Meta-regression showed no evidence of study-level covariates on the effect size. Publication bias was not statistically significant (Egger's test p=0.08). Conclusions: The results indicate a statistically significant excess of mortality due to Chagas disease that is shared among both symptomatic and asymptomatic populations

Heterogeneity of Trypanosoma cruzi infection rates in vectors and animal reservoirs in Colombia: a systematic review and meta-analysis

Background The heterogeneity of Trypanosoma cruzi infection rates among triatomines insects and animal reservoirs has been studied in independent studies, but little information has been systematised to allow pooled and comparative estimates. Unravelling the main patterns of this heterogeneity could contribute to a further understanding of T. cruzi transmission in Colombia. Methods A systematic search was conducted in PubMed, Medline, LILACS, Embase, Web of Knowledge, Google Scholar and secondary sources with no filters of language or time and until April 2018. Based on selection criteria, all relevant studies reporting T. cruzi infection rates in reservoirs or triatomines were chosen. For pooled analyses, a random effects model for binomial distribution was used. Heterogeneity among studies is reported as I2. Subgroup analyses included: taxonomic classification, ecotope and diagnostic methods. Publication bias and sensitivity analyses were performed. Results Overall, 39 studies reporting infection rates in Colombia were found (22 for potential reservoirs and 28 for triatomine insects) for a total sample of 22,838 potential animals and 11,307 triatomines evaluated for T. cruzi infection. We have found evidence of 38/71 different animal species as potential T. cruzi reservoirs and 14/18 species as triatomine vectors for T. cruzi. Among animals, the species with the highest pooled prevalence were opossum (Didelphis marsupialis) with 48.0% (95% CI: 26–71%; I2 = 88%, τ2 = 0.07, P < 0.01) and domestic dog (Canis lupus familiaris) with 22.0% (95% CI: 4–48%; I2 = 96%, τ2 = 0.01, P < 0.01). Among triatomines, the highest prevalence was found for Triatoma maculata in the peridomestic ecotope (68.0%, 95% CI: 62–74%; I2 = 0%, τ2 = 0, P < 0.0001), followed by Rhodnius prolixus (62.0%, 95% CI: 38–84%; I2 = 95%, τ2 = 0.05, P < 0.01) and Rhodnius pallescens (54.0%, 95% CI: 37–71%; I2 = 86%, τ2 = 0.035, P < 0.01) in the sylvatic ecotope. Conclusions To our knowledge, this is the first systematic and quantitative analyses of triatomine insects and potential animal reservoirs for T. cruzi infection in Colombia. The results highlight a marked heterogeneity between species and provide initial estimates of infection rates heterogeneity

Designing school reopening in the COVID-19 pre-vaccination period in Bogotá, Colombia: A modeling study

The COVID-19 pandemic has affected millions of people around the world. In Colombia, 1.65 million cases and 43,495 deaths were reported in 2020. Schools were closed in many places around the world to slow down the spread of SARS-CoV-2. In Bogotá, Colombia, most of the public schools were closed from March 2020 until the end of the year. School closures can exacerbate poverty, particularly in low- and middle-income countries. To reconcile these two priorities in health and fighting poverty, we estimated the impact of school reopening for in-person instruction in 2021. We used an agent-based model of SARS-CoV- 2 transmission calibrated to the daily number of deaths. The model includes schools that represent private and public schools in terms of age, enrollment, location, and size. We simulated school reopening at different capacities, assuming a high level of face-mask use, and evaluated the impact on the number of deaths in the city. We also evaluated the impact of reopening schools based on grade and multidimensional poverty index. We found that school at 35% capacity, assuming face-mask adherence at 75% in>8 years of age, had a small impact on the number of deaths reported in the city during a third wave. The increase in deaths was smallest when only pre-kinder was opened, and largest when secondary school was opened. At larger capacities, the impact on the number of deaths of opening pre-kinder was below 10%. In contrast, reopening other grades above 50% capacity substantially increased the number of deaths. Reopening schools based on their multidimensional poverty index resulted in a similar impact, irrespective of the level of poverty of the schools that were reopened. The impact of schools reopening was lower for pre-kinder grades and the magnitude of additional deaths associated with school reopening can be minimized by adjusting capacity in older grades.https://orcid.org/0000-0002-8165-3198Revista Internacional - No indexadaN

