Persistent poverty and incidence-based melanoma mortality in Texas.

PURPOSE: Previous studies have shown that individuals living in areas with persistent poverty (PP) experience worse cancer outcomes compared to those living in areas with transient or no persistent poverty (nPP). The association between PP and melanoma outcomes remains unexplored. We hypothesized that melanoma patients living in PP counties (defined as counties with ≥ 20% of residents living at or below the federal poverty level for the past two decennial censuses) would exhibit higher rates of incidence-based melanoma mortality (IMM). METHODS: We used Texas Cancer Registry data to identify the patients diagnosed with invasive melanoma or melanoma in situ (stages 0 through 4) between 2000 and 2018 (n = 82,458). Each patient's PP status was determined by their county of residence at the time of diagnosis. RESULTS: After adjusting for demographic variables, logistic regression analyses revealed that melanoma patients in PP counties had statistically significant higher IMM compared to those in nPP counties (17.4% versus 11.3%) with an adjusted odds ratio of 1.35 (95% CI 1.25-1.47). CONCLUSION: These findings highlight the relationship between persistent poverty and incidence-based melanoma mortality rates, revealing that melanoma patients residing in counties with persistent poverty have higher melanoma-specific mortality compared to those residing in counties with transient or no poverty. This study further emphasizes the importance of considering area-specific socioeconomic characteristics when implementing place-based interventions to facilitate early melanoma diagnosis and improve melanoma treatment outcomes.

Genetic correlations between liver fat content, metabolic health, and adiposity distribution in the Fels Longitudinal Study.

BACKGROUND AND AIMS: Hepatic steatosis is known to be heritable, but its genetic basis is mostly uncharacterized. Steatosis is associated with metabolic and adiposity features; recent studies hypothesize that shared genetic effects between these traits could account for some of the unexplained heritability. This study aimed to quantify these genetic associations in a family-based sample of non-Hispanic white adults. METHODS AND RESULTS: 704 participants (18-95 years, 55.8% female) from the Fels Longitudinal Study with an MRI assessment of liver fat were included. Quantitative genetic analyses estimated the age- and sex-adjusted heritability of individual traits and the genetic correlations within trait pairs. Mean liver fat was 5.95% (SE = 0.23) and steatosis (liver fat >5.56%) was present in 29.8% of participants. Heritability (h2± SE) of steatosis was 0.72 ± 0.17 (p = 6.80e-6). All other traits including liver enzymes, fasting glucose, HOMA-IR, visceral and subcutaneous adipose tissue (VAT, SAT), body mass index, body fat percent, waist circumference, lipids and blood pressure were also heritable. Significant genetic correlations were found between liver fat and all traits except aspartate aminotransferase (AST), and among most trait pairs. Highest genetic correlations were between liver fat and HOMA-IR (0.85 ± 0.08, p = 1.73e-8), fasting glucose and ALT (0.89 ± 0.26, p = 6.68e-5), and HOMA-IR with: waist circumference (0.81 ± 0.12, p = 3.76e-6), body fat percent (0.78 ± 0.12 p = 2.42e-5) and VAT (0.73 ± 0.07, p = 6.37e-8). CONCLUSIONS: Common genes may exist between liver fat accumulation, metabolic features and adiposity phenotypes.

Geospatial Analysis for Prehospital Extracorporeal Cardiopulmonary Resuscitation in Houston, Texas.

