Abstract
Background
Hunger is an important public health issue. This study aims to explore the impact of childhood hunger experiences (CHEs) on activities of daily living (ADL) disability and to discover the mediating role of depression on this correlation.
Methods
Data were derived from the China Health and Retirement Longitudinal Study 2011, 2013, 2015, 2018, and 2020. Depression was assessed using the Centre for Epidemiological Studies Depression Scale. ADL disability, basic ADL (BADL) disability and instrumental ADL (IADL) disability were assessed using the ADL scale. The relationships between CHEs, depression and ADL disability were estimated using probit methods, and the mediating effect of depression was estimated using the stepwise regression test and bootstrap methods.
Results
A total of 9,905 sample were included in the study. The study found that CHEs increased ADL disability by approximately 9.1% (β = 0.091, 95% CI: 0.028, 0.154), and increased the probability of IADL disability by approximately 8.0% (β = 0.080, 95% CI: 0.016, 0.144). CHEs increased ADL disability by approximately 29.8% (β = 0.298, 95% CI: 0.202, 0.393) in samples under 60 years of age. CHEs indirectly increased ADL (β = 0.062, 95% CI: 0.000, 0.124) by increasing the probability of depression.
Conclusion
CHEs predicted ADL disability, and depression mediated this correlation. There is a need to focus on the impact of CHEs on mental and physical health. In particular, focusing on mental health is necessary to prevent impairment of physical functioning.
Keywords: Childhood hunger experiences, Depression, Activities of daily living disability, China
Introduction
Hunger is an important public health issue [1]. The childhood hunger experiences (CHEs) refers to the situation of facing shortages of food and supplies during childhood and being exposed to malnutrition for a long time [2]. CHEs are a material deprivation of such magnitude that they can have permanent negative consequences for individual development [3], and their impacts in terms of health trajectories may be felt throughout the individual’s life course [4–7]. Activities of daily living (ADL) are skills needed to meet basic and complex physical needs [8, 9]. Previous research has found that older people who experienced frequent hunger in childhood are at a significantly higher risk of frailty, and as older people become frailer, their levels of physical activity decline [10]. Individuals who suffered hunger in childhood age more rapidly, and health shocks affect health outcomes in later life [11]. It is therefore necessary to focus on the health and mobility problems caused by CHEs.
A study found depression to be associated with CHEs and ADL. Previous research has shown that hunger is strongly associated with depression [12] and may affect all stages of life. One study found that hungry children have twice as much psychological dysfunction as non-hungry children [13]. Hunger is also an independent factor in the development of depressive symptoms in adolescents [14]. Over time, young people who experience chronic hunger have relatively higher levels of depression [15]. A previous study using the China Health and Retirement Longitudinal Study (CHARLS) data found that 13.6% of depressive symptoms in the middle and older Chinese population could be attributed to hunger [16]. Previous research has also found that depression affects physical health, especially ADL [17]. Depression is negatively associated with ADL [8, 18, 19]. The negative impact of depression on ADL even persists later in life [9]. There is a reciprocal, time-varying association between ADL disability and depressive symptoms, with more severe depressive symptoms predicting a higher level of longitudinal ADL disability over time [20].
Overall, previous studies have assessed the negative health effects of CHEs and explored the relationship between depression and CHEs as well as depression and ADL disability. However, the relationship between CHEs and ADL disability in middle and older adults remain to be discovered, and whether depression mediates the relationship between the two also needs to be explored. Therefore, the present study, based on 10-year longitudinal follow-up data, identified the effects of CHEs on ADL disability and explored whether depression is a mediating mechanism for the effects of CHEs on ADL disability. These assessments are important for discovering the relationship between mental health and physical health.
Methods
Data sources
CHARLS includes data on the health and related issues of middle and older adults (aged 45 years and above). The CHARLS data are representative in measuring ADL disability and depression [21]. The CHARLS national baseline survey was conducted in 2011, covering 150 district units and 450 village units. It was followed by 5 subsequent surveys in 2011, 2013, 2015, 2018, and 2020, and the Life Course Survey of Chinese Residents in 2014. After data cleansing, a total of 9,905 individuals participated in six surveys.
Variable selection and assignment
Independent variables
The independent variable in this study was CHEs, and the research question was derived from the responses to the question, “When you were a child before age 17 was there ever a time when your family did not have enough food to eat?” (NO = 0, YES = 1).
Dependent variable
The dependent variable in this study was ADL disability. The most widely used measure of disability is the ADL scale [17]. ADL are usually categorised as “basic activities of daily living” (BADL), which involve dressing, bathing or showering, eating, getting into or out of bed, using the toilet, and controlling urination and defecation, and “instrumental activities of daily living” (IADL), which include doing household chores, preparing hot meals, shopping, taking medications and managing money [22]. Dependence or the need for assistance with respect to at least one of these was defined as disability in daily activities [21]. Having an ADL disability was defined as 1 (0 otherwise). Similarly, having a BADL disability was defined as 1 (0 otherwise), and having a IADL disability was defined as 1 (0 otherwise).
