Geographical clustering of incident acute myocardial infarction in Denmark: A spatial analysis approach

Telemedicine as a tool for bridging geographical inequity: insights in geospatial interactions from a study on chronic heart failure patients

INTRODUCTION

Chronic heart failure patients experience large disparities in quality of and access to treatment, with rural populations receiving lower levels of care. Telemonitoring of patients is increasingly being used as an important tool for improving patient management and care and might reduce geographical inequities in healthcare.

METHODS

We investigate the presence and magnitude of a geospatial interaction effect on the health benefit of a supplementary telemedicine intervention, by analyzing the relationship between distance to regular place of treatment and the benefit of telemedicine in a secondary analysis of data from a previously conducted RCT. We use change in EQ5D health status, SF-36 Physical component score and SF-36 Mental component score as the outcomes. In the unadjusted analysis, intervention group and distance group and the interaction term are included as the independent variables, in the adjusted analysis, multiple socioeconomic and health related variables are included to account for potential confounders.

RESULTS

We find evidence of a significant interaction between the effects of telemedicine and long distance to treatment for change in EQ5D health status (unadjusted: p = 0.016, adjusted p = 0.009) and unadjusted but not adjusted mental component score (unadjusted: p = 0.013, adjusted p = 0.0728), for the change in physical component score the interaction term was not significant (unadjusted: p = 0.118, adjusted p = 0.092).

CONCLUSION

In our study we find that supplementary telemedicine is likely to reduce the health access inequities associated with geographical distance for chronic heart failure patients. However, our sample size was modest and further research is needed to confirm these findings.

Modelling the spatial risk pattern of dementia in Denmark using residential location data: A registry-based national cohort

Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia.

Association between travel distance and face-to-face consultations with general practitioners before an incident acute myocardial infarction: a nationwide register-based spatial epidemiological study

OBJECTIVES

This study examined the association between travel distance to the general practitioner's (GP) office and no face-to-face GP consultation within 1 year before an incident acute myocardial infarction (AMI).

DESIGN

A prospective cohort study using multilevel spatial logistic regression analysis of nationwide register data.

SETTING

Nationwide study including contacts to GPs in Denmark prior to an incident AMI in 2005-2017.

PARTICIPANTS

121 232 adults (≥30 years) with incident AMI were included in the study.

PRIMARY AND SECONDARY OUTCOMES MEASURES

The primary outcome was odds of not having a face-to-face GP consultation within 1 year before an incident AMI.

RESULTS

In total, 13 108 (10.8%) of the 121 232 individuals with incident AMI had no face-to-face consultation with the GP within 1 year before the AMI. Population density modified the association between travel distance and no face-to-face GP consultation. Increased odds of no face-to-face GP consultation was observed for medium (25th-75th percentile/1123-5449 m) and long (>75th percentile/5449 m) compared with short travel distance (<25th percentile/1123 m) among individuals living in small cities (OR (95% credible intervals) of 1.19 (1.10 to 1.29) and 1.19 (1.06 to 1.33), respectively) and rural areas (1.46 (1.26 to 1.68) and 1.48 (1.29 to 1.68), respectively). No association was observed for individuals living in large cities and the capital.

CONCLUSIONS

Travel distance above approximately 1 km was significantly associated with no face-to-face GP consultation before an incident AMI among individuals living in small cities and rural areas. The structure of the healthcare system should consider the importance of geographical distance between citizens and the GP in remote areas.