OBJECTIVES: Extracorporeal cardiopulmonary resuscitation (eCPR) is a promising treatment that could improve survival for refractory out-of-hospital (OHCA) patients. Healthcare systems may choose to start eCPR in the prehospital setting to optimize time to eCPR initiation and decrease low-flow time. We used geospatial modeling to evaluate different eCPR catchment strategies for a forthcoming prehospital eCPR program in Houston, Texas. METHODS: We studied OHCAs treated by the Houston Fire Department from 2013-2021. We included OHCA patients aged 18-65 years old with an initial shockable rhythm that did not have prehospital return of spontaneous circulation (ROSC). Based on the geolocation that each OHCA occurred, we used geospatial modeling to identify eCPR candidates using four mapping strategies based on distance/drive time from the eCPR center: 1) 15-minute drive time, 20-minute drive time, 10-mile drive distance, and 15-mile drive distance. RESULTS: Of 18,501 OHCAs during the study period, 881 met the eCPR inclusion criteria. Compared to non-eCPR candidates, eCPR candidates were younger (median age 52.3 years vs 62.7 years, p < 0.01) and had a higher proportion of males (76.6% v 59.8%, p < 0.01). Of eCPR candidate OHCAs, OHCAs occurred more frequently during the weekdays and the daytime, with 5:00 PM being the most common time. Using geospatial modeling and based on drive time, 219 OHCAs (24.9% of 881) were within a 15-minute drive, and 454 (51.5%) were within a 20-minute drive. Using drive distance, 383 eCPR candidates (43.5%) were within 10 miles, and 703 (79.8%) were within 15 miles. CONCLUSIONS: Using geospatial modeling, we demonstrated a process to estimate potential eCPR patient volumes for a geographic region. Geospatial modeling represents a viable strategy for healthcare systems to delineate eCPR catchment areas.

Assessment of a SARS-CoV-2 wastewater monitoring program in El Paso, Texas, from November 2020 to June 2022.

The border city of El Paso, Texas, and its water utility, El Paso Water, initiated a SARS-CoV-2 wastewater monitoring program to assess virus trends and the appropriateness of a wastewater monitoring program for the community. Nearly weekly sample collection at four wastewater treatment facilities (WWTFs), serving distinct regions of the city, was analyzed for SARS-CoV-2 genes using the CDC 2019-Novel coronavirus Real-Time RT-PCR diagnostic panel. Virus concentrations ranged from 86.7 to 268,000 gc/L, varying across time and at each WWTF. The lag time between virus concentrations in wastewater and reported COVID-19 case rates (per 100,00 population) ranged from 4-24 days for the four WWTFs, with the strongest trend occurring from November 2021 - June 2022. This study is an assessment of the utility of a geographically refined SARS-CoV-2 wastewater monitoring program to supplement public health efforts that will manage the virus as it becomes endemic in El Paso.

The social vulnerability index as a risk stratification tool for health disparity research in cancer patients: a scoping review.

PURPOSE: The social vulnerability index (SVI), developed by the Centers for Disease Control and Prevention, is a novel composite measure encompassing multiple variables that correspond to key social determinants of health. The objective of this review was to investigate innovative applications of the SVI to oncology research and to employ the framework of the cancer care continuum to elucidate further research opportunities. METHODS: A systematic search for relevant articles was performed in five databases from inception to 13 May 2022. Included studies applied the SVI to analyze outcomes in cancer patients. Study characteristics, patent populations, data sources, and outcomes were extracted from each article. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: In total, 31 studies were included. Along the cancer care continuum, five applied the SVI to examine geographic disparities in potentially cancer-causing exposures; seven in cancer diagnosis; fourteen in cancer treatment; nine in treatment recovery; one in survivorship care; and two in end-of-life care. Fifteen examined disparities in mortality. CONCLUSION: In highlighting place-based disparities in patient outcomes, the SVI represents a promising tool for future oncology research. As a reliable geocoded dataset, the SVI may inform the development and implementation of targeted interventions to prevent cancer morbidity and mortality at the neighborhood level.

A Novel Bayesian Spatial-Temporal Approach to Quantify SARS-CoV-2 Testing Disparities for Small Area Estimation.

Objectives. To propose a novel Bayesian spatial-temporal approach to identify and quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing disparities for small area estimation. Methods. In step 1, we used a Bayesian inseparable space-time model framework to estimate the testing positivity rate (TPR) at geographically granular areas of the census block groups (CBGs). In step 2, we adopted a rank-based approach to compare the estimated TPR and the testing rate to identify areas with testing deficiency and quantify the number of needed tests. We used weekly SARS-CoV-2 infection and testing surveillance data from Cameron County, Texas, between March 2020 and February 2022 to demonstrate the usefulness of our proposed approach. Results. We identified the CBGs that had experienced substantial testing deficiency, quantified the number of tests that should have been conducted in these areas, and evaluated the short- and long-term testing disparities. Conclusions. Our proposed analytical framework offers policymakers and public health practitioners a tool for understanding SARS-CoV-2 testing disparities in geographically small communities. It could also aid COVID-19 response planning and inform intervention programs to improve goal setting and strategy implementation in SARS-CoV-2 testing uptake. (Am J Public Health. 2023;113(1):40-48. https://doi.org/10.2105/AJPH.2022.307127).

