Spatial patterns and sociodemographic predictors of chronic obstructive pulmonary disease in Florida.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic, inflammatory respiratory disease that obstructs airflow and decreases lung function and is a leading cause death globally. In the United States (US), the prevalence among adults is 6.2%, but increases with age to 12.8% among those 65 years or older. Florida has one of the largest populations of older adults in the US, accounting for 4.5 million adults 65 years or older. This makes Florida an ideal geographic location for investigating COPD as disease prevalence increases with age. Understanding the geographic disparities in COPD and potential associations between its disparities and environmental factors as well as population characteristics is useful in guiding intervention strategies. Thus, the objectives of this study are to investigate county-level geographic disparities of COPD prevalence in Florida and identify county-level socio-demographic predictors of COPD prevalence. Methods: This ecological study was performed in Florida using data obtained from the US Census Bureau, Florida Health CHARTS, and County Health Rankings and Roadmaps. County-level COPD prevalence for 2019 was age-standardized using the direct method and 2020 US population as the standard population. High-prevalence spatial clusters of COPD were identified using Tango's flexible spatial scan statistics. Predictors of county-level COPD prevalence were investigated using multivariable ordinary least squares model built using backwards elimination approach. Multicollinearity of regression coefficients was assessed using variance inflation factor. Shapiro-Wilks, Breusch Pagan, and robust Lagrange Multiplier tests were used to assess for normality, homoskedasticity, and spatial autocorrelation of model residuals, respectively. Results: County-level age-adjusted COPD prevalence ranged from 4.7% (Miami-Dade) to 16.9% (Baker and Bradford) with a median prevalence of 9.6%. A total of 6 high-prevalence clusters with prevalence ratios >1.2 were identified. The primary cluster, which was also the largest geographic cluster that included 13 counties, stretched from Nassau County in north-central Florida to Charlotte County in south-central Florida. However, cluster 2 had the highest prevalence ratio (1.68) and included 10 counties in north-central Florida. Together, the primary cluster and cluster 2 covered most of the counties in north-central Florida. Significant predictors of county-level COPD prevalence were county-level percentage of residents with asthma and the percentage of current smokers. Conclusions: There is evidence of spatial clusters of COPD prevalence in Florida. These patterns are explained, in part, by differences in distribution of some health behaviors (smoking) and co-morbidities (asthma). This information is important for guiding intervention efforts to address the condition, reduce health disparities, and improve population health.

Estimation of population age structure, daily survival rates, and potential to support dengue virus transmission for Florida Keys Aedes aegypti via transcriptional profiling.

Aedes aegypti is an important vector of dengue virus and other arboviruses that affect human health. After being ingested in an infectious bloodmeal, but before being transmitted from mosquito to human, dengue virus must disseminate from the vector midgut into the hemocoel and then the salivary glands. This process, the extrinsic incubation period, typically takes 6-14 days. Since older mosquitoes are responsible for transmission, understanding the age structure of vector populations is important. Transcriptional profiling can facilitate predictions of the age structures of mosquito populations, critical for estimating their potential for pathogen transmission. In this study, we utilized a two-gene transcript model to assess the age structure and daily survival rates of three populations (Key West, Marathon, and Key Largo) of Ae. aegypti from the Florida Keys, United States, where repeated outbreaks of autochthonous dengue transmission have recently occurred. We found that Key Largo had the youngest Ae. aegypti population with the lowest daily survival rate, while Key West had the oldest population and highest survival rate. Across sites, 22.67% of Ae. aegypti females were likely old enough to transmit dengue virus (at least 15 days post emergence). Computed estimates of the daily survival rate (0.8364 using loglinear and 0.8660 using non-linear regression), indicate that dengue vectors in the region experienced relatively low daily mortality. Collectively, our data suggest that Ae. aegypti populations across the Florida Keys harbor large numbers of older individuals, which likely contributes to the high risk of dengue transmission in the area.

Concurrent substance use among cancer patients with and without a history of cannabis use since cancer diagnosis at an NCI-Designated Cancer Center in Florida.