Measuring Accessibility to Healthcare Using Taxi Trajectories Data: A Case Study of Acute Myocardial Infarction Cases in Beijing

Several methods have been applied to measure healthcare accessibility, ie, the Euclidean distance, the network distance, and the transport time based on speed limits. However, these methods generally produce less accurate estimates than actual measurements. This research proposed a method to estimate historical healthcare accessibility more accurately by using taxi Global Positioning System (GPS) traces. The proposed method's advantages were evaluated vis a case study using acute myocardial infarction (AMI) cases in Beijing in 2008. Comparative analyses of the new measure and three conventionally used measures suggested that the median estimated transport time to the closest hospital with percutaneous coronary intervention (PCI) capability for AMI patients was 5.72 minutes by the taxi GPS trace-based measure, 2.42 minutes by the network distance-based measure, 2.28 minutes by the speed limit-based measure, 1.73 minutes by the Euclidean distance-based measure; and the estimated proportion of patients who lived within 5 minutes of a PCI-capable hospital was 38.17%, 89.20%, 92.52%, 95.05%, respectively. The three conventionally used measures underestimated the travel time cost and overestimated the percentage of patients with timely access to healthcare facilities. In addition, the new measure more accurately identifies the areas with low or high access to healthcare facilities. The taxi GPS trace-based accessibility measure provides a promising start for more accurately estimating accessibility to healthcare facilities, increasing the use of medical records in studying the effects of historical healthcare accessibility on health outcomes, and evaluating how accessibility to healthcare changes over time.

Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach

In 2016, the British government acknowledged the importance of reducing antimicrobial prescriptions to avoid the long-term harmful effects of overprescription. Prescription needs are highly dependent on the factors that have a spatiotemporal component, such as bacterial outbreaks and urban densities. In this context, density-based clustering algorithms are flexible tools to analyze data by searching for group structures and therefore identifying peer groups of GPs with similar behavior. The case of Scotland presents an additional challenge due to the diversity of population densities under the area of study. We propose here a spatiotemporal clustering approach for modeling the behavior of antimicrobial prescriptions in Scotland. Particularly, we consider the density-based spatial clustering of applications with noise algorithm (DBSCAN) due to its ability to include both spatial and temporal data. We extend this approach into two directions. For the temporal analysis, we use dynamic time warping to measure the dissimilarity between time series while taking into account effects such as seasonality. For the spatial component, we propose a new way of weighting spatial distances with continuous weights derived from a Kernel density estimation-based process. This makes our approach suitable for cases with different local densities, which presents a well-known challenge for the original DBSCAN. We apply our approach to antibiotic prescription data in Scotland, demonstrating how the findings can be used to compare antimicrobial prescription behavior within a group of similar peers and detect regions of extreme behaviors.

Examining Predictors of Myocardial Infarction

Cardiovascular diseases are the leading cause of death in the United States. This study analyzed predictors of myocardial infarction (MI) for those aged 35 and older based on demographic, socioeconomic, geographic, behavioral, and risk factors, as well as access to healthcare variables using the Center for Disease (CDC) Control Behavioral Risk Factor Surveillance System (BRFSS) survey for the year 2019. Multiple quasibinomial models were generated on an 80% training set hierarchically and then used to forecast the 20% test set. The final training model proved somewhat capable of prediction with a weighted F1-Score = 0.898. A complete model based on statistically significant variables using the entirety of the dataset was compared to the same model built on the training set. Models demonstrated coefficient stability. Similar to previous studies, age, gender, marital status, veteran status, income, home ownership, employment status, and education level were important demographic and socioeconomic predictors. The only geographic variable that remained in the model was associated with the West North Central Census Division (in-creased risk). Statistically important behavioral and risk factors as well as comorbidities included health status, smoking, alcohol consumption frequency, cholesterol, blood pressure, diabetes, stroke, chronic obstructive pulmonary disorder (COPD), kidney disease, and arthritis. Three access to healthcare variables proved statistically significant: lack of a primary care provider (Odds Ratio, OR = 0.853, p < 0.001), cost considerations prevented some care (OR = 1.232, p < 0.001), and lack of an annual checkup (OR = 0.807, p < 0.001). The directionality of these odds ratios is congruent with a marginal effects model and implies that those without MI are more likely not to have a primary provider or annual checkup, but those with MI are more likely to have missed care due to the cost of that care. Cost of healthcare for MI patients is associated with not receiving care after accounting for all other variables.

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.