Associations between actigraphy-derived rest-activity rhythm characteristics and hypertension in United States adults.

People with disrupted circadian rhythms, such as shift workers, have shown a higher risk of hypertension. However, it is unclear whether more subtle differences in diurnal rest-activity rhythms in the population are associated with hypertension. Clarifying the association between the rest-activity rhythm, a modifiable behavioural factor, and hypertension could provide insight into preventing hypertension and possibly cardiovascular diseases. In this study, we investigated the association between rest-activity rhythm characteristics and hypertension in a large representative sample of United States adults. Cross-sectional data were obtained from the National Health and Nutrition Examination Survey 2011-2014 (N = 6726; mean [range] age 49 [20-79] years; 52% women). Five rest-activity rhythm parameters (i.e., pseudo F statistic, amplitude, mesor, amplitude:mesor ratio, and acrophase) were derived from 24-h actigraphy data using the extended cosine model. We performed multiple logistic regression to assess the associations between the rest-activity rhythm parameters and hypertension. Subgroup analysis stratified by age, gender, race/ethnicity, body mass index and diabetes status was also conducted. A weakened overall rest-activity rhythm, characterised by a lower F statistic, was associated with higher odds of hypertension (odds ratio quintile 1 versus quintile 5 [OR Q1vs.Q5 ] 1.61, 95% confidence interval [CI] 1.26-2.05; p trend < 0.001). Similar results were found for lower amplitude (OR Q1vs.Q5 1.51, 95% CI 1.13-2.03; p trend = 0.01) and amplitude:mesor ratio (OR Q1vs.Q5 1.34, 95% CI 1.01-1.78; p trend = 0.03). The results were robust to the adjustment of confounders, individual behaviours including physical activity levels and sleep duration and appeared consistent across subgroups. Possible interaction between the rest-activity rhythm and body mass index was found. Our results support an association between weakened rest-activity rhythms and higher odds of hypertension.

U.S. Adolescent Rest-Activity patterns: insights from functional principal component analysis (NHANES 2011-2014).

BACKGROUND: Suboptimal rest-activity patterns in adolescence are associated with worse health outcomes in adulthood. Understanding sociodemographic factors associated with rest-activity rhythms may help identify subgroups who may benefit from interventions. This study aimed to investigate the association of rest-activity rhythm with demographic and socioeconomic characteristics in adolescents. METHODS: Using cross-sectional data from the nationally representative National Health and Nutrition Examination Survey (NHANES) 2011-2014 adolescents (N = 1814), this study derived rest-activity profiles from 7-day 24-hour accelerometer data using functional principal component analysis. Multiple linear regression was used to assess the association between participant characteristics and rest-activity profiles. Weekday and weekend specific analyses were performed in addition to the overall analysis. RESULTS: Four rest-activity rhythm profiles were identified, which explained a total of 82.7% of variance in the study sample, including (1) High amplitude profile; (2) Early activity window profile; (3) Early activity peak profile; and (4) Prolonged activity/reduced rest window profile. The rest-activity profiles were associated with subgroups of age, sex, race/ethnicity, and household income. On average, older age was associated with a lower value for the high amplitude and early activity window profiles, but a higher value for the early activity peak and prolonged activity/reduced rest window profiles. Compared to boys, girls had a higher value for the prolonged activity/reduced rest window profiles. When compared to Non-Hispanic White adolescents, Asian showed a lower value for the high amplitude profile, Mexican American group showed a higher value for the early activity window profile, and the Non-Hispanic Black group showed a higher value for the prolonged activity/reduced rest window profiles. Adolescents reported the lowest household income had the lowest average value for the early activity window profile. CONCLUSIONS: This study characterized main rest-activity profiles among the US adolescents, and demonstrated that demographic and socioeconomic status factors may shape rest-activity behaviors in this population.

Rest-activity profiles among U.S. adults in a nationally representative sample: a functional principal component analysis.

