Psychometric evaluation of the 8-item Altarum Consumer Engagement (ACE) Measure™ in community-dwelling adults in Singapore.

BACKGROUND: A valid and reliable measure is essential to assess patient engagement and its impact on health outcomes. This study aimed to examine the psychometric properties of the 8-item Altarum Consumer Engagement Measure™ (ACE Measure) among English-speaking community-dwelling adults in Singapore. METHODS: This cross-sectional study involved 400 randomly selected community-dwelling adults (mean age: 49.7 years, 50.0% were female, 72.3% were Chinese) who completed the English version of the 8-item ACE Measure independently. The item-level statistics were described. The internal consistency of the measure was measured by Cronbach alpha and item-rest correlations. Validity of the tool was assessed by 1) factorial validity using confirmatory factor analysis (CFA), 2) hypothesis-testing validity by correlating ACE subscales (Commitment and Navigation) with health-related outcomes, and 3) criterion validity against the Patient Activation Measure and Health Confidence Measure. RESULTS: There was no floor or ceiling effect for Commitment and Navigation subscales, and the Cronbach alpha for each subscale was 0.76 and 0.54, respectively. The two-factor structure was confirmed by CFA. In general, Commitment and Navigation subscales were positively correlated with frequency of activity participation (rho = 0.30 - 0.33) and EQ-5D visual analog scale (rho = 0.15 - 0.30). Individuals who perceived better health than peers had higher subscale scores (p < 0.01). Each subscale score had moderate and positive correlations with activation score (rho = 0.48 - 0.55) and health confidence score (rho = 0.35 - 0.47). CONCLUSIONS: The two-subscale ACE Measure demonstrated good construct validity in English-speaking Singapore community-dwelling adults. Evidence in internal consistency was mixed, indicating further investigation.

Deaths in dementia: a scoping review of prognostic variables.

OBJECTIVES: To identify the types of factors included in research examining mortality in patients with dementia, and to stratify the identified factors by care settings. DESIGN: We systematically searched PubMed, Embase, PsycINFO and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases, and identified grey literature from the Networked Digital Library of Theses and Dissertations, Open Grey and Grey Literature Report. Two authors independently screened for eligibility of studies. Independent reviewers extracted relevant study information. We conducted a narrative synthesis of the data. RESULTS: We identified 8254 articles, of which 94 met the inclusion criteria. More than half (n=53) were published between 2009 and 2018 with half from Europe. Studies were conducted across hospices/nursing homes (n=25), hospital (n=23), outpatient clinics (n=21), mixed settings (n=15) and in the community (n=10). Nearly 60% adopted a prospective cohort study design with 87% performing multivariable analysis. Overall, 239 variables were identified and classified into six themes-individual factors, health status, functional ability, cognition and mental health, treatments and health system factors. Although a general set of factors were common across all studies, when stratified by care settings, variations were seen in the specific variables included. CONCLUSION: Identifying prognostic variables relevant to the dementia population in each setting is key to facilitate appropriate care plans and to ensure timely access to palliative care options. Future research should also focus on ensuring the replicability of prognostic models and to generate a better understanding of the direct and interacting influence of the identified factors on mortality.

Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.

BACKGROUND: For population health management, it is important to have health indices that can monitor prevailing health trends in the population. Traditional health indices are generally measurable at different geographical levels with varied number of health dimensions. The aim of this work was to develop and validate a scalable and extendable multi-dimensional health index based on individual data. METHODS: We defined health to be made up of five different domains: Physical, Mental, Social, Risk, and Healthcare utilization. Item response theory was used to develop models to compute domain scores and a health index. These were normalized to represent an individual's health percentile relative to the population (0 = worst health, 100 = best health). Data for the models came from a longitudinal health survey on 1,942 participants. The health index was validated using age, frailty, post-survey one-year healthcare utilization and one-year mortality. RESULTS: The Spearman rho between the health index and age, frailty and post-survey one-year healthcare utilization were -0.571, -0.561 and -0.435, respectively, with all p<0.001. The area under the Receiver Operating Characteristic curve (AUROC) for post-survey one-year mortality was 0.930. An advantage of the health index is that it can be calculated using different sets of questions and the number of questions can be easily expanded. CONCLUSION: The health index can be used at the individual, program, local, regional or national level to track the state of health of the population. When used together with the domain scores, it can identify regions with poor health and deficiencies within each of the five health domains.

Effects of chronic diseases on health-related quality of life and self-rated health among three adult age groups.

Little is known about whether there is any difference in associations of chronic diseases with health-related quality of life and self-rated health across age groups. The purpose of the present study was to examine the associations of one specific and multiple chronic diseases with health-related quality of life and self-rated health (measured using the 5-level EQ-5D version) in three age groups: young (21-44 years), middle-aged (45-64 years), and older adults (≥65 years). Secondary data analysis of 1932 participants in the Population Health Index Survey was performed. Linear regression results showed that different chronic diseases had a characteristic effect on health-related quality of life and self-rated health among different age groups. The presence of a single chronic disease was associated with lower health-related quality of life and self-rated health in young adults. Multi-morbidity was consistently associated with decreased health-related quality of life and self-rated health in all age groups. Our findings suggest that although young adults have a lower prevalence of chronic diseases, their impacts on health-related quality of life and self-rated health can be as significant as that in middle-aged and older adults.

Social isolation, loneliness and their relationships with depressive symptoms: A population-based study.

OBJECTIVES: To assess the relationship between various social isolation indicators and loneliness, and to examine the differential associations that social isolation indicators, loneliness have with depressive symptoms. METHODS: Baseline data for 1,919 adults (aged 21 years and above) from a representative health survey in the Central region of Singapore was used for this study. The association between social isolation indicators (marital status, living arrangement, social connectedness with relatives and friends) and loneliness (the three-item UCLA Loneliness) were assessed, and their differential associations with depressive symptoms (the Patient Health Questionnaire-9) were examined using multiple linear regression, controling for relevant covariates. RESULTS: There was significant overlap between loneliness and social isolation. Social connectedness with relatives and friends were mildly correlated with loneliness score (|r| = 0.14~0.16). Social isolation in terms of weak connectedness with relatives and with friends and loneliness were associated with depressive symptoms even after controling for age, gender, employment status and other covariates. The association of loneliness with depressive symptoms (ß = 0.33) was independent of and stronger than that of any social isolation indicators (|ß| = 0.00~0.07). CONCLUSIONS: The results of the study establishes a significant and unique association of different social isolation indicators and loneliness with depressive symptoms in community-dwelling adults aged 21 and above.