Adherence to medication treatment plans is important for chronic disease (CD) management. Cost-related nonadherence (CRN) puts patients at risk for complications. Native Hawaiians and Pacific Islanders (NHPI) suffer from high rates of CD and socioeconomic disparities that could increase CRN behaviors.Examine factors related to CRN to medication treatment plans within an understudied population.Using 2014 NHPI-National Health Interview Survey data, we examined CRN among a nationally representative sample of NHPI adults. Bonferroni-adjusted Wald test and multivariable logistic regression were performed to examine associations among financial burden-related factors, CD status, and CRN.Across CD status, NHPI engaged in CRN behaviors had, on an average, increased levels of perceived financial stress, financial insecurity with health care, and food insecurity compared with adults in the total NHPI population. Regression analysis indicated perceived financial stress [adjusted odds ratio (AOR)=1.16; 95% confidence intervals (CI), 1.10-1.22], financial insecurity with health care (AOR=1.96; 95% CI, 1.32-2.90), and food insecurity (AOR=1.30; 95% CI, 1.06-1.61) all increase the odds of CRN among those with CD. We also found significant associations between perceived financial stress (AOR=1.15; 95% CI, 1.09-1.20), financial insecurity with health care (AOR=1.59; 95% CI, 1.19-2.12), and food insecurity (AOR=1.31; 95% CI, 1.04-1.65) and request for lower cost medication.This study demonstrated health-related and non-health-related financial burdens can influence CRN behaviors. It is important for health care providers to collect and use data about the social determinants of health to better inform their conversations about medication adherence and prevent CRN.
OBJECTIVE Marshallese adults experience high rates of type 2 diabetes. Previous diabetes self-management education (DSME) interventions among Marshallese were unsuccessful. This study compared the extent to which two DSME interventions improved glycemic control, measured on the basis of change in glycated hemoglobin (HbA1c). RESEARCH DESIGN AND METHODS A two-arm randomized controlled trial compared a standard-model DSME (standard DSME) with a culturally adapted family-model DSME (adapted DSME). Marshallese adults with type 2 diabetes (n = 221) received either standard DSME in a community setting (n = 111) or adapted DSME in a home setting (n = 110). Outcome measures were assessed at baseline, immediately after the intervention, and at 6 and 12 months after the intervention and were examined with adjusted linear mixed-effects regression models. RESULTS Participants in the adapted DSME arm showed significantly greater declines in mean HbA1c immediately (−0.61% [95% CI −1.19, −0.03]; P = 0.038) and 12 months (−0.77% [95% CI −1.38, −0.17]; P = 0.013) after the intervention than those in the standard DSME arm. Within the adapted DSME arm, participants had significant reductions in mean HbA1c from baseline to immediately after the intervention (−1.18% [95% CI −1.55, −0.81]), to 6 months (−0.67% [95% CI −1.06, −0.28]), and to 12 months (−0.87% [95% CI −1.28, −0.46]) (P < 0.001 for all). Participants in the standard DSME arm had significant reductions in mean HbA1c from baseline to immediately after the intervention (−0.55% [95% CI −0.93, −0.17]; P = 0.005). CONCLUSIONS Participants receiving the adapted DSME showed significantly greater reductions in mean HbA1c immediately after and 12 months after the intervention than the reductions among those receiving standard DSME. This study adds to the body of research that shows the potential effectiveness of culturally adapted DSME that includes participants’ family members.
The first paper by Zhengda Pei, Ningping Xiao, and Pei Yang, entitled "Cost-Effectiveness Analysis of Tumor Treating Fields for Lung Cancer Treatment in China," uses a Markov simulation model with a theoretical population of 276 patients metastatic non-small cell lung cancer (mNSCLC) over a 15-year time horizon to evaluate the cost-effectiveness of Tumor Treating Fields (TTFields) combined with standard-of-care for treating this cancer. The economic evaluation, including total costs, life-years, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER) values, reveals that the high costs of TTFields do not justify the modest survival benefits under current pricing models. This study aligns well with our research topic by applying a cost-effectiveness model providing insights into the economics of healthcare treatments. Future studies could combine simulation methods with AI techniques to optimize treatment decision models and dynamically update cost-effectiveness analyses as treatment parameters or patient demographics change. Additionally, research could use patient characteristics with mNSCLC to personalize these analyses using ML, AI, or DL analytics.The next article by Wenjie Liu, Gengwei Huo, and Peng Chen evaluates the cost-effectiveness of brigatinib and lorlatinib for treating ALK-positive non-small cell lung cancer (NSCLC), comparing the costs and outcomes of using brigatinib followed by lorlatinib before chemotherapy versus after. The study uses past trial data to recompose individual patient-level data and estimate survival models, with cost figures derived from publicly available data and agent administration expenses. They authors employ Markov models to simulate disease progression, deriving transition probabilities between states, each with associated costs and quality-adjusted life years (QALYs). Their findings show that using brigatinib as a first-line treatment followed by lorlatinib is more cost-effective than using brigatinib as a second-line option. This approach improves QALYs and health outcomes while optimizing healthcare costs. The study supports a treatment strategy prioritizing first-line brigatinib to enhance the costeffectiveness of NSCLC care.In the third paper titled "Demand prediction of medical services in home and community-based services for older adults in China using machine learning", Yucheng Huang and colleagues use cross-sectional data from the 2018 Longitudinal Healthy Longevity Survey of 15,312 Chinese adults aged 65+ to predict demand for medical services in home and community-based services (HCBS) based on Anderson's behavioral model of health services utilization. Results from five predictive machine-learning methods (logistic regression, logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting) unveiled that enabling factors, need factors, and behavioral factors were significant predictors of HCBS use. The authors argue that a robust conceptual model combined with rigorous machine learning methods could help better allocate limited medical resources to this segment of China's population with increasing demand for healthcare services. Future studies could employ machine learning or deep-learning methods based on longitudinal data to make causal inferences and integrate other dimensions of Andersen's Model of Health Services Use, such as social support, an enabling factor, to predict healthcare demand.Entitled "Hospital efficiency in the Eastern Mediterranean region: A systematic review and metaanalysis", the fourth article by Hamid Ravaghi and colleagues analyzes hospital efficiency in the Eastern Mediterranean, focusing on management's impact on healthcare quality and costs. Using a systematic review and meta-analysis of 37 studies, the authors evaluate factors like resource allocation, technology use, and hospital size through data envelopment analysis. They find significant heterogeneity across countries. Oman, for instance, has the highest mean technical efficiency among high-income nations and Bahrain the lowest. Among low-and middle-income nations, Iraq and Iran rank the highest, with Pakistan the lowest. Key factors affecting efficiency include internal structure, regional differences, and decision-maker participation in sustainability assessments. The authors recommend developing outpatient care, reducing supplier-induced demand, and strengthening hospital management and governance to improve efficiency and resilience against crises like COVID-19.In the fifth article entitled "The direct and indirect effects of length of hospital stay on the costs of inpatients with stroke in Ningxia, China," Ming Su and colleagues analyze data from 129,444 ischemic stroke patients and 15,525 hemorrhagic stroke patients. Using quantile regression and structural equation models, they assess how length of hospital stay (LOHS) impacts hospitalization costs, considering indirect social factors. The study compares ischemic and hemorrhagic stroke patients, examining variables like age, hospital level, admission and discharge details, payment method, LOHS, and surgery. Findings show LOHS as the largest contributor to inpatient costs, with other factors such as the Charlson Comorbidity Index, payment type, surgery, hospital level, and discharge method also having significant direct and indirect effects.Finally, the systematic review of 144 papers by Ali Imani, Roghayeh Alibabayee, Mina Golestani and Koustuv Dalal identifies key indicators affecting hospital efficiency, categorized into input, process, and output indicators. Input indicators include hospital capacity, structure, and costs; process indicators cover quality-oriented processes and educational activities; and output indicators focus on activity-related and quality-related outcomes. The study emphasizes the importance of both quantitative and qualitative measures for a comprehensive analysis of hospital efficiency, providing a structured framework for healthcare providers and administrators to enhance patient care and optimize cost. Future research can build on this framework to develop predictive models for real-time decision-making and cost management, especially in response to dynamic hospital demands and potential pandemics.The articles collectively highlight innovative applications of data science including machine and statistical learning techniques aiming at enhancing patient care and optimizing resource allocation in clinical and non-clinical settings. They provide actionable insights for healthcare providers and hospital administrators. They revolve around cost-effectiveness and efficiency and recommendations for improvements.
Little is known about health limitations and service utilization among the Native Hawaiian and Pacific Islander (NHPI) children with developmental disabilities (DDs) due to limited data. Our study examined the prevalence of DDs, health limitations, services used, and the unmet needs of NHPI children aged 3 to 17 years using cross-sectional data from the 2014 NHPI National Health Interview Survey. Results showed that prevalence of DDs among NHPI children was lower than American children of other races. DDs were negatively associated with health and functioning of NHPI children. There is a need to promote understanding of DDs among NHPI families and to inform public policy makers to identify appropriate intervention services for NHPI children.
Diabetes self-management education (DSME) programs that engage the families of patients with diabetes have shown to be effective in improving diabetes-related outcomes of the patients. The health effects of these "family models" of DSME on participating family members are rarely studied. Opportunity exists for the participating family members to benefit from the healthy lifestyle recommendations offered through such programs.Using data from a randomized controlled trial to assess the effect of family DSME compared to standard DSME among Marshallese adults with Type 2 diabetes, this study examined baseline to 12-month changes in A1c, body mass index (BMI), food consumption, and physical activity among participating family members, comparing outcomes of family members based on attended at least 1 (n = 98) versus attended no (n = 44) DSME sessions.Overall, family member attendance was low. There were no differences in the level of change from baseline to 12 months for A1c, BMI, food consumption, and physical activity between groups. After controlling for attendance and sociodemographic measures, lowering of BMI was the only significant predictor of not having an A1c level indicative of diabetes at 12 months.Future research on family DSME should consider ways to improve family member attendance; have them set their own health improvement goals; and integrate healthy lifestyle education, such as healthy eating and being physically active, along with the DSME core content to create an added benefit of diabetes prevention for participating family members. The limitations of this study and recommendations for future research are provided. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Native Hawaiians and Pacific Islanders (NHPIs) are one of the fasting growing racial groups in the United States (US). NHPIs have a significantly higher disease burden than the US population as a whole, yet they remain underrepresented in research. The purpose of this study is to examine factors associated with health care utilization among NHPIs.Drawing from the 2014 NHPI-National Health Interview Survey, we used stereotype logistic regressions to examine utilization of emergency department (ED) and outpatient services among 2172 individuals aged 18 and older.NHPIs with chronic diseases were twice as likely to be multiple ED users and nearly four times as likely to be frequent-users of outpatient services. Social support played a protective role in preventing multiple use of ED. Having a usual source of care made it more than eight times as likely to be a frequent-user of outpatient services. Use of eHealth information increased the odds of using ED and outpatient services. Ability to afford health care increased the odds of using outpatient services. There was no association between health insurance coverage and use of ED and outpatient services among NHPIs.This research provides the first available national estimates of health services use by NHPIs. Efforts to improve appropriate use of health services should consider leveraging the protective factors of social support to reduce the odds of frequent ED use, and having a usual source of care to increase use of outpatient services.
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