Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content.

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.

Pulmonary Vein Thrombosis and Pulmonary Embolus in a Pregnant Patient: A Case Report.

We report a case of a pregnant woman who presented to the emergency department complaining of dyspnea and syncope and was ultimately diagnosed with pulmonary vein thrombosis and a saddle pulmonary embolus on computed tomography pulmonary angiography. Proper identification is critical for prompt management to avoid significant life-threatening sequela.

Community Assessment for Public Health Emergency Response (CASPER) following Hurricane Michael: Gadsden, Calhoun, and Jackson Counties, Florida, 2020.

OBJECTIVES: To assess community preparedness and ongoing recovery efforts in the rural counties most severely impacted by Hurricane Michael, including structural and economic losses, injury and illness, healthcare access, and suicide risk and ideation. DESIGN: The Florida Department of Health conducted a Community Assessment for Public Health Emergency Response (CASPER) in January 2020, 15 months after Hurricane Michael made landfall in October 2018. SETTING: A total of 30 clusters were randomly selected from three rural counties in the Panhandle of Florida, including Jackson (15 clusters), Gadsden (11), and Calhoun (four) counties. PARTICIPANTS: A total of 185 face-to-face and two phone interviews were conducted with residents 18 years of age or older. MAIN OUTCOME MEASURE: Hurricane preparedness, structural and economic losses, access to care, and physical and mental health. RESULTS: Around 43 percent of respondents evacuated as a result of Hurricane Michael, and at least two-thirds of all respondents reported having an emergency supply kit and enough nonperishable food, water, and medication. Structural damage was extensive with 63 percent reporting home damage, averaging over $32,000. Few injuries or illnesses were reported post-landfall (9 percent), with the most common being minor injuries and bacterial infections. Most respondents reported continued access to healthcare if needed. The most common stress-related issues reported were difficulty sleeping (19 percent) and agitated behaviors (10 percent). Seven percent of respondents reported being at moderate to high risk for suicide. CONCLUSIONS: Rural areas may lack resources, such as healthcare facilities, skilled workers, and supplies, that hinder their ability to recover from storms when compared to more urban counties. Many residents reported that 15 months after the storm, their homes were still not fully repaired. A majority of residents were prepared with adequate supplies, had minimal disruption in employment or healthcare access, and had few illnesses or injuries during the storm or the recovery efforts.

The Genomics and Population Genomics of the Light Brown Apple Moth, Epiphyas postvittana, an Invasive Tortricid Pest of Horticulture.

The light brown apple moth, Epiphyas postvittana is an invasive, polyphagous pest of horticultural systems around the world. With origins in Australia, the pest has subsequently spread to New Zealand, Hawaii, California and Europe, where it has been found on over 500 plants, including many horticultural crops. We have produced a genomic resource, to understand the biological basis of the polyphagous and invasive nature of this and other lepidopteran pests. The assembled genome sequence encompassed 598 Mb and has an N50 of 301.17 kb, with a BUSCO completion rate of 97.9%. Epiphyas postvittana has 34% of its assembled genome represented as repetitive sequences, with the majority of the known elements made up of longer DNA transposable elements (14.07 Mb) and retrotransposons (LINE 17.83 Mb). Of the 31,389 predicted genes, 28,714 (91.5%) were assigned to 11,438 orthogroups across the Lepidoptera, of which 945 were specific to E. postvittana. Twenty gene families showed significant expansions in E. postvittana, including some likely to have a role in its pest status, such as cytochrome p450s, glutathione-S-transferases and UDP-glucuronosyltransferases. Finally, using a RAD-tag approach, we investigated the population genomics of this pest, looking at its likely patterns of invasion.

Prevalence and predictors of stroke among individuals with prediabetes and diabetes in Florida.

