The validation of culturally appropriate scales to assess the family health climate in a multi-ethnic Asian population.

Background: The Family Health Climate (FHC) is a family environment attribute postulated to influence the health behaviors of family members. It can be measured by domain scales for physical activity (FHC-PA) and nutrition (FHC-NU), which have been validated and used to identify health climate patterns in families in Western populations. To extend the use of the scales to Asian settings, this study aimed to adapt and validate the instruments for use in the multi-ethnic population of Singapore, accounting for language and cultural differences. Methods: In Part A (n = 40) to adapt the scales for the Singapore population, we performed cognitive interviews, face validity testing and pre-testing of the instruments (n = 40). Besides English, the scales were translated into Chinese and Malay. In Part B (n = 400), we performed exploratory and confirmatory factor analyses respectively on two random samples. We also tested for item discriminant validity, internal consistency reliability, construct validity, and measurement invariance. Results: The findings from the cognitive interviews in Part A led to scale adaptations to accommodate cultural and linguistic factors. In Part B, EFA on Sample I resulted in a three-factor model for the PA scale (accounting for 71.2% variance) and a four-factor model for the NU scale (accounting for 72.8% variance). CFA on Sample II indicated acceptable model fits: FHC-PA: χ2 = 192.29, df = 101, p < 0.001, χ2/df = 1.90; SRMR = 0.049; RMSEA = 0.067; CFI = 0.969; TLI = 0.963; FHC-NU: χ2 = 170.46, df = 98, p < 0.001, χ2/df = 1.74; SRMR = 0.036; RMSEA = 0.061; CFI = 0.967; TLI = 0.960. The scores of family members demonstrated significant agreement on the FHC-PA (Sg) [ICC(2, 2) = 0.77] and FHC-NU (Sg) [ICC(2, 2) = 0.75] scales. Findings suggest good evidence for item discriminant validity, internal consistency reliability, construct validity, and measurement invariance. Short versions of the scales were also developed. Conclusion: We adapted, translated and validated the scales for assessing the health climate of families in Singapore, including the development of short versions. The results showed good psychometric properties and the constructs had significant relationships with health behaviors and routines. Improving our understanding of family influences on individual health behavior will be important in developing multi-level strategies for health promotion and chronic disease prevention.

Towards a Parsimonious Pathway Model of Modifiable and Mediating Risk Factors Leading to Diabetes Risk.

Modifiable risk factors are of interest for chronic disease prevention. Few studies have assessed the system of modifiable and mediating pathways leading to diabetes mellitus. We aimed to develop a pathway model for Diabetes Risk with modifiable Lifestyle Risk factors as the start point and Physiological Load as the mediator. As there are no standardised risk thresholds for lifestyle behaviour, we derived a weighted composite for Lifestyle Risk. Physiological Load was based on an index using clinical thresholds. Sociodemographics are non-modifiable risk factors and were specified as covariates. We used structural equation modeling to test the model, first using 2014/2015 data from the Indonesian Family Life Survey. Next, we fitted a smaller model with longitudinal data (2007/2008 to 2014/2015), given limited earlier data. Both models showed the indirect effects of Lifestyle Risk on Diabetes Risk via the mediator of Physiological Load, whereas the direct effect was only supported in the cross-sectional analysis. Specifying Lifestyle Risk as an observable, composite variable incorporates the cumulative effect of risk behaviour and differentiates this study from previous studies assessing it as a latent construct. The parsimonious model groups the multifarious risk factors and illustrates modifiable pathways that could be applied in chronic disease prevention efforts.

Examining the heterogeneity inexcess risks of coronary heart disease, stroke, dialysis, and lower extremity amputation associated with type 2 diabetes mellitus across demographic subgroups in an Asian population: A population-based matched cohort study.

AIMS: To examine whether the excess risks of coronary heart disease (CHD), stroke, dialysis, and lower extremity amputation (LEA) associated with type 2 diabetes mellitus (T2DM) differ across age, sex, and ethnicity in Singapore. METHODS: Using a 10-year administrative data, we matched individuals with T2DM using nearest neighbour matching, to those without, in 1:2 ratio. To examine whether the excess risks were heterogeneous across age, sex, and ethnicity, we generated interaction terms of age, sex, and ethnicity with T2DM status in Cox proportional hazard (PH) models. RESULTS: The main analyses included ~1 million person years, comprising 66,099 and 120,485 individuals with and without T2DM, respectively. The associations of T2DM with CHD and dialysis, split into two time periods to address violation of PH assumption, were higher with older age in short-term but lower with older age in long-term. The association of T2DM with stroke and LEA were lower with older age. The associations of T2DM with CHD and stroke were also consistently higher in women than men. The associations of T2DM with LEA were higher in ethnic Malays than ethnic Chinese. CONCLUSIONS: The excess risks of CHD, stroke, dialysis, and LEA associated with T2DM were heterogeneous across some demographic subgroups.