BACKGROUND: Although substance use may have adverse impacts on cancer outcomes, little is known regarding patterns of concurrent substance use with cannabis among cancer patients. Our objective was to examine predictors of concurrent substance use with cannabis among cancer patients since their cancer diagnosis and explore perceptions of cannabis among these patients. METHODS: Patients treated at a National Cancer Institute-designated comprehensive cancer center were invited to participate in an electronic survey regarding medical cannabis from August to November 2021. Survey data were linked to internal data resources including electronic health records and patient intake forms to obtain history of substance use (defined as within at least 3 months of cancer diagnosis) of cigarettes, injection drugs, high levels of alcohol, or clinically unsupervised prescription drugs (total n = 1094). Concurrent substance users were defined as those with any reported substance use and cannabis use at the time of cancer diagnosis. We used descriptive statistics (χ2 or exact tests) to compare groups and estimated adjusted odds ratios (AORs) with 95% confidence intervals (CIs) to identify predictors of substance use among users and nonusers of cannabis. RESULTS: Approximately 45% (n = 489) of the sample reported cannabis use since their cancer diagnosis. Of patients who reported using cannabis, 20% self-reported concurrent polysubstance use, while 8% of cannabis nonusers reported substance use (P < .001). Among patients who use cannabis, those who reported 2 or more self-reported treatment-related symptoms (eg, pain, fatigue) were more likely to have self-reported concurrent substance use (AOR = 3.15, 95% CI = 1.07 to 9.27) compared with those without any symptoms. Among nonusers, those with lower educational background were more likely to have a history of concurrent substance use (AOR = 3.74, 95% CI = 1.57 to 8.92). Patients who use cannabis with concurrent substance use were more likely to report improved sleep (P = .04), increased appetite (P = .03), and treatment of additional medical conditions (P = .04) as perceived benefits of cannabis use. CONCLUSIONS: High symptom burden may be associated with concurrent substance use with cannabis among cancer patients.

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

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.

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system.

OBJECTIVE: In order to predict at hospital admission the prognosis of patients with serious and life-threatening COVID-19 pneumonia, we sought to understand the clinical characteristics of hospitalised patients at admission as the SARS-CoV-2 pandemic progressed, document their changing response to the virus and its variants over time, and identify factors most importantly associated with mortality after hospital admission. DESIGN: Observational study using a prospective hospital systemwide COVID-19 database. SETTING: 15-hospital US health system. PARTICIPANTS: 26 872 patients admitted with COVID-19 to our Northeast Ohio and Florida hospitals from 1 March 2020 to 1 June 2022. MAIN OUTCOME MEASURES: 60-day mortality (highest risk period) after hospital admission analysed by random survival forests machine learning using demographics, medical history, and COVID-19 vaccination status, and viral variant, symptoms, and routine laboratory test results obtained at hospital admission. RESULTS: Hospital mortality fell from 11% in March 2020 to 3.7% in March 2022, a 66% decrease (p<0.0001); 60-day mortality fell from 17% in May 2020 to 4.7% in May 2022, a 72% decrease (p<0.0001). Advanced age was the strongest predictor of 60-day mortality, followed by admission laboratory test results. Risk-adjusted 60-day mortality had all patients been admitted in March 2020 was 15% (CI 3.0% to 28%), and had they all been admitted in May 2022, 12% (CI 2.2% to 23%), a 20% decrease (p<0.0001). Dissociation between observed and predicted decrease in mortality was related to temporal change in admission patient profile, particularly in laboratory test results, but not vaccination status or viral variant. CONCLUSIONS: Hospital mortality from COVID-19 decreased substantially as the pandemic evolved but persisted after hospital discharge, eclipsing hospital mortality by 50% or more. However, after accounting for the many, even subtle, changes across the pandemic in patients' demographics, medical history and particularly admission laboratory results, a patient admitted early in the pandemic and predicted to be at high risk would remain at high risk of mortality if admitted tomorrow.

Estimating the potential value of MSM-focused evidence-based implementation interventions in three Ending the HIV Epidemic jurisdictions in the United States: a model-based analysis.

