Water Sanitation and Hygiene Institute

BackgroundLung cancer remains the leading cause of cancer-related mortality in the United States, yet disparities in lung cancer screening (LCS) outcomes exist and remain understudied, particularly in the Midwestern region. Our objective was to investigate disparities in lung radiology outcomes and follow-up care based on patient, provider, and place-based characteristics (i.e., area deprivation index; ADI).MethodsThis cross-sectional study used data from the LCS program at Siteman Cancer Center (SCC) in St. Louis, Missouri, from January to December 2023. SCC’s catchment area includes 82 counties in Missouri and Illinois; approximately 15% of the population reside in a rural zip code, and 29% reside in medically underserved areas 80% are White. The study included 1,946 individuals aged 50–80, meeting LCS eligibility criteria based on smoking history and age. Lung radiology findings were assessed as primary outcomes, and timely follow-up adherence (i.e., return for follow-up visit) was analyzed among patients with high-risk findings (Lung-RADS 3 [”Probably Benign”] and 4 [”Suspicious”]) from January to June 2023, requiring follow-up by December 2023. Multivariable logistic regression was conducted, adjusting for patient and provider characteristics and ADI.ResultsOf the 1,946 individuals who accessed LCS, 57% were Black, 41% White, 1% Asian, and 1% of another race; 54% were male and 46% female. Lung findings were classified as “probably benign/suspicious” (high risk) for 14%. Annual visits were associated with higher likelihood of high-risk scores compared to baseline visits (AOR = 2.10 (1.40–3.15); p < 0.001). Racial differences were noted in the association between provider type and lung outcomes. Among White individuals only, specialist compared to primary care provider referral was associated with increased odds of being high risk (AOR = 1.58 (1.00–2.50); p = 0.048). Sex, insurance, smoking status, park-years and ADI were not associated with lung radiology outcomes. Timely adherence to return follow-up visit among high-risk patients was suboptimal, with only 20.0% returning within 3–6 months for their repeat LCS. Individuals residing in moderate ADI (distress) areas were less likely to have timely follow-up (for high-risk findings) compared to those in high distress (AOR = 0.393, 95% CI = 0.155–1.001, p = 0.050). There were no differences by sex, insurance, smoking status, and pack-years for being classified as high risk.ConclusionsSCC’s LCS program successfully captured Black populations and individuals from highly distressed areas, in a predominantly White catchment area. There were no observable race disparities in timely follow-up of high-risk findings, reflecting progress toward equity in access and outcomes. Place-based disparities in follow-up were observed that warrant further characterization and risk assessment to improve follow-up of patients undergoing LCS.

Triglyceride (TG) /High density lipoprotein cholesterol (HDL-C) ratio (THR) represents a single surrogate predictor of hyperinsulinemia or insulin resistance that is associated with premature aging processes, risk of diabetes and increased mortality. To identify novel genetic loci for THR change over time (ΔTHR), we conducted genome-wide association study (GWAS) and genome-wide linkage scan (GWLS) among subjects of European ancestry who had complete data from two exams collected about seven years apart from the Long Life Family Study (LLFS, n=1384), a study with familial clustering of exceptional longevity in the US and Denmark. Subjects with diabetes or using medications for dyslipidemia were excluded from this analysis. ΔTHR was derived using growth curve modeling, and adjusted for age, sex, field centers, and principal components (PCs). GWAS was conducted using a linear mixed model accounted for familial relatedness. Our linkage scan was built on haplotype-based IBD estimation with 0.5 cM average spacing. Heritability of ΔTHR was moderate (46%). Our GWAS identified a significant locus at the LPL (p=1.58e-9) for ΔTHR; this gene locus has been reported before influencing baseline THR levels. Our GWLS found evidence for a significant linkage with a logarithm of the odds (LODs) exceeding 3 on 3q28 (LODs=4.1). Using a subset of 25 linkage enriched families (pedigree-specific LODs>0.1), we assessed sequence elements under 3q28 and identified two novel variants (EIF4A2/ADIPOQ-rs114108468, p=5e-6, MAF=1.8%; TPRG1-rs16864075, p=3e-6, MAF=8%; accounted for ~28% and ~29% of the linkage, respectively, and 57% jointly). While the former variant was associated with EIF4A2 (p=7e-5) / ADIPOQ (p=3.49e-2) RNA transcriptional levels, the latter variant was not associated with TPRG1 (p=0.23) RNA transcriptional levels. Replication in FHS OS observed modest effect of these loci on ΔTHR. Of 188 metabolites from 13 compound classes assayed in LLFS, we observed multiple metabolites (e.g., DG.38.5, PE.36.4, TG.58.3) that were significantly associated with the variants (p<3e-4). our linkage-guided sequence analysis approach permitted our discovery of two novel gene variants EIF4A2/ADIPOQ-rs114108468 and TPRG1-rs16864075 on 3q28 for ΔTHR among subjects without diabetes selected for exceptional survival and healthy aging.

