Implementation and evaluation of the peer-training program for village health volunteers to improve chronic disease management among older adults in rural Thailand

Building a learning health care community in rural and remote areas: a systematic review

BACKGROUND

A Learning Health Care Community (LHCC) is a framework to enhance health care through mutual accountability between the health care system and the community. LHCC components include infrastructure for health-related data capture, care improvement targets, a supportive policy environment, and community engagement. The LHCC involves health care providers, researchers, decision-makers, and community members who work to identify health care needs and address them with evidence-based solutions. The objective of this study was to summarize the barriers and enablers to building an LHCC in rural areas.

METHODS

A systematic review was conducted by searching electronic databases. Eligibility criteria was determined by the research team. Published literature on LHCCs in rural areas was systematically collected and organized. Screening was completed independently by two authors. Detailed information about rural health care, activities, and barriers and enablers to building an LHCC in rural areas was extracted. Qualitative analysis was used to identify core themes.

RESULTS

Among 8169 identified articles, 25 were eligible. LHCCs aimed to increase collaboration and co-learning between community members and health care providers, integrate community feedback in health care services, and to share information. Main barriers included obtaining adequate funding and participant recruitment. Enablers included meaningful engagement of stakeholders and stakeholder collaboration.

CONCLUSIONS

The LHCC is built on a foundation of meaningful use of health data and empowers health care practitioners and community members in informed decision-making. By reducing the gap between knowledge generation and its application to practice, the LHCC has the potential to transform health care delivery in rural areas.

Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional study

Objective

To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.

Methods

The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ2 and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.

Results

The χ2 analyses showed that age and eating behavior were the predicting features of UHTN occurrence. The binary logistic regression revealed that taking food supplements/vitamins, using seasoning powder, and eating bean products were related to normotensive and hypertensive classifications. The RF, XGB, and SVM accuracy were 0.90, 0.89, and 0.57, respectively. The SHAP identified the importance of salt intake and food/vitamin supplements. Vitamin B6, B12, and selenium in the UHTN were lower than in the normotensive group.

Conclusion

ML indicates that salt intake, soybean consumption, and food/vitamin supplements are primary factors for UHTN classification in older adults.

Community-embedded follow-up management intervention for geriatric primary care: a mixed-methods study of an integrated health services model

BACKGROUND

To propose a community-embedded follow-up management model to provide health services for elderly patients with osteoporosis who live alone.

METHODS

Researchers randomly selected 396 people with osteoporosis living alone from five communities in Nantong, China, for the study. These participants were randomly assigned to control and intervention groups. Twenty-four community physicians in five communities provided professional support based on a community-embedded follow-up management model. Participants completed quantitative questionnaires at baseline and after the 6-month follow-up intervention, and some participants underwent semi-structured face-to-face interviews. The primary outcome is the effectiveness of the community-embedded follow-up management model in improving the quality of life of elderly patients with osteoporosis living alone. Based on an objective quantitative assessment, the qualitative study explains and adds essential components of this community-based follow-up management model.

RESULTS

The quantitative study showed that scores in physical functioning, ability to perform daily activities, self-efficacy, and mental status were significantly improved in the intervention group compared to the control group (p < 0.05). The most significant improvements were found in "mental status" (p = 0.012) and "self-care skills" (p = 0.003). The qualitative study reported the essential elements of a community healthcare model for older people living alone with osteoporosis, including professional support, personalized services, social support, and empowerment.

CONCLUSIONS

Community-embedded follow-up management meets the need for elderly patients with osteoporosis living alone. It helps to improve health perception, promote physical and mental health, and optimize the quality of life in this population. Personalized services and professional support are two major contributing factors to effective embedded follow-up management in the community.

A 9-year retrospective cohort study of the monitoring and screening of childhood developmental delay in Thailand

BACKGROUND

Developmental delay in early childhood can have negative long-term cognitive and psychiatric sequelae, along with poor academic achievement, so early screening and surveillance are paramount. The aim of this study is to evaluate the impact of screening and surveillance on child developmental delay using the Developmental Surveillance and Promotion Manual (DSPM) and the Thai Early Developmental Assessment for Intervention (TEDA4I) for Thai children aged 0-5 years old.

METHODS

Data were obtained from the routine developmental screening for specific disorders at ages 9, 18, 30, 42 and 60 months conducted using DSPM and TEDA4I from 2013 to 2021. Descriptive statistics were used to analyse the data, and the results are visualised graphically herein.

RESULTS

Only 56% of the children were screened for child developmental delay using DSPM. The proportion of children screened increased from <1% in 2013 to 90% in 2021. Suspected developmental delay prevalence increased significantly from 3.91% in 2013-2015 to 10.00% in 2016-2018 and 26.48% in 2019-2021. Moreover, of the children with suspected developmental delay who received developmental stimulation within a month, only 87.9% returned for follow-up visits when they were evaluated again using TEDA4I to ascertain any abnormalities and specific areas of deficit. The overall proportion of children diagnosed with developmental delay was 1.29%. During the pandemic, the proportion of screening tests for child developmental delay at routine vaccination visits and follow-ups decreased but was still at least 80% in each region.

CONCLUSIONS

Since 1%-3% of children have suspected developmental delay, early detection is key to treating it as soon as possible. We anticipate that our findings will raise awareness in parents and caregivers about childhood developmental delay and lead to the implementation of early intervention and follow-up at the rural level in Thailand.

Developing a Depression Care Model for the Hill Tribes: A Family- and Community-Based Participatory Research

A high prevalence of depression has been detected among individuals from the hill tribes in Thailand. However, there are no proper interventions to address this problem. Using a community-based participatory research (CBPR) design, the study team developed a model of depression care for this population. The study involved 45 people in the model development and 65 people in the model testing, who were patients, family members, village health volunteers (VHVs), community and religious leaders, healthcare personnel, NGOs, and local administrative staff. The model development was divided into three phases: understanding the current situation of depression and care, model development, and evaluation of its effectiveness using psychological and relevant outcomes. Questionnaires, observations, focus groups, and in-depth interviews were used for data collection, and content analysis was employed for qualitative data. The Wilcoxon signed-rank test was used to analyze changes in VHVs' knowledge and skills before and after training. The resulting model, "SMILE," consists of stakeholders' readiness (S), external and internal motivations (M), interpersonal relationship (I), life and community assets (L), and empowerment (E). VHVs underwent training on the model, and after training, their knowledge increased significantly from 3.50 ± 1.14 to 8.28 ± 0.81 (p < 0.001). Moreover, their basic counselling and depression screening skills showed improvement from 3.39 ± 1.23 to 7.64 ± 3.76 (p < 0.001). The developed model can be applied to other hill tribe communities in Northern Thailand to improve depression care.