BACKGROUND: The 24-h rest and activity behaviors (i.e., physical activity, sedentary behaviors and sleep) are fundamental human behaviors essential to health and well-being. Functional principal component analysis (fPCA) is a flexible approach for characterizing rest-activity rhythms and does not rely on a priori assumptions about the activity shape. The objective of our study is to apply fPCA to a nationally representative sample of American adults to characterize variations in the 24-h rest-activity pattern, determine how the pattern differs according to demographic, socioeconomic and work characteristics, and examine its associations with general health status. METHODS: The current analysis used data from adults 25 or older in the National Health and Nutrition Examination Survey (NHANES, 2011-2014). Using 7-day 24-h actigraphy recordings, we applied fPCA to derive profiles for overall, weekday and weekend rest-activity patterns. We examined the association between each rest-activity profile in relation to age, gender, race/ethnicity, education, income and working status using multiple linear regression. We also used multiple logistic regression to determine the relationship between each rest-activity profile and the likelihood of reporting poor or fair health. RESULTS: We identified four distinct profiles (i.e., high amplitude, early rise, prolonged activity window, biphasic pattern) that together accounted for 86.8% of total variation in the study sample. We identified numerous associations between each rest-activity profile and multiple sociodemographic characteristics. We also found evidence suggesting the associations differed between weekdays and weekends. Finally, we reported that the rest-activity profiles were associated with self-rated health. CONCLUSIONS: Our study provided evidence suggesting that rest-activity patterns in human populations are shaped by multiple demographic, socioeconomic and work factors, and are correlated with health status.

Effects of aerosol particle size on the measurement of airborne PM2.5 with a low-cost particulate matter sensor (LCPMS) in a laboratory chamber.

Previous validation studies found a good linear correlation between the low-cost particulate matter sensors (LCPMS) and other research grade particulate matter (PM) monitors. This study aimed to determine if different particle size bins of PM would affect the linear relationship and agreement between the Dylos DC1700 (LCPMS) particle count measurements (converted to PM2.5 mass concentrations) and the Grimm 11R (research grade instrument) mass concentration measurements. Three size groups of PM2.5 (mass median aerodynamic diameters (MMAD): < 1 µm, 1-2 µm, and > 2 µm) were generated inside a laboratory chamber, controlled for temperature and relative humidity, by dispersing sodium chloride crystals through a nebulizer. A linear regression comparing 1-min average PM2.5 particle counts from the Dylos DC1700 (Dylos) to the Grimm 11R (Grimm) mass concentrations was estimated by particle size group. The slope for the linear regression was found to increase as MMAD increased (< 1 µm, 0.75 (R2 = 0.95); 1-2 µm, 0.90 (R2 = 0.93); and > 2 µm, 1.03 (R2 = 0.94). The linear slopes were used to convert Dylos counts to mass concentration, and the agreement between converted Dylos mass and Grimm mass was estimated. The absolute relative error between converted Dylos mass and the Grimm mass was smaller in the < 1 µm group (16%) and 1-2 µm group (16%) compared to the > 2 µm group (32%). Therefore, the bias between converted Dylos mass and Grimm mass varied by size group. Future studies examining particle size bins over a wider range of coarse particles (> 2.5 µm) would provide useful information for accurately converting LCPMS counts to mass concentration.

The association between overnight fasting and body mass index in older adults: the interaction between duration and timing.

BACKGROUND: Circadian rhythms play an important role in the regulation of eating and fasting, and mistimed dietary intakes may be detrimental to metabolic health. Extended overnight fasting has been proposed as a strategy to better align the eating-fasting cycle with the internal circadian clock, and both observational and experimental studies have linked longer overnight fasting with lower body weight. However, it remains unclear if the timing of overnight fasting modifies the relationship between fasting duration and weight outcomes. METHODS: The current study included 495 men and 499 women age 50-74 years. Dietary intake over 12 months was assessed by 24-h dietary recalls every two months, and body-mass index was measured at the beginning, middle and end of the study. Logistic regression was used to estimate the relationship between overnight fasting duration and the likelihood of being overweight or obesity adjusted for multiple confounders, and assessed whether the relationship was modified by the timing of overnight fasting, measured as the midpoint of the fasting period. RESULTS: Among participants with early overnight fasting (midpoint < 02:19 am), a longer fasting duration was associated with lower odds of overweight and obesity; while among those with late fasting (≥02:19 am), longer fasting was associated with higher odds of overweight and obesity. Specifically, when compared to the shortest quintile of overnight fasting duration, the longest quintile was associated with a 53% reduction in the odds of overweight and obesity in the early fasting group (OR = 0.47, 95% CI = 0.23, 0.97), but a 2.36-fold increase in the late fasting group (OR = 3.36, 95% CI = 1.48, 7.62). Additionally adjusting for dietary intakes during morning and late evening periods did not affect the observed associations. CONCLUSIONS: Longer overnight fasting was associated with a reduced likelihood of being overweight or obese, but only among those with an early timing of fasting.

