Understanding the Patient Landscape: A Ten-Year Retrospective Examination of Electroconvulsive Therapy in Romania's Largest Psychiatric Hospital.

The aim of this analysis was to investigate the socio-demographic and clinical profile, the effectiveness, and the association of pharmacological treatment in patients who underwent electroconvulsive therapy during the last 10 years in the largest psychiatric hospital in Romania. This study includes 249 patients aged between 18 and 73 years old. Recurrent depression was the most frequent diagnosis for which ECT was performed (T = 96, 38.55%), followed by schizophrenia (T = 72, 28.91%). The most frequent indication for ECT was treatment resistance (T = 154, 61.84%), followed by persistent suicidal ideation (T = 54, 21.68%) and catatonia (T = 42, 16.86%). In 111 (44.60%) cases included in this study, re-hospitalization was required after performing ECT, while 138 (55.40%) participants did not require any further hospital readmissions. Significant differences were found between these groups in terms of socio-demographic data, diagnosis, number of ECT sessions performed, and association of psychotropic medication during and after the procedure, therefore two separate patient profiles were found based on these characteristics. Patients necessitating re-hospitalization post-ECT were mainly males aged 25-44 diagnosed with schizophrenia and underwent a greater number of ECT sessions (7-12), whereas those not requiring re-hospitalization were predominantly females aged 45-64 with recurrent depressive disorder for which 4-6 ECT sessions were performed.

Understanding social needs screening and demographic data collection in primary care practices serving Maryland Medicare patients.

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.

A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes.

Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes, which are included in electronic health records. We sought to identify problematic opioid use from a full range of clinical notes and compare the demographic and clinical characteristics of patients identified as having problematic opioid use exclusively in clinical notes to patients documented through ICD opioid use disorder diagnostic codes. We developed and applied a natural language processing (NLP) tool that combines rule-based pattern analysis and a trained support vector machine to the clinical notes of a patient cohort (n = 222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. The NLP tool achieved 96.6% specificity, 90.4% precision/PPV, 88.4% sensitivity/recall, and 94.4% accuracy on unseen test data. NLP exclusively identified 57,331 patients; 6997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, and more care encounters, and were less likely to be married. Patients in both these groups had substantially elevated comorbidity levels compared with patients not documented through either method as experiencing problematic opioid use. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.

Lower extremity muscle strength equation of older adults assessed by Five Time Sit to Stand Test (FTSST).

Background: The decline in lower limb muscle strength, one of the risk factors for falling in the older adults, puts older persons at an increased risk of falling. The assessment of the lower limb muscle strength is very important. Objective: The purpose of this study was to construct the equation for predicting knee extensor muscle strength based on demographic data and the results of the Five-Time Sit-to-Stand Test (FTSST). Methods: A total of 121 healthy elders (mean age 68.00±7.26) were asked to complete the FTSST and submit the demographic information. By using a stationary push-pull dynamometer, the knee extensor strength of each participant was assessed. The multiple regression analysis was used to explore knee extensor strength prediction equation. Results: The findings demonstrated that the knee extensor strength equation was developed using variables obtained from gender, weight, and time to complete the FTSST. The equation was found to have a high correlation (r=0.838) and 70.1% estimation power. Its formula was as follows: Knee extensor strength=32.735+3.688 (gender; female=0 or male=1) + 0.189 (weight) - 2.617 (time to complete the FTSST). However, there was an estimating error in this equation of 4.72 kg. Conclusion: The determining factors influencing knee extensor strength, which can be utilized to estimate the strength in elderly individuals, are demographic variables including gender, weight, and the time taken to complete the FTSST.

Ecologically sustainable removal of pharmaceuticals: A mechanistic study of bismuth sulfide-graphene oxide/silver nanocomposite.

Bismuth sulfide nanoparticles (BiS NPs) were synthesized via the hydrothermal method, and reduced graphene oxide(rGO) and silver nanoparticles (Ag), which acted as substrates, have prepared using the chemical reduction method. The synthesized nanoparticles have been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet-visible spectroscopy, and photoluminescence spectroscopy. Commercially available paracetamol-500 mg (PAM) and aspirin-300 mg (ASP) were selected for photodegradation under visible light using the as-prepared composites in an aqueous solution. Photoluminescence spectroscopy was used to detect PAM and ASP using the photo-excited electron transfer (PET) process, and the limit of detection (LOD) has obtained for PAM(8.70 ppm) and ASP(4.43 ppm) with a sensitivity of 0.9954 and 0.8002, respectively. Fourier transform infrared spectroscopy (FTIR) was used to analyze the before and after degradation products and to confirm the disintegrated products such as -COOH and -CH- both before and after disintegration.. The experimental data were found to fit well with the Freundlich isotherm, suggesting that the as-prepared nanocomposites exhibited a heterogeneous nature for PAM (5119 mg/L), and the pseudo-first-order kinetic model suggests ASP (1030 mg/L) with R2 values of 0.9119 and 0.7075. The risk assessment analysis of PAM was 9.823 µg/L(RQ > 1) and that of ASP was 0.2106 µg/L(RQ < 1), indicating that PAM has a higher potential risk than ASP. The demographic data of the participants indicated that PAM was the most stockpiled medicine at home; this work also encompasses the action of a single PAM and ASP tablet toward the environment, if it is accidently disposed of improperly could create massive water/soil pollution; hence, the care/duty of each person should follow the proper disposal of medical waste because we cannot replace this environment.