Forecasting stock prices is crucial in financial markets, with significant ramifications for investors, traders, and financial institutions. Making accurate predictions allows for well-informed decision-making and effective risk management. In recent years, there has been a notable increase in the utilization of machine learning methods within the financial sector to augment the accuracy of stock price forecasting. The expansion is driven by the growing accessibility of historical market data and the processing capacity necessary for its analysis. Using machine learning techniques inside financial markets has brought about a notable shift in the prevailing paradigm. Conventional approaches, such as time series analysis and fundamental analysis, have played a crucial role but had inherent limitations in comprehensively capturing the intricate dynamics of stock markets. Machine learning has promise in capturing complex patterns and correlations present in data, potentially enhancing the precision of predictive outcomes. The primary objective of this research study is to comprehensively evaluate the efficacy of machine learning models in predicting stock prices. The central research question is: "How effective are machine learning models in predicting the price of stocks?" This question is paramount as it directly impacts investment decisions and risk management strategies. Understanding the capabilities and limitations of machine learning models in this context is crucial for financial professionals and individual investors. This research will primarily consider publicly traded stocks in major stock markets (e.g., NYSE, NASDAQ) over the last decade to maintain a focused investigation. It will explore a range of machine learning techniques, including time series models like ARIMA, deep learning models such as LSTM, and ensemble methods like Random Forest. The analysis will encompass various evaluation metrics commonly used in forecasting, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). This paper is structured as follows: Section 2, provides a comprehensive literature review, delving into traditional stock price prediction methods, the application of machine learning in finance, and previous research relevant to the study. Section 3 analyzes the data collection process, feature selection, model selection, and the chosen evaluation metrics. The experimental data are reported in Section 4, followed by a comprehensive analysis and discussion of these results in Section 5. In conclusion, the present study culminates in Section 6, wherein it provides a comprehensive summary of the principal findings, deliberate on the potential ramifications, and provide valuable perspectives on the prospective advancements in the domain of machine learning as applied to the prediction of stock prices. This study illuminates the dynamic terrain of the banking industry, where the utilization of data-driven decision-making, helped by machine learning, assumes a more central position. Through thoroughly evaluating machine learning models, our research aims to enhance our comprehension of their efficacy and constraints in stock price prediction.

High treatment costs associated with breast cancer are a substantial burden to patients and society. Despite mounting awareness, patient perspectives about the value of cost discussions in breast reconstruction and risk factors for financial distress are unknown. The authors performed a single-institution, cross-sectional survey of all women who underwent breast reconstruction following mastectomy or lumpectomy for breast cancer or risk reduction. Questions were derived from previously published survey items, and the authors leveraged regression analysis to identify patient-level risk factors for major financial distress. A total of 647 of 2293 patients returned the survey questionnaires (28.2 percent response rate). From the 647 respondents, 399 (62 percent) underwent breast reconstruction, and of these, 140 (35 percent) reported that total treatment expenses were higher than expected. One hundred twenty-nine breast reconstruction patients (32 percent) paid over $5000 in out-of-pocket costs. Two hundred eighty-four (71 percent) felt that surgeons should explain the estimated out-of-pocket costs when choosing a type of breast reconstruction and 205 (51 percent) believed that a financial consultation should be scheduled with every new cancer diagnosis. However, only 52 patients (13 percent) reported having had cost discussions with the treatment team. The incidence of major financial distress was n = 70 (18 percent), and following regression analysis, higher credit score and annual income were associated with a 66 percent and 69 percent risk reduction, respectively. Recipients of breast reconstruction demonstrate unanticipated and unplanned financial strain related to out-of-pocket expenses and believe that cost-consciousness should impact treatment decisions. Lower income and credit score are associated with financial distress. Cost discussions may optimize decision-making in preference sensitive conditions.