Habitat Characteristics as Potential Drivers of the Angiostrongylus daskalovi Infection in European Badger (Meles meles) Populations

From 2016 to 2020, an investigation was carried out to identify the rate of Angiostrongylus spp. infections in European badgers in Hungary. During the study, the hearts and lungs of 50 animals were dissected in order to collect adult worms, the morphometrical characteristics of which were used for species identification. PCR amplification and an 18S rDNA-sequencing analysis were also carried out. Global and local spatial autocorrelation methods were used to detect high-rated and low-rated infected animal clusters. We conducted a binary logistic regression analysis along with hierarchical agglomerative clustering to determine the relation between selected biotic and abiotic variables, and the prevalence of an A. daskalovi infection. We found a high prevalence (72%) and moderate mean intensity (14.1) of Angiostrongylus sp. infection. Morphology and sequencing revealed that all animals were infected by A. daskalovi. The results of both spatial autocorrelations suggested that the spatial distribution of infected badgers was more spatially clustered than random. The results of an analysis of the correlation between habitat characteristics and infection showed that the infected animals could be associated with dry and open landscape habitats without extended and connected canopy. It is suggested that the territorial behaviour of badgers and the landscape-directed aggregation of potential intermediate hosts might be the drivers of an A. daskalovi infection.

Geographical variation and clustering are found in atrial fibrillation beyond socioeconomic differences: a Danish cohort study, 1987-2015

BACKGROUND

The prevalence and incidence rate of atrial fibrillation (AF) increase worldwide and AF is a risk factor for more adverse cardiovascular diseases including stroke. Approximately 44% of AF cases cannot be explained by common individual risk factors and risk might therefore also be related to the environment. By studying geographical variation and clustering in risk of incident AF adjusted for socioeconomic position at an individual level, potential neighbourhood risk factors could be revealed.

METHODS

Initially, yearly AF incidence rates 1987-2015 were estimated overall and stratified by income in a register-based cohort study. To examine geographical variation and clustering in AF, we used both spatial scan statistics and a hierarchical Bayesian Poisson regression analysis of AF incidence rates with random effect of municipalities (n = 98) in Denmark in 2011-2015.

RESULTS

The 1987-2015 cohort included 5,453,639 individuals whereof 369,800 were diagnosed with an incident AF. AF incidence rate increased from 174 to 576 per 100,000 person-years from 1987 to 2015. Inequality in AF incidence rate ratio between highest and lowest income groups increased from 23% in 1987 to 38% in 2015. We found clustering and geographical variation in AF incidence rates, with incidence rates at municipality level being up to 34% higher than the country mean after adjusting for socioeconomic position.

CONCLUSIONS

Geographical variations and clustering in AF incidence rates exist. Compared to previous studies from Alberta, Canada and the United States, we show that geographical variations exist in a country with free access to healthcare and even when accounting for socioeconomic differences at an individual level. An increasing social inequality in AF was seen from 1987 to 2015. Therefore, when planning prevention strategies, attention to individuals with low income should be given. Further studies focusing on identification of neighbourhood risk factors for AF are needed.

Geographical inequalities in acute myocardial infarction beyond neighbourhood-level and individual-level sociodemographic characteristics: a Danish 10-year nationwide population-based cohort study

OBJECTIVE

This study examined whether geographical patterns in incident acute myocardial infarction (AMI) were explained by neighbourhood-level and individual-level sociodemographic characteristics.

DESIGN

An open cohort study design of AMI-free adults (age ≥30 years) with a residential location in Denmark in 2005-2014 was used based on nationwide administrative population and health register data linked by the unique personal identification number. Poisson regression of AMI incidence rates (IRs) with a geographical random effect component was performed using a Bayesian approach. The analysis included neighbourhood-level variables on income, ethnic composition, population density and population turnover and accounted for individual-level age, sex, calendar year, cohabitation status, income and education.

SETTING

Residents in Denmark (2005-2014).