The impact of vaccination strategies for COVID-19 in the context of emerging variants and increasing social mixing in Bogotá, Colombia: a mathematical modelling study

In Bogotá by August 1st, more than 27,000 COVID-19 deaths have been reported, 29 while complete and partial vaccination coverage reached 30% and 37%, respectively. Although 30 reported cases are decreasing, the potential impact of new variants is uncertain.In Bogotá by August 1st, more than 27,000 COVID-19 deaths have been reported, 29 while complete and partial vaccination coverage reached 30% and 37%, respectively. Although 30 reported cases are decreasing, the potential impact of new variants is uncertain.Revista Nacional - Indexad

Complementary paths to Chagas disease elimination: the impact of combining vector control with etiological treatment

Background. The World Health Organization's 2020 goals for Chagas disease are (1) interrupting vector-borne intradomiciliary transmission and (2) having all infected people under care in endemic countries. Insecticide spraying has proved efficacious for reaching the first goal, but active transmission remains in several regions. For the second, treatment has mostly been restricted to recently infected patients, who comprise only a small proportion of all infected individuals. Methods. We extended our previous dynamic transmission model to simulate a domestic Chagas disease transmission cycle and examined the effects of both vector control and etiological treatment on achieving the operational criterion proposed by the Pan American Health Organization for intradomiciliary, vectorial transmission interruption (ie, <2% seroprevalence in children <5 years of age). Results. Depending on endemicity, an antivectorial intervention that decreases vector density by 90% annually would achieve the transmission interruption criterion in 2-3 years (low endemicity) to >30 years (high endemicity). When this strategy is combined with annual etiological treatment in 10% of the infected human population, the seroprevalence criterion would be achieved, respectively, in 1 and 11 years. Conclusions. Combining highly effective vector control with etiological (trypanocidal) treatment in humans would substantially reduce time to transmission interruption as well as infection incidence and prevalence. However, the success of vector control may depend on prevailing vector species. It will be crucial to improve the coverage of screening programs, the performance of diagnostic tests, the proportion of people treated, and the efficacy of trypanocidal drugs. While screening and access can be incremented as part of strengthening the health systems response, improving diagnostics performance and drug efficacy will require further research

Spatial and temporal invasion dynamics of the 2014-2017 Zika and chikungunya epidemics in Colombia

Zika virus (ZIKV) and chikungunya virus (CHIKV) were recently introduced into the Americas resulting in significant disease burdens. Understanding their spatial and temporal dynamics at the subnational level is key to informing surveillance and preparedness for future epidemics. We analyzed anonymized line list data on approximately 105,000 Zika virus disease and 412,000 chikungunya fever suspected and laboratory-confirmed cases during the 2014-2017 epidemics. We first determined the week of invasion in each city. Out of 1,122, 288 cities met criteria for epidemic invasion by ZIKA and 338 cities by CHIKV. We analyzed risk factors for invasion using linear and logistic regression models. We also estimated that the geographic origin of both epidemics was located in Barranquilla, north Colombia. We assessed the spatial and temporal invasion dynamics of both viruses to analyze transmission between cities using a suite of (i) gravity models, (ii) Stouffer's rank models, and (iii) radiation models with two types of distance metrics, geographic distance and travel time between cities. Invasion risk was best captured by a gravity model when accounting for geographic distance and intermediate levels of density dependence; Stouffer's rank model with geographic distance performed similarly well. Although a few long-distance invasion events occurred at the beginning of the epidemics, an estimated distance power of 1.7 (95% CrI: 1.5-2.0) from the gravity models suggests that spatial spread was primarily driven by short-distance transmission. Similarities between the epidemics were highlighted by jointly fitted models, which were preferred over individual models when the transmission intensity was allowed to vary across arboviruses. However, ZIKV spread considerably faster than CHIKV

Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease

Q1Q1Background: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues. Methodology/principal findings: We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty. Conclusions/significance: The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.https://orcid.org/0000-0002-8165-3198Revista Internacional - IndexadaA1N