Introduction
As a subjective measure of overall health, self-reported health (SRH) provides an unspecific, yet comprehensive, measure of population health. Although researchers have no control over which aspects of health the individual emphasizes in their assessments of SRH (Jylhä
2009), the question itself can capture dimensions of health that more detailed health questions often miss. SRH has consistently been shown to be an important independent predictor of mortality, even after the analyses are adjusted with other more objective health indicators such as biological markers (Idler and Benyamini
1997; Kuhn et al.
2006).
Because SRH is subjective, several studies have suggested that there are differences in how different groups perceive their health, where for example women put more emphasis on disability than mortality compared to men (Deeg and Kriegsman
2003). There might also be differences between countries because cultural and linguistic factors might affect how the respondents answer the SRH question (Jylhä
2009; Jylhä et al.
1998). These differences in reporting SRH between population groups might lead to difficulties in comparing different studies.
A large number of cross-sectional studies have identified predictors of SRH. These predictors include socioeconomic variables such as income (Simons et al.
2013), education (Mirowsky and Ross
2008), occupation (Gueorguieva et al.
2009), social capital (Eriksson and Ng
2015; Giordano et al.
2012), and transition into divorce or widowhood (Liu
2012); lifestyle variables such as smoking, alcohol use, and physical inactivity (Hämmig et al.
2014; Rosenkranz et al.
2013); and other health-related variables such as body mass index (Wang and Arah
2015). In summary, these earlier results indicate that better socioeconomic status and healthier lifestyle are positively associated with SRH, and either very low or very high body mass index is negatively associated with SRH.
Evidence for predictors of change in SRH over time has also been provided in longitudinal studies (Cullati et al.
2014; Svedberg et al.
2005). These predictors include age; sex (Rohlfsen and Kronenfeld
2014); socioeconomic level, including income (Giordano and Lindstrom
2010), education level (Lee et al.
2012), and labor market participation (Gueorguieva et al.
2009); health, including both physical and psychological health (Ayyagari et al.
2012; Verropoulou
2012); cognitive status; social capital (Giordano and Lindstrom
2010); and lifestyle such as alcohol consumption and physical activity (Sargent-Cox et al.
2014).
Dropout occurs in most follow-up studies, especially in studies of aging. Most missing data methods assume an ignorable dropout mechanism, i.e., that dropout is unrelated to the outcome given some measured covariates. It is well known that if this ignorability assumption is not fulfilled, the results can be biased (Little and Rubin
2002). Moreover, an ignorable dropout mechanism is unrealistic in follow-up studies where the outcome of interest is related to health, since then one expect dropout to be directly dependent on deteriorating health; see, e.g., Josefsson et al. (
2016) for a practical example in a study of cognitive decline. Methods taking into account non-ignorable dropout are either based on strong identifying assumptions and/or using extra information like instrumental variables (Molenberghs et al.
2015), or, as we propose herein, estimating bounds under milder assumptions (Genbäck et al.
2015; Vansteelandt et al.
2006).
Even though there is a vast literature covering transitions in SRH, there are few covering more than one country at a time and, importantly, even fewer consider possible biases due to non-ignorable dropout. The main objectives of this study were to identify predictors of decline in SRH in older populations (50 years or older) in three European countries, while showing that evidence obtained from the data may depend on whether we ignore dropout or whether we account for the probable situation that SRH decliners are overrepresented among those dropping out at follow-up. We focused on three countries from different parts of Europe, the north (Sweden), west (Netherlands), and south (Italy), in order to corroborate results across different contexts and sampling schemes.
Results
Description of the study subjects
The baseline characteristics of female and male respondents are summarized in Tables
1 and
2, respectively. There were slightly more female than male respondents in all countries studied.
