Engaging Communities in Emerging Infectious Disease Mitigation to Improve Public Health and Safety.

The COVID-19 pandemic highlighted the need for potent community-based tools to improve preparedness. We developed a community health-safety climate (HSC) measure to assess readiness to adopt health behaviors during a pandemic. We conducted a mixed-methods study incorporating qualitative methods (e.g., focus groups) to generate items for the measure and quantitative data from a February 2021 national survey to test reliability, multilevel construct, and predictive and nomologic validities. The 20-item HSC measure is unidimensional (Cronbach α = 0.87). All communities had strong health-safety climates but with significant differences between communities (F = 10.65; p<0.001), and HSC levels predicted readiness to adopt health-safety behaviors. HSC strength moderated relationships between HSC level and behavioral indicators; higher climate homogeneity demonstrated stronger correlations. The HSC measure can predict community readiness to adopt health-safety behaviors in communities to inform interventions before diseases spread, providing a valuable tool for public health authorities and policymakers during a pandemic.

Risk Factors for Elder Mistreatment Among Older Korean Americans.

Using data from a sample of older Korean Americans (n = 2,150), we examined the prevalence and associated factors of physical, emotional, and financial mistreatment. Given the importance of contextual factors, we examined the effect of immigration-related (years in the U.S. and acculturation) and interpersonal/community-related (family solidarity, social network, and ethnic community social cohesion) factors in addition to sociodemographic and health-related characteristics. The rate of experiencing physical, emotional, and financial mistreatment during the past year was 3%, 37.9%, and 16.1%, respectively. Younger age and lower family solidarity were common risk factors for emotional and financial mistreatment. The experience of emotional mistreatment was also more likely among females and those with higher level of acculturation, smaller social networks, and lower ethnic community social cohesion. Chronic disease was an additional risk factor for financial mistreatment. The findings suggest targeted prevention and intervention strategies for elder mistreatment.

Intervention to promote preventive dental care for older Korean-American Medi-Cal enrollees in Los Angeles.

BACKGROUND: In California, preventive dental care is covered by Medi-Cal (California's Medicaid program). However, many beneficiaries do not use their dental benefits. Given that a lack of knowledge about oral health and insurance coverage contributes to this underutilization, promoting the use of dental benefits among eligible individuals via an educational program is imperative. Responding to the particular needs of older immigrants with limited English proficiency, we developed a digital oral health intervention for older Korean-American Medi-Cal enrollees in Los Angeles. This educational intervention is designed to be delivered via computers and the Internet. It consists of a 15-min self-running PowerPoint presentation narrated in Korean with links to additional information on the Internet. The slides contain information about the basic etiology of oral diseases, oral hygiene, common myths about oral health and dental care, Medi-Cal coverage of preventive dental care, and how to find a dental clinic. METHODS: We pilot tested the intervention with 12 participants to examine its feasibility and acceptability. We also obtained participants' qualitative feedback about the intervention. RESULTS: A post-intervention quantitative assessment yielded high participant satisfaction and improved oral health and dental care knowledge. Participant responses to the intervention yielded four themes: (1) content and structure, (2) linguistic and cultural aspects, (3) delivery mode, and (4) additional concerns and suggestions. CONCLUSIONS: Our findings confirm the intervention's feasibility and acceptability and suggest further refinement.

"Do Your Best to Avoid it": Appraisals and Coping with Discrimination-Related Stress Among Older Chinese Immigrants in Affordable Senior Housings During the COVID-19 Pandemic.

This study investigated how older Chinese immigrants in affordable senior housing facilities (ASHs) appraise and cope with anti-Asian discrimination and related stress during the COVID-19 pandemic. Qualitative interviews were conducted with 26 participants aged over 65, analyzing verbatim transcripts and field notes using thematic analysis. Four major domains were identified: (1) discriminatory experience, (2) appraisals of discrimination stress, (3) coping strategies, and (4) rationales behind coping. Among the participants, 11 (42%) reported experiencing discrimination either personally or by someone they know. Participants' appraisals of discrimination stress varied, and most of them opted for disengagement coping strategies (e.g., avoidance, rationalization) attributing them to health concerns, individual characteristics, environmental and societal factors, and low acculturation. The findings suggest the need for more social and community resources to enhance coping capacity and resilience in this vulnerable population.

Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma.

BACKGROUND: Accurately predicting which patients are most likely to benefit from massive transfusion protocol (MTP) activation may help patients while saving blood products and limiting cost. The purpose of this study is to explore the use of modern machine learning (ML) methods to develop and validate a model that can accurately predict the need for massive blood transfusion (MBT). METHODS: The institutional trauma registry was used to identify all trauma team activation cases between June 2015 and August 2019. We used an ML framework to explore multiple ML methods including logistic regression with forward and backward selection, logistic regression with lasso and ridge regularization, support vector machines (SVM), decision tree, random forest, naive Bayes, XGBoost, AdaBoost, and neural networks. Each model was then assessed using sensitivity, specificity, positive predictive value, and negative predictive value. Model performance was compared to that of existing scores including the Assessment of Blood Consumption (ABC) and the Revised Assessment of Bleeding and Transfusion (RABT). RESULTS: A total of 2438 patients were included in the study, with 4.9% receiving MBT. All models besides decision tree and SVM attained an area under the curve (AUC) of above 0.75 (range: 0.75-0.83). Most of the ML models have higher sensitivity (0.55-0.83) than the ABC and RABT score (0.36 and 0.55, respectively) while maintaining comparable specificity (0.75-0.81; ABC 0.80 and RABT 0.83). CONCLUSIONS: Our ML models performed better than existing scores. Implementing an ML model in mobile computing devices or electronic health record has the potential to improve the usability.

Social network and mental health of Chinese immigrants in affordable senior housing during the COVID-19 pandemic: a mixed-methods study.

OBJECTIVE: Chinese immigrant older adults who live in affordable housing are at high risk of experiencing social isolation during the COVID-19 pandemic, which can affect their mental health. Using a triangulation mixed-methods approach, this study describes Chinese immigrant older adults' social network, mental health status, and their associations during the pandemic. METHODS: Semi-structured in-depth interviews were conducted with 26 Chinese immigrant older adults from June to August 2021. The structure and characteristics of participants' social networks were assessed with a name-generating approach. Mental health status was self-reported with Geriatric Depression Scale and UCLA loneliness scale. RESULTS: This sample (mean age = 78.12, 69.23% female), on average, had 5.08 social ties in their network, and 58% were family ties. Participants reported decreased social contact, family and friends interaction patterns specific to immigrants, and constantly being in a low mood and bored. Having closer relationships with others and maintaining the same or higher contact frequency after COVID-19 onset was associated with fewer depressive symptoms. Resilience from religious beliefs, neighbors as role models, and wisdom learned from past experiences were reported. CONCLUSION: Knowledge built in this study can inform respondence to future crises like the COVID-19 pandemic in affordable housing settings serving older immigrant populations.

Lipid Droplets in Lung Cancers Are Crucial for the Cell Growth and Starvation Survival.

For rapid and unlimited cell growth and proliferation, cancer cells require large quantities of nutrients. Many metabolic pathways and nutrient uptake systems are frequently reprogrammed and upregulated to meet the demand from cancer cells, including the demand for lipids. The lipids for most adult normal cells are mainly acquired from the circulatory system. Whether different cancer cells adopt identical mechanisms to ensure sufficient lipid supply, and whether the lipid demand and supply meet each other, remains unclear, and was investigated in lung cancer cells. Results showed that, despite frequent upregulation in de novo lipogenesis and the lipid transporter system, different lung cancer cells adopt different proteins to acquire sufficient lipids, and the lipid supply frequently exceeds the demand, as significant amounts of lipids stored in the lipid droplets could be found within lung cancer cells. Lipid droplet surface protein, PLIN3, was found frequently overexpressed since the early stage in lung cancer tissues. Although the expression is not significantly associated with a specific gender, age, histology type, disease stage, and smoking habit, the frequently elevated expression of PLIN3 protein indicates the importance of lipid droplets for lung cancer. These lipid droplets are not only for nutrient storage, but are also crucial for tumor growth and proliferation, as well as survival in starvation. These results suggest that manipulation of lipid droplet formation or TG storage in lung cancer cells could potentially decrease the progression of lung cancer. Further exploration of lipid biology in lung cancer could help design novel treatment strategies.

