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.

Machine Evaluation of Catchment Area Relevance through Text Mining.

The University of Miami Sylvester Comprehensive Cancer Center Community Outreach and Engagement Office has developed an algorithm to aid in identifying catchment area relevant trials. We have developed this tool to capture a catchment area (South Florida) that represents the most racially, ethnically, and geographically diverse region in the US. Unfortunately, the area's tumor burden is also significant with many notable disparities, necessitating a prioritization of trials within Sylvester's catchment area. These trials address the needs of the population Sylvester serves by targeting cancers that are locally prevalent.

The Florida Scoring System for stratifying children with suspected Sjögren's disease: a cross-sectional machine learning study.

BACKGROUND: Childhood Sjögren's disease is a rare, underdiagnosed, and poorly-understood condition. By integrating machine learning models on a paediatric cohort in the USA, we aimed to develop a novel system (the Florida Scoring System) for stratifying symptomatic paediatric patients with suspected Sjögren's disease. METHODS: This cross-sectional study was done in symptomatic patients who visited the Department of Pediatric Rheumatology at the University of Florida, FL, USA. Eligible patients were younger than 18 years or had symptom onset before 18 years of age. Patients with confirmed diagnosis of another autoimmune condition or infection with a clear aetiological microorganism were excluded. Eligible patients underwent comprehensive examinations to rule out or diagnose childhood Sjögren's disease. We used latent class analysis with clinical and laboratory variables to detect heterogeneous patient classes. Machine learning models, including random forest, gradient-boosted decision tree, partial least square discriminatory analysis, least absolute shrinkage and selection operator-penalised ordinal regression, artificial neural network, and super learner were used to predict patient classes and rank the importance of variables. Causal graph learning selected key features to build the final Florida Scoring System. The predictors for all models were the clinical and laboratory variables and the outcome was the definition of patient classes. FINDINGS: Between Jan 16, 2018, and April 28, 2022, we screened 448 patients for inclusion. After excluding 205 patients due to symptom onset later than 18 years of age, we recruited 243 patients into our cohort. 26 patients were excluded because of confirmed diagnosis of a disorder other than Sjögren's disease, and 217 patients were included in the final analysis. Median age at diagnosis was 15 years (IQR 11-17). 155 (72%) of 216 patients were female and 61 (28%) were male, 167 (79%) of 212 were White, and 20 (9%) of 213 were Hispanic, Latino, or Spanish. The latent class analysis identified three distinct patient classes: class I (dryness dominant with positive tests, n=27), class II (high symptoms with negative tests, n=98), and class III (low symptoms with negative tests, n=92). Machine learning models accurately predicted patient class and ranked variable importance consistently. The causal graphical model discovered key features for constructing the Florida Scoring System. INTERPRETATION: The Florida Scoring System is a paediatrician-friendly tool that can be used to assist classification and long-term monitoring of suspected childhood Sjögren's disease. The resulting stratification has important implications for clinical management, trial design, and pathobiological research. We found a highly symptomatic patient group with negative serology and diagnostic profiles, which warrants clinical attention. We further revealed that salivary gland ultrasonography can be a non-invasive alternative to minor salivary gland biopsy in children. The Florida Scoring System requires validation in larger prospective paediatric cohorts. FUNDING: National Institute of Dental and Craniofacial Research, National Institute of Arthritis, Musculoskeletal and Skin Diseases, National Heart, Lung, and Blood Institute, and Sjögren's Foundation.

Highly pathogenic avian influenza A(H5N1) virus in a common bottlenose dolphin (Tursiops truncatus) in Florida.