BACKGROUND: The prevalence of both prediabetes and diabetes have been increasing in Florida. These increasing trends will likely result in increases of stroke burden since both conditions are major risk factors of stroke. However, not much is known about the prevalence and predictors of stroke among adults with prediabetes and diabetes and yet this information is critical for guiding health programs aimed at reducing stroke burden. Therefore, the objectives of this study were to estimate the prevalence and identify predictors of stroke among persons with either prediabetes or diabetes in Florida. METHODS: The 2019 Behavioral Risk Factor Surveillance System (BRFSS) survey data were obtained from the Florida Department of Health and used for the study. Weighted prevalence estimates of stroke and potential predictor variables as well as their 95% confidence intervals were computed for adults with prediabetes and diabetes. A conceptual model of predictors of stroke among adults with prediabetes and diabetes was constructed to guide statistical model building. Two multivariable logistic models were built to investigate predictors of stroke among adults with prediabetes and diabetes. RESULTS: The prevalence of stroke among respondents with prediabetes and diabetes were 7.8% and 11.2%, respectively. The odds of stroke were significantly (p ≤ 0.05) higher among respondents with prediabetes that were ≥ 45 years old (Odds ratio [OR] = 2.82; 95% Confidence Interval [CI] = 0.74, 10.69), had hypertension (OR = 5.86; CI = 2.90, 11.84) and hypercholesterolemia (OR = 3.93; CI = 1.84, 8.40). On the other hand, the odds of stroke among respondents with diabetes were significantly (p ≤ 0.05) higher if respondents were non-Hispanic Black (OR = 1.79; CI = 1.01, 3.19), hypertensive (OR = 3.56; CI = 1.87, 6.78) and had depression (OR = 2.02; CI = 1.14, 3.59). CONCLUSIONS: Stroke prevalence in Florida is higher among adults with prediabetes and diabetes than the general population of the state. There is evidence of differences in the importance of predictors of stroke among populations with prediabetes and those with diabetes. These findings are useful for guiding health programs geared towards reducing stroke burden among populations with prediabetes and diabetes.

Sex pheromone receptors of the light brown apple moth, Epiphyas postvittana, support a second major pheromone receptor clade within the Lepidoptera.

Sex pheromones facilitate species-specific sex communication within the Lepidoptera. They are detected by specialised pheromone receptors (PRs), most of which to date fall into a single monophyletic receptor lineage (frequently referred to as "the PR clade") within the odorant receptor (OR) family. Here we investigated PRs of the invasive horticultural pest, Epiphyas postvittana, commonly known as the light brown apple moth. Ten candidate PRs were selected, based on their male-biased expression in antennae or their relationship to the PR clade, for functional assessment in both HEK293 cells and Xenopus oocytes. Of these, six ORs responded to compounds that include components of the E. postvittana ('Epos') sex pheromone blend or compounds that antagonise sex pheromone attraction. In phylogenies, four of the characterised receptors (EposOR1, 6, 7 and 45) fall within the PR clade and two other male-biased receptors (EposOR30 and 34) group together well outside the PR clade. This new clade of pheromone receptors includes the receptor for (E)-11-tetradecenyl acetate (EposOR30), which is the main component of the sex pheromone blend for this species. Interestingly, receptors of the two clades do not segregate by preference for compounds associated with behavioural response (agonist or antagonist), isomer type (E or Z) or functional group (alcohol or acetate), with examples of each scattered across both clades. Phylogenetic comparison with PRs from other species supports the existence of a second major clade of lepidopteran ORs including, EposOR30 and 34, that has been co-opted into sex pheromone detection in the Lepidoptera. This second clade of sex pheromone receptors has an origin that likely predates the split between the major lepidopteran families.

Community Assessment for Public Health Emergency Response (CASPER) Following Hurricane Michael, Bay and Gulf Counties, Florida, 2019.

