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Background
Identifying disparities in myocardial infarction (MI) burden and assessing its temporal changes are critical for guiding resource allocation and policies geared towards reducing/eliminating health disparities. Our objectives were to: (a) investigate the spatial distribution and clusters of MI mortality risk in Florida; and (b) assess tem...
Context in source publication
Context 1
... in the North ( Fig. 2) and large low-risk clusters predominantly in South Florida (Fig. 3). A total of 6-11 high-risk clusters were identified during each of the three-year time intervals between 2000 and 2014. The largest high-risk clusters were located in northwest and north central parts of Florida ( Fig. 2), which are predominantly rural ( Fig. 4) based on the Florida Department of Health Office of Rural Health definition of rural areas i.e. population density < 100 people/sq. mile [22]. Smaller high-risk clusters were identified in Central, West Central, Northeast, and Southeast Florida, with the urban high-risk cluster in Miami-Dade County being the most prominent ( Fig. 2). ...
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Background: There are limited studies assessing rural–urban disparities among older adults in Africa including South Africa.
Aim: This study explores rural–urban health disparities among older adults in a population-based survey in South Africa.
Setting: Data for this study emanated from the 2008 study on ‘Global Ageing and Adult Health (SAGE) wa...
Citations
... Population density, which was used as a measure of rurality in this study, was significantly associated with DRH rates, with more densely populated areas tending to have lower rates of hospitalizations. In the present study, the association between population density and DRH rates was strongest in northern Florida, where spatial clusters of pre-diabetes and diabetes prevalence [20,22,88], as well as stroke prevalence [89] and myocardial infarction mortality [90] have been identified. Previous research suggests that rural residents are more likely to report delaying health care due to cost than urban residents [91]. ...
Background
Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales.
Methods
Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients.
Results
Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access).
Conclusions
The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations.
... It is worth noting that despite reduced opportunities for the SARS-COV-2 virus to spread in the sparsely populated rural Florida counties, these locales have, on average, larger proportions of older (> 65 years) residents and higher burdens of underlying chronic health conditions such as obesity [117] diabetes [81,101], hypertension [100], and heart disease [102,118,119] compared to urban counties. Rural areas also tend to have low health insurance rates and a less robust healthcare infrastructure [120][121][122][123]. ...
Background
Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.
Methods
Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff’s circular spatial scan statistics or Tango’s flexible spatial scan statistics and their locations were visually displayed using QGIS.
Results
Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1–3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases.
Conclusions
Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.
... Our study had several notable strengths. First, data analysis in most previous geographic studies has focused either on AMI hospital admissions or mortality, which may lead to underestimation of the total AMI burden [37][38][39]. We obtained data on AMI incidence that covered both hospitalized AMI cases and out-of-hospital deaths from the BCDSS [12,14], thus providing more accurate depictions of spatiotemporal patterns for the AMI burden. ...
Acute myocardial infarction (AMI) poses a serious disease burden in China, but studies on small-area characteristics of AMI incidence are lacking. We therefore examined temporal trends and geographic variations in AMI incidence at the township level in Beijing. In this cross-sectional analysis, 259,830 AMI events during 2007–2018 from the Beijing Cardiovascular Disease Surveillance System were included. We estimated AMI incidence for 307 consistent townships during consecutive 3-year periods with a Bayesian spatial model. From 2007 to 2018, the median AMI incidence in townships increased from 216.3 to 231.6 per 100,000, with a greater relative increase in young and middle-aged males (35–49 years: 54.2%; 50–64 years: 33.2%). The most pronounced increases in the relative inequalities was observed among young residents (2.1 to 2.8 for males and 2.8 to 3.4 for females). Townships with high rates and larger relative increases were primarily located in Beijing’s northeastern and southwestern peri-urban areas. However, large increases among young and middle-aged males were observed throughout peri-urban areas. AMI incidence and their changes over time varied substantially at the township level in Beijing, especially among young adults. Targeted mitigation strategies are required for high-risk populations and areas to reduce health disparities across Beijing.
... Spatial analyses of the incidence of MI will generate new knowledge to identify high risk areas and investigate the potential impact of environmental risk factors on the incidence of MI [10][11][12]. Geographic Information Systems (GIS) is a powerful tool to support geocoding MI patients' locations, conducting spatial analysis and visualizing the incidence of MI patterns at a finer geography level. GIS has the capacity to link spatial data with different source of MI attribute data to develop a big picture for the incidence of MI pattern across communities. ...
