Efecto de factores sociodemográficos en la mortalidad del COVID-19 en Costa Rica: un enfoque geográfico

Introducción: La enfermedad por coronavirus (COVID-19) se ha extendido entre la población de todo el país y ha tenido un gran impacto a nivel mundial. Sin embargo, existen diferencias geográficas importantes en la mortalidad de COVID-19 entre las diferentes regiones del mundo y en Costa Rica. Objetivo: Explorar el efecto de algunos de los factores sociodemográficos en la mortalidad de COVID-19 en pequeñas divisiones geográficas o cantones de Costa Rica. Métodos: Usamos registros oficiales y aplicamos un modelo de regresión clásica de Poisson y un modelo de regresión ponderada geográficamente. Resultados: Obtuvimos un criterio de información de Akaike (AIC) más bajo con la regresión ponderada (927.1 en la regresión de Poison versus 358.4 en la regresión ponderada). Los cantones con un mayor riesgo de mortalidad por COVID-19 tuvo una población más densa; bienestar material más alto; menor proporción de cobertura de salud y están ubicadas en el área del Pacífico de Costa Rica. Conclusiones: Una estrategia de intervención de COVID-19 específica debería concentrarse en áreas de la costa pacífica con poblaciones más densas, mayor bienestar material y menor población por unidad de salud.

Introduction: The coronavirus disease (COVID-19) has spread among the population of Costa Rica and has had a great global impact. However, there are important geographic differences in mortality from COVID-19 among world regions and within Costa Rica. Objective: To explore the effect of some sociodemographic factors on COVID-19 mortality in the small geographic divisions or cantons of Costa Rica. Methods: We used official records and applied a classical epidemiological Poisson regression model and a geographically weighted regression model. Results: We obtained a lower Akaike Information Criterion with the weighted regression (927.1 in Poisson regression versus 358.4 in weighted regression). The cantons with higher risk of mortality from COVID-19 had a denser population; higher material well-being; less population by health service units and are located near the Pacific coast. Conclusions: A specific COVID-19 intervention strategy should concentrate on Pacific coast areas with denser population, higher material well-being and less population by health service units.

A Systematic Review of Methodology: Time Series Regression Analysis for Environmental Factors and Infectious Diseases

Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.

Systematic review on methodology: time series regression analysis for environmental factors and infectious diseases

Background: Time series analysis is suitable forinvestigations of relatively direct and short-term effects of exposures on outcomes.In environmental epidemiology studies, this method has been one of the standardapproaches to assess impacts of environmental factors on acute non-infectious diseases(e.g. cardiovascular deaths), with conventionally generalized linear or additivemodels (GLM and GAM). However, the same manner of practices of this method is observedwith infectious diseases despite of the substantial differences fromnon-infectious diseases which may result in analytical challenges. Methods: Following Preferred ReportingItems for Systematic Reviews and Meta-Analyses guideline, systematic review wasconducted to elucidate important issues in assessing the associations betweenenvironmental factors and infectious diseases using time series analysis withGLM or GAM. Published studies in relation to associations between weatherfactors, and malaria, cholera, dengue, or influenza were targeted. Findings: Issues regarding theestimation of susceptible population and exposure lag times, adequacy ofseasonal adjustments, the presence of strong autocorrelations, and a lack of smallerobservation time unit of outcomes (i.e. daily data) were raised from our review.These concerns may be attributed to the features specific to infectious diseases,such as transmissions among individuals and complicated causal mechanisms. Conclusion: The consequence of not takingadequate measures to address these issues is distortion of the appropriate riskquantifications of exposures factors. The future studies are required careful attentionsto details, and recommended to examine alternative models or methods thatimprove studies with time series regression analysis for environmental determinantsof infectious diseases.

On comparison of SAS codes with GLM and MIXED for the crossover studies with QT interval data

The structural complexity of crossover studies for bioequivalence test confuses analysts and leaves them a hard choice among various programs. Our study reviews PROC GLM and PROC MIXED in SAS and compares widely used SAS codes for crossover studies. PROC MIXED based on REML is more recommended since it provides best linear unbiased estimator of the random between-subject effects and its variance. Our study also considers the covariance structure within subject over period which most PK/PD studies and crossover studies ignore. The QT interval data after the administration of moxifloxacin for a fixed time point are analyzed for the comparison of representative SAS codes for crossover studies.

Influence of relative humidity in Vibrio cholerae infection: A time series model.

Background & objectives : Spread of cholera in West Bengal is known to be related to its ecosystem which favours Vibrio cholerae. Incidence of cholera has not been correlated with temperature, relative humidity and rainfall, which may act as favourable factors. The aim of this study was to investigate the relational impact of climate changes on cholera. Methods : Monthly V. cholerae infection data for of the past 13 years (1996-2008), average relative humidity (RH), temperature and rainfall in Kolkata were considered for the time series analysis of Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model to investigate relational impact of climatic association of V. cholerae infection and General Linear Model (GLM) for point estimation. Results : The SARIMA (1,0,0)(0,1,1) model revealed that monthly average RH was consistently linear related to V. cholerae infection during monsoon season as well as temperature and rainfall were non-stationary, AR(1), SMA(1) and SI(1) (P<0.001) were highly significant with seasonal difference. The GLM has identified that consistent (<10%) range of RH (86.78 ± 4.13, CV=5.0, P <0.001) with moderate to highest (>7 cm) rainfall (10.1 ± 5.1, CV=50.1, P <0.001) and wide (>5-10°C) range of temperature (29.00 ± 1.64, CV=5.6, P <0.001) collectively acted as an ideal climatic condition for V. cholerae infection. Increase of RH to 21 per cent influenced an unusual V. cholerae infection in December 2008 compared to previous years. Interpretation & conclusions : V. cholerae infection was associated higher RH (>80%) with 29°C temperature with intermittent average (10 cm) rainfall. This model also identified periodicity and seasonal patterns of cholera in Kolkata. Heavy rainfall indirectly influenced the V. cholerae infection, whereas no correlation was found with high temperature.