Mediating variables
The mediating variable for this study was depression. The CHARLS data assessed depression using the Centre for Epidemiological Studies Depression Scale (CES-D-10), which involves 10 questions about the respondent’s feelings of annoyance and difficulty in concentrating in the past week. The CES-D-10 scores ranged from 0 to 30, with a higher score indicating more severe depression. According to previous studies [23, 24], a CES-D-10 score of less than 10 is considered to indicate no depression and given a value of 0, and a score greater than or equal to 10 indicates depression and is given a value of 1.
Based on prior research [25, 26], this study included gender (Female = 0, Male = 1), age (< 60 = 0, ≥ 60 = 1), income (Lower = 0, Higher = 1), residence (Rural = 0, Urban = 1), marital status (Unmarried = 0, Married = 1), educational level (Primary school or below = 0, Middle school or above = 1), health insurance (NO = 0, YES = 1), pension insurance (NO = 0, YES = 1), and chronic disease (NO = 0, YES = 1) as control variables. Previous studies have found that financial support for older adults is inversely associated with depressive symptoms [27], and we took this factor into account to minimize confounding and used child financial support (Lower = 0, Higher = 1) as the control variable. In addition, this study controlled for fixed effects of time and region (Eastern region = 0, Central region = 1, and Western region = 2).
Statistical methods
In this study, probit models were used to test the relationship between CHEs, depression and ADL disability. Based on previous studies, the stepwise regression test (BK) method was used for mediated effects analysis [28]. Moreover, the bootstrap method was used to assess the robustness of the results. Regarding descriptive statistics, categorical variables were expressed as frequencies and percentages. All operations were performed using STATA 15.1. The significance level for all hypothesis tests was set at 0.05.
Results
Descriptive statistics
Table 1 shows the descriptive statistical analysis. A total of 9,905 respondents were included in this study, 7,117 (71.853%) individuals experienced childhood hunger, and 2,788 (28.147%) did not. 5,873 (59.293%) respondents were younger than 60 years old, 4,209 (42.494%) respondents with lower incomes, and 7,782 samples had lower child financial support. A total of 6,622 (66.855%) individuals lived in rural areas and 3,283 (33.145%) in urban areas; 8,915 were married (90.005%), 6,771 (68.359%) had primary education or below, and 9,412 (95.023%) were covered by health insurance. 1,024 (10.338%) samples were covered by pension insurance, and 6,629 (66.926%) samples had chronic disease.
Table 1.
Descriptive statistical analysis (2011, N = 9,905)
| Variable | TOTAL | CHEs | NOT CHEs | P |
|---|---|---|---|---|
| Gender | < 0.001 | |||
| Female | 5,312 (53.629%) | 3,667 (51.525%) | 1,645 (59.003%) | |
| Male | 4,593(46.371%) | 3,450 (48.475%) | 1,143 (40.997%) | |
| Age | < 0.001 | |||
| < 60 | 5,873(59.293%) | 4,050(56.906%) | 1,823(65.387%) | |
| ≥ 60 | 4,032(40.707%) | 3,067(43.094%) | 965(34.613%) | |
| Income | < 0.001 | |||
| Lower | 4,209(42.494%) | 3,149(44.246%) | 1,060(38.020%) | |
| Higher | 5,696(57.506%) | 3,968(55.754%) | 1,728(61.980%) | |
| Child financial support | < 0.001 | |||
| Lower | 7,782(78.566%) | 5,516(77.505%) | 2,266(81.277%) | |
| Higher | 2,123(21.434%) | 1,601(22.495%) | 522(18.723%) | |
| Residence | < 0.001 | |||
| Rural | 6,622(66.855%) | 4,868(68.400%) | 1,754(62.912%) | |
| Urban | 3,283(33.145%) | 2,249(31.600%) | 1,034(37.088%) | |
| Marital status | 0.024 | |||
| Married | 8,915(90.005%) | 6,436(90.431%) | 2,479(88.917%) | |
| Unmarried | 990(9.995%) | 681(9.569%) | 309(11.083%) | |
| Educational level completed | < 0.001 | |||
| Primary school or below | 6,771(68.359%) | 5,086(71.463%) | 1,685(60.438%) | |
| Middle school or above | 3,134(31.641%) | 2,031(28.537%) | 1,103(39.562%) | |
| Health insurance | 0.248 | |||
| YES | 9,412 (95.023%) | 6,774(95.181%%) | 2,638(94.620%) | |
| NO | 493 (4.977%) | 343(4.819%) | 150(5.380%) | |
| Pension insurance | < 0.001 | |||
| YES | 1,024(10.338%) | 652(9.161%) | 372(13.343%) | |
| NO | 8,881(89.662%) | 6,465(90.839%) | 2,416(86.657%) | |
| Chronic disease | < 0.001 | |||
| YES | 6,629(66.926%) | 1,772(68.245%) | 4,857(63.558%) | |
| NO | 3,276(33.074%) | 1,016(31.755%) | 2,260(36.442%) | |
| N | 9,905(100%) | 7,117 (71.853%) | 2,788(28.147%) |
NOTE: Table 1 based on cross-tabulation, and showing the distribution of CHEs in different feature samples. All variables were expressed as N (%), and chi-squared tests were used
Benchmark regression: an analysis of the impact of CHEs on ADL disability
Table 2 shows that, CHEs increased ADL disability by approximately 9.1% (β = 0.091, 95% CI: 0.028, 0.154), and increased the probability of IADL disability by approximately 8.0% (β = 0.080, 95% CI: 0.016, 0.144), and were not significantly associated with BADL disability(β = 0.034, 95% CI: − 0.053, 0.120). CHEs increased ADL disability by approximately 29.8% (β = 0.298, 95% CI: 0.202, 0.393) in samples under 60 years of age, and were not significantly associated with ADL disability in samples aged 60 years and older (β = − 0.062, 95% CI: − 0.140, 0.017).