An informatics approach to examine decision-making impairments in the daily life of individuals with depression.

Mental health informatics studies methods that collect, model, and interpret a wide variety of data to generate useful information with theoretical or clinical relevance to improve mental health and mental health care. This article presents a mental health informatics approach that is based on the decision-making theory of depression, whereby daily life data from a natural sequential decision-making task are collected and modeled using a reinforcement learning method. The model parameters are then estimated to uncover specific aspects of decision-making impairment in individuals with depression. Empirical results from a pilot study conducted to examine decision-making impairments in the daily lives of university students with depression are presented to illustrate this approach. Future research can apply and expand on this approach to investigate a variety of daily life situations and psychiatric conditions and to facilitate new informatics applications. Using this approach in mental health research may generate useful information with both theoretical and clinical relevance and high ecological validity.

Serial cardiopulmonary exercise testing in an asymptomatic young female receiving percutaneous balloon dilatation for cor triatriatum sinistrum at an early age.

A young female was diagnosed as classic cor triatriatum sinistrum (CTS) at 38 months old incidentally and she received percutaneous catheter-based balloon dilatation twice at 41 and 48 months old. She took regular follow-up by echocardiography biannually with no re-stenosis of the orifice in the membrane between two chambers in the left atrium and she denied any cardiac-related symptoms. Serial cardiopulmonary exercise testing (CPET) by treadmill under Ramped-Bruce protocol was done at her 13, 19, and 23-year old. She could reach maximal effort and complete the three CPETs. No significant change of metabolic equivalent at anaerobic (MET) threshold, peak MET, and pulmonary function were noted in the serial CPETs and all of them were within normal limits comparing to the reference values of Chinese specific to her age. Our case report demonstrated that the concept of percutaneous catheter-based balloon dilatation of obstructive membrane for classic CTS without other associated congenital heart diseases is sound and feasible. The prognosis is well without re-obstruction and the cardiopulmonary fitness after that could be maintain as healthy peers for up to 18 years.

Efficacy and Safety of a Single Intra-articular Injection of Platelet-rich Plasma on Pain and Physical Function in Patients With Ankle Osteoarthritis-A Prospective Study.

Ankle osteoarthritis (OA) can cause disabling symptoms, and some patients prefer to be treated with minimally invasive procedures. The aim was to evaluate the efficacy and safety of a single intraarticular injection of platelet-rich plasma (PRP) for patients with ankle OA. In a prospective study done in a university-affiliated tertiary care medical center, 44 patients with symptomatic ankle OA for at least 6 months were recruited. Patients received a single injection of PRP (3 mL) into symptomatic ankles. The primary outcome was the change from baseline in the visual analog scale (VAS) pain (0-10 cm) at 6 months. Secondary outcomes included the Ankle Osteoarthritis Scale (AOS) score, American Orthopedic Foot and Ankle Society (AOFAS) hindfoot-ankle score, single-leg stance test (SLS), rescue analgesics consumption and patient satisfaction. Thirty-nine participants (88.64%) completed the study. Significantly improvement in the VAS and AOS was noted at 1-, 3-, and 6-month follow-ups (p < .001). The mean VAS pain decreased significantly from 4.1 ± 1.7 at baseline to 2.2 ± 1.9, 1.7 ± 1.5, and 1.8 ± 1.6 at 1, 3, and 6 months (p < .001). The mean total AOS score reduced by 1.5, 2.2, and 2.1 from baseline respectively postinjection (p < .001). The mean AOFAS hindfoot-ankle score improved from 80.3 points at baseline to 87.2, 91.6, and 89.7 points at 1, 3, and 6 months (p < .001). SLS tests improved significantly (p < .001) at each follow-up. Acetaminophen consumption dropped significantly (p < .001) and no serious adverse events occurred. The study showed promise for a single intraarticular injection of PRP in the treatment of ankle OA.

Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study.

