Health Disparities in the Aftermath of Flood Events: A Review of Physical and Mental Health Outcomes with Methodological Considerations in the USA.

PURPOSE OF REVIEW: This review applies an environmental justice perspective to synthesize knowledge of flood-related health disparities across demographic groups in the USA. The primary aim is to examine differential impacts on physical and mental health outcomes while also assessing methodological considerations such as flood exposure metrics, baseline health metrics, and community engagement. RECENT FINDINGS: In our review (n = 27), 65% and 72% of studies identified racial, ethnic, or socio-economic disparities in physical and mental health outcomes post-flooding, respectively. The majority of racial/ethnic disparities were based on Black race, while most socio-economic disparities were based on lower household income. Forty-two percent of studies lacked flood exposure metrics, but often identified disparities. Common flood exposure metrics included self-reported flooding, flood risk models, and satellite-based observations. Seventy percent of studies lacked baseline health measurements or suitable alternatives, and only 19% incorporated community engagement into their research design. The literature consistently finds that both physical and mental health burdens following flooding are unequally shared across racial, ethnic, and socio-economic groups. These findings emphasize the need for disaster risk reduction policies that address underlying vulnerabilities to flooding, unequal exposure to flooding, and progressive funding for recovery efforts. Findings also underscore the importance of methodological enhancements to facilitate precise assessments of flood exposure and health outcomes.

Redlining, racism and food access in US urban cores.

In the 1930s, the Home Owners' Loan Corporation (HOLC) graded the mortgage security of urban US neighborhoods. In doing so, the HOLC engaged in the practice, imbued with racism and xenophobia, of "redlining" neighborhoods deemed "hazardous" for lenders. Redlining has caused persistent social, political and economic problems for communities of color. Linkages between redlining and contemporary food access remain unexamined, even though food access is essential to well-being. To investigate this, we used a census tract-level measure of low-income and low grocery store food access from the US Department of Agriculture Food Access Research Atlas, redlining data from Mapping Inequality Project, and demographic data from the American Community Survey. We employed generalized estimating equations with robust covariance estimates to analyze data pertaining to 10,459 census tracts in 202 US cities. Tracts that the HOLC graded as "C" ("decline in desirability") and "D" ("hazardous") had reduced contemporary food access compared to those graded "A" ("best"). Increases in contemporary census tract proportions of Black, Hispanic, or other racial/ethnic minority residents, as well as disabled residents, were associated with reduced food access. Increases in contemporary proportions of residents age 75 years and older or those without a car were associated with better food access. Tracts that underwent housing redevelopment since being graded had better food access, while those undergoing gentrification had reduced food access. Results suggest that issues of redlining, housing discrimination, racism, ableism, displacement, and food inaccessibility are deeply intertwined.

Predicting Actions of Users Using Heterogeneous Online Signals.

Advertising platforms have a growing need for improving prediction quality, as missing out on ad opportunities can have a negative effect on their performance. To that end, prediction tasks such as conversion prediction need to be continuously advanced through the inclusion of data from new sources or through algorithmic development that tackles existing challenges. The introduction of different data sources naturally brings unwanted noise, whereas underexplored areas still exist in modeling approaches, such as temporal information of events in sequences. In this study, we propose extensions for modeling online user activity trails that address two very important aspects of activities-time and noise, through dedicated layers that can be used in existing deep sequence-learning approaches. Our proposed method exhibited area under the receiver operating characteristic curve improvement of up to 3% and 1.75% compared with production and best baseline approaches, respectively, across two major advertiser data sets and several predictive tasks.

Social disparities in neighborhood heat in the Northeast United States.

Upward trends in ground-level warming are expected to intensify, affecting the health of human populations. Specific to the United States, the Northeast (NE) region is one of the most vulnerable to these warming trends. Previous research has found social disparities in the distribution of heat, while recent studies have examined associations between metropolitan racial/ethnic segregation and heat exposures. We advance upon previous research by including a novel measure of neighborhood-level racial/ethnic diversity in our examination of social inequalities in heat for NE neighborhoods (census tracts). We paired data derived from the United States Geological Survey on mean land surface temperature (LST) for the summer months of 2013-2017 with sociodemographic data from the American Community Survey (5-year estimates, 2013-2017). We use multivariable generalized estimating equations (GEEs) that adjust for geographic clustering. Findings reveal heat exposure disparities across NE neighborhoods. Neighborhoods with higher proportions of racial/ethnic minorities, people of lower socioeconomic status, households without access to an automobile, and greater diversity experience higher temperatures. Diversity was more strongly related to increased heat in neighborhoods with lower Latinx and lower Black composition suggesting that neighborhood homogeneity confers a differentially greater cooling effect based on higher White composition. The social groups that carry the unequal thermal burdens are also those who are most vulnerable. Interventions to reduce heat risks in the NE should therefore prioritize reducing the burden on historically disadvantaged communities.

Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County.

Non-governmental air quality monitoring networks include low-cost, networked air pollution sensors hosted at homes and schools that display real-time pollutant concentration estimates on publicly accessible websites. Such networks can empower people to take health-protective actions, but their unplanned organization may produce an uneven spatial distribution of sensors. Barriers to acquiring sensors may disenfranchise particular social groups. To test this directly, we quantitatively examine if there are social inequalities in the distribution of sensors in a non-governmental air quality monitoring network (PurpleAir) in Los Angeles County, California. We paired sociodemographic data from the American Community Survey and estimates of PM2.5 concentrations from the USEPA's Downscaler model at the census tract level (n = 2203) with a sensors per capita (SPC) variable, which is based on population proximity to PurpleAir sensors (n = 696) in Los Angeles County. Findings from multivariable generalized estimating equations (GEEs) controlling for clustering by housing age and value reveal patterns of environmental injustice in the distribution of PurpleAir sensors across Los Angeles County census tracts. Tracts with higher percentages of Hispanic/Latino/a and Black residents and lower median household income had decreased SPC. There was a curvilinear (concave) relationship between the percentage of renter-occupants and SPC. Sensors were concentrated in tracts with greater percentages of adults and seniors (vs. children), higher occupied housing density, and higher PM2.5 pollution. Results reveal social inequalities in the self-organizing PurpleAir network, suggesting another layer of environmental injustice such that residents of low-income and minority neighborhoods have reduced access to information about local air pollution.

Disparities in Health Effects and Access to Health Care Among Houston Area Residents After Hurricane Harvey.

OBJECTIVES: Although research shows that public health is substantially affected during and after disasters, few studies have examined the health effects of Hurricane Harvey, which made landfall on the Texas coast in August 2017. We assessed disparities in physical health, mental health, and health care access after Hurricane Harvey among residents of the Houston-The Woodlands-Sugar Land, Texas, metropolitan statistical area (ie, Houston MSA). METHODS: We used structured survey data collected through telephone and online surveys from a population-based random sample of Houston MSA residents (n = 403) collected from November 29, 2017, through January 6, 2018. We used descriptive statistics to describe the prevalence of physical health/mental health and health care access outcomes and multivariable generalized linear models to assess disparities (eg, based on race/ethnicity, socioeconomic status, disability) in health outcomes. RESULTS: Physical health problems disproportionately affected persons who did not evacuate (odds ratio [OR] = 0.41; 95% confidence interval [CI], 0.19-0.87). Non-Hispanic black persons were more likely than non-Hispanic white persons to have posttraumatic stress (OR = 5.03; 95% CI, 1.90-13.10), as were persons in households that experienced job loss post-Harvey (vs did not experience job loss post-Harvey; OR = 2.89; 95% CI, 1.14-7.32) and older persons (OR = 1.04; 95% CI, 1.01-1.06). Health care access was constrained for persons whose households lost jobs post-Harvey (vs did not lose jobs post-Harvey; OR = 2.73; 95% CI, 1.29-5.78) and for persons with disabilities (vs without disabilities; OR = 3.19; 95% CI, 1.37-7.45). CONCLUSIONS: Our findings underscore the need to plan for and ameliorate public health disparities resulting from climate change-related disasters, which are expected to occur with increased frequency and magnitude.

Hurricane Harvey and Greater Houston households: comparing pre-event preparedness with post-event health effects, event exposures, and recovery.

Most disaster studies rely on convenience sampling and 'after-only' designs to assess impacts. This paper, focusing on Hurricane Harvey (2017) and leveraging a pre-/post-event sample of Greater Houston households (n=71) in the United States, establishes baselines for disaster preparedness and home structure flood hazard mitigation, explores household-level ramifications, and examines how preparedness and mitigation relate to health effects, event exposures, and recovery. Between 70 and 80 per cent of participants instituted preparedness measures. Mitigation actions varied: six per cent had interior drainage systems and 83 per cent had elevated indoor heating/cooling components. Sixty per cent reported home damage. One-half highlighted allergies and two-thirds indicated some level of post-traumatic stress (PTS). Three-quarters worried about family members/friends. The results of generalised linear models revealed that greater pre- event mitigation was associated with fewer physical health problems and adverse experiences, lower PTS, and faster recovery. The study design exposed the broad benefits of home structure flood hazard mitigation for households after Harvey.