CONTEXT: On October 10, 2018, Hurricane Michael made landfall near Mexico Beach, Florida, as one of the strongest storms on record to hit the US mainland. Hurricane Michael brought strong winds, heavy rain, and life-threatening storm surge, causing extensive damage across the Florida Panhandle. OBJECTIVES: To assess community preparedness and effects experienced by Panhandle residents, including structural and economic losses, injury and illness, health care access, and suicide risk and ideation in the counties most severely impacted by Hurricane Michael. DESIGN: The Florida Department of Health conducted a Community Assessment for Public Health Emergency Response (CASPER) in October and November 2019, a year after Hurricane Michael made landfall. CASPER is a 2-stage cluster sampling method designed to provide household-level information about a community's needs in a timely, inexpensive, and representative manner. SETTING: A total of 30 clusters were randomly selected from Bay and Gulf Counties, Florida. PARTICIPANTS: In total, 178 face-to-face interviews were completed with adult residents 18 years or older. MAIN OUTCOME MEASURES: Hurricane-related impacts, including structural and economic losses, injury and illness, health care access; and mental health. RESULTS: Almost half of respondents did not evacuate despite mandatory evacuation orders. Most houses (78.1%) received some damage, with more than half still not repaired 1 year later. Access to emergency supply kits, water, nonperishable foods, medications, and health care was common, though many reported needing supplies not included in their kit. Less than half reported having working household carbon monoxide detectors. Injuries and illnesses associated with the hurricane were uncommon; however, anxiety, depression, and insomnia were reported as occurring or worsening by more than one-third of respondents posthurricane. CONCLUSIONS: Increased education and communication regarding hurricane preparedness and recovery, which include clearer messaging on evacuation, improving emergency supply kits, importance of carbon monoxide detectors, and proper generator use, could enhance the safety of the community.

Kidney Recovery in Patients With Acute Kidney Injury Treated in Outpatient Hemodialysis or Rehabilitation Facilities.

RATIONALE & OBJECTIVE: Since January 2017, patients with acute kidney injury requiring dialysis (AKI-D) can be discharged to outpatient dialysis centers for continued hemodialysis (HD) support. We aimed to examine the rate of kidney recovery, time to recovery, and hospitalization-related clinical parameters associated with kidney recovery in patients with AKI-D. STUDY DESIGN: Single-center prospective cohort study. SETTING & PARTICIPANTS: 111 adult patients who were admitted to the University of Kentucky Hospital, experienced AKI-D, and were discharged with need of outpatient HD. EXPOSURE: Hospitalization-related clinical parameters were evaluated. OUTCOME: Kidney recovery as a composite of being alive and no longer requiring HD or other form of kidney replacement therapy. ANALYTICAL APPROACH: Discrete-time survival analysis and logistic regression were used to determine adjusted probabilities of kidney recovery at prespecified time points and to evaluate clinical parameters associated with recovery. RESULTS: 45 (41%) patients recovered kidney function, 25 (55.5%) within the first 30 days following discharge, 16 (35.5%) within 30 to 60 days, and 4 (9%) within 60 to 90 days. Adjusted probabilities of recovery were 36.7%, 27.4%, and 6.3%, respectively. Of the remaining patients, 49 (44%) developed kidney failure requiring chronic kidney replacement therapy and 17 (15%) died or went to hospice. Patients who did not recover kidney function were older, had more comorbid conditions, had lower estimated glomerular filtration rates at baseline, and received more blood transfusions during hospitalization when compared with those who recovered kidney function. LIMITATIONS: Selection bias given that patients included in the study were all eligible for AKI management with outpatient HD as part of Medicare/Medicaid services. CONCLUSIONS: At least one-third of AKI-D survivors discharged from an acute care hospital dependent on HD recovered kidney function within the first 90 days of discharge, more commonly in the first 30 days postdischarge. Future studies should elucidate clinical parameters that can inform risk classification and interventions to promote kidney recovery in this vulnerable and growing population.

Heat illness data strengthens vulnerability maps.