INTRODUCTION: Improving the delivery of existing evidence-based interventions to prevent and diagnose HIV is key to Ending the HIV Epidemic in the United States. Structural barriers in the access and delivery of related health services require municipal or state-level policy changes; however, suboptimal implementation can be addressed directly through interventions designed to improve the reach, effectiveness, adoption or maintenance of available interventions. Our objective was to estimate the cost-effectiveness and potential epidemiological impact of six real-world implementation interventions designed to address these barriers and increase the scale of delivery of interventions for HIV testing and pre-exposure prophylaxis (PrEP) in three US metropolitan areas. METHODS: We used a dynamic HIV transmission model calibrated to replicate HIV microepidemics in Atlanta, Los Angeles (LA) and Miami. We identified six implementation interventions designed to improve HIV testing uptake ("Academic detailing for HIV testing," "CyBER/testing," "All About Me") and PrEP uptake/persistence ("Project SLIP," "PrEPmate," "PrEP patient navigation"). Our comparator scenario reflected a scale-up of interventions with no additional efforts to mitigate implementation and structural barriers. We accounted for potential heterogeneity in population-level effectiveness across jurisdictions. We sustained implementation interventions over a 10-year period and evaluated HIV acquisitions averted, costs, quality-adjusted life years and incremental cost-effectiveness ratios over a 20-year time horizon (2023-2042). RESULTS: Across jurisdictions, implementation interventions to improve the scale of HIV testing were most cost-effective in Atlanta and LA (CyBER/testing cost-saving and All About Me cost-effective), while interventions for PrEP were most cost-effective in Miami (two of three were cost-saving). We estimated that the most impactful HIV testing intervention, CyBER/testing, was projected to avert 111 (95% credible interval: 110-111), 230 (228-233) and 101 (101-103) acquisitions over 20 years in Atlanta, LA and Miami, respectively. The most impactful implementation intervention to improve PrEP engagement, PrEPmate, averted an estimated 936 (929-943), 860 (853-867) and 2152 (2127-2178) acquisitions over 20 years, in Atlanta, LA and Miami, respectively. CONCLUSIONS: Our results highlight the potential impact of interventions to enhance the implementation of existing evidence-based interventions for the prevention and diagnosis of HIV.

Lipoprotein(a) and Coronary Plaque in Asymptomatic Individuals: The Miami Heart Study at Baptist Health South Florida.

BACKGROUND: Elevated levels of lipoprotein(a) (Lp(a)) are independently associated with an increased risk of atherosclerotic cardiovascular disease events. However, the mechanisms driving this association are poorly understood. We aimed to evaluate the association between Lp(a) and coronary plaque characteristics in a contemporary US cohort without clinical atherosclerotic cardiovascular disease, undergoing coronary computed tomography angiography, the noninvasive gold standard for the assessment of coronary atherosclerosis. METHODS: We used baseline data from the Miami Heart Study-a community-based, prospective cohort study-which included asymptomatic adults aged 40 to 65 years evaluated using coronary computed tomography angiography. Those taking any lipid-lowering therapies were excluded. Elevated Lp(a) was defined as ≥125 nmol/L. Outcomes included any plaque, coronary artery calcium score >0, maximal stenosis ≥50%, presence of any high-risk plaque feature (positive remodeling, spotty calcification, low-attenuation plaque, napkin ring), and the presence of ≥2 high-risk plaque features. RESULTS: Among 1795 participants (median age, 52 years; 54.3% women; 49.6% Hispanic), 291 (16.2%) had Lp(a) ≥125 nmol/L. In unadjusted analyses, individuals with Lp(a) ≥125 nmol/L had a higher prevalence of all outcomes compared with Lp(a) <125 nmol/L, although differences were only statistically significant for the presence of any coronary plaque and ≥2 high-risk features. In multivariable models, elevated Lp(a) was independently associated with the presence of any coronary plaque (odds ratio, 1.40, [95% CI, 1.05-1.86]) and with ≥2 high-risk features (odds ratio, 3.94, [95% CI, 1.82-8.52]), although only 35 participants had this finding. Among participants with a coronary artery calcium score of 0 (n=1200), those with Lp(a) ≥125 nmol/L had a significantly higher percentage of any plaque compared with those with Lp(a) <125 nmol/L (24.2% versus 14.2%; P<0.001). CONCLUSIONS: In this contemporary analysis, elevated Lp(a) was independently associated with the presence of coronary plaque. Larger studies are needed to confirm the strong association observed with the presence of multiple high-risk coronary plaque features.

Investigation of geographic disparities of diabetes-related hospitalizations in Florida using flexible spatial scan statistics: An ecological study.

