Rural-urban differences in stroke risk

Long-term exposure to air pollution and risk of stroke by ecoregions: The REGARDS study

Several cohort studies have found associations between long-term exposure to air pollution and stroke risk. However, it is unclear whether the surrounding ecology may modify these associations. This study evaluates associations of air pollution with stroke risk by ecoregions, which are areas of similar type, quality, and quantity of environmental resources in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. We assessed the incidence of stroke in 26,792 participants (45+ yrs) from the REGARDS study, a prospective cohort recruited across the contiguous United States. One-yr and 3-yr means of PM2.5, PM10, O3, NO2, SO2, and CO were estimated at baseline using data from the Center for Air, Climate, & Energy Solution, and assigned to participants at the census block group level. Incident stroke was ascertained through September 30, 2020. Relations of air pollutants with the risk of incident stroke were estimated using Cox proportional hazards models, adjusting for relevant demographics, behavioral risk factors, and neighborhood urbanicity. Models were stratified by EPA designated ecoregions. A 5.4 μg/m3 (interquartile range) increase in 1-yr PM10 was associated with a hazard ratio (95 %CI) for incident stroke of 1.07 (1.003, 1.15) in the overall study population. We did not find evidence of positive associations for PM2.5, O3, NO2, SO2, and CO in the fully adjusted models. In our ecoregion-specific analysis, associations of PM2.5 with stroke were stronger in the Great Plains ecoregion (HR = 1.44) than other ecoregions, while associations for PM10 were strongest in the Eastern Temperate Forests region (HR = 1.15). The associations between long-term exposure to air pollution and risk of stroke varied by ecoregion. Our results suggests that the type, quality, and quantity of the surrounding ecology can modify the effects of air pollution on risk of stroke.

Differences in health characteristics and health behaviors between rural and non-rural community-dwelling stroke survivors aged ≥65 years in the USA

OBJECTIVES

To examine differences in health characteristics and health behaviors between rural and non-rural stroke survivors in the USA.

METHODS

Data were extracted from the 2017 and 2019 Behavioral Risk Factor Surveillance System (BRFSS) to compare prevalences of health characteristics (i.e., diabetes, disability, poor health, high cholesterol, hypertension, no health care coverage, weight status) and health behaviors (i.e., fruit consumption, vegetable consumption, physical inactivity, high alcohol consumption, smoking) among community-dwelling stroke survivors, stratified by rural status (i.e., rural vs. non-rural). Logistic regression was used to calculate odds ratios (ORs) for health characteristics and health behaviors to examine the association of rural status with each variable of interest (reference group=non-rural).

RESULTS

Data from 14,599 respondents (rural: n = 5,039; non-rural: n = 9,560) were available for analysis. The majority of respondents were female (61.4%), non-Hispanic white (83.2%), previously married (56.1%), had at least some college education (55.2%), and had an annual household income ≥USD $25,000 (56.9%). Prevalences of disability, poor health, weekly aerobic exercise, and smoking were higher among rural respondents compared to non-rural respondents. Logistic regression showed increased odds (odds ratio range: 1.1-1.2) for these variables among rural respondents; however, odds ratios were attenuated after controlling for sociodemographic and health characteristics.

CONCLUSIONS

We did not find evidence of differences in the investigated health characteristics and health behaviors between rural and non-rural community-dwelling stroke survivors in the USA. Additional research is needed to confirm these findings and to identify alternative sociodemographic and health factors that may differ between rural and non-rural community-dwelling stroke survivors.

The Epidemic Characteristics of Stroke Death from 2012 to 2021 in Chongqing, China

INTRODUCTION

Stroke has become a major disease that threatens the global population's health and is a major public health problem that needs to be solved in China. Therefore, it is essential to analyze the trend of the mortality of stroke and its epidemic characteristic of stroke death.

METHODS

Death cases of stroke were reported to the national death registry system by the medical staff of all medical institutions, and the population data every year were obtained from District or County's Statistic Bureau in Chongqing. They were analyzed to calculate the mortality, age-standardized mortality rate by Chinese standardization population (ASMRC), age-specific mortality, proportion, and annual percent of change (APC) according to the ICD-10 code. ASMRC was based on the standard population of the 6th census in China, 2010. The stroke mortality of each subgroup was compared using the χ2 test. Trend analysis was presented by APC.