A Novel Imputation Approach for Sharing Protected Public Health Data.

Objectives. To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. Methods. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing. We modeled 4 methods and compared the results to baseline mean imputation. Results. We assessed performance by comparing root mean squared error (RMSE), mean absolute error (MAE), and proportional variance between imputed and suppressed values. Our method outperformed mean imputation; we retained 46% of the suppressed value's proportional variance with better precision (22% lower RMSE and 26% lower MAE) than simple mean imputation. Conclusions. Our easy-to-implement imputation technique largely overcomes the adverse effects of low count value suppression with superior results to simple mean imputation. This novel method is generalizable to researchers sharing protected public health surveillance data. (Am J Public Health. 2021; 111(10):1830-1838. https://doi.org/10.2105/AJPH.2021.306432).

United States prevalence of pediatric asthma by environmental tobacco smoke exposure, 2016-2017.

OBJECTIVES: We sought to update the prevalence estimates of parent-reported asthma diagnosis by Environmental Tobacco Smoke (ETS) exposure in the United States (US) pediatric population. METHODS: This cross-sectional study included 71,811 families with children who participated in the 2016-2017 National Survey of Children's Health (NSCH). Weighted asthma prevalence estimates were calculated for ETS-exposed and non-exposed children. Chi-square analysis compared asthma prevalence between the two exposure groups and logistic regression analysis generated adjusted odds ratios (aORs) of asthma diagnosis by ETS exposure by sex, race/ethnicity, and household education and income level. RESULTS: Asthma prevalence estimates were significantly higher in ETS-exposed vs. non-exposed children (10.7% vs. 7.8%, p < 0.001). Children with a smoker in the house are 30% more likely to have an asthma diagnosis vs. children with no smokers in the house (aOR 1.29, 95% Confidence Interval [CI] 1.09-1.52). Significant predictors for ETS exposure included < high school education and lower family income. Conversely, non-Hispanic black and Hispanic children were less likely to have ETS exposure vs. non-Hispanic white children. CONCLUSIONS: ETS exposure is a significant risk factor for asthma in the US pediatric population. Smoking cessation initiatives targeting non-Hispanic white parents from lower socioeconomic may improve children's chronic pulmonary health risk.

Time series modeling of pathogen-specific disease probabilities with subsampled data.

Many diseases arise due to exposure to one of multiple possible pathogens. We consider the situation in which disease counts are available over time from a study region, along with a measure of clinical disease severity, for example, mild or severe. In addition, we suppose a subset of the cases are lab tested in order to determine the pathogen responsible for disease. In such a context, we focus interest on modeling the probabilities of disease incidence given pathogen type. The time course of these probabilities is of great interest as is the association with time-varying covariates such as meteorological variables. In this set up, a natural Bayesian approach would be based on imputation of the unsampled pathogen information using Markov Chain Monte Carlo but this is computationally challenging. We describe a practical approach to inference that is easy to implement. We use an empirical Bayes procedure in a first step to estimate summary statistics. We then treat these summary statistics as the observed data and develop a Bayesian generalized additive model. We analyze data on hand, foot, and mouth disease (HFMD) in China in which there are two pathogens of primary interest, enterovirus 71 (EV71) and Coxackie A16 (CA16). We find that both EV71 and CA16 are associated with temperature, relative humidity, and wind speed, with reasonably similar functional forms for both pathogens. The important issue of confounding by time is modeled using a penalized B-spline model with a random effects representation. The level of smoothing is addressed by a careful choice of the prior on the tuning variance.