BACKGROUND: Hospitalizations due to diabetes complications are potentially preventable with effective management of the condition in the outpatient setting. Diabetes-related hospitalization (DRH) rates can provide valuable information about access, utilization, and efficacy of healthcare services. However, little is known about the local geographic distribution of DRH rates in Florida. Therefore, the objectives of this study were to investigate the geographic distribution of DRH rates at the ZIP code tabulation area (ZCTA) level in Florida, identify significant local clusters of high hospitalization rates, and describe characteristics of ZCTAs within the observed spatial clusters. METHODS: Hospital discharge data from 2016 to 2019 were obtained from the Florida Agency for Health Care Administration through a Data Use Agreement with the Florida Department of Health. Raw and spatial empirical Bayes smoothed DRH rates were computed at the ZCTA level. High-rate DRH clusters were identified using Tango's flexible spatial scan statistic. Choropleth maps were used to display smoothed DRH rates and significant high-rate spatial clusters. Demographic, socioeconomic, and healthcare-related characteristics of cluster and non-cluster ZCTAs were compared using the Wilcoxon rank sum test for continuous variables and Chi-square test for categorical variables. RESULTS: There was a total of 554,133 diabetes-related hospitalizations during the study period. The statewide DRH rate was 8.5 per 1,000 person-years, but smoothed rates at the ZCTA level ranged from 0 to 101.9. A total of 24 significant high-rate spatial clusters were identified. High-rate clusters had a higher percentage of rural ZCTAs (60.9%) than non-cluster ZCTAs (41.8%). The median percent of non-Hispanic Black residents was significantly (p < 0.0001) higher in cluster ZCTAs than in non-cluster ZCTAs. Populations of cluster ZCTAs also had significantly (p < 0.0001) lower median income and educational attainment, and higher levels of unemployment and poverty compared to the rest of the state. In addition, median percent of the population with health insurance coverage and number of primary care physicians per capita were significantly (p < 0.0001) lower in cluster ZCTAs than in non-cluster ZCTAs. CONCLUSIONS: This study identified geographic disparities of DRH rates at the ZCTA level in Florida. The identification of high-rate DRH clusters provides useful information to guide resource allocation such that communities with the highest burdens are prioritized to reduce the observed disparities. Future research will investigate determinants of hospitalization rates to inform public health planning, resource allocation and interventions.

Headgear mandates in high school girls' lacrosse: investigating differences in impact rates and game play behaviors.

BACKGROUND/OBJECTIVE: Headgear designed to protect girls' lacrosse athletes is widely available and permitted for voluntary use; however, it remains unknown how policies mandating headgear use may change the sport and, particularly regarding impacts during game-play. Therefore, this study compares the impact rates and game play characteristics of girls' high school lacrosse in Florida which mandates headgear use (HM), with states having no headgear mandate (NHM). MATERIALS AND METHODS: Video from 189 randomly-selected games (HM: 64, NHM: 125) were analyzed. Descriptive statistics, Impact Rates (IR), Impact Rate Ratios (IRR), Impact Proportion Ratios (IPR), and 95% Confidence Intervals (CI) were calculated. IRRs and IPRs with corresponding CIs that excluded 1.00 were deemed statistically significant. RESULTS: 16,340 impacts (HM:5,821 NHM: 10,519; 86.6 impacts/game, CI: 88.6-93.3) were identified using the Lacrosse Incident Analysis Instrument (LIAI). Most impacts directly struck the body (n = 16,010, 98%). A minority of impacts directly struck a player's head (n = 330, 2%). The rate of head impacts was significantly higher in the HM cohort than NHM cohort (IRR = 2.1; 95% CI = 1.7-2.6). Most head impacts (n = 271, 82%) were caused by stick contact in both groups. There was no difference in the proportion of penalties administered for head impacts caused by stick contact between the HM and NHM cohorts (IPR IRRHM/NHM = 0.98; CI = 0.79-1.16). However, there was a significantly greater proportion of head impacts caused by player contact that resulted in a penalty administered in the HM cohort (IPR = 1.44 CI = 1.17-1.54). CONCLUSION: These findings demonstrate that mandating headgear use was associated with a two-fold greater likelihood of sustaining a head impact during game play compared to NHM states. A majority of head impacts in both HM and NHM states were caused by illegal stick contact that did not result in penalty.

High school girls' lacrosse athletes participating in a state with a headgear mandate was twice as likely to sustain a head impact than those participating in states without headgear mandates.Stick contact remains the most common mechanism of head impacts in girls' lacrosse, regardless of mandating headgear.Regardless of whether headgear was or was not mandated, most head impacts caused by stick contact did not result in a penalty.

Agent-based modeling of the COVID-19 pandemic in Florida.

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like "Should we lockdown?" During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.

Psychosocial and socioeconomic changes among low-income people with HIV during the COVID-19 pandemic in Miami-Dade County, Florida: racial/ethnic and gender differences.