RESULTS

The crude mortality of stroke increased from 96.29 per 100,000 in 2012 to 115.93 per 100,000 significantly, with the APC of 2.02% (t = 2.82, p = 0.022) in Chongqing. ASMRC of stroke was 56.47 per 100,000 in 2012 and 54.70 per 100,000 in 2021, and its trend change was stable (APC = -0.01, t = 0.07, p = 0.947). The crude mortality of stroke in males was higher than that in females every year (p < 0.05). The death proportion of intracerebral hemorrhage dwindled from 60.53% in 2012 to 49.88% in 2021, whereas the death proportion of ischemic stroke increased from 20.92% in 2012 to 39.96% in 2021. The average age of stroke death was delayed from 73.43 years old in 2012 to 76.52 years old in 2021 significantly (t = 18.12, p < 0.001). The percentage of stroke death at home increased from 75.23% in 2012 to 79.23% in 2021, while the percentage of stroke death at hospitals decreased from 17.89% in 2012 to 15.89% in 2021.

CONCLUSION

The crude mortality of stroke surged, and intracerebral hemorrhage was the main death cause of all subtypes. The mortality of stroke in males and rural residents was higher than that in females and urban residents. Most stroke deaths occurred at home. Male and rural residents were crucial populations for stroke prevention and control. There should be improved medical resources in rural areas and enhanced capability of stroke diagnosis and treatment.

Relationship between perceptions of recurrence risk and depression state among first-episode ischemic stroke patients in rural areas: The mediating role of coping style

AIMS

To investigate the relationship between stroke survivors' perceptions of recurrence risk, coping styles and depression state, and the role coping styles play in mediating that relationship.

DESIGN

A cross-sectional descriptive study.

METHODS

From one hospital in Huaxian, China, 320 stroke survivors were randomly selected as a convenience sample. In this research, the Simplified Coping Style Questionnaire, the Patient Health Questionnaire-9 and the Stroke Recurrence Risk Perception Scale were all used. Structural equation modelling and correlation analysis were used to analyse the data. This research followed the EQUATOR and STROBE checklists.

RESULTS

There were 278 valid survey responses. There were mild to severe depressive symptoms in 84.8% of stroke survivors. In stroke survivors, there was a significant negative relationship (p < 0.01) between the positive coping of perceptions of recurrence risk and their depression state. Recurrence risk perception's impact on depression state was partly mediated, according to mediation studies, by coping style, with the mediation effect accounting for 44.92% of the overall effect.

CONCLUSIONS

The connection between perceptions of recurrence risk and depression state was mediated by the coping mechanisms of stroke survivors. A lower degree of depression state among survivors was connected with positive coping to the beliefs of recurrence risk.

Perception of recurrent risk versus objective measured risk of ischemic stroke in first-ever stroke patients from a rural area in China: A cross-sectional study

OBJECTIVE

Risk perception is critical to the formation of individual health prevention behaviors. A long-term accurate perception of stroke recurrent risks is imperative for stroke secondary prevention. This study aims to explore the level of recurrence risk perceptions and the influential factors of inaccuracy between perceived and objective risk in first-ever ischemic stroke patients from a rural area.

METHODS

From May to November 2020, 284 first-ever ischemic stroke patients were conveniently recruited in a rural area of Henan Province, China. Perceived risk was measured based on self-reported using a numerical rating scale, whereas the objective risk was measured by the Essen Stroke Risk Score. Patients' perceived risk was compared with their objective risk and categorized as "Accurate," "Underestimated," and "Overestimated." The influencing factors of inaccuracy were further evaluated using multivariate regression analyses.

RESULTS

46% of the participants underestimated their stroke risk, while 15.9% overestimated their risks. Patients who were younger (≤65 years), didn't worry about recurrent stroke, and had a low actual recurrent risk were more likely to underestimate their recurrent risk. Patients who were employed, had lower independence, and had greater anxiety were more likely to overestimate their recurrent risk.

CONCLUSIONS

The majority of participants were unable to accurately perceive their own risk of stroke recurrence. Patients' age, working status, worry about recurrent stroke, actual recurrent risk, level of dependence, and anxiety played a role in perception inaccuracy.

