Disparities in Temporal and Geographic Patterns of Myocardial Infarction Hospitalization Risks in Florida

Odoi, Evah W; Nagle, Nicholas N.; DuClos, Chris; Kintziger, Kristina W.

doi:10.3390/ijerph16234734

Cited by 5 publications

(5 citation statements)

References 98 publications

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“…It is also possible that geographic differences in comorbidities may play a role in the observed DRMR clustering. Previous research indicated that the northern parts of Florida exhibit significantly higher rates of comorbidities, including hypertension, heart disease, and kidney disease, compared to other regions within the state ( Smith et al, 2018 ; Odoi et al, 2019 ). Comorbidities in individuals with diabetes can further increase the risk of mortality through their adverse effects on blood pressure, myocardial infarction, cancer risks, kidney function, and blood sugar control.…”

Section: Discussionmentioning

confidence: 99%

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study

Deb Nath,

Odoi

2024

PeerJ

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Background Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida. Methods Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10–E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango’s flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel. Results A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida. Conclusion Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

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Section: Discussionmentioning

confidence: 99%

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study

Deb Nath,

Odoi

2024

PeerJ

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“…The identification of high-risk clusters in Calhoun, Gadsden, and Jackson counties in Northwest Florida both when hospitalized cases were -divided by the total number of cases and by the county population suggests that despite the lower COVID-19 incidence risks in Northwest Florida compared to Southern Florida, a large proportion of the cases in Northwest Florida were severe enough to warrant hospitalization. Northwest Florida is a predominantly rural region with large proportions of black and older residents, and high burdens of comorbidities such as diabetes, hypertension and cardiovascular disease [ 81 , 100 – 102 ]. Moreover, similar to what was reported for black patients in California [ 103 ], limited access to outpatient testing sites in Northwest Florida [ 37 ] may result in a large proportion of black segments of the population accessing testing in hospitals when symptoms are already severe.…”

Section: Discussionmentioning

confidence: 99%

“…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 – 123 ].…”

Section: Discussionmentioning

confidence: 99%

Geographic disparities in COVID-19 testing and outcomes in Florida

Khan

Odoi

2023

BMC Public Health

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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.

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“…It is worth pointing out that other studies have used FSSS to identify spatial clusters of other health outcomes such as measles ( Tang et al, 2017 ), cancer ( Katayama et al, 2014 ; Amin, Nelson & McDougall, 2018 ), suicide mortality ( Yamaoka et al, 2020 ), and cardiovascular diseases ( Roberson et al, 2019 ; Odoi et al, 2019 ). Recent research in Florida has demonstrated that FSSS is a useful technique for the identification of geographic disparities in stroke prevalence ( Roberson et al, 2019 ) and hospitalizations due to myocardial infarction ( Odoi et al, 2019 ). Suffice it to say that populations living within the identified pre-diabetes clusters identified in the current study have disproportionately high risks of the condition and, therefore, control efforts should focus on these areas in order to reduce the observed disparities.…”

Section: Discussionmentioning

confidence: 99%

Geographic disparities, determinants, and temporal changes in the prevalence of pre-diabetes in Florida

Lord

Roberson

Odoi

2021

PeerJ

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Background Left unchecked, pre-diabetes progresses to diabetes and its complications that are important health burdens in the United States. There is evidence of geographic disparities in the condition with some areas having a significantly high risks of the condition and its risk factors. Identifying these disparities, their determinants, and changes in burden are useful for guiding control programs and stopping the progression of pre-diabetes to diabetes. Therefore, the objectives of this study were to investigate geographic disparities of pre-diabetes prevalence in Florida, identify predictors of the observed spatial patterns, as well as changes in disease burden between 2013 and 2016. Methods The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Counties with significant changes in the prevalence of the condition between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple comparisons using the Simes method. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Multivariable regression models were used to identify determinants of county-level pre-diabetes prevalence. Results The state-wide age-adjusted prevalence of pre-diabetes increased significantly (p ≤ 0.05) from 8.0% in 2013 to 10.5% in 2016 with 72% (48/67) of the counties reporting statistically significant increases. Significant local geographic hotspots were identified. High prevalence of pre-diabetes tended to occur in counties with high proportions of non-Hispanic black population, low median household income, and low proportion of the population without health insurance coverage. Conclusions Geographic disparities of pre-diabetes continues to exist in Florida with most counties reporting significant increases in prevalence between 2013 and 2016. These findings are critical for guiding health planning, resource allocation and intervention programs.

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Disparities in Temporal and Geographic Patterns of Myocardial Infarction Hospitalization Risks in Florida

Cited by 5 publications

References 98 publications

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study

Geographic disparities in COVID-19 testing and outcomes in Florida

Geographic disparities, determinants, and temporal changes in the prevalence of pre-diabetes in Florida

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