Table 2.
Analysis of the impact of CHEs and control variables on ADL disability
| Variables | ADL disability | BADL disability | IADL disability | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| ALL | P | < 60 | P | ≥ 60 | P | |||||
| β (95%CI) | β (95%CI) | β (95%CI) | β (95%CI) | P | β (95%CI) | P | ||||
|
CHEs (Reference group: No) |
0.091 (0.028, 0.154) |
0.005 |
0.298 (0.202, 0.393) |
< 0.001 |
− 0.062 (− 0.140, 0.017) |
0.124 |
0.034 (− 0.053, 0.120) |
0.444 |
0.080 (0.016, 0.144) |
0.014 |
|
Gender (Reference group: Female) |
− 0.281 (− 0.339, − 0.224) |
< 0.001 |
− 0.210 (− 0.301, − 0.118) |
< 0.001 |
− 0.330 (− 0.399, − 0.260) |
< 0.001 |
− 0.191 (− 0.271, − 0.112) |
< 0.001 |
− 0.251 (− 0.309, − 0.192) |
< 0.001 |
|
Age (Reference group: < 60) |
0.269 (0.217, 0.321) |
< 0.001 | — | — | — | — |
0.393 (0.316, 0.470) |
< 0.001 |
0.245 (0.192, 0.298) |
< 0.001 |
|
Income (Reference group: Lower) |
− 0.280 (− 0.332, − 0.228) |
< 0.001 |
− 0.235 (− 0.316, − 0.154) |
< 0.001 |
− 0.346 (− 0.417, − 0.276) |
< 0.001 |
− 0.243 (− 0.321, − 0.165) |
< 0.001 |
− 0.310 (− 0.365, − 0.256) |
< 0.001 |
|
Child financial support (Reference group: Lower) |
− 0.080 (− 0.123, − 0.038) |
< 0.001 |
− 0.033 (− 0.108, 0.043) |
< 0.001 |
− 0.122 (− 0.174, − 0.069) |
< 0.001 |
− 0.050 (− 0.111, 0.011) |
0.110 |
− 0.084 (− 0.128, − 0.041) |
< 0.001 |
|
Area of residence (Reference group: Rural) |
− 0.209 (− 0.270, − 0.149) |
< 0.001 |
− 0.111 (− 0.205, − 0.016) |
0.021 |
− 0.276 (− 0.350, − 0.203) |
< 0.001 |
− 0.138 (− 0.221, − 0.055) |
0.001 |
− 0.198 (− 0.259, − 0.136) |
< 0.001 |
|
Marital status (Reference group: Unmarried) |
− 0.251 (− 0.316, − 0.187) |
< 0.001 |
− 0.327 (− 0.477, − 0.177) |
< 0.001 |
− 0.213 (− 0.284, − 0.141) |
< 0.001 |
− 0.304 (− 0.390, − 0.218) |
< 0.001 |
− 0.220 (− 0.286, − 0.154) |
< 0.001 |
|
Educational level completed (Reference group: Primary school or below) |
− 0.518 (− 0.587, − 0.450) |
< 0.001 |
− 0.545 (− 0.643, − 0.448) |
< 0.001 |
− 0.435 (− 0.524, − 0.346) |
< 0.001 |
− 0.258 (− 0.353, − 0.163) |
< 0.001 |
− 0.560 (− 0.631, − 0.489) |
< 0.001 |
|
Health insurance (Reference group: NO) |
− 0.079 (− 0.161, 0.002) |
0.057 |
0.059 (− 0.101, 0.220) |
0.469 |
− 0.143 (− 0.240, − 0.045) |
0.004 |
− 0.139 (− 0.251, − 0.027) |
0.015 |
− 0.084 (− 0.168, − 0.001) |
0.049 |
|
Pension insurance (Reference group: NO) |
−0.084 (− 0.135, − 0.033) |
0.001 |
− 0.129 (− 0.216, − 0.041) |
0.004 |
− 0.036 (− 0.101, 0.029) |
0.276 |
− 0.102 (− 0.175, − 0.029) |
0.006 |
− 0.053 (− 0.106, 0.000) |
0.049 |
|
Chronic disease (Reference group: NO) |
0.469 (0.407, 0.530) |
< 0.001 |
0.461 (0.367, 0.555) |
< 0.001 |
0.468 (0.393, 0.544) |
< 0.001 |
0.613 (0.522, 0.703) |
< 0.001 |
0.424 (0.361, 0.486) |
< 0.001 |
| Time Effect | YES | YES | YES | YES | YES | |||||
| Regional Effect | YES | YES | YES | YES | YES | |||||
| Wald chi2 | 1691.13 | 466.06 | 921.33 | 837.22 | 1598.18 | |||||
| P | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | |||||
| N | 49,545 | 20,037 | 29,488 | 49,525 | 49,525 | |||||
Note: β denotes regression coefficient
Analysis of the mediating effect of depression on the relationship between CHEs and ADL disability
Table 3 analyses the mediating effect of depression. Based on the analysis results, CHEs significantly increased the probability of depression by 29.7% (β = 0.297, 95% CI: 0.247, 0.346), and thus, depression could serve as a mediating variable. Further placing both depression and CHEs in a regression model revealed that CHEs remained significantly related to ADL disability (β = 0.062, 95% CI: 0.000, 0.124) but were not significantly related to BADL disability (β = 0.012, 95% CI: − 0.074, 0.097) and IADL disability (β = 0.051, 95% CI: − 0.012, 0.114), whereas depression was significantly related to ADL disability (β = 0.325, 95% CI: 0.285, 0.364), BADL disability (β = 0.244, 95% CI: 0.188, 0.301) and IADL disability (β = 0.320, 95% CI: 0.279, 0.361). Thus, depression has a partial mediating effect regarding the effect of CHEs on ADL disability.
