Demographic Factors Research Articles

Key Predictors of Patient Satisfaction and Loyalty in Saudi Healthcare Facilities: A Cross-Sectional Analysis.

Patient satisfaction and loyalty are essential indicators of healthcare quality, directly impacting patient outcomes and the long-term success of healthcare facilities. Despite the growing importance of patient-centered care in Saudi Arabia, there is limited research exploring the factors that influence patient satisfaction and loyalty, particularly in the Riyadh region. This study aims to identify the key factors influencing patient satisfaction and loyalty among Saudi patients attending public and private healthcare facilities in the Riyadh region. The study focuses on how healthcare service quality, communication, and demographic factors contribute to patient satisfaction and loyalty. A cross-sectional study was conducted with a sample of 350 Saudi patients from 10 healthcare facilities in Riyadh. Data were collected using the Patient Satisfaction Questionnaire (PSQ-18) and the Patient Loyalty Questionnaire (PLQ). Descriptive statistics, Pearson correlation, and multiple linear regression were employed to identify predictors of patient satisfaction and loyalty. Significant predictors of patient satisfaction included general satisfaction (β = 0.48, p < 0.001), communication (β = 0.35, p < 0.001), and the frequency of healthcare visits (β = 0.13, p = 0.011). Private healthcare facilities had higher satisfaction (p < 0.001) and loyalty scores (p < 0.001) compared to public facilities. Patient loyalty was strongly predicted by general satisfaction (β = 0.55, p < 0.001) and communication (β = 0.42, p < 0.001). Communication quality and patient satisfaction are key drivers of patient loyalty in Saudi healthcare facilities. Private facilities outperform public ones in patient satisfaction and loyalty. These findings emphasize the need for healthcare providers to enhance communication and service quality to foster patient loyalty. Tailored approaches to meet the diverse needs of patients, particularly in terms of education and visit frequency, are crucial for improving healthcare outcomes in Saudi Arabia.

Overcoming Linguistic and Cultural Barriers in English Language Learning: An Exploration of Culturally Responsive Pedagogy

This study investigated the implementation and effectiveness of CRP in English language learning at Cebu Technological University-Tuburan Campus, addressing linguistic and cultural barriers for learners and explored diverse subcultures, including respondents’ religion, family structure, socioeconomic status, and educational attainment. Employing a mixed-methods design, the research utilized surveys and pre-post-tests with thirty-three respondents, and interviews with six informants to gather data. Findings revealed a broad age range, a predominance of female participants, and a religiously influenced demographic. The pre-post-test results demonstrated a significant improvement in overall written communication competencies, highlighting a substantial improvement in the application of morphological concepts in writing essays, coupled with sustained high proficiency in grammar. A multiple regression analysis identifies the varying influences of demographic factors on test scores. Teachers perceived CRP as a catalyst for inclusive education, emphasizing customized approaches aligned with cultural backgrounds. Thus, CRP positively impacts language learning outcomes in diverse educational settings. Recommendations include implementing an action plan to enhance language proficiency and overcome linguistic and cultural barriers in English language learning. Findings contribute to a broader understanding of CRP’s significant application in a culturally diverse educational context.

The Demographical Analysis Of Indonesian Buy-Now-Pay-Later Users Financial Wellbeing

This study aims to understand what causes financial wellbeing among Indonesian Buy-Now-Pay-Later Users in terms of demographical. A survey from 400 respondents was collected through an online questionnaire from January to June 2024 and analyzed using descriptive statistics, and continued by using Kruksal Wallis and Mann Whitney U in SPSS to test whether there is a statistically significant difference between two or more independent variables. This study is confirmed as the study demonstrates that demographic factors significantly influence financial well-being. These insights emphasize the importance of considering demographic variables when assessing financial health and tailoring financial education, policy-making, and service provision to effectively address the diverse needs of different demographic groups.