Table 1
Descriptive statistics for the female respondents with good or better self-rated health at baseline
Baseline variables |
Mean age in years | 63.8 | (63.3, 64.4) | 61.8 | (61.2, 62.3) | 61.9 | (61.3, 62.6) |
% who responded to the SRH question at the beginning of the interview | 47.0 | (44.2, 49.9) | 51.8 | (48.6, 55.0) | 47.7 | (43.8, 51.5) |
Socioeconomic variables |
% with high education level | 51.8 | (49.0, 54.7) | 39.6 | (36.5, 42.7) | 28.0 | (24.6, 31.4) |
% make ends meet fairly easily or easily | 80.2 | (78.0, 82.5) | 83.2 | (80.9, 85.6) | 40.5 | (36.7, 44.2) |
Cognitive function variables |
% with good numeracy test | 50.8 | (47.9, 53.6) | 50.3 | (47.1, 53.4) | 25.1 | (21.8, 28.4) |
% with good date orientation | 93.1 | (91.7, 94.6) | 87.7 | (85.6, 89.8) | 89.6 | (87.3, 91.9) |
Health-related variables |
Overweight, 25 ≤ body mass index < 30 | 35.8 | (33.1, 38.5) | 36.8 | (33.7, 39.8) | 37.0 | (33.3, 40.6) |
Obesity, body mass index ≥ 30 | 11.8 | (10.0, 13.7) | 13.2 | (11.0, 15.4) | 14.2 | (11.5, 16.9) |
Mean number of chronic diseases | 1.33 | (1.26, 1.40) | 0.97 | (0.90, 1.05) | 1.15 | (1.06, 1.24) |
Mean number of mobility problems | 0.97 | (0.88, 1.05) | 0.68 | (0.60, 0.76) | 0.92 | (0.81, 1.04) |
% with depression | 35.3 | (32.6, 38.0) | 29.1 | (26.2, 32.0) | 41.6 | (37.9, 45.4) |
Mean of maximum grip strength in kg | 27.6 | (27.3, 28.0) | 29.8 | (29.4, 30.2) | 26.0 | (25.6, 26.5) |
% with limitation in normal activities | 37.9 | (35.1, 40.7) | 35.4 | (32.4, 38.5) | 21.4 | (18.3, 24.5) |
Lifestyle variables |
% who stopped smoking | 31.7 | (29.1, 34.4) | 29.8 | (26.9, 32.8) | 16.1 | (13.3, 18.9) |
% who were current smokers | 18.5 | (16.3, 20.8) | 21.2 | (18.6, 23.8) | 16.4 | (13.6, 19.3) |
% with high alcohol usage | 1.05 | (0.47, 1.63) | 13.7 | (11.5, 15.9) | 12.9 | (10.3, 15.5) |
% with physical inactivity | 3.32 | (2.30, 4.34) | 4.26 | (2.98, 5.55) | 17.0 | (14.1, 19.9) |
Follow-up variables |
% who were lost-to follow-up | 47.7 | | 49.6 | | 43.1 | |
% with decline in SRH among those who were followed up | 24.0 | | 23.0 | | 41.8 | |
Table 2
Descriptive statistics for the male respondents with good or better self-rated health at baseline
Baseline variables |
Mean age in years | 64.7 | (64.1, 65.3) | 62.6 | (62.0, 63.2) | 63.4 | (62.7, 64.0) |
% who responded to the SRH question at the beginning of the interview | 49.0 | (46.1, 51.9) | 48.6 | (45.3, 51.9) | 45.6 | (41.8, 49.5) |
Socioeconomic variables |
% with high education level | 47.8 | (44.8, 50.7) | 57.8 | (54.5, 61.1) | 29.5 | (26.0, 33.0) |
% make ends meet fairly easily or easily | 84.1 | (81.9, 86.2) | 84.3 | (81.9, 86.7) | 39.4 | (35.7, 43.2) |
Cognitive function variables |
% with good numeracy test | 63.1 | (60.3, 65.9) | 72.0 | (69.0, 75.0) | 36.0 | (32.4, 39.7) |
% with good date orientation | 88.3 | (86.4, 90.2) | 85.5 | (83.2, 87.9) | 86.9 | (84.3, 89.5) |
Health-related variables |
Overweight, 25 ≤ body mass index < 30 | 48.5 | (45.6, 51.4) | 50.9 | (47.6, 54.3) | 53.9 | (50.1, 57.7) |
Obesity, body mass index ≥ 30 | 12.6 | (10.7, 14.6) | 10.6 | (8.5, 12.6) | 14.8 | (12.0, 17.5) |
Mean number of chronic diseases | 1.32 | (1.25, 1.39) | 0.88 | (0.81, 0.94) | 1.07 | (0.99, 1.16) |
Mean number of mobility problems | 0.54 | (0.48, 0.61) | 0.30 | (0.25, 0.35) | 0.45 | (0.38, 0.53) |
% with depression | 17.0 | (14.9, 19.2) | 16.7 | (14.2, 19.2) | 25.3 | (21.9, 28.6) |
Mean of maximum grip strength in kg | 45.6 | (45.0, 46.1) | 47.1 | (46.5, 47.7) | 42.0 | (41.3, 42.8) |
% with limitation in normal activities | 33.6 | (30.8, 36.3) | 24.9 | (22.0, 27.8) | 14.6 | (11.9, 17.3) |
Lifestyle variables |
% who stopped smoking | 45.6 | (42.7, 48.5) | 50.2 | (46.8, 53.5) | 38.0 | (34.2, 41.7) |
% who were current smokers | 13.7 | (11.7, 15.7) | 26.3 | (23.3, 29.2) | 23.9 | (20.6, 27.2) |
% with high alcohol usage | 3.05 | (2.05, 4.06) | 24.9 | (22.0, 27.8) | 43.9 | (40.1, 47.7) |
% with physical inactivity | 3.31 | (2.27, 4.35) | 4.56 | (3.17, 5.95) | 13.7 | (11.1, 16.4) |
Follow-up variables |
% who were lost-to follow-up | 50.9 | | 54.8 | | 43.9 | |
% with decline in SRH among those who were followed up | 21.9 | | 24.4 | | 35.8 | |
The Swedes were slightly older (confidence intervals in Tables
1 and
2 for Swedish respondents did not overlap with those of Dutch and Italian respondents) and reported more chronic diseases, and the men reported more limitations in normal daily activities due to health problems than their counterparts in the Netherlands and Italy. A high proportion of the Swedish women had high education level and performed well in the cognitive tests. The Swedes also had a lower proportion of individuals with high alcohol use, and the Swedish men had a lower proportion of current smokers than the other countries.
