Exploratory Temporal and Spatial Analysis of Myocardial Infarction Hospitalizations in Calgary, Canada

Spatial and temporal analysis of myocardial infarction incidence in Zanjan province, Iran

Background

Myocardial Infarction (MI) is a major important public health concern and has huge burden on health system across the world. This study aimed to explore the spatial and temporal analysis of the incidence of MI to identify potential clusters of the incidence of MI patterns across rural areas in Zanjan province, Iran.

Materials & methods

This was a retrospective and geospatial analysis study of the incidence of MI data from nine hospitals during 2014–2018. Three different spatial analysis methods (Spatial autocorrelation, hot spot analysis and cluster and outlier analysis) were used to identify potential clusters and high-risk areas of the incidence of MI at the study area.

Results

Three thousand eight hundred twenty patients were registered at Zanjan hospitals due to MI during 2014–2018. The overall age-adjusted incidence rate of MI was 343 cases per 100,000 person which was raised from 88 cases in 2014 to 114 cases in 2018 per 100,000 person-year (a 30% increase, P < 0.001). Golabar region had the highest age-adjusted incidence rate of MI (515 cases per 100,000 person). Five hot spots and one high-high cluster were detected using spatial analysis methods.

Conclusion

This study showed that there is a great deal of spatial variations in the pattern of the incidence of MI in Zanjan province. The high incidence rate of MI in the study area compared to the national average, is a warning to local health authorities to determine the possible causes of disease incidence and potential drivers of high-risk areas. The spatial cluster analysis provides new evidence for policy-makers to design tailored interventions to reduce the incidence of MI and allocate health resource to unmet need areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11695-8.

Temporal and spatial effect of air pollution on hospital admissions for myocardial infarction: a case-crossover study

BACKGROUND

In studies showing associations between ambient air pollution and myocardial infarction (MI), data have been lacking on the inherent spatial variability of air pollution. The aim of this study was to determine whether the long-term spatial distribution of air pollution influences short-term temporal associations between air pollution and admission to hospital for MI.

METHODS

We identified adults living in Calgary who were admitted to hospital for an MI between 2004 and 2012. We evaluated associations between short-term exposure to air pollution (ozone [O₃], nitrogen dioxide [NO₂], sulfur dioxide [SO₂], carbon monoxide [CO], particulate matter < 10 μm in diameter [PM₁₀] and particulate matter < 2.5 μm in diameter [PM_2.5]), and hospital admissions for MI using a time-stratified, case-crossover study design. Air Quality Health Index (AQHI) scores were calculated from a composition of O₃, NO₂ and PM_2.5. Conditional logistic regression models were stratified by low, medium and high levels of neighbourhood NO₂ concentrations derived from land use regression models; results of these analyses are presented as odds ratios (ORs) with 95% confidence intervals (CIs).

RESULTS

From 2004 to 2012, 6142 MIs were recorded in Calgary. Individuals living in neighbourhoods with higher long-term air pollution concentrations were more likely to be admitted to hospital for MI after short-term elevations in air pollution (e.g., 5-day average NO₂: OR 1.20, 95% CI 1.03-1.40, per interquartile range [IQR]) as compared with regions with lower air pollution (e.g., 5-day average NO₂: OR 0.90, 95% CI 0.78-1.04, per IQR). In high NO₂ tertiles, the AQHI score was associated with MI (e.g., 5-day average OR 1.13, 95% CI 1.02-1.24, per IQR; 3-day average OR 1.13, 95% CI 1.04-1.23, per IQR).

INTERPRETATION

Our results show that the effect of air pollution on hospital admissions for MI was stronger in areas with higher NO₂ concentrations than that in areas with lower NO₂ concentrations. Individuals living in neighbourhoods with higher traffic-related pollution should be advised of the health risks and be attentive to special air quality warnings.

Atmospheric Pollution and Hospitalization for Cardiovascular and Respiratory Diseases in the City of Manaus from 2008 to 2012

Objective

To relate the levels of air pollution and hospital admissions for cardiovascular and respiratory diseases in the city of Manaus in Brazil from 2008 to 2012.

Method

This is an ecological time-series study among children (under 5 years of age) and elderly (above 60 years of age). Data on the daily number of hospitalizations for cardiovascular and respiratory diseases, pollutants (PM_2.5), temperature, and humidity were used. Poisson generalized additive models were used to estimate the association between variables. Increases in hospitalizations for cardiovascular and respiratory diseases were estimated for the interquartile range (IQR) daily mean level of each variable studied, with a confidence interval of 95%.

Results

Respiratory diseases and children: −0.40% (95% CI: −1.11, 0.30), 0.59% (95% CI: −0.35, 1.52), and 0.47% (95% CI: −3.28, 4.21) for PM_2.5, temperature, and humidity, respectively. Respiratory diseases and elderly: 0.19% (95% CI: −0.93, 1.31), −0.10% (95% CI: −1.85, 1.65), and −6.17% (95% CI: −13.08, 0.74) for PM_2.5, temperature, and humidity, respectively. Cardiovascular diseases and elderly: −0.18% (95% CI: −0.86, 0.50), −0.04% (95% CI: −1.10, 1.03), and −3.37% (95% CI: −7.59, 0.85) for PM_2.5, temperature, and humidity, respectively.

Conclusions

The time-series study found no significant association between PM_2.5, temperature, humidity, and hospitalization, unlike the evidences provided by the present academic literature. Since there is no air quality monitoring network in Manaus and the option available in the present study was to reproduce some information obtained from remote sensing, there is a need for implementation of ground monitoring stations for health and environmental studies in the region.