Bayesian penalized spline models for the analysis of spatio-temporal count data.

In recent years, the availability of infectious disease counts in time and space has increased, and consequently, there has been renewed interest in model formulation for such data. In this paper, we describe a model that was motivated by the need to analyze hand, foot, and mouth disease surveillance data in China. The data are aggregated by geographical areas and by week, with the aims of the analysis being to gain insight into the space-time dynamics and to make short-term predictions, which will aid in the implementation of public health campaigns in those areas with a large predicted disease burden. The model we develop decomposes disease-risk into marginal spatial and temporal components and a space-time interaction piece. The latter is the crucial element, and we use a tensor product spline model with a Markov random field prior on the coefficients of the basis functions. The model can be formulated as a Gaussian Markov random field and so fast computation can be carried out using the integrated nested Laplace approximation approach. A simulation study shows that the model can pick up complex space-time structure and our analysis of hand, foot, and mouth disease data in the central north region of China provides new insights into the dynamics of the disease.

Spatiotemporal analysis of HPV vaccination and associated neighborhood-level disparities in Texas-an ecological study.

Background: HPV is responsible for most cervical, oropharyngeal, anal, vaginal, and vulvar cancers. The HPV vaccine has decreased cervical cancer incidence, but only 49% of Texas adolescents have initiated the vaccine. Texas shows great variation in HPV vaccination rates. We used geospatial analysis to identify areas with high and low vaccination rates and explored differences in neighborhood characteristics. Methods: Using Anselin's Local Moran's I statistic, we conducted an ecological analysis of hot and cold spots of adolescent HPV vaccination coverage in Texas from 2017 to 2021. Next, we utilized a Mann-Whitney U test to compare neighborhood characteristics of vaccination coverage in hot spots versus cold spots, leveraging data from the Child Opportunity Index (COI) and American Community Survey. Results: In Texas, there are 64 persistent vaccination coverage hotspots and 55 persistent vaccination coverage cold spots. The persistent vaccination coverage hot spots are characterized by ZIP codes with lower COI scores, higher percentages of Hispanic residents, higher poverty rates, and smaller populations per square mile compared to vaccine coverage cold spots. We found a more pronounced spatial clustering pattern for male adolescent vaccine coverage than we did for female adolescent vaccine coverage. Conclusion: In Texas, HPV vaccination coverage rates differ depending on the community's income level, with lower-income areas achieving higher success rates. Notably, there are also gender-based discrepancies in vaccination coverage rates, particularly among male adolescents. This knowledge can aid advocates in customizing their outreach initiatives to address these disparities.

Unraveling trends in schistosomiasis: deep learning insights into national control programs in China.

OBJECTIVES: To achieve the ambitious goal of eliminating schistosome infections, the Chinese government has implemented diverse control strategies. This study explored the progress of the 2 most recent national schistosomiasis control programs in an endemic area along the Yangtze River in China. METHODS: We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. Two traditional models were also constructed for comparison with 2 evaluation indicators: the mean-squared prediction error (MSPE) and continuous ranked probability score (CRPS). RESULTS: The CNN-IDE model was the optimal model, with the lowest overall average MSPE of 0.04 and the CRPS of 0.19. From 1997 to 2011, the prevalence exhibited a notable trend: it increased steadily until peaking at 1.6 per 1,000 in 2005, then gradually declined, stabilizing at a lower rate of approximately 0.6 per 1,000 in 2006, and approaching zero by 2011. During this period, noticeable geographic disparities in schistosomiasis prevalence were observed; high-risk areas were initially dispersed, followed by contraction. Predictions for the period 2012 to 2015 demonstrated a consistent and uniform decrease. CONCLUSIONS: The proposed CNN-IDE model captured the intricate and evolving dynamics of schistosomiasis prevalence, offering a promising alternative for future risk modeling of the disease. The comprehensive strategy is expected to help diminish schistosomiasis infection, emphasizing the necessity to continue implementing this strategy.

Impact of patient demographics and neighborhood socioeconomic variables on clinical trial participation patterns for NHL.

