METHODS: The study applied mixed-method embedded design to analyze both quantitative and qualitative data. Quantitative approach was used to evaluate sustainability perception from 20 intervention clinics via self-reported assessment form whereas qualitative data were obtained through in-depth interview (IDI) and focus group discussions (FGDs) 14 health care professionals participated in IDI session and were either care coordinators, liaison officers (LOs)/clinic managers, or medical officers-in-charge for the clinic's intervention. Nine FGDs conducted comprised 58 HCPs from various categories.
RESULTS: HCPs from all the 20 clinics involved responded to each listed Enhanced Primary Healthcare (EnPHC) intervention components as being implemented but the perceived sustainability of these implementation varies between them. Quantitative feedback showed sustainable interventions included risk stratification, non-communicable disease (NCD) screening form, referral within clinics and hospitals, family health team (FHT), MTAC services and mechanisms and medical adherence status. Qualitative feedback highlighted implementation of each intervention components comes with its challenges, and most of it are related to inadequate resources and facilities in clinic. HCPs made initiatives to adapt based on clinical setting to implement the interventions at best level possible, whereby this seems to be one of the core values for sustainability.
CONCLUSION: Overall perceptions among HCPs on sustainability of EnPHC interventions are highly influenced by current experiences with existing resources. Components perceived to have inadequate resources are seen as a challenge to sustain. It's crucial for stakeholders to understand implications affecting implementation process if concerns raised are not addressed and allocation of needed resources to ensure overall successfulness and long term sustainability.
METHODS: Case information from 192 children was collected from outpatient and inpatient clinics using a survey questionnaire. These included 90 pediatric burn cases and 102 controls who were children without burns. A stepwise logistic regression analysis was used to determine the risk factors for pediatric burns in order to establish a model. The goodness-of-fit for the model was assessed using the Hosmer and Lemeshow test as well as receiver operating characteristic and internal calibration curves. A nomogram was then used to analyze the contribution of each influencing factor to the pediatric burns model.
RESULTS: Seven variables, including gender, age, ethnic minority, the household register, mother's employment status, mother's education and number of children, were analyzed for both groups of children. Of these, age, ethnic minority, mother's employment status and number of children in a household were found to be related to the occurrence of pediatric burns using univariate logistic regression analysis (p 0.2 and variance inflation factor <5 showed that age was a protective factor for pediatric burns [odds ratio (OR) = 0.725; 95% confidence interval (CI): 0.665-0.801]. Compared with single-child parents, those with two children were at greater risk of pediatric burns (OR = 0.389; 95% CI: 0.158-0.959). The ethnic minority of the child and the mother's employment status were also risk factors (OR = 6.793; 95% CI: 2.203-20.946 and OR = 2.266; 95% CI: 1.025-5.012, respectively). Evaluation of the model used was found to be stable. A nomogram showed that the contribution in the children burns model was age > mother's employment status > number of children > ethnic minority.
CONCLUSIONS: This study showed that there are several risk factors strongly correlated to pediatric burns, including age, ethnic minority, the number of children in a household and mother's employment status. Government officials should direct their preventive approach to tackling the problem of pediatric burns by promoting awareness of these findings.
OBJECTIVE: To identify subgroups of COPD with distinct phenotypes, evaluate the distribution of phenotypes in four related regions and calculate the 1-year change in lung function and quality of life according to subgroup.
METHODS: Using clinical characteristics, we performed factor analysis and hierarchical cluster analysis in a cohort of 1676 COPD patients from 13 Asian cities. We compared the 1-year change in forced expiratory volume in one second (FEV1), modified Medical Research Council dyspnoea scale score, St George's Respiratory Questionnaire (SGRQ) score and exacerbations according to subgroup derived from cluster analysis.
RESULTS: Factor analysis revealed that body mass index, Charlson comorbidity index, SGRQ total score and FEV1 were principal factors. Using these four factors, cluster analysis identified three distinct subgroups with differing disease severity and symptoms. Among the three subgroups, patients in subgroup 2 (severe disease and more symptoms) had the most frequent exacerbations, most rapid FEV1 decline and greatest decline in SGRQ total score.
CONCLUSION: Three subgroups with differing severities and symptoms were identified in Asian COPD subjects.