Table 3.
Analysis of the mediating effect of depression on the relationship between CHEs and ADL disability
| Variables | Depression | ADL disability | BADL disability | IADL disability |
|---|---|---|---|---|
| β (95%CI) | β (95%CI) | β (95%CI) | β (95%CI) | |
|
CHEs (Reference group: NO) |
0.297 (0.247, 0.346) |
0.062 (0.000, 0.124) |
0.012 (− 0.074, 0.097) |
0.051 (− 0.012, 0.114) |
|
Depression (Reference group: NO) |
0.325 (0.285, 0.364) |
0.244 (0.188, 0.301) |
0.320 (0.279, 0.361) |
|
|
Gender (Reference group: Female) |
−0.406 (− 0.451, − 0.361) |
−0.243 (− 0.300, − 0.186) |
−0.162 (− 0.241, − 0.084) |
−0.213 (− 0.272, − 0.155) |
|
Age (Reference group: <60) |
−0.045 (− 0.085, − 0.005) |
0.278 (0.226, 0.330) |
0.396 (0.319, 0.472) |
0.255 (0.201, 0.308) |
| Income (Reference group: Lower) |
−0.127 (− 0.166, − 0.089) |
−0.275 (− 0.328, − 0.223) |
−0.240 (− 0.318, − 0.162) |
−0.305 (− 0.359, − 0.251) |
| Child financial support (Reference group: Lower) |
−0.039 (− 0.074, − 0.005) |
−0.076 (− 0.119, − 0.034) |
−0.047 (− 0.107, 0.014) |
−0.080 (− 0.124, − 0.037) |
|
Residence (Reference group: Rural) |
−0.282 (− 0.329, − 0.235) |
−0.183 (− 0.243, − 0.123) |
−0.118 (− 0.200, − 0.035) |
−0.172 (− 0.233, − 0.111) |
|
Marital status (Reference group: Unmarried) |
−0.136 (− 0.190, − 0.082) |
−0.245 (− 0.309, − 0.181) |
−0.301 (− 0.386, − 0.216) |
−0.215 (− 0.280, − 0.149) |
|
Educational level completed (Reference group: Primary school or below) |
−0.159 (− 0.210, − 0.108) |
−0.504 (− 0.572, − 0.436) |
−0.250 (− 0.343, − 0.156) |
−0.547 (− 0.617, − 0.476) |
|
Health insurance (Reference group: NO) |
−0.042 (− 0.111, 0.026) |
−0.079 (− 0.160, 0.003) |
−0.136 (− 0.248, − 0.024) |
−0.084 (− 0.168, 0.000) |
|
Pension insurance (Reference group: NO) |
−0.030 (− 0.071, 0.011) |
−0.084 (− 0.136, − 0.033) |
−0.100 (− 0.173, − 0.028) |
−0.053 (− 0.106, − 0.001) |
|
Chronic disease (Reference group: NO) |
0.379 (0.333, 0.426) |
0.434 (0.373, 0.494) |
0.586 (0.496, 0.676) |
0.389 (0.328, 0.451) |
| Time Effect | YES | YES | YES | YES |
| Regional Effect | YES | YES | YES | YES |
| Wald chi2 | 1594.96 | 1941.09 | 903.67 | 1820.20 |
| P | < 0.001 | < 0.001 | < 0.001 | < 0.001 |
| N | 49,525 | 49,525 | 49,525 | 49,525 |
Note: β denotes regression coefficient
Mediation effect test
Table 4 shows the mediation effect test. Based on the analysis results, the mediating effect of depression was robustly tested using the bootstrap method and found to be significant in the CHEs-ADL relationship (β = 0.007, P < 0.001), the CHEs-BADL relationship (β = 0.002, P < 0.001), and the CHEs-IADL relationship (β = 0.006, P < 0.001).
Table 4.
Mediation effect test
| β | P | Percentile CI | Bias-Corrected CI | |
|---|---|---|---|---|
| CHEs-depression-ADL disability | 0.007 | < 0.001 | (0.006,0.008) | (0.006, 0.008) |
| CHEs-depression-BADL disability | 0.002 | < 0.001 | (0.002, 0.003) | (0.002, 0.003) |
| CHEs-depression-IADL disability | 0.006 | < 0.001 | (0.005, 0.007) | (0.005, 0.007) |
Note: β denotes regression coefficient
Discussion
Using CHARLS data from 2011, 2013, 2015, 2018, and 2020, and the Life Course Survey of Chinese Residents in 2014, we found that CHEs increased ADL disability in middle and older adults and that depression was a mediating variable between CHEs and ADL disability. We tested this result using bootstrap methods.