ABSTRACT: Prior studies have demonstrated that certain populations including older patients, racial/ethnic minority groups, and women are underrepresented in clinical trials. We performed a retrospective analysis of patients with non-Hodgkin lymphoma (NHL) seen at MD Anderson Cancer Center (MDACC) to investigate the association between trial participation, race/ethnicity, travel distance, and neighborhood socioeconomic status (nSES). Using patient addresses, we ascertained nSES variables on educational attainment, income, poverty, racial composition, and housing at the census tract (CT) level. We also performed geospatial analysis to determine the geographic distribution of clinical trial participants and distance from patient residence to MDACC. We examined 3146 consecutive adult patients with NHL seen between January 2017 and December 2020. The study cohort was predominantly male and non-Hispanic White (NHW). The most common insurance types were private insurance and Medicare; only 1.1% of patients had Medicaid. There was a high overall participation rate of 30.5%, with 20.9% enrolled in therapeutic trials. In univariate analyses, lower participation rates were associated with lower nSES including higher poverty rates and living in crowded households. Racial composition of CT was not associated with differences in trial participation. In multivariable analysis, trial participation varied significantly by histology, and participation declined nonlinearly with age in the overall, follicular lymphoma, and diffuse large B-cell lymphoma (DLBCL) models. In the DLBCL subset, Hispanic patients had lower odds of participation than White patients (odds ratio, 0.36; 95% confidence interval, 0.21-0.62; P = .001). In our large academic cohort, race, sex, insurance type, and nSES were not associated with trial participation, whereas age and diagnosis were.

Trends in Fatal Opioid-Related Overdose in American Indian and Alaska Native Communities, 1999-2021.

INTRODUCTION: Opioid-related overdose mortality rates have increased sharply in the U.S. over the past two decades, and inequities across racial and ethnic groups have been documented. Opioid-related overdose trends among American Indian and Alaska Natives require further quantification and assessment. METHODS: Observational, U.S. population-based registry data on opioid-related overdose mortality between 1999 and 2021 were extracted in 2023 using ICD-10 codes from the U.S. Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research multiple cause of death file by race, Hispanic ethnicity, sex, and age. Segmented time series analyses were conducted to estimate opioid-related overdose mortality growth rates among the American Indian and Alaska Native population between 1999 and 2021. Analyses were performed in 2023. RESULTS: Two distinct time segments revealed significantly different opioid-related overdose mortality growth rates within the overall American Indian and Alaska Native population, from 0.36 per 100,000 (95% CI=0.32, 0.41) between 1999 and 2019 to 6.5 (95% CI=5.7, 7.31) between 2019 and 2021, with the most pronounced increase among those aged 24-44 years. Similar patterns were observed within the American Indian and Alaska Native population with Hispanic ethnicity, but the estimated growth rates were generally steeper across most age groups than across the overall American Indian and Alaska Native population. Patterns of opioid-related overdose mortality growth rates were similar between American Indian and Alaska Native females and males between 2019 and 2021. CONCLUSIONS: Sharp increases in opioid-related overdose mortality rates among American Indian and Alaska Native communities are evident by age and Hispanic ethnicity, highlighting the need for culturally sensitive fatal opioid-related overdose prevention, opioid use disorder treatment, and harm-reduction efforts. Future research should aim to understand the underlying factors contributing to these high mortality rates and employ interventions that leverage the strengths of American Indian and Alaska Native culture, including the strong sense of community.

RBD amplicon sequencing of wastewater reveals patterns of variant emergence and evolution.

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study utilized receptor-binding domain (RBD) amplicon sequencing of wastewater samples to monitor the SARS-CoV-2 community dynamics and evolution in El Paso, TX. Over 17 months, we identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Our findings demonstrated early detection of variants and identification of unreported outbreaks, while showing strong consistency with clinical genome sequencing data at the local, state, and national levels. Alpha diversity analyses revealed significant periodical variations, with the highest diversity observed in winter and the outbreak lag phases, likely due to lower competition among variants before the outbreak growth phase. The data underscores the importance of low transmission periods for rapid mutation and variant evolution. This study highlights the effectiveness of integrating RBD amplicon sequencing with wastewater surveillance in tracking viral evolution, understanding variant emergence, and enhancing public health preparedness.