Breast reconstruction in patients with a high body mass index (BMI) (≥30 kg/m) is technically challenging and is associated with increased postoperative complications. The optimal reconstructive approach for these patients remains to be determined. This study compared outcomes of prepectoral and dual-plane reconstruction in high-BMI patients to determine whether there was an association between postoperative complications and the plane of reconstruction. High-BMI patients who underwent immediate dual-plane or prepectoral expander/implant reconstruction were included in this retrospective study. Patients were stratified by reconstructive approach (dual-plane or prepectoral), and postoperative complications were compared between the groups. Multivariate logistic regression analysis was performed to determine whether the plane of reconstruction was an independent predictor of any complication after adjusting for potential confounding differences in patient variables between the groups. Of 133 patients, 65 (128 breasts) underwent dual-plane and 68 (129 breasts) underwent prepectoral reconstruction. Rates of seroma (13.3 percent versus 3.1 percent), surgical-site infection (9.4 percent versus 2.3 percent), capsular contracture (7.0 percent versus 0.8 percent), and any complication (25.8 percent versus 14.7 percent) were significantly higher in patients who had dual-plane versus prepectoral reconstruction (p < 0.05). Multivariate logistic regression identified dual-plane, diabetes, neoadjuvant radiotherapy, and adjuvant chemotherapy as significant, independent predictors of any complication (p < 0.05). Dual-plane reconstruction increased the odds of any complication by 3-fold compared with the prepectoral plane. Compared with the dual-plane approach, the prepectoral approach appears to be associated with a lower risk of postoperative complications following immediate expander/implant breast reconstruction and may be a better reconstructive option in high-BMI patients. Therapeutic, III.

Postoperative complications such as surgical site infections, dehiscence, seromas, and hematomas prolong wound care and impose significant cost increases to patients and healthcare providers. Clinicians aiming to reduce the incidence of these complications should be aware of risk factors associated with surgical type, procedures used, patient characteristics, and postoperative care. Today, improved guidelines and general practices for managing surgical incisions have reduced the incidence of complications to historic lows. In addition to these standard care options, advanced wound care approaches have been extensively studied and exist as options for clinicians to provide adjunctive postoperative support and facilitate wound healing. These systems include advanced wound dressings and closed-incision negative-pressure therapy. Advanced wound care is not appropriate in all settings, and healthcare providers must assess each case for specific needs to be addressed by the available incision management plans. Emerging therapies that are intended to improve the continuum of postoperative care should continue to be evaluated in controlled clinical trials to determine their effectiveness under different circumstances and to support the creation of more robust guidelines for their use.

Tropical montane habitats, grasslands, in particular, merit urgent conservation attention owing to the disproportionate levels of endemic biodiversity they harbour, the ecosystem services they provide, and the fact that they are among the most threatened habitats globally. The Shola Sky Islands in the Western Ghats host a matrix of native forest-grassland matrix that has been planted over the last century, with exotic timber plantations. The popular discourse on the landscape change is that mainly forests have been lost to the timber plantations and recent court directives are to restore Shola forest trees. In this study, we examine spatiotemporal patterns of landscape change over the last 40 years in the Palani Hills, a significant part of the montane habitat in the Western Ghats. Using satellite imagery and field surveys, we find that 66% of native grasslands and 31% of native forests have been lost over the last 40 years. Grasslands have gone from being the dominant, most contiguous land cover to one of the rarest and most fragmented. They have been replaced by timber plantations and, to a lesser extent, expanding agriculture. We find that the spatial pattern of grassland loss to plantations differs from the loss to agriculture, likely driven by the invasion of plantation species into grasslands. We identify remnant grasslands that should be prioritised for conservation and make specific recommendations for conservation and restoration of grasslands in light of current management policy in the Palani Hills, which favours large-scale removal of plantations and emphasises the restoration of native forests.

Water Culture in South Asia: Bangladesh Perspectives, by Suzanne Hanchett, Tofazzel Hossain Monju, Kazi Rozana Akhter, Shireen Akhter, and Anwar Islam 2014, Development Resources Press, 308 pages, ISBN 978-0-9906337-0-9 hbk (colour, US$39.99), ISBN 978-0-9906337-1-6 pbk (b/w, $19.99), eBook PDF $9.99