The study found that CHEs significantly increased ADL disability in middle and older adults, which is consistent with the results of previous studies [29]. Malnutrition early in life is detrimental to health in later life [11]. Hunger increases the odds of worsening health by 16% [30]. This may be because hunger leads to reduced health in childhood, increasing the risk of frailty in later life [31]. Hunger-induced ageing triggers biological wastage throughout the body, which affects ADL [32]. Previous research has found that the high risk of hunger may persist into adolescence [33], and the experience of hunger in youth was significantly associated with an elevated risk of depression in 3 study cycles spanning 6 years [15]. Our study extended this finding using data conducted over a 10-year period, revealing that the negative impact of CHEs persists into middle and older age adulthood in China. The negative health effects of CHEs persist throughout the life course, with persistent negative cumulative effects on the quality of life [34]. As the standard of living in China rises, there is more security for the nutritional levels and ADL of those who experience hunger; however, more food only allows for catch-up growth [35]. Complete nutritional recovery and catch-up growth in adulthood are not sufficient to compensate for the health effects of early malnutrition [36]. This may be because childhood nutrient deficiencies can lead to long-term neurodevelopmental and health problems [37, 38]. Thus, CHEs increased ADL disability even into middle and older age. Our study also found that IADL were more affected, which may be due to the fact that IADL includes more complex activities that are more susceptible to external influences [9]. Thus, intervention strategies that reduce stress or improve nutritional availability may help reduce the risk of depressive symptoms [1, 16]. Further exploration of age groups may reduce confounders [39], our study found that CHEs in the under-60 years group had a greater impact on the ADL. This reflects the difference in the ADL of the under-60 years group, and there is a need to pay more attention to the group that is about to enter old age. We also found a positive correlation between chronic disease and ADL, which is consistent with previous studies, which may be due to the inability of older adults with chronic disease to perform ADL independently [40], and it is likely that older adults with chronic disease have lower levels of ADL, making them feel stressed and ultimately leading to depression [41].
Our findings identified depression as a mediating variable in the effect of CHEs on ADL disability. Hunger was significantly associated with depression [1, 16, 42, 43]. On the one hand, experiencing hunger during childhood coincides with a period of brain development [16], and coping with stress during a period of declining health may increase the risk of depressive symptoms, which has a negative impact on mental health. On the other hand, hunger may constitute one of the burdens associated with poverty that has a unique impact on the mental health of children living in poverty [13]. Poverty in childhood increases the likelihood of negative events later in life [44]. Overall, hunger is a physical experience that develops into a psychological experience as the individual grows, evolving into fear of hunger and insecurity [45]. The greater the stress, the greater the likelihood of developing mental illnesses, such as depression and anxiety [41].
Depression in middle and older adults also raises the probability of ADL disability. Depressive symptoms are negatively associated with quality of life and ADL disability [31, 46, 47]. Our study found that depression increased ADL disability among middle and older adults. On the one hand, this may be because functional disability, including activity limitations and participation limitations, is a major symptom of depression [48]. Depression tends to lead to a lack of energy and decreased motivation, which can lead to decreased physical functioning [49]. Older adults with depression show a stronger link between pain and functional limitations [50]. On the other hand, depressive symptoms can act as a stressor, causing and exacerbating inflammatory processes and thereby increasing the risk of subsequent ADL disability [20]. Depression may exacerbate medical disorders that can lead to ADL disability. Depression is considered to be a reversible disease [51]; therefore, mental health is necessary to prevent the impairment of physical functioning [52]. We also found that depressive states are particularly intervening between CHEs and IADL. Compared to BADL, IADL includes more complex mobility [22], which means that people with IADL disability are more in need of emotional support. Therefore, it is necessary to improve community health services, pay attention to the mental health of those with CHEs, carry out early intervention, and improve health awareness and health level.
However, our findings have some shortcomings. On the one hand, respondents recalled CHEs, and depression and ADL disability were scale-generated and not medical diagnoses, which may have influenced the results. In the future, it is necessary to use medical diagnosis to determine whether depression is present. On the other hand, we used longitudinal survey data over a 10-year period, which has limitations in observing the long-term relationship between CHEs, depression and ADL disability. People with CHEs have a faster decline in health as they age [11], the level of ADL disability is also declining [53], so it is necessary to pay attention to the mediating role of depression in this long-term relationship. In addition, this study is a correlation rather than a causal relationship, and causal analysis is necessary for future research.
Conclusion
Using data from 6 surveys over a 10-year period of CHARLS, this study found that CHEs increased ADL disability in middle and older adults and that depression mediated this relationship. There is a need to focus on the mental health of people who experience childhood hunger, while preventing mental illness is necessary to protect physical functioning.
Acknowledgements
We would like to acknowledge the CHARLS team for providing the data. We are responsible for the content of this article.