PRACTICE IMPLICATIONS

The findings could help healthcare providers gain a better understanding of the level and accuracy of recurrence risk perceptions among first-ever stroke patients in the rural area. Future counseling on the perceived risk of stroke recurrence and individual objective risk assessment could be conducted to help patients better understand their risk of recurrence. Individualized risk communication and multidisciplinary teamwork can be developed to improve the accuracy of recurrence risk perceptions and health behaviors.

Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence

BACKGROUND

Limited studies have focused on the risk assessment of stroke in rural regions. Moreover, the application of artificial intelligence in stroke risk scoring system is still insufficient. This study aims to develop a simplified and visualized risk score with good performance and convenience for rural stroke risk assessment, which is combined with a machine learning (ML) algorithm.

METHODS

Participants of the Henan Rural Cohort were enrolled in this study. The total participants (n = 38,322) were randomly split into a train set and a test set in the ratio of 7:3. An ML algorithm was used to select variables and the logistic regression was then applied to construct the scoring system. The C-statistic and the Brier score (BS) were used to evaluate the discrimination and calibration. The Framingham stroke risk profile (FSRP) and the self-reported stroke risk function (SRSRF) were chosen to be compared.

RESULTS

The Rural Stroke Risk Score (RSRS) was produced in this study, including age, drinking status, triglyceride, type 2 diabetes mellitus, hypertension, waist circumference, and family history of stroke. On validation, the C-statistic was 0.757 (95% CI 0.749-0.765) and the BS was 0.058 in the test set. In addition, the discrimination of RSRS was 6.02% and 7.34% higher than that of the FSRP and SRSRF, respectively.

CONCLUSIONS

A well-performed scoring system for assessing stroke risk in rural residents was developed in this study. This risk score would facilitate stroke screening and the prevention of cardiovascular disease in economically underdeveloped areas.

Demographic and Regional Trends in Stroke-Related Mortality in Young Adults in the United States, 1999 to 2019

Background Despite improvements in the management and prevention of stroke, increasing hospitalizations for stroke and stagnant mortality rates have been described in young adults. However, there is a paucity of contemporary national mortality estimates in young adults. Methods and Results Trends in mortality related to stroke in young adults (aged 25-64 years) were assessed using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database. Age-adjusted mortality rates per 100 000 people with associated annual percentage change were calculated. Joinpoint regression was used to assess the trends in the overall sample and different demographic (sex, race and ethnicity, and age) and geographical (state, urban-rural, and regional) subgroups. Between 1999 and 2019, a total of 566 916 stroke-related deaths occurred among young adults. After the initial decline in mortality in the overall population, age-adjusted mortality rate increased from 2013 to 2019 with an associated annual percentage change of 1.5 (95% CI, 1.1-2.0). Mortality rates were higher in men versus women and in non-Hispanic Black people versus individuals of other races and ethnicities. Non-Hispanic American Indian or Alaskan Native people had a marked increase in stroke-related mortality (annual percentage change 2010-2019: 3.3). Furthermore, rural (nonmetropolitan) counties experienced the greatest increase in mortality (annual percentage change 2012-2019: 3.1) compared with urban (metropolitan) counties. Conclusions Following the initial decline in stroke-related mortality, young adults have experienced increasing mortality rates from 2013 to 2019, with considerable differences across demographic groups and regions.

Stroke Risk Prediction with Machine Learning Techniques

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

Behavior change, health, and health disparities 2021: Rural addiction and health

This Special Issue of Preventive Medicine (PM) is the 8th in a series on behavior change, health, and health disparities. This is a topic of critical importance to improving U.S. population health. There is broad consensus that personal behavior patterns or lifestyle such as substance abuse, poor food choices, physical inactivity, and non-adherence with medical regimens are among the most important modifiable causes of chronic disease and premature death and contributors to recent decreases in U.S. longevity. While no U.S region is free of these problems, they disproportionately impact rural communities. As in prior Special Issues in this series, we devote considerable space to the ongoing U.S. opioid epidemic while also examining selected issues in rural health disparities involving tobacco use, cancer, and cardiovascular disease. Across each of these topics we have recruited contributions from accomplished investigators, clinicians, and policymakers to acquaint readers with recent advances while also noting knowledge gaps and unresolved challenges.