Since late 2021, highly pathogenic avian influenza (HPAI) viruses of A/goose/Guangdong/1/1996 (H5N1) lineage have caused widespread mortality in wild birds and poultry in the United States. Concomitant with the spread of HPAI viruses in birds are increasing numbers of mammalian infections, including wild and captive mesocarnivores and carnivores with central nervous system involvement. Here we report HPAI, A(H5N1) of clade 2.3.4.4b, in a common bottlenose dolphin (Tursiops truncatus) from Florida, United States. Pathological findings include neuronal necrosis and inflammation of the brain and meninges, and quantitative real time RT-PCR reveal the brain carried the highest viral load. Virus isolated from the brain contains a S246N neuraminidase substitution which leads to reduced inhibition by neuraminidase inhibitor oseltamivir. The increased prevalence of A(H5N1) viruses in atypical avian hosts and its cross-species transmission into mammalian species highlights the public health importance of continued disease surveillance and biosecurity protocols.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

Investigating Disparities in Hypopharyngeal/Laryngeal Cancer Survival in Florida With Geospatial Mapping Analysis.

OBJECTIVE: Identify predictors of overall survival (OS) after hypopharyngeal/laryngeal cancer in Florida. MATERIAL AND METHODS: We conducted a retrospective cohort study using data from the Florida Cancer Data System (FCDS) on patients diagnosed with hypopharyngeal or laryngeal cancer from 2010-2017. Primary outcome was OS. Hazard ratios (HRs) were estimated from univariable and multivariable Cox regression models for OS. Data was analyzed from November 1, 2022, to June 30, 2023. RESULTS: We analyzed 6771 patients, who were primarily male (81.2%), White non-Hispanic (WNH) (78.2%), publicly insured (70.1%), married (51.8%), and residents of urban counties (73.6%). Black patients were more likely to be younger at diagnosis (38.9%), single (43.4%), to have distant SEER stage disease (25.6%). Median OS were lowest among patients who were uninsured (34 months), with hypopharyngeal site disease (18 months), and a smoking history (current: 34 months, former: 46 months, no smoking: 63 months). Multivariable Cox regression analysis showed worse OS for single/unmarried vs married (HR 1.47 [95%CI: 1.36-1.59], P < .001), history of tobacco use (current: HR 1.62 [95%CI: 1.440-1.817], P < .001; former smokers: (HR 1.28 [95%CI: 1.139-1.437], P < .001) vs no history). Improved OS was observed among White Hispanics (WH) vs WNH (HR .73 [95%CI: .655-.817], P < .001) and women vs men (HR .88 [95%CI: .807-.954], P = .002). Geographical mapping showed that mortality rates were highest in census tracts with low income and education. CONCLUSION: Our findings suggest that sociodemographic and clinical factors impact OS from hypopharyngeal/laryngeal cancer in Florida and vary geographically within the state. These results will help guide future public health interventions.

Disparities in incidence and survival for patients with Ewing sarcoma in Florida.

BACKGROUND: Ewing sarcoma (ES) is a malignant bone tumor most commonly affecting non-Hispanic White (NHW) adolescent males, though recognition among Hispanic individuals is rising. Prior population-based studies in the United States (US), utilizing Surveillance, Epidemiology, and End Results (SEER) have shown higher all-cause mortality among White Hispanics, Blacks, and those of low socioeconomic status (SES). Florida is not part of SEER but is home to unique Hispanic populations including Cubans, Puerto Ricans, South Americans that contrasts with the Mexican Hispanic majority in other US states. This study aimed to assess racial/ethnic disparities on incidence and survival outcomes among this diverse Florida patient population. METHODOLOGY: Our study examined all patients diagnosed with osseous ES (2005-2018) in Florida (n = 411) based on the state's population-based cancer registry dataset. Florida Age-adjusted Incidence Rates (AAIRs) were computed by sex and race-ethnicity and compared to the equivalent populations in SEER. Cause-specific survival disparities among Florida patients were examined using Kaplan-Meier analysis. Univariable and multivariable analyses using Cox regression were performed for race/ethnicity, with adjustment for age, sex, year of diagnosis, site of disease, staging, SES, and insurance type. RESULTS: There was a significantly higher incidence of osseous ES in Florida Hispanic males (AAIR 2.6/1,000,000); (95% CI: 2.0-3.2 per 1,000,000; n = 84) compared to the SEER Hispanic males (AAIR 1.2/1,000,000;1.1-1.4 per 1,000,000; n = 382). Older age, distant metastasis, lack of chemotherapy or surgical resection were statistically significant determinants of poor survival while SES, insurance status and race-ethnicity were not. However, among nonmetastatic ES, Florida Hispanics had an increased risk of death compared to Florida NHW (adjusted Hazard Ratio 2.32; 95%CI: 1.20-4.46; p = 0.012). CONCLUSIONS: Florida Hispanic males have a higher-than-expected incidence of osseous ES compared to the US. Hispanics of both sexes show remarkably worse survival for nonmetastatic disease compared to NHW. This disparity is likely multifactorial and requires further in-depth studies.