Abbreviations
- ADL
Activities of daily living
- BADL
Basic activities of daily living
- CES-D
The Centre for Epidemiological Studies Depression Scale
- CHARLS
The China Health and Retirement Longitudinal Study
- CHEs
Childhood hunger experiences
- IADL
Instrumental activities of daily living
Author contributions
Weile Zhang, Min Su, and Dongxu Li had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Weile Zhang, Min Su, Dongxu Li, Xi Guo, Zhengrong Li, and Tianjiao Zhang participated in concept and design. Weile Zhang, Zhengrong Li, Tianjiao Zhang, and Zheru Hu participated in acquisition, analysis, or interpretation of data. Weile Zhang, Min Su, Dongxu Li, Xi Guo, and Zhengrong Li contributed to drafting of the manuscript. Weile Zhang, Dongxu Li, Tianjiao Zhang, and Zheru Hu participated in statistical analysis. Min Su, and Dongxu Li contributed to supervision.
Funding
This study was funded by the National Natural Science Foundation of China (72164031&72204128). The funding bodies had no role in the design of the study or collection, analysis, and interpretation of data, and they did not contribute to the writing of the manuscript.
Data availability
Data is provided within the manuscript files.
Declarations
Ethical approval and consent to participate
The ethics review board of Peking University approved the CHARLS study (approval number IRB00001052–11015). Informed consent was obtained, and the data were anonymized for analysis. All methods were carried out in accordance with relevant guidelines and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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Contributor Information
Min Su, Email: sumin1227@126.com.
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References
- 1.Liang J, Li X, Huang X, Xie W, Zheng F. Progression of depressive symptoms after early exposure to famine: the China health and retirement longitudinal study. J Affect Disord. 2023;322:46–51. 10.1016/j.jad.2022.11.025. [DOI] [PubMed] [Google Scholar]
- 2.Bertoni M. Hungry today, unhappy tomorrow? Childhood hunger and subjective wellbeing later in life. J Health Econ. 2015;40:40–53. 10.1016/j.jhealeco.2014.12.006 [DOI] [PubMed] [Google Scholar]
- 3.Almond D, Currie J. Killing me softly: the fetal origins hypothesis. J Econ Perspect. 2011;25(3):53–172. 10.1257/jep.25.3.153. [DOI] [PMC free article] [PubMed]
- 4.Haddad L, Ross J, Oshaug A, Torheim LE, Cogill B. Report on the world nutrition situation: nutrition for improved development outcomes. 2004. 10.1111/j.1751-0813.1963.tb04208.x
- 5.Wang Z, Zou Z, Yang Z, et al. The association between fetal-stage exposure to the China famine and risk of diabetes mellitus in adulthood: results from the China health and retirement longitudinal study. BMC Public Health. 2018;18(1):1205. 10.1186/s12889-018-6134-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Félix-Beltrán L, Seixas BV. Childhood hunger experiences and chronic health conditions later in life among Brazilian older adults. Rev Panam Salud Publica. 2021;45:e39. 10.26633/RPSP.2021.39 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ye C, Aihemaitijiang S, Wang R, Halimulati M, Zhang Z. Associations between early-life food deprivation and risk of frailty of middle-age and elderly people: evidence from the China health and retirement longitudinal study. Nutrients. 2021;13(9):3066. 10.3390/nu13093066 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Green SL, Gignac GE, Watson PA, et al. Apathy and depression as predictors of activities of daily living following stroke and traumatic brain injuries in adults: a meta-analysis. Neuropsychol Rev. 2022;32(1):51–69. 10.1007/s11065-021-09501-8 [DOI] [PubMed] [Google Scholar]
- 9.Mograbi DC, Morris RG, Fichman HC, et al. The impact of dementia, depression and awareness on activities of daily living in a sample from a middle-income country. Int J Geriatr Psychiatry. 2018;33(6):807–13. 10.1002/gps.4765 [DOI] [PubMed] [Google Scholar]
- 10.Li A, Tu MT, Sousa AC, et al. Early life adversity and C-reactive protein in diverse populations of older adults: a cross-sectional analysis from the international mobility in aging study (IMIAS). BMC Geriatr. 2015;15:102. 10.1186/s12877-015-0104-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Abeliansky AL, Strulik H. Hungry children age faster. Econ Hum Biol. 2018;29:211–20. 10.1016/j.ehb.2018.03.005 [DOI] [PubMed] [Google Scholar]
- 12.Sulaiman TM, Drishti KM, Srivastava D. Food insecurity and associated depression among older adults in India: evidence from a population-based study. BMJ Open. 2022;12(4):e052718. 10.1136/bmjopen-2021-052718 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kleinman RE, Murphy JM, Little M, et al. Hunger in children in the United States: potential behavioral and emotional correlates. Pediatrics. 1998;101(1):E3. 10.1542/peds.101.1.e3 [DOI] [PubMed] [Google Scholar]
- 14.Galler JR, Bryce CP, Waber D, et al. Early childhood malnutrition predicts depressive symptoms at ages 11–17. J Child Psychol Psychiatry. 2010;51(7):789–98. 10.1111/j.1469-7610.2010.02208.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.McIntyre L, Wu X, Kwok C, Patten SB. The pervasive effect of youth self-report of hunger on depression over 6 years of follow up. Soc Psychiatry Psychiatr Epidemiol. 2017;52(5):537–47. 10.1007/s00127-017-1361-5 [DOI] [PubMed] [Google Scholar]
