Spatial analysis of cardiovascular mortality and associated factors around the world

Bayesian spatial-temporal analysis and determinants of cardiovascular diseases in Tanzania mainland

BACKGROUND

Cardiovascular Diseases (CVDs) are health-threatening conditions that account for high mortality in the world. Approximately 23.6 million deaths due to CVD is expected in the year 2030 worldwide. The CVD burden is more severe in developing countries, including Tanzania.

OBJECTIVES

This study analyzed the spatial-temporal trends and determinants of cardiovascular diseases in Tanzania from 2010 to 2019.

METHODS

Individual data were extracted from Jakaya Kikwete Cardiac Institute (JKCI), Mbeya Zonal Referral Hospital (MZRH), Kilimanjaro Christian Medical Centre (KCMC) and Bugando hospitals and the geographical data from TMA. The model containing spatial and temporal components was analyzed using the Bayesian hierarchical method implemented using Integrated Nested Laplace Approximation (INLA).

RESULTS

The results found that the incidence of CVD increased from 2010 to 2014 and decreased from 2015 to 2019. The southern highlands, lake, central and coastal zones were more likely to have CVD problems than others. It was also revealed that people aged 60-64 years OR = 1.49, females OR = 1.51, smokers OR = 1.76, alcohol drinkers OR = 1.48, and overweight OR = 1.89 were more likely to have CVD problems. Additionally, a 1oC increase in the average annual air maximum temperature was related to a 14% risk of developing CVD problems. The study revealed that the model, which included spatial and temporal random effects, was the best-predicting model.

CONCLUSION

The study shows a decreased CVD incidence rate from 2015 to 2019. The CVD incidences occurred more in Tanzania's coastal and lake areas between 2010 and 2019. The demographic, lifestyle and geographical risk factors were significantly associated with the CVD.

The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation

Cardiovascular Disease (CVD) is currently the major challenge to people's health and the world's top cause of death. In Tanzania, deaths due to CVD account for about 13% of the total deaths caused by the non-communicable diseases. This study examined the spatio-temporal clustering of CVDs from 2010 to 2019 in Tanzania for retrospective spatio-temporal analysis using the Bernoulli probability model on data sampled from four selected hospitals. Spatial scan statistics was performed to identify CVD clusters and the effect of covariates on the CVD incidences was examined using multiple logistic regression. It was found that there was a comparatively high risk of CVD during 2011-2015 followed by a decline during 2015-2019. The spatio-temporal analysis detected two high-risk disease clusters in the coastal and lake zones from 2012 to 2016 (p<0.001), with similar results produced by purely spatial analysis. The multiple logistic model showed that sex, age, blood pressure, body mass index (BMI), alcohol intake and smoking were significant predictors of CVD incidence.

Spatial analysis of the impact of urban built environment on cardiovascular diseases: a case study in Xixiangtang, China

BACKGROUND

The built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs).

METHODS

Taking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk.

RESULTS

The results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran's I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities.

CONCLUSIONS

The spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

The Role of Pan-Immune-Inflammation Value in Determining the Severity of Coronary Artery Disease in NSTEMI Patients

BACKGROUND

Even though medication and interventional therapy have improved the death rate for non-ST elevation myocardial infarction (NSTEMI) patients, these patients still have a substantial residual risk of cardiovascular events. Early identification of high-risk individuals is critical for improving prognosis, especially in this patient group. The focus of recent research has switched to finding new related indicators that can help distinguish high-risk patients. For this purpose, we examined the relationship between the pan-immune-inflammation value (PIV) and the severity of coronary artery disease (CAD) defined by the SYNTAX score (SxS) in NSTEMI patients.

METHODS

Based on the SxS, CAD patients were split into three groups. To evaluate the risk variables of CAD, multivariate logistic analysis was employed.

RESULTS

The PIV (odds ratio: 1.003; 95% CI: 1.001-1.005; p = 0.005) was found to be an independent predictor of a high SxS in the multivariate logistic regression analysis. Additionally, there was a positive association between the PIV and SxS (r: 0.68; p < 0.001). The PIV predicted the severe coronary lesion in the receiver-operating characteristic curve analysis with a sensitivity of 91% and specificity of 81.1%, using an appropriate cutoff value of 568.2.

CONCLUSIONS

In patients with non-STEMI, the PIV, a cheap and easily measured laboratory variable, was substantially correlated with a high SxS and the severity of CAD.

Antibody responses after BNT162b2 vaccination in Japanese geriatric intermediate care facilities

Background

To evaluate antibody responses against the primary series of vaccination of severe acute respiratory syndrome coronavirus-2 [SARS-CoV-2] vaccines in the staff and residents of Japanese geriatric intermediate care facilities.

Methods

All subjects (159 staff and 96 residents) received two doses of the BNT162b2 mRNA vaccine 3 weeks apart. Baseline data of subject were collected using a structured form. Serum samples were collected three times: before vaccination, 3 weeks after the first dose, and 4 weeks after the second dose, and anti-receptor binding domain of the spike protein of SARS-CoV-2 [anti-RBD] IgG was measured using two immunoassays.

Results

After the second dose, geometric mean titers [GMT] of anti-RBD with both the Abbott and Roche assay were significantly lower in residents than staff (2282 AU/mL vs. 8505 AU/mL, and 258 U/mL vs. 948 U/mL, respectively). Multivariate analysis of characteristics affecting antibody responses (≥1280 AU/mL for Abbott and > 210 U/mL for Roche) showed lower odds ratios [ORs] for older age (adjusted OR per 10 year increase [aOR] = 0.62, 95 % confidence interval [95 %CI]; 0.38-1.02), steroid usage (aOR = 0.09, 95 %CI; 0.01-0.60) and regular nonsteroidal anti-inflammatory drugs [NSAIDs] usage (aOR = 0.16, 95 %CI; 0.03-0.88).

Conclusions

Elderly people and steroid and NSAID users had lower antibody responses following the second vaccine dose.

Food insecurity is associated with glycemic markers, and socioeconomic status and low-cost diets are associated with lipid metabolism in Mexican mothers

The association between socioeconomic status (SES) and chronic disease has recently become more evident in middle- and low-income countries. We hypothesized that poor socioeconomic conditions, such as food insecurity, low educational level, or low SES, may restrict access to a healthy diet and may be associated with cardiometabolic risk independently of body fat. This study examined the relation between socioeconomic indicators, body fat, and cardiometabolic disease risk markers in a random sample of mothers living in Queretaro, Mexico. Young and middle-aged mothers (n = 321) answered validated questionnaires to determine SES, food insecurity, and educational level and a semiquantitative food frequency questionnaire to determine dietary patterns and the cost of individual diet. Clinical measurements included anthropometry, blood pressure, lipids profile, glucose, and insulin. Obesity was present in 29% of the participants. Women with moderate food insecurity had higher waist circumference, glucose, insulin, and homeostasis model assessment of insulin resistance than women with food security. High triglyceride concentration and lower levels of high-density lipoprotein and low-density lipoprotein cholesterol were associated with lower SES and lower educational level. Women who consumed a lower carbohydrate diet had higher SES, higher education, and better cardiovascular risk markers. The higher carbohydrate diet profile was the least expensive diet. There was an inverse association between the cost and energy-density of foods. In conclusion, food insecurity was associated with glycemic control markers, and lower SES and education were related to a low-cost, higher carbohydrate diet and to a greater cardiovascular risk. The influence of the social environment on obesity and cardiovascular diseases needs to be further explored.