Drug use and COVID-19 testing, vaccination, and infection among underserved, minority communities in Miami, Florida.

The Coronavirus Disease 2019 (COVID-19) pandemic has disproportionately impacted people who use drugs (PWUD). This study explored relationships between drug use, COVID-19 testing, vaccination, and infection. This cross-sectional study was conducted in Miami, Florida between March 2021 and October 2022 as part of the National Institutes of Health (NIH) Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) initiative and the Miami Adult Studies on HIV (MASH) cohort. Users of cannabis, cocaine/crack, heroin/fentanyl, methamphetamines, hallucinogens, and/or prescription drug misuse in the previous 12 months were considered PWUD. Sociodemographic data, COVID-19 testing history, and vaccination-related beliefs were self-reported. Vaccinations were confirmed with medical records and positivity was determined with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. Statistical analyses included chi-square tests and logistic regression. Of 1,780 participants, median age was 57 years, 50.7% were male, 50.2% Non-Hispanic Black, and 66.0% reported an annual income less than $15,000. Nearly 28.0% used drugs. PWUD were less likely than non-users to self-report ever testing positive for SARS-CoV-2 (14.7% vs. 21.0%, p = 0.006). However, 2.6% of participants tested positive for SARS-CoV-2, with no significant differences between PWUD and non-users (3.7% vs. 2.2%, p = 0.076). PWUD were more likely than non-users to experience difficulties accessing testing (10.2% vs. 7.1%, p = 0.033), vaccine hesitancy (58.9% vs. 43.4%, p = 0.002) and had lower odds of receiving any dose of a COVID-19 vaccine compared to non-users (aOR, 0.63; 95% CI, 0.49-0.81; p<0.001). PWUD presented with greater difficulties accessing COVID-19 testing, greater vaccine hesitancy, and lower odds of vaccination. Testing and immunization plans that are tailored to the needs of PWUD and consider access, trust-building campaigns, and education may be needed.

The shifting impact and response to COVID-19 in Florida.

The first cases of COVID-19 in Florida were diagnosed on March 1, 2020. Three years later, more than 7.3 million people have had COVID-19 in Florida, and more than 93,000 individuals have died from this illness. When considering the impact of COVID-19 on Florida, several key factors need to be considered, including that Florida was one of the most medically vulnerable states due to a substantial proportion of older individuals and those with underlying medical conditions. Florida also has a centralized Department of Health and Division of Emergency Management structure that facilitated response activities. Looking at the impact of COVID-19 on Florida, two distinct phases need to be considered: the pre-Delta variant phase from March 2020 to July 2021 and the Delta variant and beyond phase that began July 2021 and still continues. During the 16-month first phase, about 38,000 people died. Yet, 24,000 people died during the 5-months of the Delta variant wave from July to November 2021. During the Omicron waves that followed Delta, an additional 31,000 people died. Florida thus went from ranking 26th in death per capita in the United States at the end of the first phase to 10th a few months into the Delta wave and now ranks 8th. Why did these phases differ so dramatically in terms of mortality? During the first phase of the pandemic, adherence to established nonpharmacological and older adult protection measures was recommended. When COVID-19 vaccines became available in December 2020, there was an aggressive campaign to promote COVID-19 vaccination, and public acceptance was high. The second phase followed political opposition to CDC and public health expert guidelines, the rise of anti-vaccine sentiment and misinformation, and falling vaccination rates. These factors contributed to considerable population vulnerability to severe disease when the Delta variant hit. As the former State Surgeon General and Secretary of Health of Florida from June 2019 to September 2021, this report provides perspective on the shifting impact and response to COVID-19 in Florida, which is the third most populous state in the United States. This perspective shows the clear consequences of shifting from standard public health practices and vaccine promotion to attacks on public health and vaccines.