- 16.Li C, Miles T, Shen L, et al. Early-life exposure to severe famine and subsequent risk of depressive symptoms in late adulthood: the China health and retirement longitudinal study. Br J Psychiatry. 2018;213(4):579–86. 10.1192/bjp.2018.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Klompstra L, Ekdahl AW, Krevers B, Milberg A, Eckerblad J. Factors related to health-related quality of life in older people with multimorbidity and high health care consumption over a two-year period. BMC Geriatr. 2019;19(1):187. 10.1186/s12877-019-1194-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Wu Y, Li S, Han D, et al. The mediating role of depression in association between total sleep time and instrumental activities of daily living in China. Int J Public Health. 2023;68:1605678. 10.3389/ijph.2023.1605678 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Du S, Qin Y, Han M, et al. Longitudinal mediating effect of depression on the relationship between excessive daytime sleepiness and activities of daily living in Parkinson’s disease. Clin Gerontol Published Online August. 2022;11. 10.1080/07317115.2022.2111014 [DOI] [PubMed]
- 20.Wang J, Luo N, Sun Y, et al. Exploring the reciprocal relationship between activities of daily living disability and depressive symptoms among middle-aged and older Chinese people: a four-wave, cross-lagged model. BMC Public Health. 2023;23(1):1180. 10.1186/s12889-023-16100-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wu C. The mediating and moderating effects of depressive symptoms on the prospective association between cognitive function and activities of daily living disability in older adults. Arch Gerontol Geriatr. 2021;96:104480. 10.1016/j.archger.2021.104480 [DOI] [PubMed] [Google Scholar]
- 22.Paula JJ, Bertola L, Ávila RT, et al. Development, validity, and reliability of the general activities of daily living scale: a multidimensional measure of activities of daily living for older people. Braz J Psychiatry. 2014;36(2):143–52. 10.1590/1516-4446-2012-1003 [DOI] [PubMed] [Google Scholar]
- 23.Ni Y, Tein JY, Zhang M, Yang Y, Wu G. Changes in depression among older adults in China: a latent transition analysis. J Affect Disord. 2017;209:3–9. 10.1016/j.jad.2016.11.004 [DOI] [PubMed] [Google Scholar]
- 24.Guo J, Guan L, Fang L, et al. Depression among Chinese older adults: a perspective from Hukou and health inequities. J Affect Disord. 2017;223:115–20. 10.1016/j.jad.2017.07.032 [DOI] [PubMed] [Google Scholar]
- 25.Lin L, Cao B, Chen W, Li J, Zhang Y, Guo VY. Association of adverse childhood experiences and social isolation with later-life cognitive function among adults in China. JAMA Netw Open. 2022;5(11):e2241714. 10.1001/jamanetworkopen.2022.41714 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Wang Q. Association of childhood intrafamilial aggression and childhood peer bullying with adult depressive symptoms in China. JAMA Netw Open. 2020;3(8):e2012557. 10.1001/jamanetworkopen.2020.12557 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Xia W, van Wijngaarden JDH, Huijsman R, Buljac-Samardžić M. Effect of receiving financial support from adult children on depression among older persons and the mediating role of social participation. Int J Environ Res Public Health. 2022;19(19):12974. 10.3390/ijerph191912974 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–82. 10.1037/0022-3514.51.6.1173 [DOI] [PubMed] [Google Scholar]
- 29.Kirkpatrick SI, McIntyre L, Potestio ML. Child hunger and long-term adverse consequences for health. Arch Pediatr Adolesc Med. 2010;164(8):754–62. 10.1001/archpediatrics.2010.117 [DOI] [PubMed] [Google Scholar]
- 30.Halmdienst N, Winter-Ebmer R. Long-run relations between childhood shocks and health in late adulthood-evidence from the survey of health, ageing, and retirement in Europe. CESifo Econ Stud. 2014;60(2):402–34. 10.1093/cesifo/ifu015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Li Y, Xue QL, Odden MC, Chen X, Wu C. Linking early life risk factors to frailty in old age: evidence from the China health and retirement longitudinal study. Age Ageing. 2020;49(2):208–17. 10.1093/ageing/afz160 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Scherrer Junior G, Passos KG, Oliveira LM et al. Elderly’s activities of daily living, depressive symptoms and quality of life. Acta Paulista De Enfermagem. 35:eAPE0237345. 10.37689/acta-ape/2022AO0237345
- 33.Paquin V, Muckle G, Bolanis D, et al. Longitudinal trajectories of food insecurity in childhood and their associations with mental health and functioning in adolescence. JAMA Netw Open. 2021;4(12):e2140085. 10.1001/jamanetworkopen.2021.40085 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Dong H, Du C, Wu B, Wu Q. Multi-state analysis of the impact of childhood starvation on the healthy life expectancy of the elderly in China. Front Public Health. 2021;9:690645. 10.3389/fpubh.2021.690645 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Hermanussen M, Bogin B, Scheffler C. Stunting, starvation and refeeding: a review of forgotten 19th and early 20th century literature. Acta Paediatr. 2018;107(7):1166–76. 10.1111/apa.14311 [DOI] [PubMed] [Google Scholar]
