Socioeconomic disparities play an important role in the development of severe clinical outcomes including deaths from COVID-19. However, the current scientific evidence in regard the association between measures of poverty and COVID-19 mortality in hospitalized patients is scant. The objective of this study was to investigate whether there is an association between the Colombian Multidimensional Poverty Index (CMPI) and mortality from COVID-19 in hospitalized patients in Colombia from May 1, 2020 to August 15, 2021. This was an ecological study using individual data on hospitalized patients from the National Institute of Health of Colombia (INS), and municipal level data from the High-Cost Account and the National Administrative Department of Statistics. The main outcome variable was mortality due to COVID-19. The main exposure variable was the CMPI that ranges from 0 to 100% and was categorized into five levels: (i) level I (0%−20%), (ii) level II (20%−40%), (iii) level III (40%−60%), (iv) level IV (60%−80%); and (v) level V (80%−100%). The higher the level, the higher the level of multidimensional poverty. A Bayesian multilevel logistic regression model was applied to estimate Odds Ratio (OR) and their corresponding 95% credible intervals (CI). In addition, a subgroup analysis was performed according to the epidemiological COVID-19 waves using the same model. The odds for dying from COVID-19 was 1.46 (95% CI 1.4–1.53) for level II, 1.41 (95% CI 1.33–1.49) for level III and 1.70 (95% CI 1.54–1.89) for level IV hospitalized COVID-19 patients compared with the least poor patients (CMPI level I). In addition, age and male sex also increased mortality in COVID-19 hospitalized patients. Patients between 26 and 50 years-of-age had 4.17-fold increased odds (95% CI 4.07–4.3) of death compared with younger than 26-years-old patients. The corresponding for 51–75 years-old patients and those above the age of 75 years were 9.17 (95% CI 8.93–9.41) and 17.1 (95% CI 16.63–17.56), respectively. Finally, the odds of death from COVID-19 in hospitalized patients gradually decreased as the pandemic evolved. In conclusion, socioeconomic disparities were a major risk factor for mortality in patients hospitalized for COVID-19 in Colombia.

Predictions of hospital beds occupancy depends on hospital admission rates and the length of stay (LoS) according to bed type (hospital and intensive care unit beds). The objective of this study was to describe the LoS of COVID-19 hospital patients in Colombia during 2020-2021. Accelerated failure time models were used to estimate the LoS distribution according to each bed type and throughout each bed pathway. Acceleration factors and 95% confidence intervals were calculated to measure the effect on LoS of the outcome, sex, age, admission period during the epidemic (i.e., epidemic waves, peaks or valleys, and before/after vaccination period), and patients’ geographic origin. Most of the admitted COVID-19 patients occupied just hospital bed. Recovered patients spent more time in the hospital and intensive care unit than deceased patients. Men had longer LoS than women. In general, the LoS increased with age. Finally, the LoS varied along epidemic waves. It was lower in epidemic valleys than peaks, and became shorter after vaccinations began in Colombia than before. Our study highlights the necessity of analyzing local data on hospital admission rates and LoS to design strategies to prioritize hospital beds resources during the current and future pandemics.

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Biological noise results from heterogeneous gene expression levels among a group of cells [1]. This heterogeneity is due to the variation in gene expression that occurs over time at the single-cell level. Some noise-filtering mechanisms like redundancy in genetic circuits have been identified. Likewise, the feed-forward loop network motif has been found to have noise-filtering capacities in animal development. On the other hand, previous studies have contradictory conclusions about the noise-filtering capacities of the feedback loop and none of them have studied this capacity in the activator inhibitor regulatory system. Here we studied some dynamical properties, such as noise and expression levels, in self-activated and activator-inhibitor regulatory systems, both at the unicellular and multicellular levels. These systems are essential in the self-patterning and community effect processes occurring in development and differentiation. We used the three-stage model to represent the expression of a gene with promoter regulation and Hill functions to represent the regulatory connections between genes. We used Gillespie's Algorithm and the Chemical Langevin Equation for simulations. The regulatory systems evaluated do not reduce the biological noise. On the contrary, the noise remains at the same level or increases in comparison with an unregulated gene. The noise levels in these systems depend on the gene expression type of both the regulatorand the regulated gene. In this way, the particular forms in which genes connect to each other in these regulatory systems do not explain the noise in expression. However, the noise has a propagation pattern different for activation and inactivation types of regulation. Finally, the diffusion and colony size could be mechanisms of noise filtering in gene expression in a colony of cells. The increase in diffusion rate and colony size are necessary to synchronize gene expression and perform the community effect in embryonic development.