... There are several clustering analysis methods to determine the high-risk areas such as hot spot analysis, cluster and outlier analysis (Anselin Local Moran's I), K-means, geographical weighted regression (GWR) [19]. Hot spot analysis and Anselin Local Moran's I statistic were common spatial techniques to examine the clustering in the pattern of AAIR MI at Zanjan province [11,16]. ...
... For example, Khalkhal County was recognized as a LH outlier of MI in the first 3 years of the study, while no clusters was defined among the counties of Ardabil province [27]. The results of studies conducted in Iran [16], Canada [14], Denmark [15], USA [11], Sub-Saharan Africa [22], Korea [31] and Sweden [13] support the efficiency of Anselin Local Moran I analyses to identify the potential high-risk areas of diseases and their results are in line with our findings. The results of this study showed that the highest AAIRMI was observed in rural areas compared to urban areas. ...
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.
... Tango's flexible spatial scan statistic (FSSS) was used to identify the geographic locations of high-risk clusters of pertussis using FleXScan (Tango & Takahashi, 2005). Since the flexible spatial scanning window detects both circular and irregularly shaped clusters (Jacquez, 2009;Odoi et al., 2019;Lord, Roberson & Odoi, 2021), it is ideal for situations like % Population Living in a Rural Area this one involving investigation of clusters whose shapes are unknown at the outset . It has been shown that Tango's flexible scan statistics has good power as well as the ability to detect irregularly shaped clusters more accurately than Kulldorff's spatial scan statistics (Tango & Takahashi, 2005). ...
Background: Pertussis is a toxin-mediated respiratory illness caused by Bordetella pertussis that can result in severe complications and death, particularly in infants. Between 2008 and 2011, children less than 3 months old accounted for 83% of the pertussis deaths in the United States. Understanding the geographic disparities in the distribution of pertussis risk and identifying high risk geographic areas is necessary for guiding resource allocation and public health control strategies. Therefore, this study investigated geographic disparities and temporal changes in pertussis risk in Florida from 2010 to 2018. It also investigated socioeconomic and demographic predictors of the identified disparities. Methods: Pertussis data covering the time period 2010-2018 were obtained from Florida HealthCHARTS web interface. Spatial patterns and temporal changes in geographic distribution of pertussis risk were assessed using county-level choropleth maps for the time periods. Tango's flexible spatial scan statistics were used to identify high-risk spatial clusters which were displayed in maps. Ordinary least squares (OLS) regression was used to identify significant predictors of county-level risk. Residuals of the OLS model were assessed for model assumptions including spatial autocorrelation.
Results: County-level pertussis risk varied from 0 to 116.31 cases per 100,000 people during the study period. A total of 11 significant (p < 0.05) spatial clusters were identified with risk ratios ranging from 1.5 to 5.8. Geographic distribution remained relatively consistent over time with areas of high risk persisting in the western panhandle, northeastern coast, and along the western coast. Although county level pertussis risks generally increased from 2010–2012 to 2013–2015, risk tended to be lower during the 2016–2018 time period. Significant predictors of county-level pertussis risk were rurality, percentage of females, and median income. Counties with high pertussis risk tended to be rural (p = 0.021), those with high median incomes (p = 0.039), and those with high percentages of females (p < 0.001).
Conclusion: There is evidence that geographic disparities exist and have persisted over time in Florida. This study highlights the application and importance of Geographic Information Systems (GIS) technology and spatial statistical/epidemiological tools in identifying areas of highest disease risk so as to guide resource allocation to reduce health disparities and improve health for all.
... Knowledge of the shocking extent of this difference, as well as local initiative, resulted in the launch of the North Karelia Project, which eventually demonstrated that the incidence of myocardial infarction could be decreased by the population-level control of the risk factors (21). Several publications appeared following these classical studies, whose authors called attention to regional differences regarding the incidence and treatment of myocardial infarction (13,(22)(23)(24)(25)(26)(27)(28)(29). Upon examining the same problem, several articles underlined the role of the social environment of the population (30)(31)(32)(33). ...