The Impact of Social Media Marketing on Consumer Purchase Intention and Customer Loyalty on E-commerce Websites

Thailand’s e-commerce industry has significantly improved in recent years, driven by widespread Internet access and smartphone adoption. The diverse online marketplace caters to the changing needs and preferences of Thai consumers. However, the impact of social media marketing on consumer purchase intention and loyalty to e-commerce sites has not received sufficient attention. This study focuses on online shopping platforms in Thailand to investigate the role of social media marketing in influencing consumer purchase intention and loyalty to e-commerce platforms. For this purpose, 400 online questionnaires were required for data collection and used for testing the hypothesis, based on the suitable statistics through the advanced statistical program. The findings indicated that demographic factors, consumer behavior, and social media engagement have significantly influenced consumer purchase intention. This research also highlighted implications for how e-commerce sites can enhance their marketing strategies and foster customer loyalty to their products. Keywords: social media marketing, purchase intention, customer loyalty

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

Psychological Well-Being and Quality of Life in Laryngeal Cancer Patients across Tumor.

Background/Objectives: Laryngeal cancer significantly impacts patients' psychological well-being and quality of life (QoL). This study aims to evaluate the psychological impact and QoL in patients with laryngeal cancer, focusing on differences based on tumor stage and treatment. Methods: This longitudinal study included 75 patients diagnosed with laryngeal cancer. Participants were assessed at diagnosis and 3 months post-treatment using validated tools such as the Hospital Anxiety and Depression Scale (HADS) and the EORTC QLQ-H&N35 questionnaire. This study analyzed the impact of tumor stage, treatment type, and demographic factors on psychological well-being and QoL. Results: Patients with early-stage tumors (Stage I) reported significantly better psychological well-being and QoL compared to those with more advanced tumors (Stages III and IV) both before and after treatment. The non-significant p-values in advanced stages suggest a uniformity of severe distress and poor QoL among these patients. Treatment led to significant reductions in anxiety and depression in early-stage patients, while those with advanced-stage disease showed less improvement. Conclusions: The findings highlight the critical need for early psychological intervention, particularly in advanced-stage laryngeal cancer patients who continue to experience substantial psychological distress and poor QoL despite treatment. Integrating comprehensive psychological support into standard care is essential to improve overall outcomes for these patients.

The association between different leisure-time physical activity patterns and the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio in adults: national health and nutrition examination survey 2007–2018

BackgroundDespite the potential superiority of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a diagnostic and predictive marker, no study has investigated the link between different leisure-time physical activity (LTPA) patterns and the NHHR. This study aims to explore this relationship.MethodsData was extracted from the National Health and Nutrition Examination Survey (NHANES) cycles spanning from 2007 to 2008 to 2017–2018. Participants (N = 14,211) were classified into four groups based on their LTPA patterns: (1) inactive (LTPA = 0 min/week); (2) insufficiently active (LTPA < 150 min/week); (3) weekend warrior (LTPA ≥ 150 min/week within 1 or 2 sessions); and (4) regularly active (LTPA ≥ 150 min/week in more than 2 sessions). Weighted multiple linear regression analysis was employed twice, using inactive and regular active groups as reference groups, respectively. Weighted stratification analyses and interaction tests were performed by demographics.ResultsCompared to the inactive group, each additional unit of LTPA time was associated with a significant 0.23-unit greater decrease in the NHHR in the regularly active group [-0.23 (-0.29; -0.16)]. However, no significant decrease was observed in the “Weekend Warrior” [-0.11 (-0.22; 0.008)] or insufficiently active groups [-0.03 (-0.11; 0.04)]. Moreover, compared to the regularly active group, the insufficiently active [0.21 (0.13; 0.29)], “Weekend Warrior” [0.13 (0.004; 0.25)], and inactive [0.26 (0.20; 0.32)] groups had significantly higher NHHR. The associations between the NHHR and various LTPA patterns did not significantly differ by demographic factors, except for race.ConclusionThe regularly active pattern is significantly associated with a lower NHHR, but no significant difference in the NHHR was detected between the insufficiently active and “Weekend Warriors” patterns. The study suggests that frequency and regularity of PA are crucial for optimal lipid management, supporting clinical recommendations to meet or exceed 150 min of PA in more than two sessions per week.

