Data
Data were abstracted from the health section of a survey developed by students and researchers at the Ecole Nationale Supérieure de Statistique et d'Economie Appliquée (ENSEA - national school of statistics and applied economics in English) and administered to a sample of 15 to 65 years old in Dabou and its vicinity, a small town at 60 kilometers from Abidjan, Côte d’Ivoire, in May 2017.
A multistage sampling strategy was used to collect data on the living condition, resilience and health among elderly in Dabou and its vicinity. We randomly selected households stratified according to the following categories: 1) group 1: Dabou (town itself), 2) group 2: 4 villages (Dabou’s vicinity) with more than 700 households, 3) group 3: 3 villages with 400 to 700 households, 4) group 4: 8 villages with 150 to 400 households, and 5) group 5: 1 village with less than 150 households. We used the following respective sampling rate: 5%, 20%, 33%, 50% and 100%. This drawing allowed us to obtain a sample of 2776 households, among which 663 from group 1 (Dabou), 794 from group 2, 427 from group 3, 823 from group 4 and 69 in from group 5. Then, at the level of each household, a simple random draw was used to capture one individual among those 15 and above in age.
Our sample size (n=2775) was calculated to ensure that with an alpha of 0.05 and a power of 0.9, a rate of resilience of 50%, the minimum detectable variation is under 3%. However, due to a 9% nonresponse rate, the final sample size was n= 2523 for the entire survey. We extracted information from this dataset regarding individuals aged 50 to 65 (n= 267).
Two pre-tested questionnaires were used in the survey. Informed consent was obtained from all subjects (parent and/or legal guardian for participants under 18 ) before administration of the survey. The first questionnaire was administered to the head of the household to gather information on the members of the household, including demographic characteristics, education and employment status, and dwelling/living conditions. The second questionnaire gathered information about the participants’ health, including overall self-assessed health, conditions, physical functioning, health behaviors (e.g. smoking, alcohol, diet, physical exercise), and self-assessed survival probabilities for four target ages (70, 75, 80 and 85). The dataset also contains some socioeconomic variables that include gender, age, employment status, and living conditions From this dataset, we extracted information about all individuals aged 50 to 65 to target elderly populations.
Measures
Outcome variable
SLE is the outcome in this study. This variable is not directly collected in the questionnaire of the survey, but was estimated based on SSPs. The SSPs were assessed by four items:
- On a scale of 0 to 100, what do you estimate your chances of being alive at target age 70 is?
- On a scale of 0 to 100, what do you estimate your chances of being alive at target age 75 is?
- On a scale of 0 to 100, what do you estimate your chances of being alive at target age 80 is?
- On a scale of 0 to 100, what do you estimate your chances of being alive at target age 85 is?
Independent variables
We included a comprehensive list of independent variables informed by the literature [13,14]. These variables were categorized into seven group of variables: sociodemographic, economic, comorbidities, behavioral, quality of life, living conditions, and genetic information.
Sociodemographic variables: Age was a continuous variable, measured in years. Two gender categories were created (1 if male, 0 if female). Two living with partner categories were created (1 if lives with a partner, 0 otherwise). Two educational levels categories were created (1 if primary school at most, 0 otherwise).
Economic variables: Two employment categories were created (1 if the individual is employed, 0 otherwise). Income per capita was a continuous variable, measured in dollars.
Comorbidities: Two comorbidities variables were considered: (1) Obese [1 if obese (Body Mass Index ≥ 30 Kg/m2), 0 otherwise]; (2) Chronic condition (1 if individual reports at least one chronic condition, 0 otherwise).
Behavioral variables: Four categorical variables were used: (1) Current smoker (1 if the individual is currently a smoker, 0 otherwise), (2) Alcohol intake (1 if the individual consumes at least 2 glasses of alcohol per day, 0 otherwise), (3) Physical activity (1 if individual exercises, 0 otherwise), (4) Diet (1 if the individual observes a diet, 0 otherwise).
Quality of life variables: Three categorical variables were created: (1) health conditions limiting adults' ability to perform activities of daily living (1 if the individual reports that his health condition limits his/her working ability, 0 otherwise), (2) Health status 1 (1 if individual reports poor health status, 0 otherwise), (3) Health status 2 (1 if the individual reports that his health is deteriorating compared to the previous year, 0 otherwise).
Living conditions variables: Three categorical variables were used: (1) Toilet with flush (1 if the individual has access to a flush toilet, 0 otherwise), (2) Underground water (1 if the individual uses underground water as drinking water, 0 otherwise), (3) Septic tank (1 if the individual uses a septic tank for wastes, 0 otherwise).
Genetic information variables: Three categorical variables were created: (1) Mother alive (1 if the individual’s mother is alive, 0 otherwise), (2) Father alive (1 if the individual’s father is alive, 0 otherwise), (3) Both parents alive (1 if the individual’s both parents are alive, 0 otherwise).
Estimation of self-assessed life expectancy
We estimated SLE and its determinants using a two-pronged approach by: (i) estimating individuals’ life expectancy using the self-assessed survival probabilities (SSPs) (approach by Bellemare et al. (2012)) [15], and (ii) applying a finite mixture of regression models to form homogenous groups of individuals (clusters/components) and investigate the determinants. A technical note on the methodological approach is presented in Appendix.
The approach by Bellemare et al. is based on a cubic spline smoothing around each SSP value. This smoothing helps to estimate the cumulative distribution function of the SLE for each individual. Given that the cumulative distribution function is strictly monotonic, the function can be approximated by a cubic polynomial around each SSP. Subsequently, the cumulative distribution function of the SLE is estimated by connecting these local polynomials. Finally, the average life expectancy for each individual is calculated from the estimated cumulative distribution function. Suppose that a person reports 100 as its SSP for the target age of 70, 60 as its SSP for the target age of 75, 30 as its SSP for the target age of 80, and 0 as its SSP for the target age of 85. Then, on each of the intervals ( , and ) the cumulative distribution function is approximated by a cubic polynomial and the junction of these polynomials provides the overall cumulative distribution function for the individual.
Statistical Analyses
For comparison purposes, we plotted the distribution of the estimated SLE for both males and females (stratified by age groups) using kernel density estimation. In the second step of our methodological approach, we used a finite mixture model to assess the determinants of the estimated life expectancy, accounting for the heterogeneity in the distribution of the life expectancy among elders. This heterogeneity exists because the perception of life expectancy can differ among elders, even if they have the same characteristics. The finite mixture model allows creating homogenous clusters of SLE for elders based on individual characteristics, which are called concomitant variables. Variables, such as self-assessed health status, self-assessed health variation, or having a parent alive might affect the individual’s SLE and would be used as concomitant variables. Then, instead of a constant effect of the determinants on SLE for the whole population, this framework allows the determinants of the SLE to vary across clusters.
The study was approved by the National Ethical Committee of Côte d’Ivoire (Comité Consultatif National de Bioéthique de la République de Côte d'Ivoire in French) and written informed consent forms were obtained from participants. All methods were carried out in accordance with relevant guidelines and regulations.