BACKGROUND: SMS text messaging is an inexpensive, private, and scalable technology-mediated assessment mode that can alleviate many barriers faced by the safety net population to receive depression screening. Some existing studies suggest that technology-mediated assessment encourages self-disclosure of sensitive health information such as depressive symptoms while other studies show the opposite effect. OBJECTIVE: This study aimed to evaluate the validity of using SMS text messaging to screen depression and related conditions, including anxiety and functional disability, in a low-income, culturally diverse safety net primary care population. METHODS: This study used a randomized design with 4 study groups that permuted the order of SMS text messaging and the gold standard interview (INTW) assessment. The participants for this study were recruited from the participants of the prior Diabetes-Depression Care-management Adoption Trial (DCAT). Depression was screened by using the 2-item and 8-item Patient Health Questionnaire (PHQ-2 and PHQ-8, respectively). Anxiety was screened by using the 2-item Generalized Anxiety Disorder scale (GAD-2), and functional disability was assessed by using the Sheehan Disability Scale (SDS). Participants chose to take up the assessment in English or Spanish. Internal consistency and test-retest reliability were evaluated by using Cronbach alpha and intraclass correlation coefficient (ICC), respectively. Concordance was evaluated by using an ICC, a kappa statistic, an area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. A regression analysis was conducted to examine the association between the participant characteristics and the differences in the scores between the SMS text messaging and INTW assessment modes. RESULTS: Overall, 206 participants (average age 57.1 [SD 9.18] years; females: 119/206, 57.8%) were enrolled. All measurements except the SMS text messaging-assessed PHQ-2 showed Cronbach alpha values ≥.70, indicating acceptable to good internal consistency. All measurements except the INTW-assessed SDS had ICC values ≥0.75, indicating good to excellent test-retest reliability. For concordance, the PHQ-8 had an ICC of 0.73 and AUROC of 0.93, indicating good concordance. The kappa statistic, sensitivity, and specificity for major depression (PHQ-8 ≥8) were 0.43, 0.60, and 0.86, respectively. The concordance of the shorter PHQ-2, GAD-2, and SDS scales was poor to fair. The regression analysis revealed that a higher level of personal depression stigma was associated with reporting higher SMS text messaging-assessed PHQ-8 and GAD-2 scores than the INTW-assessed scores. The analysis also determined that the differences in the scores were associated with marital status and personality traits. CONCLUSIONS: Depression screening conducted using the longer PHQ-8 scale via SMS text messaging demonstrated good internal consistency, test-retest reliability, and concordance with the gold standard INTW assessment mode. However, care must be taken when deploying shorter scales via SMS text messaging. Further regression analysis supported that a technology-mediated assessment, such as SMS text messaging, may create a private space with less pressure from the personal depression stigma and therefore encourage self-disclosure of depressive symptoms. TRIAL REGISTRATION: ClinicalTrials.gov NCT01781013; https://clinicaltrials.gov/ct2/show/NCT01781013. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/12392.

Cost-Effectiveness of a Technology-Facilitated Depression Care Management Adoption Model in Safety-Net Primary Care Patients with Type 2 Diabetes.

BACKGROUND: The Diabetes-Depression Care-Management Adoption Trial is a translational study of safety-net primary care predominantly Hispanic/Latino patients with type 2 diabetes in collaboration with the Los Angeles County Department of Health Services. OBJECTIVES: To evaluate the cost-effectiveness of an information and communication technology (ICT)-facilitated depression care management program. METHODS: Cost-effectiveness of the ICT-facilitated care (TC) delivery model was evaluated relative to a usual care (UC) and a supported care (SC) model. TC added automated low-intensity periodic depression assessment calls to patients. Patient-reported outcomes included the 12-Item Short Form Health Survey converted into quality-adjusted life-years (QALYs) and the 9-Item Patient Health Questionnaire-calculated depression-free days (DFDs). Costs and outcomes data were collected over a 24-month period (-6 to 0 months baseline, 0 to 18 months study intervention). RESULTS: A sample of 1406 patients (484 in UC, 480 in SC, and 442 in TC) was enrolled in the nonrandomized trial. TC had a significant improvement in DFDs (17.3; P = 0.011) and significantly greater 12-Item Short Form Health Survey utility improvement (2.1%; P = 0.031) compared with UC. Medical costs were statistically significantly lower for TC (-$2328; P = 0.001) relative to UC but not significantly lower than for SC. TC had more than a 50% probability of being cost-effective relative to SC at willingness-to-pay thresholds of more than $50,000/QALY. CONCLUSIONS: An ICT-facilitated depression care (TC) delivery model improved QALYs, DFDs, and medical costs. It was cost-effective compared with SC and dominant compared with UC.