Correlates of public compliance with COVID-19 regulations in Taiwan

PurposeThis study explores the determinants of public willingness to comply with COVID-19 regulations to address the research gap at the intersection of public health and law enforcement within the unique sociocultural context of Taiwan.Design/methodology/approachUtilizing survey data from New Taipei City in 2021, the analysis involved multiple linear regression models to assess the influences of psychological conditions (i.e. distress and self-efficacy), community compliance and perceptions of government (i.e. general trust in government and specific perceptions of police procedural justice) on compliance tendencies while controlling for individual demographics.FindingsThe results indicated that self-efficacy, perceived community compliance, trust in government, and police procedural justice are positively associated with public compliance with COVID-19 regulations. Among these variables, trust in government and police procedural justice were identified as the most prominent factors, followed by self-efficacy and perceived community compliance. As demographic factors such as age, gender and education did not significantly affect willingness to comply, psychological, social and governmental influences are more powerful determinants of compliance than static demographic characteristics.Originality/valueThis study provides empirical evidence from Taiwan on the factors shaping public compliance during an unprecedented global pandemic. It highlights the importance of fostering governmental trust and enhancing police procedural justice during periods of stability to secure compliance with public health directives in times of crisis.

Factors Influencing Job Satisfaction among Mental Health Nurses: A Systematic Review.

Job satisfaction is crucial for healthcare professionals, and understanding its influencing factors is essential for fostering a positive work environment, reducing turnover rates, and improving the overall quality of patient care. This systematic review examined the factors linked to nurse job satisfaction in psychiatric hospitals and diverse psychiatric settings, analyzing the findings through the lens of Herzberg's theory. Despite existing evidence, gaps remain in understanding the differences in factors affecting job satisfaction across various settings. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a comprehensive systematic review by searching six databases, namely PubMed, CINAHL, Cochrane Library, PsycINFO, Scopus, and APA PsycNet. Our search yielded an initial 567 studies published between January 2014 and February 2024, which were subsequently screened and evaluated based on predetermined inclusion criteria. Following this process, a total of 16 studies were deemed eligible for final analysis. Each of these selected studies underwent an independent review by two authors, utilizing the Joanna Briggs Institute checklist tool to ensure rigorous assessment. The findings revealed that interpersonal relationships, working conditions, and recognition were the most frequently reported factors associated with nurse job satisfaction, along with various extrinsic, intrinsic, personal, emotional, and psychosocial factors. Notably, psychiatric hospitals showed a mix of personal, extrinsic, intrinsic, and psychosocial factors influencing job satisfaction, with demographic factors being the most frequently examined. In contrast, diverse psychiatric settings focused more on external aspects impacting job satisfaction. The review highlights the importance of both intrinsic and extrinsic factors and suggests that future research should employ more robust methods and consider psychiatric hospitals and other mental health contexts. Herzberg's theory provides a valuable framework for understanding the factors associated with nurse job satisfaction.

Stablecoins for Transferring Value in the Cryptocurrency Market

Stablecoins have gained prominence as an effective means to mitigate the inherent volatility in the cryptocurrency markets by providing a stable and reliable medium for value preservation and transfer. This research paper investigates the attributes that render stablecoins a preferable vehicle for value retention and transaction within these markets. By employing a quantitative methodology, this study collected data through surveys, with a sample size of 714 individuals chosen via purposive sampling techniques. To analyze the data, both descriptive and inferential statistical methods were applied. The demographic profiles of the sample were summarized using descriptive statistics such as frequencies and percentages. For examining the relationships between demographic factors (such as gender, age, education, income, and place of residence) and the perceived efficacy of stablecoins, inferential techniques including Chi-square tests and logistic regression were utilized. The logistic regression analysis specifically assessed how these variables influenced the likelihood of participants favoring stablecoins for financial transactions. The results indicate that the preference for stablecoins as a means of holding or transferring value within the cryptocurrency domain in Thailand varies significantly across different demographic groups, specifically influenced by factors such as gender, education, income, and place of residence. These outcomes provide insightful implications concerning the demographic influences on the adoption and utilization of stablecoins in financial activities. Keywords: stablecoin, holding, transferring, value, cryptocurrency market

Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study.