COVID-19-related stress response among adult females: Relevance of sociodemographics, health-related behaviors and COVID-19 contact.

Women were more affected than men during the COVID-19 pandemic. This study aimed to investigate COVID-19-related stress response in adult women and its association with the relevant socioeconomic, lifestyle and COVID-19-related factors. This research was carried out in eight randomly chosen cities from September 2020 to October 2021. To examine stress, we distributed the COVID Stress Scales (CSS) and the Perceived Stress Scale (PSS). Women also fulfilled a general socio-epidemiologic questionnaire. The study included 1,264 women. Most women were healthy, highly educated, employed, married, nonsmokers who consumed alcohol. The average total CSS score suggested a relatively low COVID-19 related stress), while 1.7% of women had CSS ≥ 100. The mean PSS was around the mid-point value of the scale. Older women, who were not in a relationship, didn't smoke, didn't drink alcohol, but used immune boosters, had chronic illnesses and reported losing money during the pandemic had higher CSS scores. A higher level of stress was also experienced by women exposed to the intense reporting about COVID-19, had contact with COVID-19 positive people or took care of COVID-19 positive family members. In this sample of predominantly highly educated women few women experienced very high stress level, probably due to the study timing (after the initial wave) when the pandemic saw attenuated stress levels. To relieve women from stress, structural organization and planning in terms of health care delivery, offsetting economic losses, controlled information dissemination and psychological support for women are needed.

Upper body thermal images and associated clinical data from a pilot cohort study of COVID-19.

OBJECTIVES: The data was collected for a cohort study to assess the capability of thermal videos in the detection of SARS-CoV-2. Using this data, a published study applied machine learning to analyze thermal image features for Covid-19 detection. DATA DESCRIPTION: The study recorded a set of measurements from 252 participants over 18 years of age requesting a SARS-CoV-2 PCR (polymerase chain reaction) test at the Hospital Zambrano-Hellion in Nuevo León, México. Data for PCR results, demographics, vital signs, food intake, activities and lifestyle factors, recently taken medications, respiratory and general symptoms, and a thermal video session where the volunteers performed a simple breath-hold in four different positions were collected. Vital signs recorded include axillary temperature, blood pressure, heart rate, and oxygen saturation. Each thermal video is split into 4 scenes, corresponding to front, back, left and right sides, and is available in MPEG-4 format to facilitate inclusion into pipelines for image processing. Raw JPEG images of the background between subjects are included to register variations in room temperatures.

Quality of life in children with Down syndrome and its association with parent and child demographic characteristics: Parent-reported measures.

BACKGROUND: This study aims to explore the association between the quality of life (QoL) in children with Down syndrome (DS) and its relationship with demographic characteristics of both parents and children. The investigation encompasses five domains: physical and psychological well-being, autonomy and parental relationship, social well-being, and peers, as well as school and the learning environment. METHOD: An online questionnaire, the KIDSCREEN-27, was used to measure the QoL of 112 families with DS in Saudi Arabia, referred to as "Parent-Reported Measures." Descriptive statistics were analyzed using the Statistical Package for Social Sciences. RESULTS: The study found that the QoL of children with DS showed high scores in the psychological well-being, autonomy, parental relations, school, and learning environment domains. However, the physical and social well-being and peer domains had lower scores, although still considered "good scores." Family income had a positively significant influence on all QoL domains. Specifically, higher family income was associated with better QoL outcomes, except for social well-being. Parental age was found to influence psychological well-being, while parental education and the relationship between the parent and child influenced social well-being. Lastly, the child's gender was found to have an impact on the school and learning environment domain. CONCLUSION: The study highlights the importance of understanding the impact of the demographic variability of children with DS and their parents on the QoL of their children. It emphasizes the need to address the needs of families with lower incomes and the importance of parental education and relationships with their children in improving social well-being. The findings could aid policymakers and healthcare providers in improving the QoL for families with children who have DS.

Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data.

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.

Mortality trends and sociodemographic factors associated with early death in sickle cell disease patients in the state of São Paulo / Tendência da mortalidade e fatores sociodemográficos associados à morte precoce em pacientes com doença falciforme no estado de São Paulo

ABSTRACT Objective: To estimate trends in mortality rate and average age of death, and identify sociodemographic factors associated with early death in patients with sickle cell disease (SCD). Methods: An ecological and cross-sectional study was conducted using data from the Mortality Information System. All deaths of patients residing in the state of São Paulo from 1996 to 2015 with at least one International Disease Code for SCD in any field of the death certificate were included. Simple linear regression was used to estimate trends. The Log-rank test and multiple Cox regression were used to identify factors associated with early death. Results: The age-standardized mortality rate per million inhabitants increased by 0.080 per year (R2=0.761; p<0.001). When the events were stratified by age at death, the increase was 0.108 per year for those occurring at age 20 years or older, (R2=0.789; p<0.001) and 0.023 per year for those occurring before age 20 years old (R2=0.188; p=0.056). The average age at death increased by 0.617 years (7.4 months) per year (R2=0.835; p<0.001). Sociodemographic factors associated with early death identified were male gender (hazard ratio — HR=1.30), white race (HR=1.16), death occurring in the hospital (HR=1.29), and living in the Greater São Paulo (HR=1.13). Conclusions: The mortality rate and the average age of death in patients with SCD have increased over the last two decades. Sociodemographic factors such as gender, race, place of occurrence, and residence were found to be associated with early death.

RESUMO Objetivo: Estimar as tendências da taxa de mortalidade e da idade média de morte e identificar os fatores sociodemográficos associados ao óbito precoce em pacientes com doença falciforme (DF). Métodos: Estudo ecológico e transversal realizado com dados do Sistema de Informações sobre Mortalidade. Foram incluídos todos os eventos de óbitos de pacientes residentes no estado de São Paulo de 1996 a 2015, que continham pelo menos um Código Internacional de Doenças para DF, em qualquer campo do atestado de óbito. As tendências foram estimadas por meio da regressão linear simples. Para a identificação dos fatores associados ao óbito precoce, foram realizadas análises de sobrevida, por meio da regressão de Cox simples e múltipla. Resultados: A taxa de mortalidade, padronizada pela idade, por milhão de habitantes, aumentou 0,080 ao ano (R²=0,761; p<0,001). Quando os eventos foram estratificados por idade do óbito, naqueles que ocorreram com 20 anos ou mais, o aumento foi de 0,108 ao ano (R²=0,789; p<0,001) e, nos que ocorreram antes de 20 anos, foi de 0,023 ao ano (R²=0,188; p=0,056). A idade média ao morrer aumentou 0,617 ano por ano (R²=0,835; p<0,001). Os fatores associados ao óbito precoce identificados no modelo múltiplo foram: sexo masculino (hazard ratio — HR=1,30), raça branca (HR=1,16), morte dentro do hospital (HR=1,29) e moradia na Grande São Paulo (HR=1,13). Conclusões: Houve aumento da taxa de mortalidade e da idade média de óbito com DF nas duas últimas décadas estudadas. Os fatores sociodemográficos sexo, raça, local de ocorrência e município de residência estiveram associados com a faixa etária do óbito.

A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images.

Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances. In this work, we introduce two novel datasets: Consumers comprises 145 video sequences compliant to personal information regulations as far as facial images are concerned and BID is a set of cropped body images from each sequence that can be used for numerous computer vision tasks. We also propose an end-to-end framework which comprises CNNs as object detectors, LSTMs for motion forecasting of the tracklet association component in a sequence, along with a multi-attribute classification model for apparent demographic estimation of the detected outputs, aiming to capture useful metadata of consumer product preferences. Obtained results on tracking and age/gender prediction are promising with respect to reference systems while they indicate the proposed model's potential for practical consumer metadata extraction.

Using a Standing Heel-Rise Test as a Predictor of Ankle Muscle Strength in the Elderly.