Spatial and temporal analysis of HIV clinical outcomes in Florida reveals counties with persistent racial and ethnic disparities during 2012-2019.

BACKGROUND: Racial/ethnic disparities in the HIV care continuum have been well documented in the US, with especially striking inequalities in viral suppression rates between White and Black persons with HIV (PWH). The South is considered an epicenter of the HIV epidemic in the US, with the largest population of PWH living in Florida. It is unclear whether any disparities in viral suppression or immune reconstitution-a clinical outcome highly correlated with overall prognosis-have changed over time or are homogenous geographically. In this analysis, we 1) investigate longitudinal trends in viral suppression and immune reconstitution among PWH in Florida, 2) examine the impact of socio-ecological factors on the association between race/ethnicity and clinical outcomes, 3) explore spatial and temporal variations in disparities in clinical outcomes. METHODS: Data were obtained from the Florida Department of Health for 42,369 PWH enrolled in the Ryan White program during 2008-2020. We linked the data to county-level socio-ecological variables available from County Health Rankings. GEE models were fit to assess the effect of race/ethnicity on immune reconstitution and viral suppression longitudinally. Poisson Bayesian hierarchical models were fit to analyze geographic variations in racial/ethnic disparities while adjusting for socio-ecological factors. RESULTS: Proportions of PWH who experienced viral suppression and immune reconstitution rose by 60% and 45%, respectively, from 2008-2020. Odds of immune reconstitution and viral suppression were significantly higher among White [odds ratio =2.34, 95% credible interval=2.14-2.56; 1.95 (1.85-2.05)], and Hispanic [1.70 (1.54-1.87); 2.18(2.07-2.31)] PWH, compared with Black PWH. These findings remained unchanged after accounting for socio-ecological factors. Rural and urban counties in north-central Florida saw the largest racial/ethnic disparities. CONCLUSIONS: There is persistent, spatially heterogeneous, racial/ethnic disparity in HIV clinical outcomes in Florida. This disparity could not be explained by socio-ecological factors, suggesting that further research on modifiable factors that can improve HIV outcomes among Black and Hispanic PWH in Florida is needed.

Concurrent Outbreaks of Hepatitis A, Invasive Meningococcal Disease, and Mpox, Florida, USA, 2021-2022.

In 2022, concurrent outbreaks of hepatitis A, invasive meningococcal disease (IMD), and mpox were identified in Florida, USA, primarily among men who have sex with men. The hepatitis A outbreak (153 cases) was associated with hepatitis A virus genotype IA. The IMD outbreak (44 cases) was associated with Neisseria meningitidis serogroup C, sequence type 11, clonal complex 11. The mpox outbreak in Florida (2,845 cases) was part of a global epidemic. The hepatitis A and IMD outbreaks were concentrated in Central Florida and peaked during March--June, whereas mpox cases were more heavily concentrated in South Florida and had peak incidence in August. HIV infection was more common (52%) among mpox cases than among hepatitis A (21%) or IMD (34%) cases. Where feasible, vaccination against hepatitis A, meningococcal disease, and mpox should be encouraged among at-risk groups and offered along with program services that target those groups.