Loneliness is increasingly emerging as a significant public health problem in children and adolescents. Predicting loneliness and finding its risk factors in children and adolescents is lacking and necessary, and would greatly help determine intervention actions. This study aimed to find appropriate machine learning techniques to predict loneliness and its associated risk factors among schoolchildren. The data were collected from an ongoing prospective puberty cohort that was established in Chongqing, Southwest China. This study used 822 subjects (46.84% boys, age range: 11-16) followed in 2019. Five models, (a) random forest, (b) extreme gradient boosting (XGBoost), (c) logistic regression, (d) neural network, and (e) support vector machine were applied to predict loneliness. A total of 39 indicators were collected and 28 predictors were finally included for prediction after data pre-processing, including demographic, parental relationship, mental health, pubertal development, behaviors, and environmental factors. Model performance was determined by accuracy and AUC. Additionally, random forest and XGBoost were applied to identify the important factors. The XGBoost algorithm with SHAP was also used to interpret the results of our ML model. All machine learning performed with favorable accuracy. Compared to random forest (AUC: 0.87 (95%CI: 0.80, 0.93)), logistic regression (AUC: 0.80 (95%CI: 0.70, 0.89)), neural network (AUC: 0.80 (95%CI: 0.71, 0.89)), and support vector machine (AUC: 0.79 (95%CI: 0.79, 0.89)), XGBoost algorithm had the highest AUC values 0.87 (95%CI: 0.80, 0.93) in the test set, although the difference was not significant between models. Peer communication, index of general affect, peer alienation, and internet addiction were the top four significant factors of loneliness in children and adolescents. The results of this study suggest that machine learning has considerable potential to predict loneliness in children. This may be valuable for the early identification and intervention of loneliness.

Autonomic Reactivity to Mental Stress is Associated with Cardiovascular Mortality

Abstract Background and Aims The mechanisms linking acute psychological stress to cardiovascular disease (CVD) mortality are incompletely understood. We studied the relationship of electrocardiographic measures of autonomic dysfunction during acute mental stress provocation and CVD death. Methods In a pooled cohort of 765 participants with stable CVD from two related studies, we collected Holter data during standardized laboratory-based mental stress testing with a speech task and followed them for events. We assessed autonomic function using low frequency (LF) heart rate variability (HRV) in five-minute intervals before, during, and after stress induction, and specifically examined changes from rest to stress. We employed cause-specific survival models to examine its association with CVD and all-cause mortality, controlling for demographic and CVD risk factors. Results The mean (SD) age was 58 (10) years, 35% were women, and 44% self-identified as Black. After a median follow-up of 5.6 years, 37 (5%) died from CVD causes. A stress-induced low frequency (LF) HRV decrease (67% of sample), versus increase, was associated with a hazard ratio (HR) of 3.48 (95% confidence interval, 3.25, 3.73) for CVD mortality. Low rest LF HRV (bottom quartile) was also independently associated with CVD mortality, HR = 1.75 (1.58, 1.94), versus normal rest LF HRV (upper three quartiles). The combination of stress-induced LF HRV decrease and low rest LF HRV was associated with HR = 5.73 (5.33, 6.15) versus the normal stress/rest LF HRV reference. We found similar results with HF HRV. Conclusions Stress-induced LF HRV decrease and low rest LF HRV are both independently and additively associated with a higher CVD mortality risk. Additional research is needed to assess whether targeting autonomic dysfunction may improve CVD outcomes.

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

Family Resilience and Its Influencing Factors in Patients With Cancer and Their Family Members: A Systematic Review.