BACKGROUND: COVID-19 profoundly and uniquely impacted people with HIV. People with HIV experienced significant psychosocial and socioeconomic impacts, yet a limited amount of research has explored potential differences across gender and racial/ethnic groups of people with HIV. OBJECTIVE: The objective of this study was to examine psychosocial and socioeconomic stressors related to the COVID-19 pandemic among a diverse sample of people with HIV in South Florida and to determine if the types of stressors varied across gender and racial/ethnic groups. METHODS: We analyzed data from a cross-sectional survey with Miami-Dade County, Ryan White Program recipients. Outcomes included mental health, socioeconomic, drug/alcohol, and care responsibility/social support changes. Weighted descriptive analyses provided an overview of stressors by gender and racial/ethnic group and logistic regressions estimated associations between demographics and stressors. RESULTS: Among 291 participants, 39% were Non-Hispanic Black, 18% were Haitian, and 43% were Hispanic. Adjusting for age, sex, language, and foreign-born status, Hispanics were more likely to report several worsened mental health (i.e. increased loneliness, anxiety) and socioeconomic stressors (i.e. decreased income). Spanish speakers were more likely to report not getting the social support they needed. Women were more likely to report spending more time caring for children. CONCLUSIONS: Findings highlight ways in which cultural and gender expectations impacted experiences across people with HIV and suggest strategies to inform interventions and resources during lingering and future public health emergencies. Results suggest that public health emergencies have different impacts on different communities. Without acknowledging and responding to differences, we risk losing strides towards progress in health equity.

Overdose deaths before and during the COVID-19 pandemic in a US county.

Introduction: Globally, overdose deaths increased near the beginning of the COVID-19 pandemic, which created availability and access barriers to addiction and social services. Especially in times of a crisis like a pandemic, local exposures, service availability and access, and system responses have major influence on people who use drugs. For policy makers to be effective, an understanding at the local level is needed. Methods: This retrospective epidemiologic study from 2019 through 2021 compares immediate and 20-months changes in overdose deaths from the pandemic start to 16 months before its arrival in Pinellas County, FL We examine toxicologic death records of 1,701 overdoses to identify relations with interdiction, and service delivery. Results: There was an immediate 49% increase (95% CI 23-82%, p < 0.0001) in overdose deaths in the first month following the first COVID deaths. Immediate increases were found for deaths involving alcohol (171%), heroin (108%), fentanyl (78%), amphetamines (55%), and cocaine (45%). Overdose deaths remained 27% higher (CI 4-55%, p = 0.015) than before the pandemic through 2021.Abrupt service reductions occurred when the pandemic began: in-clinic methadone treatment dropped by two-thirds, counseling by 38%, opioid seizures by 29%, and drug arrests by 56%. Emergency transport for overdose and naloxone distributions increased at the pandemic onset (12%, 93%, respectively) and remained higher through 2021 (15%, 377%,). Regression results indicate that lower drug seizures predicted higher overdoses, and increased 911 transports predicted higher overdoses. The proportion of excess overdose deaths to excess non-COVID deaths after the pandemic relative to the year before was 0.28 in Pinellas County, larger than 75% of other US counties. Conclusions: Service and interdiction interruptions likely contributed to overdose death increases during the pandemic. Relaxing restrictions on medical treatment for opioid addiction and public health interventions could have immediate and long-lasting effects when a major disruption, such as a pandemic, occurs. County level data dashboards comprised of overdose toxicology, and interdiction and service data, can help explain changes in overdose deaths. As a next step in predicting which policies and practices will best reduce local overdoses, we propose using simulation modeling with agent-based models to examine complex interacting systems.

Cancer Registry Enrichment via Linkage with Hospital-Based Electronic Medical Records: A Pilot Investigation.

