Settings and study design
The 10/66 Dementia Research Group’s (10/66 DRG) population-based studies of ageing and dementia in LMICs comprised baseline surveys of all older people aged 65 years and over living in geographically-defined catchment areas in seven countries, with a follow-up 3–5 years later. For the current analyses, this comprises urban and rural sites in China, Mexico and Peru, and urban sites in Cuba, Dominican Republic, Venezuela and India. Baseline population-based surveys were carried out between 2003 and 2007, and incidence wave follow-up assessments between 2008 and 2010. For India, the follow-up comprised a mortality sweep only. The design of the baseline and follow-up phases of the 10/66 DRG research program have been described in detail elsewhere . Here, we will describe aspects directly relevant to the analyses presented in this paper.
Participants were recruited following informed signed consent. Persons with dementia who lacked capacity for consent were recruited on the basis of a relative’s signed agreement. Illiterate persons were read the information sheet and consent form, and invited to express their consent verbally, which was witnessed. Studies were approved by local ethical committees as well as by the ethical committee of the Institute of Psychiatry, King’s College London.
Exposures – Frailty
We assessed seven indicators of frailty, namely exhaustion, weight loss, slow walking speed, low energy expenditure (physical inactivity), undernutrition, and cognitive and sensory impairment. These were operationalized as follows:
Exhaustion: assessed using a single item (Q.48.1) from the Geriatric Mental Status examination. Participants who reported feeling worn out or exhausted were considered to have this frailty .
Weight loss: Self-reported weight loss was assessed using a single item from the Geriatric Mental State (Q53.1) “Have you lost any weight in the last three months?” Those reporting weight loss of 10 lbs (4.5 kg) or more in last three months were considered to have this frailty .
Slow walking speed: assessed using a standard timed walking test in which the participant was asked to walk 5 metres at usual speed, turn, and return to the starting point. Those taking 16 seconds or longer to complete the task were considered to have a slow walking speed.
Low energy expenditure: in response to the question “Taking into account both work and leisure, would you say that you are: very, fairly, not very or not at all physically active?” Those that rated themselves not at all physical active were considered physically inactive.
Undernutrition: assessed through the measurement of mid-upper arm circumference, those with a circumference of <22 cm were considered to be frail. This cut-point is used in the Mini Nutritional Assessment® to identify the most severe level of undernutrition according to this index .
Cognitive impairment: cognitive function was assessed using the Community Screening Instrument for Dementia COGSCORE, which tests multiple domains of cognitive function, and has been found to have robust cross-cultural measurement properties in the 10/66 study sites . Frailty was defined according to the higher of two possible cut-points (29.5, for ‘possible dementia’) in order to identify cognitive impairment beyond dementia.
Sensory impairment: assessed according to self-report (from two separate items) of having ‘eyesight problems’ or ‘hearing problems or deafness’, which interfered with activities to at least some extent.
Physical frailty model
Fried et al.’s physical frailty model  proposes five specific and measurable indicators to identify frailty (exhaustion, weight loss, weak grip strength, slow walking speed, and low energy expenditure). Individuals are identified as frail if they meet three or more of the five criteria, as intermediate if they meet one or two, and as non-frail if they meet none of the five criteria . We applied our exhaustion, weight loss, slow walking speed, and low energy expenditure indicators. Since handgrip strength was not measured we considered participants as frail if they fulfilled two or more of the four frailty indicators.
The approach developed in the Alameda County study comprised 16 self-reported items grouped into four domains of functioning (physical, nutrition, cognitive, and sensory) . The physical functioning domain included dizziness, loss of balance, weakness in the arms, and weakness in the legs. The nutritive functioning domain included loss of appetite and unexplained weight loss. The cognitive functioning domain included memory and attention difficulties. The sensory functioning domain included vision and hearing difficulties in different situations. Participants were classified as frail if they had difficulties in two or more domains. We applied our slow walking speed, undernutrition, cognitive impairment, and sensory impairment indicators.
Covariates – measures of socio-demographic circumstances, morbidity, and disability
Age, sex, and educational level were important determinants of mortality  and dependence in our LMIC sites . Participants’ ages were established during the baseline interview, from stated age, official documentation, informant report, and, in the case of discrepancy, age according to an event calendar. We also recorded the participant’s gender and educational level (none; some but did not complete primary; completed primary; completed secondary; tertiary).
