Psychosocial comorbidities of diabetes during the COVID-19 pandemic in Iran.

BACKGROUND: The psychosocial impacts of the COVID-19 pandemic are mainly focused on the general population, while pandemics do not impact the mental health of the entire population uniformly, especially vulnerable populations with underlying health conditions. This study aimed to investigate diabetes psychosocial comorbidities among Iranians with type 1 diabetes (T1D) during the COVID-19 pandemic. This study aimed to investigate diabetes psychosocial comorbidities among Iranians with type 1 diabetes (T1D) during the COVID-19 pandemic. MATERIALS AND METHODS: This was a cross-sectional study of 212 adults with T1D in different cities in Iran. Study participants completed an online survey in April-June 2020. The survey collected self-reported data on diabetes psychosocial comorbidities (i.e. diabetes burnout, diabetes distress, and depressive symptoms). Demographic and COVID-19 data before and during the pandemic were also collected. Responses were analyzed using ordinary least squares and logistic regression methods. RESULTS: Around 17.5% reported being tested for COVID-19 virus, 8% were diagnosed positive, 10.8% were hospitalized, and 92.9% followed precaution recommendations during the pandemic. Participants had high levels of diabetes distress (57.1%), depressive symptoms (60.8%), and diabetes burnout (mean score = 3.1 out of 5). During the pandemic, trouble paying for the very basic needs was a consistent factor increasing the risk of diabetes distress, diabetes burnout, and depressive symptoms. Lack of access to diabetes care was only associated with diabetes burnout, while diabetes hospitalization/emergency department (ED) visit was associated with diabetes distress. Existing diabetes disparities before the pandemic were also associated with higher scores of diabetes psychosocial comorbidities [accessing diabetes supplies and medications (P < 0.0001) and places for physical exercise (P < 0.0333)]. CONCLUSION: The negative impact of the COVID-19-related changes on individuals with diabetes, as one of the most vulnerable populations, must be recognized alongside the physical, financial, and societal impact on all those affected. Psychological interventions should be implemented urgently in Iran, especially for those with such characteristics.

Geographic disparities in COVID-19 testing and outcomes in Florida.

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.

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

Knowledge of geographical disparities in myocardial infarction (MI) is critical for guiding health planning and resource allocation. The objectives of this study were to identify geographic disparities in MI hospitalization risks in Florida and assess temporal changes in these disparities between 2005 and 2014. This study used retrospective data on MI hospitalizations that occurred among Florida residents between 2005 and 2014. We identified spatial clusters of hospitalization risks using Kulldorff's circular and Tango's flexible spatial scan statistics. Counties with persistently high or low MI hospitalization risks were identified. There was a 20% decline in hospitalization risks during the study period. However, we found persistent clustering of high risks in the Big Bend region, South Central and southeast Florida, and persistent clustering of low risks primarily in the South. Risks decreased by 7%-21% in high-risk clusters and by 9%-28% in low-risk clusters. The risk decreased in the high-risk cluster in the southeast but increased in the Big Bend area during the last four years of the study. Overall, risks in low-risk clusters were ahead those for high-risk clusters by at least 10 years. Despite MI risk declining over the study period, disparities in MI risks persist. Eliminating/reducing those disparities will require prioritizing high-risk clusters for interventions.

Geographic disparities and temporal changes in risk of death from myocardial infarction in Florida, 2000-2014.

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 temporal changes in geographic disparities in MI mortality risks in Florida from 2000 to 2014. METHODS: This is a retrospective ecologic study with county as the spatial unit of analysis. We obtained data for MI deaths occurring among Florida residents between 2000 and 2014 from the Florida Department of Health, and calculated county-level age-adjusted MI mortality risks and Spatial Empirical Bayesian smoothed MI mortality risks. We used Kulldorff's circular spatial scan statistics and Tango's flexible spatial scan statistics to identify spatial clusters. RESULTS: There was an overall decline of 48% in MI mortality risks between 2000 and 2014. However, we found substantial, persistent disparities in MI mortality risks, with high-risk clusters occurring primarily in rural northern counties and low-risk clusters occurring exclusively in urban southern counties. MI mortality risks declined in both low- and high-risk clusters, but the latter showed more dramatic decreases during the first nine years of the study period. Consequently, the risk difference between the high- and low-risk clusters was smaller at the end than at the beginning of the study period. However, the rates of decline levelled off during the last six years of the study, and there are signs that the risks may be on an upward trend in parts of North Florida. Moreover, MI mortality risks for high-risk clusters at the end of the study period were on par with or above those for low-risk clusters at the beginning of the study period. Thus, high-risk clusters lagged behind low-risk clusters by at least 1.5 decades. CONCLUSION: Myocardial infarction mortality risks have decreased substantially during the last 15 years, but persistent disparities in MI mortality burden still exist across Florida. Efforts to reduce these disparities will need to target prevention programs to counties in the high-risk clusters.