BACKGROUND: Previous extreme heat and human health studies have investigated associations either over time (e.g. case-crossover or time series analysis) or across geographic areas (e.g. spatial models), which may limit the study scope and regional variation. Our study combines a case-crossover design and spatial analysis to identify: 1) the most vulnerable counties to extreme heat; and 2) demographic and socioeconomic variables that are most strongly and consistently related to heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, acute renal disease, and respiratory disease) across 67 counties in the state of Florida, U. S over 2008-2012. METHODS: We first used a case-crossover design to examine the effects of air temperature on daily counts of health outcomes. We employed a time-stratified design with a 28-day comparison window. Referent periods were extracted from ±7, ±14, or ± 21 days to address seasonality. The results are expressed as odds ratios, or the change in the likelihood of each health outcome for a unit change in heat exposure. We then spatially examined the case-crossover extreme heat and health odds ratios and county level demographic and socioeconomic variables with multiple linear regression or spatial lag models. RESULTS: Results indicated that southwest Florida has the highest risks of cardiovascular disease, dehydration, acute renal disease, and respiratory disease. Results also suggested demographic and socioeconomic variables were significantly associated with the magnitude of heat-related health risk. The counties with larger populations working in farming, fishing, mining, forestry, construction, and extraction tended to have higher risks of dehydration and acute renal disease, whereas counties with larger populations working in installation, maintenance, and repair workers tended to have lower risks of cardiovascular, dehydration, acute renal disease, and respiratory disease. Finally, our results showed that high income counties consistently have lower health risks of dehydration, heat-related illness, acute renal disease, and respiratory disease. CONCLUSIONS: Our study identified different relationships with demographic/socioeconomic variables for each heat-sensitive health outcome. Results should be incorporated into vulnerability or risk indices for each health outcome.

Geographic disparities and temporal changes of diabetes prevalence and diabetes self-management education program participation in Florida.

BACKGROUND: Although Diabetes Self-Management Education (DSME) programs are recommended to help reduce the burden of diabetes and diabetes-related complications, Florida is one of the states with the lowest DSME participation rates. Moreover, there is evidence of geographic disparities of not only DSME participation rates but the burden of diabetes as well. Understanding these disparities is critical for guiding control programs geared at improving participation rates and diabetes outcomes. Therefore, the objectives of this study were to: (a) investigate geographic disparities of diabetes prevalence and DSME participation rates; and (b) identify predictors of the observed disparities in DSME participation rates. METHODS: Behavioral Risk Factor Surveillance System (BRFSS) data for 2007 and 2010 were obtained from the Florida Department of Health. Age-adjusted diabetes prevalence and DSME participation rates were computed at the county level and their geographic distributions visualized using choropleth maps. Significant changes in diabetes prevalence and DSME participation rates between 2007 and 2010 were assessed and counties showing significant changes were mapped. Clusters of high diabetes prevalence before and after adjusting for common risk factors and DSME participation rates were identified, using Tango's flexible spatial scan statistics, and their geographic distribution displayed in maps. Determinants of the geographic distribution of DSME participation rates and predictors of the identified high rate clusters were identified using ordinary least squares and logistic regression models, respectively. RESULTS: County level age-adjusted diabetes prevalence varied from 4.7% to 17.8% while DSME participation rates varied from 26.6% to 81.2%. There were significant (p≤0.05) increases in both overall age-adjusted diabetes prevalence and DSME participation rates from 2007 to 2010 with diabetes prevalence increasing from 7.7% in 2007 to 8.6% in 2010 while DSME participation rates increased from 51.4% in 2007 to 55.1% in 2010. Generally, DSME participation rates decreased in rural areas while they increased in urban areas. High prevalence clusters of diabetes (both adjusted and unadjusted) were identified in northern and central Florida, while clusters of high DSME participation rates were identified in central Florida. Rural counties and those with high proportion of Hispanics tended to have low DSME participation rates. CONCLUSIONS: The findings confirm that geographic disparities in both diabetes prevalence and DSME participation rates exist. Specific attention is required to address these disparities especially in areas that have high diabetes prevalence but low DSME participation rates. Study findings are useful for guiding resource allocation geared at reducing disparities and improving diabetes outcomes.

Using social security number to identify sub-populations vulnerable to the health impacts from extreme heat in Florida, U.S.