Aim: To examine the incidence and treatment of acute myocardial infarction (AMI) as well as 30-day and 1-year prognoses of patients in three major regions of Hungary by analysing data from the country’s continuous and mandated infarction registry. Methods and results: The total population of Hungary is currently 9.8 million: 39% live in the eastern region (ER), 31% in the Central region (CR) and 30% in the western region (WR). These regions exhibited significant differences in income and people exposed to poverty. Population over 30 years, the age-standardised incidence of AMI was 177.5 (175.7–179.3) per 100,000 person-year. During hospital treatment, 82.5%–84.6% of patients with ST-elevation (STEMI) and 54.8%–81.8% without ST-elevation (NSTEMI) underwent PCI. The total ischaemic time was shortest in WR:221 minutes. In the STEMI group, the 30-day mortality rates of males were lowest in the WR (p = 0.03). If PCI was performed, mortality rates for both sexes were lowest in the WR (p < 0.01; p = 0.04). The 1-year mortality rate in the male population who received PCI was lowest in the WR. In the NSTEMI group, the 30-day mortality rate exhibited no differences. Regarding 1-year mortality, those who underwent PCI in the WR showed the lowest mortality. Conclusion: The major regions of Hungary exhibited significant differences regarding the prehospital delay, the incidence, treatment and mortality of AMI. Logistic regression analysis confirmed the independent prognostic significance of the region on the 30-day mortality of patients with STEMI (Hazard ratio = 0.88, p = 0.0114).
... (9) Spatial analyses of MI incidence will generate new knowledge to identify high risk areas and investigate potential impact of environmental risk factors on MI incidence. (10)(11)(12) Geographic Information Systems (GIS) is a powerful tool to support geocoding MI patients' locations, conducting spatial analysis and visualizing the MI incidence patterns at ner geography level. GIS has the capacity to link spatial data with different source of MI attribute data to develop a big picture for the MI incidence pattern across communities. ...
... (19) Hotspot analysis and Anselin Local Moran's I statistic were common spatial techniques which was used in the present study to determine clustering patterns of MI incidence at Zanjan province. (11,16) Spatial autocorrelation (Global Moran's I) Spatial autocorrelation is the natural tendency of a variable to represent similar values as a function of distance between the geographical locations at which it is being measured. (20) Strong autocorrelation occurs when there was a relationship between the values of a variable that are geographically close to each other.(21) ...
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 variation of MI incidence and investigate if there is a spatial clusters in the MI patterns among rural areas in Zanjan province, Iran.Materials & Methods
This was a retrospective and geospatial analysis study using MI incidence data from 2014-2018 from nine hospital information system databases. Three different spatial analysis methods (Spatial autocorrelation, hotspot analysis and cluster and outlier analysis) were used to identify potential clusters and high-risk areas of MI incidence at the study area.Results3,820 patients were registered at Zanjan hospitals due to MI during 2014-2018. The age-adjusted incidence rate of MI was 343 cases per 100,000 person which raised from 88 cases in 2014 to 114 cases in 2018 per 100,000 person (a 30% increase, P<0.001). Golabar region had the highest incidence rate of MI (515 cases per 100,000 person). Five hotspot and one high-high cluster were detected using spatial analysis methods.ConclusionThis showed that there is a great deal of spatial variations in the pattern of MI incidence 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 provided new evidence for policy-makers to design tailored interventions to reduce MI incidence and allocate health resource to unmet need areas.
... Elderly people living in small metropolitan or rural areas were known for lacking adequate health care. 32 In the time of emerging pandemic such as COVID-19, these problems may be aggravated when health-care resources were under pressure. Many small metropolitan or rural hospitals were not equipped to manage infectious patients. ...
Objective
Health inequalities were often exacerbated during the emerging epidemic. This study examined urban and non-urban inequalities in health services among COVID-19 patients aged 65 years or above in Florida, USA, from 2 March to 27 May 2020.
Methods
A retrospective time series analysis was conducted using individual patient records. Multivariable Poisson’s and logistic models were used to calculate adjusted incidence of COVID-19 and the associated rates of emergency department visits, hospitalizations, and deaths.
Results
As of 27 May 2020, there were 13,659 elderly COVID-19 patients (people aged 65 years or above) in Florida and 14.9% of them died. Elderly people living in small metropolitan areas might be less likely to be confirmed with COVID-19 infection than those living in large metropolitan areas. The emergency department visit and hospitalization rates decreased significantly across metropolitan statuses for both men and women. Those patients living in small metropolitan or rural areas were less likely to be hospitalized than those living in large metropolitan areas (35% and 34% vs 41%). Elderly women aged 75 years or above living in rural areas had 113% higher adjusted incidence of COVID-19 than those living in large metropolitan areas, and the rates of hospitalizations were lower compared with those counterparts living in large metropolitan areas (29% vs 46%; odds ratio: 0.37 (0.25–0.54), p < 0.001).