Differential Health Care Use, Diabetes-Related Complications, and Mortality Among Five Unique Classes of Patients With Type 2 Diabetes in Singapore: A Latent Class Analysis of 71,125 Patients.

OBJECTIVE: With rising health care costs and finite health care resources, understanding the population needs of different type 2 diabetes mellitus (T2DM) patient subgroups is important. Sparse data exist for the application of population segmentation on health care needs among Asian T2DM patients. We aimed to segment T2DM patients into distinct classes and evaluate their differential health care use, diabetes-related complications, and mortality patterns. RESEARCH DESIGN AND METHODS: Latent class analysis was conducted on a retrospective cohort of 71,125 T2DM patients. Latent class indicators included patient's age, ethnicity, comorbidities, and duration of T2DM. Outcomes evaluated included health care use, diabetes-related complications, and 4-year all-cause mortality. The relationship between class membership and outcomes was evaluated with the appropriate regression models. RESULTS: Five classes of T2DM patients were identified. The prevalence of depression was high among patients in class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden) and class 5 (older patients with moderate-to-long T2DM duration and high disease burden with end-organ complications). They were the highest tertiary health care users. Class 5 patients had the highest risk of myocardial infarction (hazard ratio [HR] 12.05, 95% CI 10.82-13.42]), end-stage renal disease requiring dialysis initiation (HR 25.81, 95% CI 21.75-30.63), stroke (HR 19.37, 95% CI 16.92-22.17), lower-extremity amputation (HR 12.94, 95% CI 10.90-15.36), and mortality (HR 3.47, 95% CI 3.17-3.80). CONCLUSIONS: T2DM patients can be segmented into classes with differential health care use and outcomes. Depression screening should be considered for the two identified classes of patients.

Epidemiologic Characteristics of Multimorbidity and Sociodemographic Factors Associated With Multimorbidity in a Rapidly Aging Asian Country.

Importance: Multimorbidity is a growing health care problem in aging societies and is strongly associated with epidemiologic characteristics and sociodemographic factors. Knowledge of these associations is important for the design of effective preventive and management strategies. Objectives: To determine the association between multimorbidity and sociodemographic factors (age, socioeconomic status [SES], sex, and race/ethnicity) and the association between mental health diseases and physical diseases, as well as their implications for the types and costs of health care use. Design, Setting, and Participants: This population-based cross-sectional study used deidentified Singapore Eastern Regional Health System data collected between January 1, 2012, and December 31, 2016. Patients who were alive as of January 1, 2016, and residing in the Regional Health System region in 2016 (N = 1 181 024) were included. Patients who had no year of birth records (n = 573), were born in 2017 (n = 93), or died before January 1, 2016 (n = 47 322), were excluded. Main Outcomes and Measures: Multimorbidity, age, sex, SES, mental health, race/ethnicity, and health care use. Results: In the study population of 1 181 024 individuals, the mean (SD) age was 39.6 (22.1) years, 51.2% were women, 70.1% were Chinese, 7.1% were Indian, 13.5% were Malayan, and 9.3% were other races/ethnicities. Multimorbidity, present in 26.2% of the population, was more prevalent in female (26.8%; 95% CI, 26.7%-26.9%) than in male (25.6%; 95% CI, 25.5%-25.7%) patients and among patients with low SES (41.6%) than those with high SES (20.1%). Mental health diseases were significantly more prevalent among individuals with low SES (5.2%; 95% CI, 5.1%-5.2%) than high SES (2.1%; 95% CI, 2.0%-2.1%; P < .001). The 3 most prevalent disease combinations were chronic kidney disease and hypertension, chronic kidney disease and lipid disorders, and hypertension and lipid disorders. Although chronic kidney disease, hypertension, lipid disorders, and type 1 and/or type 2 diabetes-related diseases had a low cost per capita, the large number of patients with these conditions caused the overall proportion of the cost incurred by health care use to be more than twice that incurred in other diseases. Conclusions and Relevance: These findings emphasize the association between multimorbidity and sociodemographic factors such as increasing age, lower SES, female sex, and increasing number of mental disorders. Health care policies need to take sociodemographic factors into account when tackling multimorbidity in a population.