Changing COVID-19 cases and deaths detection in Florida.

Epidemic data are often difficult to interpret due to inconsistent detection and reporting. As these data are critically relied upon to inform policy and epidemic projections, understanding reporting trends is similarly important. Early reporting of the COVID-19 pandemic in particular is complicated, due to changing diagnostic and testing protocols. An internal audit by the State of Florida, USA found numerous specific examples of irregularities in COVID-19 case and death reports. Using case, hospitalization, and death data from the the first year of the COVID-19 pandemic in Florida, we present approaches that can be used to identify the timing, direction, and magnitude of some reporting changes. Specifically, by establishing a baseline of detection probabilities from the first (spring) wave, we show that transmission trends among all age groups were similar, with the exception of the second summer wave, when younger people became infected earlier than seniors, by approximately 2 weeks. We also found a substantial drop in case-fatality risk (CFR) among all age groups over the three waves during the first year of the pandemic, with the most drastic changes seen in the 0 to 39 age group. The CFR trends provide useful insights into infection detection that would not be possible by relying on the number of tests alone. During the third wave, for which we have reliable hospitalization data, the CFR was remarkably stable across all age groups. In contrast, the hospitalization-to-case ratio varied inversely with cases while the death-to-hospitalization ratio varied proportionally. Although specific trends are likely to vary between locales, the approaches we present here offer a generic way to understand the substantial changes that occurred in the relationships among the key epidemic indicators.

Recent Latinx immigrants to Miami/Dade County (FL): Travel patterns before, during, and one year after the COVID-19 pandemic lockdown.

INTRODUCTION: Understanding the transportation needs of immigrants is crucial for the design and promotion of safe, equitable, and sustainable living environments. This study examines the transportation patterns from a sample of Recent Latinx Immigrants (RLIs) upon arrival to Miami/Dade Co (MDC), Florida. METHODS: Collected between 2018 and 2021, data came from a longitudinal study examining drinking and driving trajectories among 540 RLIs to MDC. Retrospective pre-immigration data (T0) were obtained simultaneously with the first-year post-immigration data (T1). Follow up surveys were conducted one year later, before (T2-BC) or during a pandemic lockout (T2-DC), and two years later (T3). Descriptive and repeated measures mixed-model regression were used to examine the data. RESULTS: Driving declined from T0 to T1, although remained higher than previously reported for other locations. Not having a valid driver's license was the main reason for the decline. The initial reduction in driving was paralleled by an increase in the use of transit, riding as passengers in private vehicles, and walking. A year later (T2), as RLIs' income and access to a driver's license grew, driving rates increased (even during the pandemic lockdown), while the use of other transportation modes decreased. A year after the pandemic lockdown (T3), driving as well as the use of other transportation modes receded. Reasons for this decline are unclear. CONCLUSIONS: RLIs reported elevated driving rates upon their arrival to MDC. The COVID-19 pandemic seems to have altered the RLIs' transportation patterns, provoking an overall decline in mobility that lasted even after the pandemic lockdown ceased. PRACTICAL APPLICATIONS: Transportation planners working on developing safe and equitable transportation systems in MDC should: (1) identify and address barriers to the use of transportation modes other than driving by RLIs; and (2) understand reasons for the broad decline in transportation modes after the pandemic lockdown.

Field Isolation and Laboratory Vector-Host Studies of Brazoran Virus (Peribunyaviridae: Orthobunyavirus) from Florida.