Conclusion
For elderly people living in Florida, USA, those living in small metropolitan or rural areas were less likely to receive adequate health care than those living in large or medium metropolitan areas during the COVID-19 pandemic.
... Our results showing lower MI hospitalization risks in counties with high proportions of rural populations compared with those with low proportions of rural populations are inconsistent with recent ecologic studies showing higher mortality risks from MI 103 and heart disease and ischemic heart disease in rural counties compared with urban counties in Florida, 104,105 and in southeastern United States in general. 106 Our results are also inconsistent with Downloaded from http://ahajournals.org by on April 5, 2021 lower SES, 107,108 lower prevalence of protective health-related behaviors, 109 and higher prevalence of several MI risk factors reported for rural counties in Florida and in the United States in general compared with urban counties. ...
Background
Identifying social determinants of myocardial infarction ( MI ) hospitalizations is crucial for reducing/eliminating health disparities. Therefore, our objectives were to identify sociodemographic determinants of MI hospitalization risks and to assess if the impacts of these determinants vary by geographic location in Florida.
Methods and Results
This is a retrospective ecologic study at the county level. We obtained data for principal and secondary MI hospitalizations for Florida residents for the 2005–2014 period and calculated age‐ and sex‐adjusted MI hospitalization risks. We used a multivariable negative binomial model to identify sociodemographic determinants of MI hospitalization risks and a geographically weighted negative binomial model to assess if the strength of associations vary by location. There were 645 935 MI hospitalizations (median age, 72 years; 58.1%, men; 73.9%, white). Age‐ and sex‐adjusted risks ranged from 18.49 to 69.48 cases/10 000 persons, and they were significantly higher in counties with low education levels (risk ratio [ RR ]=1.033, P <0.0001) and high divorce rate ( RR , 0.995; P =0.018). However, they were significantly lower in counties with high proportions of rural ( RR , 0.996; P <0.0001), black (RR, 1.026; P =0.032), and uninsured populations ( RR , 0.983; P =0.040). Associations of MI hospitalization risks with education level and uninsured rate varied geographically ( P for non‐stationarity test=0.001 and 0.043, respectively), with strongest associations in southern Florida ( RR for <high school education, 1.036–1.041; RR for uninsured rate, 0.971–0.976).
Conclusions
Black race, divorce, rural residence, low education level, and lack of health insurance were significant determinants of MI hospitalization risks, but associations with the latter 2 were stronger in southern Florida. Thus, interventions for addressing MI hospitalization risks need to prioritize these populations and allocate resources based on empirical evidence from global and local models for maximum efficiency and effectiveness.
... Our findings confirmed deficiency in providing health care to elderly people living outside of large or medium metropolitan areas (Casper et al., 2016;Singh et al., 2019). Elderly people living in small towns or rural areas were known for lacking adequate health care (Odoi, Nagle, Roberson, & Kintziger, 2019). In the time of emerging epidemic such as COVID-19, such problems may be aggravated when health care resources were under pressure of a run. ...
Background: Health disparities were often overlooked during the emerging epidemic.
Objectives: This study examined geographic differences in the rates of health care use and deaths among elderly patients.
Methods: Based on individual patient records, multivariate Poisson and logistic models were used to calculate adjusted incidences of COVID-19 and probabilities of emergency department (ED) visits, hospitalizations and deaths.
Results: Of 8,203 elderly patients, 11% died. Elderly people living in small metropolitan areas were half as likely to be diagnosed with COVID-19. Elderly female patients living in small metropolitan areas had much lower rates of ED visits (23% vs. 34%; Odds Ratio (OR): 0.58; 95%confidence interval (CI): 0.41-0.81; p=0.002) and hospitalizations (22% vs. 31%; OR: 0.62; 95%CI: 0.44 - 0.87; p=0.006) than those living in large metropolitan areas. Furthermore, those living in non-metropolitan areas were more likely to be hospitalized than those living in large metropolitan areas (44% vs. 33%; OR: 1.46; 95%CI: 1.07-1.99; p=0.016), especially among elderly men (51% vs. 35%; OR:1.86; 95%CI: 1.18-2.93; p=0.008). Finally, there was a significant linear trend in hospitalization rates among elderly male patients (p for trend = 0.01).
Conclusions: Profound health disparities exist in the time of emerging epidemic.