This review aims to explore factors influencing family resilience in families providing care for patients with cancer and to provide suggestions for future research directions. Six electronic databases were searched including Web of Science, CINAHL, EMBASE, PsycINFO, PubMed and CNKI from their inception to December 2023. The article reference lists were also manually searched. The Mixed Method Appraisal Tool was used to assess the included studies in this review. The 27-item checklist Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was followed to report this review. Twenty-one studies from six online databases reported that either individual or family factors have effects on family resilience and were described into five clusters on the basis of the Walsh model of family resilience, including demographic and clinical factors, personal strengths and resources, family stressors, family resilient coping processes and family resilient adaptation outcomes. Family resilience in cancer families plays a pivotal role in coping with family stressors and facilitating positive outcomes through domains of coping. Future researches need to explore factors related to family resilience from dyadic perspectives and to establish multidisciplinary intervention strategies for developing levels of family resilience in cancer families. PROSPERO: CRD42024535349.

Frailty Indicator over the Adult Life Cycle as a Predictor of Healthcare Expenditure and Mortality in the Short to Midterm.

Assessing frailty from middle age onward offers valuable insights into predicting healthcare expenditures throughout the life cycle. This paper examines the use of physical frailty as an indicator of healthcare demand across all age groups. The originality of this work lies in extending the analysis of frailty indicators beyond the typical focus on individuals under 50 years old to include those in mid-life and older. For this study, we used a database where frailty was measured in 2012 in a sample of individuals aged 15 to over 90. These individuals were tracked for their healthcare expenditures from 2012 to 2016. Among the sample of 6928 individuals, frailty in 2012 resulted in a statistically significant increase in costs at the 5% level for the population aged 15 to 65. We applied multilevel linear regression models with year fixed effects, controlling for demographic factors, education level, precarity, social dimensions, lifestyle factors (e.g., vegetable consumption), physical activity, emotional well-being, and medical history. A Hausman test was conducted to validate the model choice. For mortality rate analysis, Cox models were used. Our findings demonstrate that physical frailty provides valuable information for understanding its impact on healthcare expenditure. The effect of frailty on mortality is particularly significant for the elderly population. Moreover, frailty is a predictor of healthcare costs not only in older adults but also across the entire life cycle.

Social isolation and likelihood of becoming centenarians: evidence from the Chinese longitudinal healthy longevity survey

BackgroundSocial isolation, defined as an individual’s lack of social connections, is particularly prevalent among older adults. However, its association with health outcomes among the oldest-old population (aged 80 and above) was understudied.AimsTo examine the association between social isolation and the likelihood of becoming a centenarian among the oldest-old people in China, aiming to provide novel insights into promoting healthy aging and longevity.MethodsUsing data from The Chinese Longitudinal Healthy Longevity Survey, conducted in 22 provinces in mainland China since 1998, we performed a community-based, prospective nested case-control study. The primary outcome was survival to the age of 100 by 2018 (the end of follow-up). Information on social isolation and other covariates was collected via a questionnaire at baseline. The degree of social isolation was categorized as low, moderate, and high. Included (n = 5,716) were 1,584 identified centenarians and 4,132 controls (deceased before reaching 100 years), matched by age, sex, and year of entry. A conditional logistic regression model was used to evaluate the association between social isolation and the likelihood of becoming a centenarian, adjusting for demographic factors, lifestyle factors, chronic disease, potential disability, optimistic attitude, and perceived loneliness.ResultsIndividuals with the highest social isolation score had lower odds of becoming centenarians (adjusted OR:0.82; 95% CI: 0.68, 0.98), relative to those with the least social isolation (P-value < 0.05), and this association persisted in sensitivity analyses. The association was more pronounced among ever smokers, compared to never smokers (P-value = 0.001). We did not observe significant interactions between social isolation and other covariates (P-value > 0.05 for all).ConclusionsThis study highlights the inverse association between social isolation and the likelihood of becoming a centenarian, emphasizing the need for public health initiatives to combat isolation in the older population.