- 36.Galler JR, Bryce C, Waber DP, Zichlin ML, Fitzmaurice GM, Eaglesfield D. Socioeconomic outcomes in adults malnourished in the first year of life: a 40-year study. Pediatrics. 2012;130(1):e1–7. 10.1542/peds.2012-0073 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Barks A, Hall AM, Tran PV, Georgieff MK. Iron as a model nutrient for understanding the nutritional origins of neuropsychiatric disease. Pediatr Res. 2019;85(2):176–82. 10.1038/s41390-018-0204-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Georgieff MK, Ramel SE, Cusick SE. Nutritional influences on brain development. Acta Paediatr. 2018;107(8):1310–21. 10.1111/apa.14287 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Li C, Lumey LH. Exposure to the Chinese famine of 1959-61 in early life and long-term health conditions: a systematic review and meta-analysis. Int J Epidemiol. 2017;46(4):1157–70. 10.1093/ije/dyx013 [DOI] [PubMed] [Google Scholar]
- 40.Xiao S, Shi L, Dong F, Zheng X, Xue Y, Zhang J, Xue B, Lin H, Ouyang P, Zhang C. The impact of chronic diseases on psychological distress among the older adults: the mediating and moderating role of activities of daily living and perceived social support. Aging Ment Health. 2022;26(9):1798–804. 10.1080/13607863.2021.1947965 [DOI] [PubMed] [Google Scholar]
- 41.Yuan J, Wang Y, Liu Z. Chronic disease and depression among the elderly in China: the mediating role of instrumental activities of daily living and the moderating role of area of residence. Current psychology (New Brunswick, N.J.). 2022:1–8. Advance online publication. 10.1007/s12144-022-03782-9 [DOI] [PMC free article] [PubMed]
- 42.Hammami N, Leatherdale ST, Elgar FJ. Does social support moderate the association between hunger and mental health in youth? A gender-specific investigation from the Canadian Health Behaviour in School-aged children study. Nutr J. 2020;19(1):134. 10.1186/s12937-020-00648-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Romo ML, Abril-Ulloa V, Kelvin EA. The relationship between hunger and mental health outcomes among school-going Ecuadorian adolescents. Soc Psychiatry Psychiatr Epidemiol. 2016;51(6):827–37. 10.1007/s00127-016-1204-9 [DOI] [PubMed] [Google Scholar]
- 44.Liu Y, Zhao Y, Tian Y, Xu L. Does the duration of childhood starvation affect life at age 45 or older in China? The mediating role of socioeconomic and health statuses. J Health Psychol. 2021;26(13):2520–37. 10.1177/1359105320914364 [DOI] [PubMed] [Google Scholar]
- 45.Zhao Y, Wang D, Du F. The moderating effect of contact with children on the relationship between adverse childhood experiences and depression in adulthood among a Chinese adult population. Int J Environ Res Public Health. 2022;19(15):8901. 10.3390/ijerph19158901 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Penninx BW, Leveille S, Ferrucci L, van Eijk JT, Guralnik JM. Exploring the effect of depression on physical disability: longitudinal evidence from the established populations for epidemiologic studies of the elderly. Am J Public Health. 1999;89(9):1346–52. 10.2105/ajph.89.9.1346 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Yochim BP, Lequerica A, MacNeill SE, Lichtenberg PA. Cognitive initiation and depression as predictors of future instrumental activities of daily living among older medical rehabilitation patients. J Clin Exp Neuropsychol. 2008;30(2):236–44. 10.1080/13803390701370006 [DOI] [PubMed] [Google Scholar]
- 48.Leibold ML, Holm MB, Raina KD, Reynolds CF 3rd, Rogers JC. Activities and adaptation in late-life depression: a qualitative study. Am J Occup Ther. 2014;68(5):570–7. 10.5014/ajot.2014.011130 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Chan SW, Chiu HF, Chien WT, Goggins W, Thompson D, Hong B. Predictors of change in health-related quality of life among older people with depression: a longitudinal study. Int Psychogeriatr. 2009;21(6):1171–9. 10.1017/s1041610209990950 [DOI] [PubMed] [Google Scholar]
- 50.Martín-Abreu CM, Hernández R, Cruz-Castellanos P, et al. Dignity and psychosocial related variables in elderly advanced cancer patients. BMC Geriatr. 2022;22(1):732. 10.1186/s12877-022-03423-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Ko KT, Yu SH, Lee CY, Tseng HY, Chiu YF, Hsiung CA. Moderating effect of depression on the association between pain and activities of daily living in older adults. Psychogeriatrics. 2018;18(5):379–87. 10.1111/j.1479-8301.2007.00216.x [DOI] [PubMed] [Google Scholar]
- 52.Peng S, Wang S, Feng XL. Multimorbidity, depressive symptoms and disability in activities of daily living amongst middle-aged and older Chinese: evidence from the China health and retirement longitudinal study. J Affect Disord. 2021;295:703–10. 10.1016/j.jad.2021.08.072 [DOI] [PubMed] [Google Scholar]
- 53.Gao T, Han S, Mo G, Sun Q, Zhang M, Liu H. A positive association between hunger in childhood and frailty in old age: findings from the Chinese longitudinal healthy longevity survey. Front Med. 2022;9:955834. 10.3389/fmed.2022.955834 [DOI] [PMC free article] [PubMed] [Google Scholar]
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Data is provided within the manuscript files.