The Dutch, on the other hand, reported fewer chronic diseases and had a higher grip strength compared to their counterparts in Sweden and Italy. Also, a lower proportion of the Dutch women had depressive symptoms. The Dutch men had higher education level and a higher proportion with a good score in the numerical test compared to the men in Sweden and Italy.
The Italians had the lowest proportion of respondents with high education level and good score on the numerical test, and a higher proportion of individuals with difficulty to make ends meet compared to Sweden and the Netherlands. Even though the Italians had a higher proportion of physically inactive individuals (17 and 14% among women and men, respectively, versus less than 5% in the other two countries), they had a lower proportion of individuals who felt limited in their daily activities due to health problems. The Italians also reported more depressive symptoms and had the lowest grip strength. In the longitudinal analysis, we observed a higher proportion of Italians reporting a decline in SRH (42% among women and 36% among men) compared to at most 24% in the other two countries.
Predictors of decline in SRH
This study highlights age and number of chronic diseases as two predictors of decline in SRH across countries (Tables
3 and
4). Female respondents in all three countries had a higher risk of decline in SRH with increasing age.
Table 3
Odds ratios and 95% confidence intervals from the multiple logistic regressions modeling decline in SRH against all covariates for females
Table 4
Odds ratios and 95% confidence intervals from the multiple logistic regressions modeling decline in SRH against all covariates for males
For each additional self-reported chronic condition at baseline, the odds of decline in SRH increased, ranging from 23% (odds ratio (OR) 1.23 95% confidence interval (CI) [1.03–1.46]) among the Swedish women to 79% (1.79 [1.39–2.30]) among the Dutch men. For the Italian women, the association between number of chronic diseases and decline in SRH was sensitive to non-ignorable dropout because the 95% CI assuming ignorable dropout ([1.03–1.59]) did not contain 1, but the corresponding uncertainty interval ([0.93–1.59], shown in black in Table
3), allowing for non-ignorable dropout did.
The complete case analysis indicates that being obese is positively associated with decline in SRH in Sweden (2.07 [1.10–3.90] among women and 2.75 [1.36–5.58] among men) and Italy (2.19 [1.10–4.37] among women and 2.38 [1.15–4.91] among men). The corresponding uncertainty intervals are [0.89–3.90], [0.95–5.58], [0.97–4.37] and [1.04–4.91]. In fact, all uncertainty intervals except for the interval for Italian males include 1. Therefore, an association between obesity and decline in SRH is not supported by the sensitivity analysis, and we conclude that this result is sensitive to non-ignorable dropout. No significant association between being obese and decline in SRH was found for men and women in the Netherlands.
Self-reported limitations in normal activities due to health problems were associated with decline in SRH among the Swedish women (1.90 [1.20–3.02]) and men (2.66 [1.61–4.40]). High maximum grip strength protected against decline in SRH in Sweden (0.96 [0.93–0.99] among women and 0.94 [0.91–0.97] among men) and Italy (0.96 [0.92–1.00 among women and 0.96 [0.94–0.99] among men). These results were not sensitive to non-ignorable dropout.
Somewhat surprisingly, we found that being inactive seemed to be protective against decline in SRH among the Italian women (0.42 [0.22–0.83]). However, this effect was not apparent in the other country and sex combinations.