An agent-based simulation model of patient choice of health care providers in accountable care organizations.

Accountable care organizations (ACO) in the United States show promise in controlling health care costs while preserving patients' choice of providers. Understanding the effects of patient choice is critical in novel payment and delivery models like ACO that depend on continuity of care and accountability. The financial, utilization, and behavioral implications associated with a patient's decision to forego local health care providers for more distant ones to access higher quality care remain unknown. To study this question, we used an agent-based simulation model of a health care market composed of providers able to form ACO serving patients and embedded it in a conditional logit decision model to examine patients capable of choosing their care providers. This simulation focuses on Medicare beneficiaries and their congestive heart failure (CHF) outcomes. We place the patient agents in an ACO delivery system model in which provider agents decide if they remain in an ACO and perform a quality improving CHF disease management intervention. Illustrative results show that allowing patients to choose their providers reduces the yearly payment per CHF patient by $320, reduces mortality rates by 0.12 percentage points and hospitalization rates by 0.44 percentage points, and marginally increases provider participation in ACO. This study demonstrates a model capable of quantifying the effects of patient choice in a theoretical ACO system and provides a potential tool for policymakers to understand implications of patient choice and assess potential policy controls.

Comparative Effectiveness of a Technology-Facilitated Depression Care Management Model in Safety-Net Primary Care Patients With Type 2 Diabetes: 6-Month Outcomes of a Large Clinical Trial.

BACKGROUND: Comorbid depression is a significant challenge for safety-net primary care systems. Team-based collaborative depression care is effective, but complex system factors in safety-net organizations impede adoption and result in persistent disparities in outcomes. Diabetes-Depression Care-management Adoption Trial (DCAT) evaluated whether depression care could be significantly improved by harnessing information and communication technologies to automate routine screening and monitoring of patient symptoms and treatment adherence and allow timely communication with providers. OBJECTIVE: The aim of this study was to compare 6-month outcomes of a technology-facilitated care model with a usual care model and a supported care model that involved team-based collaborative depression care for safety-net primary care adult patients with type 2 diabetes. METHODS: DCAT is a translational study in collaboration with Los Angeles County Department of Health Services, the second largest safety-net care system in the United States. A comparative effectiveness study with quasi-experimental design was conducted in three groups of adult patients with type 2 diabetes to compare three delivery models: usual care, supported care, and technology-facilitated care. Six-month outcomes included depression and diabetes care measures and patient-reported outcomes. Comparative treatment effects were estimated by linear or logistic regression models that used generalized propensity scores to adjust for sampling bias inherent in the nonrandomized design. RESULTS: DCAT enrolled 1406 patients (484 in usual care, 480 in supported care, and 442 in technology-facilitated care), most of whom were Hispanic or Latino and female. Compared with usual care, both the supported care and technology-facilitated care groups were associated with significant reduction in depressive symptoms measured by scores on the 9-item Patient Health Questionnaire (least squares estimate, LSE: usual care=6.35, supported care=5.05, technology-facilitated care=5.16; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.02); decreased prevalence of major depression (odds ratio, OR: supported care vs usual care=0.45, technology-facilitated care vs usual care=0.33; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.007); and reduced functional disability as measured by Sheehan Disability Scale scores (LSE: usual care=3.21, supported care=2.61, technology-facilitated care=2.59; P value: supported care vs usual care=.04, technology-facilitated care vs usual care=.03). Technology-facilitated care was significantly associated with depression remission (technology-facilitated care vs usual care: OR=2.98, P=.04); increased satisfaction with care for emotional problems among depressed patients (LSE: usual care=3.20, technology-facilitated care=3.70; P=.05); reduced total cholesterol level (LSE: usual care=176.40, technology-facilitated care=160.46; P=.01); improved satisfaction with diabetes care (LSE: usual care=4.01, technology-facilitated care=4.20; P=.05); and increased odds of taking an glycated hemoglobin test (technology-facilitated care vs usual care: OR=3.40, P<.001). CONCLUSIONS: Both the technology-facilitated care and supported care delivery models showed potential to improve 6-month depression and functional disability outcomes. The technology-facilitated care model has a greater likelihood to improve depression remission, patient satisfaction, and diabetes care quality.

