Spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level

Spatio-temporal patterns of the mortality of diseases associated with malnutrition and their relationship with food establishments in Mexico

This study explores the spatio-temporal behavior of mortality due to multiple causes associated with several diseases and their relationship with the physical availability of food. We analyze data for the 2010-2020 period at the municipality level in Mexico. After collecting and standardizing national databases for each disease, we perform SATSCAN temporal and FleXScan spatial cluster analyses. We use the he Kruskal-Wallis test to analyze the differences between municipalities with high relative risk of mortality and their relationship with food retail units and food establishments. We found statistically significant relationships between clusters by disease and the physical availability of food per hundred thousand inhabitants. The main pattern is a higher average density of convenience stores, supermarkets, fast food chains and franchises, and Mexican snack restaurants in high-risk municipalities, while a higher density of grocery stores and inns, cheap kitchens, and menu restaurants exists in the municipalities with low risk. The density of convenience stores, fast food chains and franchises, and Mexican snack restaurants plays a very important role in mortality behavior, so measures must exist to regulate them and encourage and protect convenience stores, grocery stores, and local food preparation units.

Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia

This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.

Urban COVID-19 endemism in Petrópolis: detection of an endemic focus by spatial analysis

OBJECTIVES.

Motivation for the study. During the COVID-19 pandemic, spatial analysis methodologies were used to identify the territorial determinants of its distribution and social inequalities in access to medical care. Main findings. We found a concentration of cases in a specific location of the municipality, which remained constant throughout the study period, with sporadic outbreaks in other areas. Implications. It is necessary to pay attention to possible endemic foci of viral diseases in urban settings and to take measures to eliminate them as well as to prevent their spread within and outside the area.

Potential Epidemic Vulnerability and Socioepidemiological Profile of SARS-CoV2 in the Brazilian Northeast Region

Background: COVID-19 is a significant public health problem that can have a negative impact, especially in vulnerable regions. Objective: This study aimed to provide evidence that could positively influence coping with COVID-19 based on the relationship between the potential epidemic vulnerability index (PEVI) and socioepidemiological variables. This could be used as a decision-making tool for the planning of preventive initiatives in regions with relevant vulnerability indices for the spread of SARS-CoV-2. Methodology: We performed a cross-sectional study, with the analysis of the population characteristics of COVID-19 cases associated with neighborhoods’ PEVIs in the conurbation region of Crajubar, northeastern Brazil, through the mapping of socioeconomic–demographic factors and spatial autocorrelation. Results: The PEVI distribution indicated low vulnerability in areas with high real estate and commercial value; as communities moved away from these areas, the vulnerability levels increased. As for the number of cases, three of the five neighborhoods with a high–high autocorrelation, and some other neighborhoods showed a bivariate spatial correlation with a low–low PEVI but also high–low with indicators that make up the PEVI, representing areas that could be protected by public health measures to prevent increases in COVID-19 cases. Conclusions: The impact of the PEVI revealed areas that could be targeted by public policies to decrease the occurrence of COVID-19.

Spatiotemporal patterns of mortality associated with chronic non-communicable diseases and child malnutrition at the municipal level in Mexico

Malnutrition is one of the main risk factors related to chronic non-communicable diseases and child undernourishment on a planetary scale. Mexico is one of the countries with the highest levels of malnutrition, but there is also an accelerated increase in overweight or obesity. This study explored the spatiotemporal behaviour of mortality associated with chronic non-communicable diseases such as type II diabetes mellitus, hypertension, ischemic heart disease and cerebrovascular disease. The analysis was carried out at the municipality level for the 2000-2020 period targeting two age groups: ≥60-year olds and 20-59-year olds. In addition, 0-4-year olds were investigated with respect to undernourishment. National databases were gathered and standardized for each disease and SaTScan spatiotemporal cluster analyses were performed. We found that mortality associated with most of the diseases evaluated has increased since 2016 except for mortality caused by child undernourishment, which showed a downward trend during the study period. To focus on active conglomerates of diseases is important as they currently represent a threat to public health. Our results contribute to the potential spatial prioritization of the allocation of resources and campaigns for prevention and treatment of chronic non-communicable diseases and child undernourishment. Generally, geographical studies are fundamental for the discovery of disease aetiology and they provide valuable and timely information to multiple stakeholders.