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

Cancer-related information behavior among black and hispanics in an NCI-designated comprehensive cancer center catchment

OBJECTIVE

This study aims to better understand health behaviors, particularly health information seeking, and how this impacts cancer care within underserved minority populations in a specific catchment area in Florida.

METHODS

We conducted an analysis of survey data from a 2019 community health survey conducted by the Moffit Cancer Center (MCC). We utilized the Comprehensive Model of Information Seeking (CMIS) as a framework and performed structural equation modeling (SEM) and related statistical analyses.

RESULTS

Our findings confirm that characteristics and demographics present a positive relationship to Online Health Information Seeking (OHIS). We also found that Utility had a negative significant relationship to OHIS.

CONCLUSIONS

We concluded that the CMIS is a useful framework for studying cancer-related information seeking, and that when properly executed in the confines of a study, can lend itself to in-depth statistical analyses as found in SEM.

IMPLICATIONS

The SEM revealed the CMIS to be promising with results in our analysis worthy of further investigation of cancer care and healthcare information access considering undeserved and minority populations.

PRACTICE IMPLICATIONS

Models such as the CMIS can be useful for understanding information seeking behaviors and to design information and communication interventions to improve access and health outcomes.

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.

Developing the Florida Academic Cancer Center Alliance Health Disparities Common Measure: The Florida Health and Ancestry Survey

Purpose

Our specific aim was to develop and assess the consensus-based validity of common measures for understanding health behaviors and ancestry in Florida’s population subgroups and establish the feasibility of wide-scale implementation of the measures and biospecimen collection within three cancer centers’ catchment areas.

Methods

Using the National Cancer Institute’s Grid-Enabled Measures web-based platform and an iterative process, we developed the Florida Health and Ancestry Survey (FHAS). We then used three sampling approaches to implement the FHAS: community-engaged, panel respondent, and random digit dialing (RDD). We asked a subset of participants to provide a saliva sample for future validation of subjective ancestry report with DNA-derived ancestry markers.

Results

This process supported the FHAS content validity. As an indicator of feasibility, the goals for completed surveys by sampling approach were met for two of the three cancer centers, yielding a total of 1438 completed surveys. The RDD approach produced the most representative sample. The panel sampling approach produced inadequate representation of older individuals and males. The community-engaged approach along with social media recruitment produced extreme underrepresentation only for males. Two of the cancer centers mailed biospecimen kits, whereas one did not due to resource constraints. On average, the community engaged approach was more productive in obtaining returned biospecimen samples (80%) than the panel approach (48%).

Conclusions

We successfully developed and implemented the FHAS as a common measure to show its feasibility for understanding cancer health disparities in Florida. We identified sampling approach successes and challenges to obtaining biospecimens for ancestry research.

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

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.