PARTICIPANTS

The study population included 4 128 079 persons (33 907 796 person-years at risk) out of whom 98 265 experienced an incident AMI.

OUTCOME MEASURE

Incident AMI registered in the National Patient Register or the Register of Causes of Death.

RESULTS

Including individual and neighbourhood sociodemographic characteristics in the model decreased the variation in IRs of AMI. However, living in certain areas was associated with up to 40% increased IRs of AMI in the adjusted model and accounting for sociodemographic characteristics only moderately changed the geographical disease patterns.

CONCLUSIONS

Differences in sociodemographic characteristics of the neighbourhood and individuals explained part, but not all of the geographical inequalities in incident AMI. Prevention strategies should address the confirmed social inequalities in incident AMI, but also target the areas with a heavy disease burden to enable efficient allocation of prevention resources.

The estimate of average age at the onset of acute myocardial infarction in Iran: A systematic review and meta-analysis study

BACKGROUND

Research indicates that the age of onset of first acute myocardial infarction (AMI) is an essential element in the life expectancy that has been decreasing in developing countries. There are various studies performed in Iran reporting a range of ages at time of AMI. Thus, this meta-analysis study is designed to determine the mean age at first AMI in the Iranian population.

METHODS

All studies that met the inclusion and exclusion criteria were reviewed using standard keywords in the databases from 2000 to 2016. Two raters verified a total of 658 articles. Sixteen studies met the inclusion criteria of this study for meta-analysis. Cochran test and I-squared (I2) were used for samples’ homogeneity. Pooled estimates of mean were calculated using the random effects inverse-variance model.

RESULTS

The mean age of AMI varied between 55.9 to 62.9 years among the primary studies. The pooled mean age of first AMI with a 95% confidence interval (CI) for the total sample, men, and women were 59 (58.9, 60.4), 58.7 (58.3, 59.2), and 64.2 (63.5, 64.8), respectively.

CONCLUSION

Our meta-analysis shows that the mean age of first onset of AMI in Iranian people is slightly lower than that reported elsewhere; and it is lower for men than for women.

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

Spatial and temporal analyses are critical to understand the pattern of myocardial infarction (MI) hospitalizations over space and time, and to identify their underlying determinants. In this paper, we analyze MI hospitalizations in Calgary from 2004 to 2013, stratified by age and gender. First, a seasonal trend decomposition analyzes the seasonality; then a linear regression models the trend component. Moran's I and hot spot analyses explore the spatial pattern. Though exploratory, results show that most age and gender groups feature a statistically significant decline over the 10 years, consistent with previous studies in Canada. Decline rates vary across ages and genders, with the slowest decline observed for younger males. Each gender exhibits a seasonal pattern with peaks in both winter and summer. Spatially, MI hot spots are identified in older communities, and in socioeconomically and environmentally disadvantaged communities. In the older communities, higher MI rates appear to be more highly associated with demographics. Conversely, worse air quality appears to be locally associated with higher MI incidence in younger age groups. The study helps identify areas of concern, where MI hot spots are identified for younger age groups, suggesting the need for localized public health policies to target local risk factors.

Geographical variation in a fatal outcome of acute myocardial infarction and association with contact to a general practitioner

BACKGROUND

Geographical variation in incidence and mortality of acute myocardial infarction (AMI) is present in Denmark. We aimed at examining the association between contact to a general practitioner (GP) the year before AMI and a fatal outcome of AMI.

METHODS

Register-based data and individual-level addresses including 69,608 individuals with AMI in 2006-2011. A Bayesian hierarchical logistic regression model was used to examine the association.

RESULTS

A fatal outcome of AMI was seen among 12.0% (78%) of individuals with (without) contact to a GP the year before AMI. A significant association was estimated.

CONCLUSIONS

A fatal outcome of AMI was significantly associated with contact to a GP. A high population to GP ratio and long distance to GP could not explain the increased odds of a fatal outcome of AMI for individuals with no contact to a GP.