Environmental injustice and Hurricane Harvey: A household-level study of socially disparate flood exposures in Greater Houston, Texas, USA.

Environmental justice research on flooding has relied heavily on analyses of aggregated geographic areal units and assessing exposure to 'pre-flood' risks (e.g., residence in 100-year flood zones) rather than actual flood events. To address these limitations, we examined disproportionate exposure to flooding caused by Hurricane Harvey in 2017 in Greater Houston (Texas). Using primary survey data collected from 377 representative households before Harvey and spatial data on Harvey-induced inundation developed by the US Federal Emergency Management Agency, we found that the areal extent of flooding around residents' home sites was distributed inequitably with respect to race/ethnicity and socioeconomic status (SES). Hispanic, black and other racial/ethnic minority households experienced more extensive flooding than white households, and lower SES households faced more extensive flooding than higher SES households. Findings align with prior flood risk research in Greater Houston and provide cause for concern, as social inequities in flood exposure may have influenced social disparities in flood impacts and post-disaster needs. Since flood events in Greater Houston are expected to increase in frequency and magnitude due to climate change, socially disparate impacts are likely to become an increasingly salient public policy issue. Thus, proactive approaches for reducing flood risks and ameliorating disparities should be implemented.

Psychiatric stressor recognition from clinical notes to reveal association with suicide.

Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden globally. One important type of suicide risk factor is psychiatric stress. Prior studies mainly use survey data to investigate the association between suicide and stressors. Very few studies have investigated stressor data in electronic health records, mostly due to the data being recorded in narrative text. This study takes the initiative to automatically extract and classify psychiatric stressors from clinical text using natural language processing-based methods. Suicidal behaviors were also identified by keywords. Then, a statistical association analysis between suicide ideations/attempts and stressors extracted from a clinical corpus is conducted. Experimental results show that our natural language processing method could recognize stressor entities with an F-measure of 89.01 percent. Mentions of suicidal behaviors were identified with an F-measure of 97.3 percent. The top three significant stressors associated with suicide are health, pressure, and death, which are similar to previous studies. This study demonstrates the feasibility of using natural language processing approaches to unlock information from psychiatric notes in electronic health record, to facilitate large-scale studies about associations between suicide and psychiatric stressors.

C-Reactive Protein as a Predictor of Difficult Laparoscopic Cholecystectomy in Patients with Acute Calculous Cholecystitis: A Multivariate Analysis.

BACKGROUND: Laparoscopic cholecystectomy (LC) is the treatment of choice for mild and moderate acute cholecystitis. The aim of this study was to analyze the utility of C-reactive protein (CRP) as a predictor of difficult laparoscopic cholecystectomy (DLC) in patients with acute cholecystitis. MATERIALS AND METHODS: We conducted a prospective study. All patients included were treated with emergency LC. Patients were analyzed as DLC and nondifficult laparoscopic cholecystectomy (NDLC). Multiple logistic regression and receiver-operating characteristic curve analysis were employed to explore which variables were statistically significant in predicting a DLC. Two different models were analyzed. RESULTS: A total of 66 patients were included (37.9% DLC versus 62.1% NDLC). Ideal cutoff point for CRP was calculated as 11 mg/dL, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for predicting DLC being 92% (95% CI 75-97.8), 82.9% (95% CI 68.7-91.5), 76.7%, and 94.4%, respectively. In the first model multivariate analysis, age >45 years, male sex, gallbladder wall thickness ≥5 mm, and pericholecystic fluid collection were significant predictors of DLC, with an area under the curve (AUC) of 0.89. In the second model multivariate analysis, only CRP ≥11 (odds ratio, OR = 17.9, P = .013) was significant predictor of presenting DLC, with an AUC of 0.96. CONCLUSIONS: Preoperative CRP with values ≥11 mg/dL was associated with the highest odds (OR = 17.9) of presenting DLC in our study. This value possesses good sensitivity, specificity, PPV, and NPV for predicting DLC in our population with acute calculous cholecystitis.