BACKGROUND: Some socioeconomically vulnerable groups may experience disproportionately higher risk of extreme heat illness than other groups, but no study has utilized the presence/absence of a social security number (SSN) as a proxy for vulnerable sub-populations. METHODS: This study focused on the warm season from 2008 to 2012 in Florida, U.S. With a total number of 8,256,171 individual level health outcomes, we devised separate case-crossover models for five heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease), type of health care visit (emergency department (ED) and hospitalization), and patients reporting/not reporting an SSN. Each stratified model also considered potential effect modification by sex, age, or race/ethnicity. RESULTS: Mean temperature raised the odds of five heat-sensitive health outcomes with the highest odds ratios (ORs) for heat-related illness. Sex significantly modified heat exposure effects for dehydration ED visits (Males: 1.145, 95 % CI: 1.137-1.153; Females: 1.110, 95 % CI: 1.103-1.117) and hospitalization (Males: 1.116, 95 % CI: 1.110-1.121; Females: 1.100, 95 % CI: 1.095-1.105). Patients not reporting an SSN between 25 and 44 years (1.264, 95 % CI: 1.192-1.340) exhibited significantly higher dehydration ED ORs than those reporting an SSN (1.146, 95 % CI: 1.136-1.157). We also observed significantly higher ORs for cardiovascular disease hospitalization from the no SSN group (SSN: 1.089, 95 % CI: 1.088-1.090; no SSN: 1.100, 95 % CI: 1.091-1.110). CONCLUSIONS: This paper partially supports the idea that individuals without an SSN could experience higher risks of dehydration (for those 25-45 years), renal disease, and cardiovascular disease than those with an SSN.

COVID-19 Information-Seeking and Prevention Behaviors in Florida, April 2020.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) surveillance can be enhanced by collecting population-level data on individual prevention measures. We described the use of a state-based, population-level surveillance system on COVID-19 prevention and information-seeking behaviors in Florida during the first month of survey administration. METHODS: Beginning in April 2020, respondents of the Florida Behavioral Risk Factor Surveillance System were asked a series of 8 questions about sources of COVID-19 information and prevention behaviors. We analyzed the prevalence of information-seeking and prevention behaviors among respondents who answered at least 1 of the 8 questions (N = 1,004) overall, by demographic characteristics, and by the presence of chronic conditions. RESULTS: Most respondents reported engaging in prevention behaviors, including handwashing (98.2%), reducing or avoiding travel (96.6%), avoiding crowds and public events (96.5%), and keeping household members at home (87.5%); however, the prevalence of prevention behaviors varied significantly by age, sex, and education. The most frequently reported source of COVID-19 information was the Centers for Disease Control and Prevention's website (40.8%) followed by the Florida Department of Health's website (32.9%). We found significant differences in information sources across all demographic and chronic condition subgroups. A larger proportion of respondents with chronic conditions (vs without chronic conditions) reported consulting their personal doctor for COVID-19 information. CONCLUSION: Understanding the uptake and characteristics associated with individual prevention and information-seeking behaviors at the population level facilitates COVID-19 response efforts. The rapid implementation of COVID-19-related questions in the Florida BRFSS provides a useful model for other population-based surveillance systems.

Encefalitis por Anticuerpos contra el Receptor NMDA: un diagnóstico diferencial neuropsiquiátrico

La Encefalitis por Anticuerpos contra el Receptor NMDA es un reto debido a la amplia lista de posibilidades diagnósticas a las que se asemeja la sintomatología inicial. Es una enfermedad cuya fisiopatología está dada por la generación y acción de anticuerpos, inducidos mayormente por agentes externos (virus) e internos (algunos tumores), sobre NMDAR (receptor N-metil-D-aspartato) que puede cursar con alteraciones neurológicas, psiquiátricas y autonómicas, usualmente afectando a población femenina adulta joven y que en ocasiones forma parte de un síndrome paraneoplásico. Su manejo se basa en inmunoterapia con corticosteroides, inmunoglobulina intravenosa o en casos refractarios, plasmaféresis. La eficacia de estas terapias aumenta con el diagnóstico oportuno, sin embargo con frecuencia el tratamiento se aplica tardíamente por lo difícil del acierto diagnóstico. Presentamos el caso clínico de una femenina de 22 años, que debutó con manifestaciones neuropsiquiátricas, inicialmente medicada con antipsicóticos y que desarrolló rigidez, aumento de creatina quinasa y estatus epiléptico, por lo cual se sospechó síndrome neuroléptico maligno, ameritando hospitalización en la unidad de cuidados intensivos. Ante la nula mejoría, se replanteó el diagnóstico, con sospecha de encefalitis autoinmune y se instauró el tratamiento específico, con lo cual la paciente pudo retornar a su vida diaria sin déficit. (provisto por Infomedic International)

The tuatara genome reveals ancient features of amniote evolution.

