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Background The suspension and/or termination of many programmes funded through the United States Agency for International Development (USAID) by the new US administration has severe short- and long-term negative impacts on the health of people worldwide. We draw attention to the termination of the Demographic and Health Surveys (DHS) Program, which includes nationally representative surveys of households, DHS, Malaria Indicator Surveys [MIS]) and health facilities (Service Provision Assessments [SPA]) in over 90 low- and middle-income countries. USAID co-funding and provision of technical support for these surveys has been shut down. Main body The impact of these disruptions will reverberate across local, regional, national, and global levels and severely impact the ability to understand the levels and changes in population health outcomes and behaviours. We highlight three key impacts on (1) ongoing data collection and data processing activities; (2) future data collection and consequent lack of population-level health indicators; and (3) access to existing data and lack of support for its use. Conclusions We call for immediate action on multiple fronts. In the short term, universal access to existing data and survey materials should be restored, and surveys which were planned or in progress should be completed. In the long term, this crisis should serve as a tipping point for transforming these vital surveys. We call on national governments, regional organisations, and international partners to develop sustainable alternatives that preserve the principles (standardised questionnaires, backward compatibility, open access data with rigorous documentation) which made the DHS Program an invaluable global health resource.

Introduction. Prior to 1980, U.S. national demographic and health data collection did not identify individuals of Hispanic/Latina/o heritage as a population group. Post-1990, robust immigration from Latin America (e.g., South America, Central America, Mexico) and subsequent growth in U.S. births, dynamically reconstructed the ethnoracial lines among Latinos from about 20 countries, increasing racial admixture and modifying patterns of health disparities. The increasing racial and class heterogeneity of U.S. Latina/os demands a critical analysis of sociodemographic factors associated with population health disparities. Purposes. To determine the state of available Latina/o population demographic and health data in the United States, assess demographic and health variables and trends from 1960 to the present, and identify current strengths, gaps, and areas of improvement. Method. Analysis of 101 existing data sets that included demographic, socioeconomic, and health characteristics of the U.S. Latina/o population, grouped by three, 20-year intervals: 1960–1979, 1980–1999, and 2000–2019. Results. Increased Latina/o immigration and U.S. births between 1960 and 2019 was associated with increases of Latino population samples in data collection. Findings indicate major gaps in the following four areas: children and youth younger than 18 years, gender and sexual identity, race and mixed-race measures, and immigration factors including nativity and generational status. Conclusions. The analysis of existing ethnoracial Latina/o population data collection efforts provides an opportunity for critical analysis of past trends, future directions in data collection efforts, and an equity lens to guide appropriate community health interventions and policies that will contribute to decreasing health disparities in Latina/o populations.

Background Health outcomes are strongly impacted by social determinants of health, including social risk factors and patient demographics, due to structural inequities and discrimination. Primary care is viewed as a potential medical setting to assess and address individual health-related social needs and to collect detailed patient demographics to assess and advance health equity, but limited literature evaluates such processes. Methods We conducted an analysis of cross-sectional survey data collected from n  = 507 Maryland Primary Care Program (MDPCP) practices through Care Transformation Requirements (CTR) reporting in 2022. Descriptive statistics were used to summarize practice responses on social needs screening and demographic data collection. A stepwise regression analysis was conducted to determine factors predicting screening of all vs. a targeted subset of beneficiaries for unmet social needs. Results Almost all practices (99%) reported conducting some form of social needs screening and demographic data collection. Practices reported variation in what screening tools or demographic questions were employed, frequency of screening, and how information was used. More than 75% of practices reported prioritizing transportation, food insecurity, housing instability, financial resource strain, and social isolation. Conclusions Within the MDPCP program there was widespread implementation of social needs screenings and demographic data collection. However, there was room for additional supports in addressing some challenging social needs and increasing detailed demographics. Further research is needed to understand any adjustments to clinical care in response to identified social needs or application of data for uses such as assessing progress towards health equity and the subsequent impact on clinical care and health outcomes .

Effective and thorough credit-risk management is a key factor for lending institutions, as significant financial losses can arise from the borrowers’ default. Consequently, machine learning methods can measure and analyze credit risk objectively when at the same time they face increasingly attention. This study analyzes default payment data from a credit cards’ portfolio containing some 30,000 clients from Taiwan with twenty-three attributes and with no missing information. We compare prediction accuracy of seven classification methods used, i.e. KNN, Logistic Regression, Naïve Bayes, Decision Trees, Random Forest, SVC, and Linear SVC. The results indicate that only few out of most of the typical variables used can adequately analyze default characteristics in terms of lending decisions. The results provide effective feedback to credit evaluators, lending institutions and business analysts for in-depth analysis. Also, they mention to the importance of the precautionary borrowing techniques to be used to better understand credit-card borrowers’ behavior, along with specific accounting, historical and demographical characteristics.

The focus of this article is a 2019–2020 demographic data collection, visualization, and analysis project on race and Classics. Based on the findings of the project, this article advances arguments in favor of regular, frequent, and well-publicized demographic data gathering and visualization as practices crucial for effective, research-driven diversity and inclusion efforts in Classics. By fueling and bolstering these efforts, such practices have the potential to expand and reshape the boundaries of Classics as a discipline.