Discussion
The objective of this study was to find predictors of declining self-reported health in older populations (50 years or older) in Sweden, the Netherlands, and Italy. Since we suspected that dropout may be positively related to decline in self-reported health, we also aimed at performing a sensitivity analysis of the assumption of ignorable dropout. We found that, after taking non-ignorable dropout into account, the number of chronic diseases was a predictor in all countries (except for Italian women) and maximum grip strength was a predictor associated with decline in SRH in Sweden and Italy. The complete case analysis, i.e., ignoring dropout, indicates that obesity is a predictor of decline in SRH among Swedish and Italian men and women. However, our sensitivity analysis showed that three out of four associations that were significant in the complete case analysis were sensitive to non-ignorable dropout. This is in part due to there being a higher proportion of obese individuals among those that drop out than those that participate at follow-up. We concluded that there was not enough evidence in the data to consider obesity a predictor of SRH decline. This demonstrates that taking into account non-ignorable dropout, in our case implying that those with poor health may have a larger propensity to dropout, impacts the results of our longitudinal study. These results may be contrasted with Rohlfsen and Kronenfeld (
2014) who found that being overweight was associated with decline in SRH for men in the USA. However, other studies did not confirm high body mass index to be a predictor of declining health (Ayyagari et al.
2012; Lee et al.
2012). The results obtained for women and men are qualitatively similar.
Our study showed that age is a predictor of decline in SRH, even when accounting for non-ignorable dropout, which is in line with previous research (Ayyagari et al.
2012; Cullati et al.
2014; Lee et al.
2012). Multimorbidity with chronic diseases has been shown to be a predictor of decline in SRH (Hsu
2015; Lee et al.
2012), and our results confirm these findings. This is also in line with Ayyagari et al. (
2012) who have identified diabetes, congestive heart failure, and angina as predictors for decline in SRH. In contrast, Rohlfsen and Kronenfeld (
2014) investigated the association between several chronic diseases and change in SRH, and identified only arthritis as a predictor for health decline among women.
Some predictors were found to be significant only in some of the country/sex combinations. For instance, high maximum grip strength was found to be a predictor associated with non-decline in SRH for both men and women in Sweden and Italy, and self-reported limitations due to health problems in normal activities were associated with decline in SRH for both men and women in Sweden. The variations between countries might be due to a lack of statistical power, to the different sampling schemes, or to cultural differences in the answers provided to the outcome question “How is your health?” For instance, individuals in different countries might put emphasis on different aspects of health in their self-assessment, or there might be linguistic/translation issues and the SRH question and answer alternatives might not be interpreted in comparable ways (Jylhä
2009; Jürges
2007).
We had fairly low response rates at baseline (41% in Sweden, 54% in the Netherlands, and 44% in Italy). Thus, the results obtained in this study based on the respondents at baseline might not be generalizable to those that did not respond. If the two groups differ, we have no means to know how this would change the results. For 84% of the dropout individuals, we do not have information on whether the individuals were alive or not at follow-up. This information could have been used to make a distinction between dropout due to death or other reasons (Josefsson et al.
2016). However, we do not expect that a sensitivity analysis where death and other dropout causes are distinguished would yield different conclusions. For instance, a sensitivity analysis removing the 439 individuals known to be deceased yielded similar results (results not shown).
Even though there is a vast literature covering transitions in SRH, there are only few covering more than one country at a time, allowing to corroborate results in different population and sampling schemes. Moreover, most published longitudinal studies of aging assume that dropout at follow-up is ignorable (independent of the outcome) given a set of observed characteristics at baseline. However, this assumption is not realistic when a health outcome is of interest and dropout is expected to be related to health conditions. Our study is, up to our knowledge, the first one on predictors of changes in SRH that considers non-ignorable dropout. The study demonstrates that conclusions can indeed change when dropout mechanisms and SRH decline are allowed to be dependent on each other. The methodology presented to take into account non-ignorable dropout can be applied to any study with binary outcome observed at two time points and where non-ignorable dropout is expected.
Acknowledgments
We are grateful to Marie Eriksson for helpful comments. This work was part of the project “Paths to Healthy and Active Ageing” supported by the Swedish Research Council for Health, Working Life and Welfare [Grant nr: 2013-2506]. This paper uses data from SHARE Waves 1 and 5 (DOIs:
https://doi.org/10.6103/SHARE.w1.260,
https://doi.org/10.6103/SHARE.w5.100), see (Börsch-Supan et al.
2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982). Additional funding came from the German Ministry of Education and Research, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064) and from various national funding sources (see
www.share-project.org).