The tuatara (Sphenodon punctatus)-the only living member of the reptilian order Rhynchocephalia (Sphenodontia), once widespread across Gondwana1,2-is an iconic species that is endemic to New Zealand2,3. A key link to the now-extinct stem reptiles (from which dinosaurs, modern reptiles, birds and mammals evolved), the tuatara provides key insights into the ancestral amniotes2,4. Here we analyse the genome of the tuatara, which-at approximately 5 Gb-is among the largest of the vertebrate genomes yet assembled. Our analyses of this genome, along with comparisons with other vertebrate genomes, reinforce the uniqueness of the tuatara. Phylogenetic analyses indicate that the tuatara lineage diverged from that of snakes and lizards around 250 million years ago. This lineage also shows moderate rates of molecular evolution, with instances of punctuated evolution. Our genome sequence analysis identifies expansions of proteins, non-protein-coding RNA families and repeat elements, the latter of which show an amalgam of reptilian and mammalian features. The sequencing of the tuatara genome provides a valuable resource for deep comparative analyses of tetrapods, as well as for tuatara biology and conservation. Our study also provides important insights into both the technical challenges and the cultural obligations that are associated with genome sequencing.

Publisher Correction: The tuatara genome reveals ancient features of amniote evolution.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Sociodemographic Determinants of Acute Myocardial Infarction Hospitalization Risks in Florida.

Background Identifying social determinants of myocardial infarction (MI) hospitalizations is crucial for reducing/eliminating health disparities. Therefore, our objectives were to identify sociodemographic determinants of MI hospitalization risks and to assess if the impacts of these determinants vary by geographic location in Florida. Methods and Results This is a retrospective ecologic study at the county level. We obtained data for principal and secondary MI hospitalizations for Florida residents for the 2005-2014 period and calculated age- and sex-adjusted MI hospitalization risks. We used a multivariable negative binomial model to identify sociodemographic determinants of MI hospitalization risks and a geographically weighted negative binomial model to assess if the strength of associations vary by location. There were 645 935 MI hospitalizations (median age, 72 years; 58.1%, men; 73.9%, white). Age- and sex-adjusted risks ranged from 18.49 to 69.48 cases/10 000 persons, and they were significantly higher in counties with low education levels (risk ratio [RR]=1.033, P<0.0001) and high divorce rate (RR, 0.995; P=0.018). However, they were significantly lower in counties with high proportions of rural (RR, 0.996; P<0.0001), black (RR, 1.026; P=0.032), and uninsured populations (RR, 0.983; P=0.040). Associations of MI hospitalization risks with education level and uninsured rate varied geographically (P for non-stationarity test=0.001 and 0.043, respectively), with strongest associations in southern Florida (RR for

Florida neighborhood analysis of social determinants and their relationship to life expectancy.

BACKGROUND: Social determinants of health (SDOH) contribute to unequal life expectancy (LE). Only a handful of papers have analyzed these relationships at the neighborhood level as opposed to the county level. This study draws on both the SDOH and social vulnerability literature to identify relevant factors affecting LE. METHODS: LE was calculated from mortality records for Florida from 2009 to 2013 for 3640 census tracts with reliable estimates. A spatial Durbin error model (SDEM) quantified the direction and magnitude of the factors to LE. The SDEM contains a spatial error term and jointly estimates both local and neighborhood associations. This methodology controls for non-independence between census tracts to provide unbiased statistical estimates. RESULTS: Factors significantly related to an increase in LE, include percentage (%) of the population who identify as Hispanic (beta coefficient [ß]: 0.06, p-value [P] < 0.001) and % of age dependent populations (% population < 5 years old and % population > 65) (ß: 0.13, P < 0.001). Conversely, the following factors exhibited significant negative LE associations, % of households with no automobile (ß: -0.05, P < 0.001), % of mobile homes (ß: -0.02, P < 0.001), and % of female headed households (ß: -0.11, P < 0.001). CONCLUSIONS: Results from the SDEM demonstrate social vulnerability indicators account for additional geographic LE variability beyond commonly studied SDOH. Empirical findings from this analysis can help local health departments identify drivers of spatial health disparities at the local level.