Brazoran virus was first isolated from Culex mosquitoes in Texas in 2012, yet little is known about this virus. We report the isolation of this virus from Culex erraticus from southern Florida during 2016. The Florida strain had a nucleotide identity of 96.3% (S segment), 99.1% (M segment), and 95.8% (L segment) to the Texas isolate. Culex quinquefasciatus and Aedes aegypti colonies were subsequently fed virus blood meals to determine their vector competence for Brazoran virus. Culex quinquefasciatus was susceptible to midgut infection, but few mosquitoes developed disseminated infections. Aedes aegypti supported disseminated infection, but virus transmission could not be demonstrated. Suckling mice became infected by intradermal inoculation without visible disease signs. The virus was detected in multiple mouse tissues but rarely infected the brain. This study documents the first isolation of Brazoran virus outside of Texas. Although this virus infected Ae. aegypti and Cx. quinquefasciatus in laboratory trials, their vector competence could not be demonstrated, suggesting they are unlikely vectors of Brazoran virus.

Evaluation of Automated Processing of Electronically Reported Serological Tests for Syphilis Using Current and Historical Syphilis Results Compared With Traditional Reactor Grid Processing in Florida.

BACKGROUND: Syphilis in Florida increased 49% from 2016 to 2020. Moreover, many serological tests for syphilis (STS) do not indicate current infection. Traditionally, syphilis surveillance systems used reactor grids, a method for prioritizing STS for investigation based on age, nontreponemal titer, and/or sex. In 2022, Florida's sexually transmitted disease surveillance system implemented an automated method for processing electronically reported STS (eSTS), expanding upon the reactor grid, using an individual's current STS (treponemal and nontreponemal), treatment history, and historical STS results aiming for more efficiently processing eSTS. We compared the new method of processing eSTS results against the reactor grid and determined potential value in time/cost savings of this change. METHODS: All eSTSs (n = 4144) from January 2, 2023 to January 8, 2023, were compared by how the logic-based method processed test results versus how the reactor grid processed test results. Each method was compared using measurements of accuracy (e.g., sensitivity/specificity). Time and cost savings in eSTS processing were estimated. RESULTS: Using the surveillance case definition as reference, the accuracy of the logic-based method for processing eSTS was nearly double (82.3% vs. 43.6%), had greater specificity (79.0% vs. 33.0%), and increased positive predictive value (47.5% vs. 22.0%) when compared with the reactor grid method. Sensitivity (99.5% vs. 98.6%) and negative predictive value (99.9% vs. 99.2%) remained similar. The logic-based method is estimated to save 7783 hours annually (~$185,000). CONCLUSIONS: Processing eSTS based on current and historical STS results is significantly more accurate than using a reactor grid. Moreover, these improvements save time and resources that can be better allocated to other program prevention activities.

HIV Testing Deserts and Vulnerability Among Adolescents and Young Adults in Tampa Bay, Florida.

PURPOSE: Human immunodeficiency virus (HIV) burden among Florida adolescents and young adults (AYA, aged 13-24 years), particularly in Tampa Bay, is among the highest in the nation. We sought to determine the association between zip code-level test site accessibility and AYA HIV burden, compare this association with adult (aged 25-44 years) HIV burden, and identify local AYA HIV testing deserts. We further aimed to identify the association between test site accessibility and population-level markers of social disadvantage. METHODS: We geocoded HIV test sites and determined the percent surface area per zip code within 15-minute walking distance to ≥ 1 test sites (PSA15) in Pinellas and Hillsborough counties. We calculated Pearson's correlation coefficients for the association of PSA15 and HIV burden by age group and, separately, the association of PSA15 and population-level characteristics. RESULTS: Of the 96 zip codes analyzed, 36.5% had a PSA15 for HIV testing of 0%. The association between PSA15 and HIV burden was substantially higher for adults (r = 0.51, p < .001) than for AYA (r = 0.09, p = .38). Overall, we identified four potential AYA testing deserts. We also found that greater PSA15 was correlated with greater %Black/African-American residents (r = 0.32, p = .002), greater %residents living in poverty (r = 0.27, p = .008), and lower child opportunity index scores (r = -0.29, p = .004). DISCUSSION: Walking-accessible HIV test sites in Tampa Bay were limited and geographically distributed largely based on adult HIV burden, population-level markers of social disadvantage, and among areas with higher percentages of Black/African-American residents. Test site distribution was less correlated with AYA HIV burden, leaving this population vulnerable across multiple testing deserts.

Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling.

Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tickborne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapularis L.) in the southeastern US. Eight years (2014-2021) of tick encounter data were used to fit environmental niche and generalized linear models to predict where and when ticks are likely to be encountered. Our results indicate temporal and environmental partitioning of the three species. Ixodes scapularis were more likely to be encountered in the autumn and winter seasons and associated with soil organic matter, vegetation indices, evapotranspiration, temperature, and gross primary productivity. By contrast, A. americanum and D. variabilis were more likely to be encountered in spring and summer seasons and associated with elevation, landcover, temperature, dead belowground biomass, vapor pressure, and precipitation. Regions in the southeast least suitable for encountering ticks included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Florida Coastal Plain, whereas suitable regions included the Interior Plateau, Central Appalachians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Spatial and temporal patterns of different tick species can inform outdoorsmen and the public on tick avoidance measures, reduce tick populations by managing suitable tick habitats, and monitoring areas with unsuitable tick habitat for potential missed encounters.

Preventing malaria spread in the US.

ABSTRACT: Locally acquired malaria is rare in the US; however, in 2023, cases were reported in Florida, Texas, Maryland, and Arkansas. Prompt diagnosis and treatment of malaria are essential to prevent severe malaria disease. This article details malaria and offers treatment guidelines.

Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis.

BACKGROUND: The adoption of predictive algorithms in health care comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been proposed to measure algorithmic bias, but their application to real-world tasks is limited. OBJECTIVE: This study aims to evaluate the algorithmic bias associated with the application of common 30-day hospital readmission models and assess the usefulness and interpretability of selected fairness metrics. METHODS: We used 10.6 million adult inpatient discharges from Maryland and Florida from 2016 to 2019 in this retrospective study. Models predicting 30-day hospital readmissions were evaluated: LACE Index, modified HOSPITAL score, and modified Centers for Medicare & Medicaid Services (CMS) readmission measure, which were applied as-is (using existing coefficients) and retrained (recalibrated with 50% of the data). Predictive performances and bias measures were evaluated for all, between Black and White populations, and between low- and other-income groups. Bias measures included the parity of false negative rate (FNR), false positive rate (FPR), 0-1 loss, and generalized entropy index. Racial bias represented by FNR and FPR differences was stratified to explore shifts in algorithmic bias in different populations. RESULTS: The retrained CMS model demonstrated the best predictive performance (area under the curve: 0.74 in Maryland and 0.68-0.70 in Florida), and the modified HOSPITAL score demonstrated the best calibration (Brier score: 0.16-0.19 in Maryland and 0.19-0.21 in Florida). Calibration was better in White (compared to Black) populations and other-income (compared to low-income) groups, and the area under the curve was higher or similar in the Black (compared to White) populations. The retrained CMS and modified HOSPITAL score had the lowest racial and income bias in Maryland. In Florida, both of these models overall had the lowest income bias and the modified HOSPITAL score showed the lowest racial bias. In both states, the White and higher-income populations showed a higher FNR, while the Black and low-income populations resulted in a higher FPR and a higher 0-1 loss. When stratified by hospital and population composition, these models demonstrated heterogeneous algorithmic bias in different contexts and populations. CONCLUSIONS: Caution must be taken when interpreting fairness measures' face value. A higher FNR or FPR could potentially reflect missed opportunities or wasted resources, but these measures could also reflect health care use patterns and gaps in care. Simply relying on the statistical notions of bias could obscure or underplay the causes of health disparity. The imperfect health data, analytic frameworks, and the underlying health systems must be carefully considered. Fairness measures can serve as a useful routine assessment to detect disparate model performances but are insufficient to inform mechanisms or policy changes. However, such an assessment is an important first step toward data-driven improvement to address existing health disparities.