[Clinical analysis of 200 cases of idiopathic thrombocytopenic purpura]. / Análisis de 200 casos clínicos de púrpura trombocitopénica idiopática.

BACKGROUND: Idiopathic thrombocytopenic purpura is characterized by extravasation of blood into the subcutaneous tissue, mucous membranes or skin; it generates bleeding manifestations as ecchymotic lesions, petechiae of sudden onset, epistaxis, bleeding from the gums, and serious complications such as intracranial hemorrhage. This condition is due to a platelet destruction mediated by antibodies directed against the surface of the platelets. The objective is to present the clinical characteristics of patients with idiopathic thrombocytopenic purpura in a third level medical facility in Puebla, México. METHODS: A descriptive study, which included 200 patients from the Hematology Service, was carried out. All patients included had the diagnosis of idiopathic thrombocytopenic purpura. We studied their clinical manifestations, type of diagnosis, as well as medical and/or surgical treatment. The statiscal analysis was made using descriptive measures. RESULTS: The entire sample (200 patients) had idiopathic thrombocytopenic purpura as a diagnosis established, 57 % had ecchymoses, 42 % petechiae, 23 % bleeding from the gums, 31.5 % epistaxis, 6.5 % abnormal uterine bleeding, 11 % malaise, 7.0 % hemorrhage, 0.5 % pruritus, 0.5 % seizures, 8 % two or more signs and symptoms and 73 % was asymptomatic when the diagnosis was established. CONCLUSIONS: Symptoms of idiopathic thrombocytopenic purpura are varied, a large percentage of patients can be processed unnoticed. The results described were similar to those reported in the literature.

INTRODUCCIÓN: la púrpura trombocitopénica idiopática se caracteriza por la extravasación de sangre en el tejido subcutáneo, membranas, mucosas o piel, que puede generar manifestaciones clínicas de sangrado como lesiones equimóticas, petequias de aparición brusca, epistaxis, gingivorragia y complicaciones graves como hemorragia intracraneal, debido a destrucción plaquetaria mediada por anticuerpos dirigidos contra la superficie de las plaquetas. El objetivo de este informe es presentar las características clínicas de los pacientes con púrpura trombocitopénica idiopática en un hospital de tercer nivel de atención, con la finalidad de tener estadísticas para estudios analíticos posteriores. MÉTODOS: se realizó un estudio descriptivo de 200 pacientes atendidos en el servicio de hematología con diagnóstico de púrpura trombocitopénica idiopática. Se describen sus manifestaciones clínicas, el diagnóstico y el tratamiento médico y quirúrgico empleados. RESULTADOS: de 200 pacientes con púrpura trombocitopénica idiopática, 57 % presentó equimosis, 42 % petequias, 23 % gingivorragia, 31.5 % epistaxis, 6.5 % sangrado uterino anormal, 11 % síntomas generales, 7 % hemorragia en otros sitios, 0.5 % prurito, 0.5 % convulsiones, 8 % dos o más signos y síntomas y 73 % estaba asintomático al momento del diagnóstico. CONCLUSIONES: la sintomatología de la púrpura trombocitopénica idiopática es variada y un elevado porcentaje de pacientes puede cursar desapercibido. Los resultados observados fueron similares a los informados en la literatura.

Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements.

Current research on sleep using experimental animals is limited by the expense and time-consuming nature of traditional EEG/EMG recordings. We present here an alternative, noninvasive approach utilizing piezoelectric films configured as highly sensitive motion detectors. These film strips attached to the floor of the rodent cage produce an electrical output in direct proportion to the distortion of the material. During sleep, movement associated with breathing is the predominant gross body movement and, thus, output from the piezoelectric transducer provided an accurate respiratory trace during sleep. During wake, respiratory movements are masked by other motor activities. An automatic pattern recognition system was developed to identify periods of sleep and wake using the piezoelectric generated signal. Due to the complex and highly variable waveforms that result from subtle postural adjustments in the animals, traditional signal analysis techniques were not sufficient for accurate classification of sleep versus wake. Therefore, a novel pattern recognition algorithm was developed that successfully distinguished sleep from wake in approximately 95% of all epochs. This algorithm may have general utility for a variety of signals in biomedical and engineering applications. This automated system for monitoring sleep is noninvasive, inexpensive, and may be useful for large-scale sleep studies including genetic approaches towards understanding sleep and sleep disorders, and the rapid screening of the efficacy of sleep or wake promoting drugs.