Background: Hospital electronic medical record (EMR) systems are becoming increasingly integrated for management of patient data, especially given recent policy changes issued by the Centers for Medicaid and Medicare Services. In addition to data management, these data provide evidence for patient-centered outcomes research for a range of diseases, including cancer. Integrating EMR patient data with existing disease registries strengthens all essential components for assuring optimal health outcomes. Objectives: To identify the mechanisms for extracting, linking, and processing hospital EMR data with the Florida Cancer Data System (FCDS); and to assess the completeness of existing registry treatment data as well as the potential for data enhancement. Methods: A partnership among the Florida Department of Health, FCDS, and a large Florida hospital system was established to develop methods for hospital EMR extraction and transmission. Records for admission years between 2007 and 2010 were extracted using ICD-9-CM codes as the trigger and were linked with the cancer registry for patients with invasive cancers of the breast. Results: A total of 11,506 unique patients were linked with a total of 12,804 unique breast tumors. Evaluation of existing registry treatment data against the hospital EMR produced a total of 5% of registry records with updated surgery information, 1% of records with updated radiation information, and 7% of records updated with chemotherapy information. Enhancement of registry treatment information was particularly affected by the availability of chemotherapy medications data. Conclusion: Hospital EMR linkages to cancer disease registries is feasible but challenged by lack of standards for data collection, coding and transmission, comprehensive description of available data, and the exclusion of certain hospital datasets. The FCDS standard treatment data variables are highly robust and complete but can be enhanced by the addition of detailed chemotherapy regimens that are commonly used in patient centered outcomes research.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program.

IMPORTANCE: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. OBJECTIVE: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. DESIGN: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. SETTING: Healthcare facilities in New York and Florida. PARTICIPANTS: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. EXPOSURE: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. MAIN OUTCOME(S) AND MEASURE(S): Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31-180 days after the last negative test. RESULTS: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). CONCLUSIONS AND RELEVANCE: We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Decentralized HIV testing: comparing peer and mail-based distribution strategies to improve the reach of HIV self-testing among people who use drugs in Florida.

INTRODUCTION: People who use drugs (PWUD) are at increased risk for HIV infection. HIV self-testing (HIVST) is a promising method for identifying new infections, but optimal distribution strategies remain understudied. METHODS: To characterize PWUD by HIVST distribution strategy (peers vs. mail), we examined data from July 2022 to June 2023 collected from a real-world HIVST program led by the non-profit, Florida Harm Reduction Collective. We used descriptive statistics and Poisson regressions with robust error variance to compare those who received HIVST through peers or via mail by socio-demographics, Ending the HIV Epidemic (EHE) county designation, and HIV testing experience. RESULTS: Among 728 participants, 78% received HIVST from peers, 47% identified as cisgender female, 48% as heterosexual, and 45% as non-White; 66% resided in an EHE county, and 55% had no HIV testing experience. Compared to those who received an HIV self-test from peers, those who received tests via mail were less likely to be cisgender male (vs. cisgender female; prevalence ratio [PR] = 0.59, 95% confidence interval [CI]: 0.43, 0.81), non-Hispanic Black (vs. non-Hispanic White; PR = 0.57, 95% CI: 0.36, 0.89) or from EHE counties (vs. non-EHE counties; PR = 0.33, 95% CI: 0.25, 0.44). Those who received tests via mail were also more likely to identify their sexual orientation as "Other/Undisclosed" (vs. straight/heterosexual; PR = 2.00, 95% CI: 1.51, 2.66). CONCLUSION: Our findings support the role of community-based HIVST distribution strategies in increasing HIV testing coverage among PWUD. Additional research could help inform the equitable reach of HIVST.

Structural Determinants of Health and Markers of Immune Activation and Systemic Inflammation in Sexual Minority Men With and Without HIV.

Among sexual minority men (SMM), HIV and use of stimulants such as methamphetamine are linked with immune activation and systemic inflammation. Throughout the COVID-19 pandemic, SMM encountered financial challenges and structural obstacles that might have uniquely contributed to immune dysregulation and systemic inflammation, beyond the impacts of HIV and stimulant use. Between August 2020 and February 2022, 72 SMM with and without HIV residing in South Florida enrolled in a COVID-19 prospective cohort study. Multiple linear regression analyses examined unemployment, homelessness, and history of arrest as structural correlates of soluble markers of immune activation (i.e., sCD14 and sCD163) and inflammation (i.e., sTNF-α receptors I and II) at baseline after adjusting for HIV status, stimulant use, and recent SARS-CoV-2 infection. Enrolled participants were predominantly Latino (59%), gay-identified (85%), and with a mean age of 38 (SD, 12) years with approximately one-third (38%) of participants living with HIV. After adjusting for HIV status, SARS-CoV-2 infection, and recent stimulant use, unemployment independently predicted higher levels of sCD163 (ß = 0.24, p = 0.04) and sTNF-α receptor I (ß = 0.26, p = 0.02). Homelessness (ß = 0.25, p = 0.02) and history of arrest (ß = 0.24, p = 0.04) independently predicted higher levels of sCD14 after adjusting for HIV status, SARS-CoV-2 infection, and recent stimulant use. Independent associations exist between structural barriers and immune activation and systemic inflammation in SMM with and without HIV. Future longitudinal research should further elucidate complex bio-behavioral mechanisms linking structural factors with immune activation and inflammation.