Primary Health Care (PHC) is a pillar of any health care system, significantly enhancing population health through improved access and quality of care. This study utilizes Geographic Information System (GIS) tools to examine the spatial accessibility of populations to health facilities, focusing specifically on Tangier as a case study. Employing GIS, the research investigates how demographic data corresponds to travel times to PHC centers within walking zones of 10, 20, and 30 minutes. The primary goal is to uncover patterns and derive insights into how health services are distributed in relation to population densities and locations. These findings highlight significant spatial disparities in access to health care, providing crucial information for health planners and policymakers. The results are instrumental in addressing the uneven distribution of health services and ensuring equitable access to PHC across different areas. By identifying regions with inadequate coverage, the study pinpoints where health care facilities can be improved or expanded. Additionally, this analysis supports the development of targeted strategies to enhance health service accessibility, which is vital for informed policy-making aimed at improving health outcomes and achieving equitable health service delivery across diverse communities.

Optical bone densitometry (OBD) has been developed for the early detection of osteoporosis. In recent years, machine learning (ML) techniques have been actively implemented for the areas of medical diagnosis and screening with the goal of improving diagnostic accuracy. The purpose of this study was to verify the feasibility of using the combination of OBD and ML techniques as a screening tool for osteoporosis. Dual energy X-ray absorptiometry (DXA) and OBD measurements were performed on 203 Thai subjects. From the OBD measurements and readily available demographic data, machine learning techniques were used to predict the T-score measured by the DXA. The T-score predicted using the Ridge regressor had a correlation of r = 0.512 with respect to the reference value. The predicted T-score also showed an AUC of 0.853 for discriminating individuals with osteoporosis. The results obtained suggest that the developed model is reliable enough to be used for screening for osteoporosis.

Demographic factors, statistical information, and technological innovation are prominent factors shaping energy transitions in the residential sector. Explaining these energy transitions requires combining insights from the disciplines investigating these factors. The existing literature is not consistent in identifying these factors, nor in proposing how they can be combined. In this paper, three contributions are made by combining the key demographic factors of households to estimate household energy consumption. Firstly, a mathematical formula is developed by considering the demographic determinants that influence energy consumption, such as the number of persons per household, median age, occupancy rate, households with children, and number of bedrooms per household. Secondly, a geographical position algorithm is proposed to identify the geographical locations of households. Thirdly, the derived formula is validated by collecting demographic factors of five statistical regions from local government databases, and then compared with the electricity consumption benchmarks provided by the energy regulators. The practical feasibility of the method is demonstrated by comparing the estimated energy consumption values with the electricity consumption benchmarks provided by energy regulators. The comparison results indicate that the error between the benchmark and estimated values for the five different regions is less than 8% (7.37%), proving the efficacy of this method in energy consumption estimation processes.

Demographic data are essential to construct mechanistic models to understand how populations change over time and in response to global threats like climate change. Existing demographic data are either lacking or insufficient for many species, particularly those that are challenging to obtain direct measurements from that can be used to estimate demographic rates, like marine mammals. A method for collecting accurate demographic data to construct robust demographic models at scale would fill this knowledge gap for difficult‐to‐access species. We introduce a novel, non‐invasive method to estimate the 3D body size (volume) of pinnipeds (seals, sea lions and walruses) that will allow monitoring at high spatial and temporal scales. Our method integrates 3D structure‐from‐motion photogrammetry data collected via planned flight missions using off‐the‐shelf, multirotor unmanned aerial vehicles (UAVs). We apply and validate this method on the grey seal Halichoerus grypus, a pinniped species that spends much of its time at sea but is predictably observable during its annual breeding season. We investigate the optimal ground sampling distance (GSD) for surveys by calculating the success rates and accuracy of volume estimates of individuals at different altitudes. Based on current technology, we establish an optimal GSD of at least 0.8 cm px−1 for animals similar in size to UK grey seals (~1.2–2.5 m length), making our method reproducible and applicable to other species. We found volume estimates were accurate and could be successfully estimated for up to 68% of hauled‐out seals in study areas. Our method accurately estimates individual body volume of pinnipeds in a time‐ and cost‐effective manner while minimising disturbance. While the approach is applied to pinnipeds here, the method could be adapted to further taxa that are otherwise challenging to obtain direct measurements from. Our proposed approach therefore has the potential to fill demographic research gaps, which will improve our ability to protect and conserve species into the future.

The widespread adoption of virtual reality (VR) technologies is significantly hindered by the prevalence of cybersickness, a disruptive experience causing symptoms like nausea, dizziness, and disorientation. Traditional methodologies for predicting cybersickness predominantly depend on biomedical data. While effective, these methods often require invasive data collection techniques, which can be impractical and pose privacy concerns. Furthermore, existing research integrating demographic information typically does so in conjunction with biomedical or behavioral data, not as a standalone predictive tool. Addressing this gap, we investigated machine learning techniques that exclusively use demographic data to classify and predict the likelihood of cybersickness and its severity in VR environments. This method relies on noninvasive, easily accessible demographic information like age, gender, and previous VR exposure. It offers a more user-friendly and ethically sound approach to predicting cybersickness. The study explores the potential of demographic variables as standalone predictors through comprehensive data analysis, challenging the traditional reliance on biomedical metrics. We comprehensively presented the input data and statistical analysis and later carefully selected the widely used machine learning models from different classes, including k-nearest neighbors, Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine. We evaluated their performances and presented detailed results and limitations. The research findings indicate that demographic data can be used to predict the likelihood and severity of cybersickness. This research provides critical insights into future research directions, including data collection design and optimization suggestions. It opens new avenues for personalized and inclusive VR design, potentially reducing barriers to VR adoption and enhancing user comfort and safety.