The senior population is at increased risk of falling due to a reduction in ankle muscle strength. Evaluating the strength of the ankle muscles in older adults is of paramount importance. The purpose of this study was to formulate an equation to estimate ankle muscle strength by utilizing the basic physical characteristics of the subject and the variables related to their ability to perform the standing heel-rise test (SHRT). One hundred and thirty-two healthy elderly participants (mean age 67.30 ± 7.60) completed the SHRT and provided demographic information. Ankle plantar flexor (PF) muscle strength was evaluated using a push-pull dynamometer. Multiple regression analysis was utilized to develop a prediction equation for ankle PF muscle strength. The study revealed that the ankle PF strength equation was derived from variables including the power index of the SHRT, gender, age, calf circumference, and single-leg standing balance test. The equation exhibited a strong correlation (r = 0.816) and had a predictive power of 65.3%. The equation is represented as follows: ankle PF strength = 24.31 - 0.20(A) + 8.14(G) + 0.49(CC) + 0.07(SSEO) + 0.20(BW/t-SHRT). The equation had an estimation error of 5.51 kg. The strength of ankle PFs in elderly individuals can be estimated by considering demographic variables, including gender, age, calf circumference, single-leg standing balance test, and the power index of the SHRT. These factors were identified as significant determinants of ankle PF strength in this population.

Association of ABO blood types and clinical variables with COVID-19 infection severity in Libya.

Objective: The continuing COVID-19 pandemic is a coronavirus-related health emergency (severe acute respiratory syndrome coronavirus 2). Inadequate efforts are still being made to address the illness situation in Libya, and this must change. To address these issues, we looked into the demography and trend of the disease as well as the potential risk factors for infection. Methods: This study is a retrospective case-control study conducted online among 616 COVID-19 patients. The p0.05 value, odds ratios, and 95% confidence intervals were calculated and analyzed from the drawn data. Results: Males were at high risk of COVID-19 than females (odds ratio = 1.3, 95% confidence interval: 1.042-1.622; p = 0.02). Anosmia and ageusia were more prominent in females. Patients with an "AB" blood group are significantly susceptible to infection. Adults (31 and above) are highly liable to infection. The univariate logistic regression analysis revealed that smoking is a risk factor for those above 60 years (odds ratio = 2.228, 95% confidence interval: 1.145-4.336; p = 0.018). Individuals with chronic diseases such as diabetes and/or hypertension are more prone to COVID-19 (odds ratio = 10.045, 95% confidence interval: 3.078-32.794; p = 0.000 and odds ratio = 11.508, 95% confidence interval: 3.930-33.695; p = 0.000, respectively). Conclusion: This study provided for the first time the demographic data and the trend of COVID-19 infection in Libya, which will assist the stakeholders and governmental bodies in planning protection strategies against the pandemic.

Opportunistic screening for low bone density using abdominopelvic computed tomography scans.

BACKGROUND: While low bone density is a major burden on US health system, current osteoporosis screening guidelines by the US Preventive Services Task Force are limited to women aged ≥65 and all postmenopausal women with certain risk factors. Even within recommended screening groups, actual screening rates are low (<26%) and vary across socioeconomic groups. The proposed model can opportunistically screen patients using abdominal CT studies for low bone density who may otherwise go undiagnosed. PURPOSE: To develop an artificial intelligence (AI) model for opportunistic screening of low bone density using both contrast and non-contrast abdominopelvic computed tomography (CT) exams, for the purpose of referral to traditional bone health management, which typically begins with dual energy X-ray absorptiometry (DXA). METHODS: We collected 6083 contrast-enhanced CT imaging exams paired with DXA exams within ±6 months documented between May 2015 and August 2021 in a single institution with four major healthcare practice regions. Our fusion AI pipeline receives the coronal and axial plane images of a contrast enhanced abdominopelvic CT exam and basic patient demographics (age, gender, body cross section lengths) to predict risk of low bone mass. The models were trained on lumbar spine T-scores from DXA exams and tested on multi-site imaging exams. The model was again tested in a prospective group (N = 344) contrast-enhanced and non-contrast-enhanced studies. RESULTS: The models were evaluated on the same test set (1208 exams)-(1) Baseline model using demographic factors from electronic medical records (EMR) - 0.7 area under the curve of receiver operator characteristic (AUROC); Imaging based models: (2) axial view - 0.83 AUROC; (3) coronal view- 0.83 AUROC; (4) Fusion model-Imaging + demographic factors - 0.86 AUROC. The prospective test yielded one missed positive DXA case with a hip prosthesis among 23 positive contrast-enhanced CT exams and 0% false positive rate for non-contrast studies. Both positive cases among non-contrast enhanced CT exams were successfully detected. While only about 8% patients from prospective study received a DXA exam within 2 years, about 30% were detected with low bone mass by the fusion model, highlighting the need for opportunistic screening. CONCLUSIONS: The fusion model, which combines two planes of CT images and EMRs data, outperformed individual models and provided a high, robust diagnostic performance for opportunistic screening of low bone density using contrast and non-contrast CT exams. This model could potentially improve bone health risk assessment with no additional cost. The model's handling of metal implants is an ongoing effort.