Cell Phone Ownership and Cellular Text/Email Capabilities Among Temporary and Payroll Construction Workers.

Background: With high rates of temporary workers and a transient worker population, the U.S. construction workforce presents a challenge for long-term research and outreach activities. Increasing availability of affordable cell phone technologies may provide an opportunity for research follow-up among construction workers once they leave the worksite. Using pilot study survey data we characterize and examine the association of cell phone technology ownership and cellular text/email services among a non-probabilistic sample of payroll and temporary construction workers. Methods: A cross-sectional study design was used to administer a one-time paper-based anonymous survey to construction workers working at construction sites in Florida, USA. The survey featured questions on sociodemographic characteristics, occupational history, cell phone technology ownership, and cellular text/email services capabilities. Results: Among the 223 construction worker survey respondents, 31.4% identified as temporary workers and 68.6% were on payroll and 87.4% owned a cell phone. Construction workers who own a cell phone had greater than a high school education (28.9% vs. 25.0%; p = 0.019), made >$30,000/year (27.1% vs. 14.8%; p = 0.011), had same cell phone number for >1 year (74.4% vs. 40.7%; p = 0.001), and were employed as a payroll worker (71.0% vs. 50.0%; p = 0.037). Temporary construction workers compared to their payroll counterparts were significantly less likely to have email services on their cell phone [unadjusted-odds ratio 0.41 (95% CI: 0.17-0.97)]. Conclusion: Cell phone ownership and smartphone-enabled technologies such as email/texting capabilities are higher among payroll than temporary construction workers. Further research on frequency of cell phone use and types of email/texting services used by construction workers are needed.

Using web data to improve surveillance for heat sensitive health outcomes.

BACKGROUND: Elevated and prolonged exposure to extreme heat is an important cause of excess summertime mortality and morbidity. To protect people from health threats, some governments are currently operating syndromic surveillance systems. However, A lack of resources to support time- and labor- intensive diagnostic and reporting processes make it difficult establishing region-specific surveillance systems. Big data created by social media and web search may improve upon the current syndromic surveillance systems by directly capturing people's individual and subjective thoughts and feelings during heat waves. This study aims to investigate the relationship between heat-related web searches, social media messages, and heat-related health outcomes. METHODS: We collected Twitter messages that mentioned "air conditioning (AC)" and "heat" and Google search data that included weather, medical, recreational, and adaptation information from May 7 to November 3, 2014, focusing on the state of Florida, U.S. We separately associated web data against two different sources of health outcomes (emergency department (ED) and hospital admissions) and five disease categories (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease). Seasonal and subseasonal temporal cycles were controlled using autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) and generalized linear model (GLM). RESULTS: The results show that the number of heat-related illness and dehydration cases exhibited a significant positive relationship with web data. Specifically, heat-related illness cases showed positive associations with messages (heat, AC) and web searches (drink, heat stroke, park, swim, and tired). In addition, terms such as park, pool, swim, and water tended to show a consistent positive relationship with dehydration cases. However, we found inconsistent relationships between renal illness and web data. Web data also did not improve the models for cardiovascular and respiratory illness cases. CONCLUSIONS: Our findings suggest web data created by social medias and search engines could improve the current syndromic surveillance systems. In particular, heat-related illness and dehydration cases were positively related with web data. This paper also shows that activity patterns for reducing heat stress are associated with several health outcomes. Based on the results, we believe web data could benefit both regions without the systems and persistently hot and humid climates where excess heat early warning systems may be less effective.