Study background and subject
This cross-sectional study is a part of baseline well-being assessment of the Jockey Club Community eHealth Care Project, a telecare programme promoting preventive healthcare and self-management. Specifically, the programme consists of (1) data sharing to a nurse team by cloud technology for proactive monitoring and follow-up, (2) regular health measurement with smart cards for recording, and (3) nursing caring call and regular outreach visits by a multi-disciplinary team including nurses, health workers, and social workers. All community-dwelling older adults aged 60 and above were eligible to participate in this telecare programme. We recruited participants in 24 elderly centres located at 12 districts of Hong Kong, between September 2016 and October 2017.
Data collection procedure
Each participant was given a tablet to complete an electronic survey, in a group of 6 to 8. At least one staff member of each elderly centre was trained to lead the groups by going through each question with the participants. Other staff members would further assist the participants with survey completion if necessary. Data collected were automatically uploaded to and stored in the cloud.
Geriatric syndromes included in this survey were (a) frailty, (b) sarcopenia, (c) mild cognitive impairment, and (d) urinary incontinence. Frailty was measured by the FRAIL scale , which consists of five items including fatigue, resistance, ambulation, illness, and loss of weight. The scores ranged from 0 (best) to 5 (worst), representing frail (3–5), pre-frail (1–2), or robust (0) status. The validated SARC-F scale  was adopted for sarcopenia screening. The scale consisted of five components, including strength, assistance with walking, rise from a chair, climb stairs, and fall. The scores ranged from 0 to 10, with 0 to 2 points for each component. Scores ≥ 4 indicated the presence of sarcopenia. Mild cognitive impairment was screened with the validated five-item Abbreviated Memory Inventory for Chinese (AMIC) . The scores ranged from 0 to 5, with 1 point for each item. Scores ≥ 3 indicated the presence of mild cognitive impairment. Older adults with any one of the syndromes were considered as living with geriatric syndrome.
Data regarding the presence of chronic diseases diagnosed by doctors was obtained through participants’ self-report. The chronic diseases included (a) hypertension, (b) diabetes mellitus, (c) hypercholesterolemia, (d) heart disease, (e) stroke, (f) chronic obstructive pulmonary disease, and (g) renal disease. Multimorbidity was defined as having two or more chronic diseases.
Disability was determined by the validated Chinese-version five-item Instrumental Activity of Daily Living (IADL) adopted from the Lawton IADL scale . The IADL tasks examined include ability to use telephone, shopping, food preparation, transportation, and ability to handle finance. Participants who had difficulty in performing any one of the activities were classified as living with disability.
Participants reported their healthcare use for any causes in the past 12 months, including (a) hospital admission, (b) general outpatient clinic (GOPC) attendance, and (c) specialist outpatient clinic (SOPC) attendance by responding to a “yes” “no” answer.
Sociodemographic variables including age, gender, marital status, education attainment, and living arrangement were recorded.
Descriptive statistics including prevalence of geriatric syndromes, multimorbidity, disability, and coexistence of these three conditions were computed. Pearson’s chi-squared test for trend was performed to examine trends in the prevalence rates across three age groups (60–69, 70–79, and ≥ 80). Additionally, strength of interrelations among the three conditions was determined by Cramer’s V. A Cramer’s V of < .1 was considered as weak, .1–.3 as moderate, > .3 as strong.
Multiple logistic regression was conducted to explore associations of the three conditions with hospital admission, GOPC attendance, and SOPC attendance in two approaches. First, geriatric syndromes, multimorbidity, and disability were included in the multivariate analysis for each of the three types of healthcare use. The analyses were then stratified by the three age groups. Second, associations of number and combination of conditions with the healthcare use were explored. Pearson’s chi-squared test for trend was performed to examine trends in healthcare use over increasing number of conditions. All multivariate analyses were further adjusted for the sociodemographic variables. Cases with missing data of any variables (n = 3) were excluded from the regression analyses.
Adjusted odds ratios (AORs) and 95% confidence intervals (95% CI) were reported. A p-value of < .05 was considered as statistically significant. All statistical analyses were performed by IBM SPSS Statistics 24 and weighted for age and gender.