Profile of autism spectrum disorders in Morocco: cross-sectional retrospective study of parents of children with autism.

Context and objective: In Morocco, autism is a frequent disorder and no epidemiological studies have been carried out. The aim of this work is to analyze the characteristics of the onset of autism in children. Settings and Design: We conducted a cross-sectional retrospective study, spread over a period of 10 months. Methods: We included in our survey children who, according to the DSM 5 definition, had one of the autism spectrum disorders and had a well-defined diagnosis. For the statistical analysis we used Excel® software. We used the percentages for the description of the qualitative variables. Results: Of the parents of children with ASD, 49% were biological mothers. Of these, 30% women had their child when they were between 30 and 35 years of age. Males accounted for 61% of children with a sex ratio of 2.6. The first autistic traits appeared at the age of 18 months for 22% of our population. Of mothers with children with ASD, 83% had regular medical follow-up throughout their pregnancy. All autistic children in our population were vaccinated according to the National Immunization Program. According to parent reports, 70% affirmed that the first autistic features appeared after vaccination with the measles-mumps-rubella (MMR) vaccine, knowing that this statement is not based on evidence. Conclusions: The quality of care depends on the age at which the diagnosis of autism is established.

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona.

Analytic compartmental models are currently used in mathematical epidemiology to forecast the COVID-19 pandemic evolution and explore the impact of mitigation strategies. In general, such models treat the population as a single entity, losing the social, cultural and economical specificities. We present a network model that uses socio-demographic datasets with the highest available granularity to predict the spread of COVID-19 in the province of Barcelona. The model is flexible enough to incorporate the effect of containment policies, such as lockdowns or the use of protective masks, and can be easily adapted to future epidemics. We follow a stochastic approach that combines a compartmental model with detailed individual microdata from the population census, including social determinants and age-dependent strata, and time-dependent mobility information. We show that our model reproduces the dynamical features of the disease across two waves and demonstrates its capability to become a powerful tool for simulating epidemic events.

Factors of Overweight and Obesity Among Employees of Petrochemical Manufacturing Companies.

The high prevalence of overweight and obesity is a major contributor to the global burden of disease, and create an economic burden for nations, including both direct and indirect costs. Although the Kingdom of Saudi Arabia is rapidly industrializing and the economy is now less dependent on oil, oil industry is still the main contributor to the Saudi labor force (41%). However, the productivity growth lags behind many developing countries. No studies have been conducted to explore the factors that can be associated with the lower productivity in this population of Saudi Arabia. This study aimed to estimate the prevalence of overweight/obesity and examine the association between overweight/obesity and lifestyle habits among employees of petrochemical manufacturing companies in the Kingdom of Saudi Arabia. A total of 1000 employees were surveyed. Chi-square test and logistic regression were used to predict odds ratios for obesity. About 47% of the sample was normal weight, while 28.5% was overweight and 21.2% was obese. Four factors were found to be predictive of obesity: feelings about weight, age, marital status, and childhood region. More than 52% of employees reported being happy with their weight. Those unhappy with their weight had more than six times greater odds of obesity as compared to those happy with their weight. Married employees were about twice as likely as unmarried to be obese. The findings of high prevalence of overweight and obesity among employees of petrochemical manufacturing suggest that more efforts are needed to promote healthy behaviors among employees. Workplace wellness programs and educational campaigns are recommended to encourage healthy lifestyle habits at both the workplace and in the community.

An Ethics and Social-Justice Approach to Collecting and Using Demographic Data for Psychological Researchers.

The collection and use of demographic data in psychological sciences has the potential to aid in transforming inequities brought about by unjust social conditions toward equity. However, many current methods surrounding demographic data do not achieve this goal. Some methods function to reduce, but not eliminate, inequities, whereas others may perpetuate harmful stereotypes, invalidate minoritized identities, and exclude key groups from research participation or access to disseminated findings. In this article, we aim to (a) review key ethical and social-justice dilemmas inherent to working with demographic data in psychological research and (b) introduce a framework positioned in ethics and social justice to help psychologists and researchers in social-science fields make thoughtful decisions about the collection and use of demographic data. Although demographic data methods vary across subdisciplines and research topics, we assert that these core issues-and solutions-are relevant to all research within the psychological sciences, including basic and applied research. Our overarching aim is to support key stakeholders in psychology (e.g., researchers, funding agencies, journal editors, peer reviewers) in making ethical and socially-just decisions about the collection, analysis, reporting, interpretation, and dissemination of demographic data.