Longitudinal Impacts of Precision Greenness on Alzheimer's Disease.

BACKGROUND: The potential for greenness as a novel protective factor for Alzheimer's disease (AD) requires further exploration. OBJECTIVES: This study assesses prospectively and longitudinally the association between precision greenness - greenness measured at the micro-environmental level, defined as the Census block - and AD incidence. DESIGN: Older adults living in consistently high greenness Census blocks across 2011 and 2016 were compared to those living in consistently low greenness blocks on AD incidence during 2012-2016. SETTING: Miami-Dade County, Florida, USA. PARTICIPANTS: 230,738 U.S. Medicare beneficiaries. MEASUREMENTS: U.S. Centers for Medicare and Medicaid Services Chronic Condition Algorithm for AD based on ICD-9 codes, Normalized Difference Vegetation Index, age, sex, race/ethnicity, neighborhood income, and walkability. RESULTS: Older adults living in the consistently high greenness tertile, compared to those in the consistently low greenness tertile, had 16% lower odds of AD incidence (OR=0.84, 95% CI: 0.76-0.94, p=0.0014), adjusting for age, sex, race/ethnicity, and neighborhood income. Age, neighborhood income and walkability moderated greenness' relationship to odds of AD incidence, such that younger ages (65-74), lower-income, and non-car dependent neighborhoods may benefit most from high greenness. CONCLUSIONS: High greenness, compared to low greenness, is associated with lower 5-year AD incidence. Residents who are younger and/or who reside in lower-income, walkable neighborhoods may benefit the most from high greenness. These findings suggest that consistently high greenness at the Census block-level, may be associated with reduced odds of AD incidence at a population level.

Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.

We evaluate approaches to vaccine distribution using an agent-based model of human activity and COVID-19 transmission calibrated to detailed trends in cases, hospitalizations, deaths, seroprevalence, and vaccine breakthrough infections in Florida, USA. We compare the incremental effectiveness for four different distribution strategies at four different levels of vaccine supply, starting in late 2020 through early 2022. Our analysis indicates that the best strategy to reduce severe outcomes would be to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the intermediate vaccine availability assumptions and relatively modest compared to a simple mass vaccination approach under high vaccine availability. Ring vaccination, while generally the most effective strategy for reducing infections, ultimately proved least effective at preventing deaths. We also consider using age group as a practical surrogate measure for actual disease-risk targeting; this approach also outperforms both simple mass distribution and ring vaccination. We find that quantitative effectiveness of a strategy depends on whether effectiveness is assessed after the alpha, delta, or omicron wave. However, these differences in absolute benefit for the strategies do not change the ranking of their performance at preventing severe outcomes across vaccine availability assumptions.

Analysis of injury severity of work zone crashes on rural and urban work zones: Accounting for out-of-sample prediction and temporal instability.

This research utilizes data collected in Florida to examine the differentials in injury severities among single-vehicle drivers involved in work zone-related incidents, specifically focusing on the distinctions between rural and urban areas. The study encompasses a four-year period (2016-2019) of crash dataset. A likelihood ratio test was performed to examine model estimate's temporal consistency in datasets from rural and urban areas across several time periods throughout the year. Separate statistical models were estimated for both rural and urban datasets to understand different driver injury severity outcomes (no injury, minor injury, and severe injury) using a mixed logit approach with possible heterogeneity in mean and variance of random parameters. Out-of-sample simulations were conducted to see the effect of different parameter changes on injury severity probabilities in rural and urban work zone crashes. Over multiple years, various years in both rural and urban models have generated statistically significant random factors that effectively capture the presence of heterogeneity in means, accounting for unobservable variations within the data. Clear evidence of factors such as speed limits, work zone type, and traffic volume affecting the work zone injury severities were found to vary significantly between rural and urban work zone areas. However, despite this difference, rural and urban work zones share common safety problems and countermeasures such as driver education, improved signage, and appropriate traffic controls; combining ITS technologies and enhanced law enforcement can help mitigate crash severity in urban and rural work zone areas.