Randomized controlled trial of a coordinated care intervention to improve risk factor control after stroke or transient ischemic attack in the safety net: Secondary stroke prevention by Uniting Community and Chronic care model teams Early to End Disparities (SUCCEED).

BACKGROUND: Recurrent strokes are preventable through awareness and control of risk factors such as hypertension, and through lifestyle changes such as healthier diets, greater physical activity, and smoking cessation. However, vascular risk factor control is frequently poor among stroke survivors, particularly among socio-economically disadvantaged blacks, Latinos and other people of color. The Chronic Care Model (CCM) is an effective framework for multi-component interventions aimed at improving care processes and outcomes for individuals with chronic disease. In addition, community health workers (CHWs) have played an integral role in reducing health disparities; however, their effectiveness in reducing vascular risk among stroke survivors remains unknown. Our objectives are to develop, test, and assess the economic value of a CCM-based intervention using an Advanced Practice Clinician (APC)-CHW team to improve risk factor control after stroke in an under-resourced, racially/ethnically diverse population. METHODS/DESIGN: In this single-blind randomized controlled trial, 516 adults (≥40 years) with an ischemic stroke, transient ischemic attack or intracerebral hemorrhage within the prior 90 days are being enrolled at five sites within the Los Angeles County safety-net setting and randomized 1:1 to intervention vs usual care. Participants are excluded if they do not speak English, Spanish, Cantonese, Mandarin, or Korean or if they are unable to consent. The intervention includes a minimum of three clinic visits in the healthcare setting, three home visits, and Chronic Disease Self-Management Program group workshops in community venues. The primary outcome is blood pressure (BP) control (systolic BP <130 mmHg) at 1 year. Secondary outcomes include: (1) mean change in systolic BP; (2) control of other vascular risk factors including lipids and hemoglobin A1c, (3) inflammation (C reactive protein [CRP]), (4) medication adherence, (5) lifestyle factors (smoking, diet, and physical activity), (6) estimated relative reduction in risk for recurrent stroke or myocardial infarction (MI), and (7) cost-effectiveness of the intervention versus usual care. DISCUSSION: If this multi-component interdisciplinary intervention is shown to be effective in improving risk factor control after stroke, it may serve as a model that can be used internationally to reduce race/ethnic and socioeconomic disparities in stroke in resource-constrained settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT01763203 .

An agent-based simulation model to study accountable care organizations.

Creating accountable care organizations (ACOs) has been widely discussed as a strategy to control rapidly rising healthcare costs and improve quality of care; however, building an effective ACO is a complex process involving multiple stakeholders (payers, providers, patients) with their own interests. Also, implementation of an ACO is costly in terms of time and money. Immature design could cause safety hazards. Therefore, there is a need for analytical model-based decision-support tools that can predict the outcomes of different strategies to facilitate ACO design and implementation. In this study, an agent-based simulation model was developed to study ACOs that considers payers, healthcare providers, and patients as agents under the shared saving payment model of care for congestive heart failure (CHF), one of the most expensive causes of sometimes preventable hospitalizations. The agent-based simulation model has identified the critical determinants for the payment model design that can motivate provider behavior changes to achieve maximum financial and quality outcomes of an ACO. The results show nonlinear provider behavior change patterns corresponding to changes in payment model designs. The outcomes vary by providers with different quality or financial priorities, and are most sensitive to the cost-effectiveness of CHF interventions that an ACO implements. This study demonstrates an increasingly important method to construct a healthcare system analytics model that can help inform health policy and healthcare management decisions. The study also points out that the likely success of an ACO is interdependent with payment model design, provider characteristics, and cost and effectiveness of healthcare interventions.