Prediabetes and Associated Risk Factors among Undergraduate Medical Students in Bangladesh

Background: Early detection and timely intervention of prediabetes is an important first step in preventing the disease. Objectives: To assess the prediabetes and associated risk factors in medical students of Bangladesh. Materials and Methods: A cross-sectional study was done among 252 medical students of Shaheed Ziaur Rahman Medical College, Bogura. Data related to demography with some behavioral factors, anthropometry, blood pressure, fasting plasma glucose, family history of hypertension (HTN) and diabetes mellitus (DM) were collected. Prediabetes was assessed by the American Diabetes Association (ADA) and World Health Organization (WHO) criteria. Results: The proportion of prediabetes was 13% according to ADA and 1.6% according to WHO respectively. Students with a positive family history of hypertension (HTN) had more prediabetes compared to those who didn’t have. None of the demographic factors, family history of DM or HTN, exercise and obesity were related between prediabetic and normal students, though prediabetic students had more proportion of family history of HTN. Conclusion: Prediabetes is proportionately high among students with positive family history of HTN. We recommend a regular checking of fasting plasma glucose of medical students with special attention to those who have a strong positive family history of DM and HTN. KYAMC Journal Volume: 15, No: 01, April 2024: 20-24.

Comparative Characteristics of Short-term and Long-term Adaptation by Blood Analysis of Foreign Female Students (India) to the Educational Process System

The article is devoted to the study of the comparative characteristics of the analysis of the leukocyte formula indicators of foreign female students from India during short-term and long-term sociocultural and other demographic factors of adaptation to educational process systems. We found little difference in the blood analysis of foreign Indian female students with short-term, long-term and social adaptation to the educational environment. Based on the evidence of the studied material, it is determined that the body reacts to each environmental requirement with a special effect. If, in one case, as at the initial stage of training, a student is faced with a situation of non-variability, then this requires the mobilization of many resources of the body, while in another case, at a longer and already established stage of training, the student’s body successfully copes with various influencing factors.

Factors Influencing Consumers' Brand Loyalty to Chinese Tea Drinks in Bangkok, Thailand

Tea drinks have always been a consumer favorite beverage because of the variety of product types that appeal to a wide customer base. In recent years, with the continuous expansion of China’s beverage market, some brands are looking to expand into overseas markets. They determine the brand’s overseas development strategy by investigating the factors that impact consumer brand loyalty to identify the local target market and group. This study investigates the impact of demographic factors, consumer perception in 7ps, and consumer satisfaction on brand loyalty. An online questionnaire was used to survey Chinese tea drink consumers who lived in Bangkok for over a month. Four hundred valid data were selected and analyzed using SPSS software. The research results show that most consumers will have different degrees of consumer brand loyalty based on consumer perception of the 7ps and consumer satisfaction. Products incorporating cultural attributes were found to be more attractive to consumers. Each of these factors, demographic, consumer satisfaction, and consumer perception in 7ps, impact consumer brand loyalty. Keywords: tea consumers, consumers brand loyalty, thailand, chinese tea drink

Health disparities in the risk of severe acidosis: real-world evidence from the All of Us cohort.

To assess the health disparities across social determinants of health (SDoH) domains for the risk of severe acidosis independent of demographical and clinical factors. A retrospective case-control study (n = 13310, 1:4 matching) is performed using electronic health records (EHRs), SDoH surveys, and genomics data from the All of Us participants. The propensity score matching controls confounding effects due to EHR data availability. Conditional logistic regressions are used to estimate odds ratios describing associations between SDoHs and the risk of acidosis events, adjusted for demographic features, and clinical conditions. Those with employer-provided insurance and those with Medicaid plans show dramatically different risks [adjusted odds ratio (AOR): 0.761 vs 1.41]. Low-income groups demonstrate higher risk (household income less than $25k, AOR: 1.3-1.57) than high-income groups ($100-$200k, AOR: 0.597-0.867). Other high-risk factors include impaired mobility (AOR: 1.32), unemployment (AOR: 1.32), renters (AOR: 1.41), other non-house-owners (AOR: 1.7), and house instability (AOR: 1.25). Education was negatively associated with acidosis risk. Our work provides real-world evidence of the comprehensive health disparities due to socioeconomic and behavioral contributors in a cohort enriched in minority groups or underrepresented populations. SDoHs are strongly associated with systematic health disparities in the risk of severe metabolic acidosis. Types of health insurance, household income levels, housing status and stability, employment status, educational level, and mobility disability play significant roles after being adjusted for demographic features and clinical conditions. Comprehensive solutions are needed to improve equity in healthcare and reduce the risk of severe acidosis.