We summarised the impact of physical, mental and cognitive health through measurement and control for stroke, physical impairments, dementia and depression – conditions previously shown to make a substantial contribution to disability and dependence [25, 26]. These were assessed as follows:
Dementia diagnosed according to the cross-culturally developed, calibrated and validated 10/66 dementia diagnosis algorithm, on the basis of cognitive testing, clinical mental state interview and informant interview .
Self-reported stroke, confirmed by the interviewer as having characteristic symptoms lasting for more than 24 hours .
Number of self-reported limiting physical impairments from a list of nine (arthritis or rheumatism; persistent cough; breathlessness, difficulty breathing or asthma; high blood pressure; heart trouble or angina; stomach or intestine problems; faints or blackouts; paralysis, weakness or loss of one leg or arm; skin disorders such as pressure sores, leg ulcers or severe burns).
International Classification of Diseases-10 depressive episode (mild, moderate or severe), derived using a computerised algorithm applied to a structured clinical interview, the Geriatric Mental State .
Disability was assessed as activity limitation and participation restriction measured by the World Health Organization Disability Assessment Scale 2.0, developed as a culture-fair assessment tool for use in cross-cultural comparative epidemiological and health services research . We had previously demonstrated measurement invariance across the sites included in our survey.
In the incidence wave we sought to trace and re-interview all baseline survey participants. We first called on their residence at baseline, revisiting on up to four occasions. Where the participant was no longer resident we sought information regarding their vital status (if known) and/or current residence, assisted by having recorded, at baseline, the names and addresses of three non-coresident friends or family members. Where participants had moved away, we sought to re-interview them, even if they had moved out of the original catchment area, by telephone if necessary. Where a participant had died, we recorded date of death, and completed a verbal autopsy interview with a suitable key informant.
Dependence (need for care) was identified through a series of open-ended questions to a key informant: Who shares the home? What kind of help does the participant need inside and outside of the home? Who, in the family, is available to care? What help do you provide? Do you help to organise care? Is there anyone else in the family who is more involved in helping? What do they do? What about friends and neighbours, what do they do? The interviewer then coded whether the participant required no care, care some of the time, or care much of the time . The same approach was used at baseline and follow-up surveys. Those with no needs for care at baseline were considered to be at risk for the incidence of dependence, and those among them who were rated as needing care some of the time or much of the time at follow-up were considered to have incident dependence.
All data was double entered into EPIDATA software and data analysis was performed using STATA version 10. We describe the principal characteristics of the mortality cohort (the whole baseline survey sample, at risk for mortality), and the dependence cohort (those with no needs for care at baseline, hence at risk for the onset of dependence). Person-years risk for the onset of dependence was calculated as the interval between baseline and follow-up assessment, or the mid-point of this interval for those who developed dependence. We used Poisson regression to estimate incidence rate ratios (IRR) for associations with incident dependence. We used Cox’s proportional hazards regression to estimate hazard ratios for associations with mortality. Survival times were censored on the date of death, or the date of follow-up for those who were re-interviewed, or the median date of follow-up interview in that site for those refusing interview. We first assessed the associations of the dichotomized frailty syndromes (defined according to physical and multidimensional frailty criteria) with both outcomes, controlling incrementally for age, sex and education (model 1), these factors plus health conditions (dementia, depression, number of physical impairments and stroke – model 2), and all of these factors plus disability (model 3). We ran the models in each site, and then used a fixed or random effects meta-analysis to combine them. Higgins I2 was computed, estimating the proportion of between-site variability in the estimates accounted for by heterogeneity, as opposed to sampling error; up to 40 % heterogeneity is conventionally considered negligible, while up to 60 % may reflect moderate heterogeneity . For model 2 (controlling for age, sex, education and health conditions, but not disability) we used the STATA aflogit command to calculate PAF % with 95 % confidence intervals (CIs) for the contribution of frailty syndromes to the incidence of dependence and mortality, comparing the dichotomised frailty syndrome with two alternative approaches; either using the number of indicators (0 to 4) as an ordinal scale, or the aggregate effect of the four individual indicators. We also estimated the aggregate effect of all seven frailty indicators entered simultaneously. The STATA aflogit command estimates the individual and combined attributable fractions robustly from within the Poisson regression framework. PAFs represent the proportion of the incidence of the outcome that could theoretically be avoided if the exposure could be removed from the population, assuming causal relationships estimated free of confounding. Finally, we estimated and compared the effects of each of the seven individual frailty indicators for associations with incident dependence (pooled meta-analysed IRR) and mortality (pooled meta-analysed HR) controlling as per model 2 above for demographic variables and health conditions.