Designing a Text Messaging Intervention to Improve Physical Activity Behavior Among Low-Income Latino Patients With Diabetes: A Discrete-Choice Experiment, Los Angeles, 2014-2015.

INTRODUCTION: Automated text messaging can deliver self-management education to activate self-care behaviors among people with diabetes. We demonstrated how a discrete-choice experiment was used to determine the features of a text-messaging intervention that are important to urban, low-income Latino patients with diabetes and that could support improvement in their physical activity behavior. METHODS: In a discrete-choice experiment from December 2014 through August 2015 we conducted a survey to elicit information on patient preferences for 5 features of a text-messaging intervention. We described 2 hypothetical interventions and in 7 pairwise comparisons asked respondents to indicate which they preferred. Respondents (n = 125) were recruited in person from a diabetes management program of a safety-net ambulatory care clinic in Los Angeles; clinicians referred patients to the research assistant after routine clinic visits. Data were analyzed by using conditional logistic regression. RESULTS: We found 2 intervention features that were considered by the survey respondents to be important: 1) the frequency of text messaging and 2) physical activity behavior-change education (the former being more important than the latter). Physical activity goal setting, feedback on physical activity performance, and social support were not significantly important. CONCLUSION: A discrete-choice experiment is a feasible way to elicit information on patient preferences for a text-messaging intervention designed to support behavior change. However, discrepancies may exist between patients' stated preferences and their actual behavior. Future research should validate and expand our findings.

School-based program options to promote physical activity: a response.

This commentary is a response to PM-14-1065R1 and addresses issues raised regarding research presented in PM-14-443.

Low-income minority patient engagement with automated telephonic depression assessment and impact on health outcomes.

PURPOSE: We investigated dimensions of low-income minority patient engagement in the context of diabetes-depression care-management with automated telephone assessment (ATA) calls as a facilitator. METHODS: Mixed method analyses (including regression analyses and coding of interviews) were used to examine patient engagement with technology, assess its impact on health outcomes and satisfaction with care, and analyze barriers to engagement. Patient engagement was measured by analyzing patient's ATA call response rates. We then evaluated those results in the context of the outcomes of the broader clinical trial, which compared three study arms. RESULTS: Average completed call rate throughout the study is about 50 % and decreases after 6 months. The biggest barrier to patient engagement is timing. Patient engagement levels differ by baseline depression status and have no significant effect on health outcomes and satisfaction with care at 6, 12, and 18 months. However, from the preliminary clinical trial results, the arm in which the ATA system is implemented has higher satisfaction with depression care than the two control arms. Thus, it is more likely that technology positively affects satisfaction with depression care outcomes through provider engagement rather than patient engagement. CONCLUSIONS: The study's patient engagement results and identified barriers would be useful to aid the design and implementation of future automated screening and monitoring systems to optimize patient engagement. The results also suggest that implementing a technology-supported care-management might not result in outcome disparities among patients with different levels of patient engagement.

Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making.

INTRODUCTION: Depression is a common but often undiagnosed comorbid condition of people with diabetes. Mass screening can detect undiagnosed depression but may require significant resources and time. The objectives of this study were 1) to develop a clinical forecasting model that predicts comorbid depression among patients with diabetes and 2) to evaluate a model-based screening policy that saves resources and time by screening only patients considered as depressed by the clinical forecasting model. METHODS: We trained and validated 4 machine learning models by using data from 2 safety-net clinical trials; we chose the one with the best overall predictive ability as the ultimate model. We compared model-based policy with alternative policies, including mass screening and partial screening, on the basis of depression history or diabetes severity. RESULTS: Logistic regression had the best overall predictive ability of the 4 models evaluated and was chosen as the ultimate forecasting model. Compared with mass screening, the model-based policy can save approximately 50% to 60% of provider resources and time but will miss identifying about 30% of patients with depression. Partial-screening policy based on depression history alone found only a low rate of depression. Two other heuristic-based partial screening policies identified depression at rates similar to those of the model-based policy but cost more in resources and time. CONCLUSION: The depression prediction model developed in this study has compelling predictive ability. By adopting the model-based depression screening policy, health care providers can use their resources and time better and increase their efficiency in managing their patients with depression.