DOI: https://doi.org/10.21203/rs.3.rs-2014903/v1
Background: Poor sleep quality and depression are two risk factors for the health of the older adults. Researches on the effects of long-time use of solid fuels in house on sleep quality and depression in the older adults were insufficient and had inconsistent findings. The purpose of this study was to examine the relationship between socioeconomic status, household solid fuels use, sleep quality and depression.
Methods: A total of 9325 older adults aged 60 and over were analyzed based on
the data extracted from the Chinese Longitudinal Healthy Longevity Survey in 2018. The structural equation model was used to test the association between socioeconomic status and sleep quality, socioeconomic status and depression in the older adults, and household solid fuels use was linked as a mediator.
Results: This study demonstrated the mediating role of household solid fuels use between socioeconomic status and sleep quality, socioeconomic status and depression. The result showed that low socioeconomic status was associated with more use of solid fuels in the house (β= -0.569, p<0.001), which was negatively associated with sleep quality (β= -0.044, p<0.001) and positively related to depression (β= 0.060, p<0.001).
Conclusion: Household solid fuels use contributes significantly to depressive symptoms and poor sleep quality among the older adults with low socioeconomic status. Programs and policies that facilitate access to clean fuels may help ameliorate depressive symptoms and bad sleep quality among the older adults with low socioeconomic status.
The ageing of the population has become a global phenomenon. According to the World Health Organization, older adults aged 60 and over was 1 billion in 2019. This number will increase to 1.4 billion in 2030 and 2.1 billion in 2050[1]. China, as the country with the largest number of older adults in the world, faces an even greater challenge of population ageing. The 7th National Population Census of China showed that China had 264.2 million older adults people aged 60 years and above, accounting for 18.70% of the total population. As the population of older adults grows and life expectancy increases, the social security system and public health system will face even bigger health challenges. Thus, we need to take more active and effective measures to maintain the health of the older adults and promote healthy ageing.
Depression and poor sleep quality are two issues that are highly prevalent among the older adults and often occur together which pose a huge threat to their health status[2, 3]. In terms of depression, it is a common mental health problem among older people. A large number of older adults worldwide have experienced depression. A systematic review has pointed out that the prevalence of depression among the older adults ranged from 1–16% [4]. Suffering from depression can lead to high rates of disability, high medical expenses, and reduced life quality. Additionally, sleep health is also an important public health issue of concern, especially in an ageing society. Poor sleep quality is more prevalent among older individuals than young people. About half of older people reported that they have sleep problems, including decreased sleep duration and sleep efficiency, irregular sleep schedules, and inability to fall asleep and stay asleep, ultimately leading to a decrease in sleep quality. Studies have found that poor sleep quality is associated with a wide range of adverse health outcomes, including cardiovascular disease[5, 6], stroke[7], cognitive impairment[8] and depression[9]. The high prevalence and the heavy burden of depression and poor sleep quality indicate the importance to find the risk factors that influence depression and sleep quality in the older adults, which have an important role to improve their quality of life and health status.
Depression and poor sleep quality are associated with multiple risk factors, including socio-demographic characteristics, Socioeconomic Status (SES), social support, lifestyle, stress and health status[10–12]. SES has attracted much attention for its impact on health for a long time, including mental health and sleep health. SES refers to the position of an individual or group in a class society and is usually measured by education, occupation and income. In previous studies, two different views about the mechanism between socioeconomic status and health had been put forward. One is the health selective theory which suggests that unhealthy bodies will limit an individual's educational and occupational achievement, leading to low SES. The other is the social causation theory, which suggests that the differences in SES are the underlying cause of health inequalities, the factors associated with low SES, such as stressful life events and poor living conditions, will increase the risk of diseases[13, 14]. Despite inconsistent theories, there were growing evidence proved that poor living conditions, and unaffordable for cleaner and healthier household products associated with low SES have unavoidable effects on people's health[15]. However, the exact mechanism between them is unclear.
Solid fuels used for cooking activities are a major source of indoor air pollutants, including carbon monoxide (CO), nitrogen oxides, inhalable particulate matter and some carcinogenic compounds[16, 17]. According to a report by WHO, around 2.6 billion people worldwide still use kerosene, biomass (wood, animal dung and crop waste) and coal as the main fuels for cooking. Additionally, 7 million people die prematurely each year due to the combined effects of environmental air pollution and household air pollution[18]. As the largest developing country, a large proportion of people in China still rely on solid fuels for cooking and heating. Accumulating evidence has suggested that exposure to indoor air pollution caused by household solid fuels use could be associated with worse sleep quality and depression. However, current researches on the effects of household solid fuels use on sleep health and mental health in the older adults are inadequate and the results were inconsistent.
In terms of sleep quality, increasing studies have shown that exposure to air pollution has detrimental effects on sleep quality[19, 20], but limited studies have investigated the relationship between indoor air pollution from household solid fuels use and sleep quality, and most study subjects are adults or children[21, 22], lack attention to the older adults who are more likely to develop sleeping health problems. To our knowledge, in China, there is only one study that investigated the relationship between sleep quality and household solid fuels use in the older adults, it was conducted on 1,725 older adults aged 80 years and above in Hainan Province, China[23]. Therefore, there is a lack of relevant studies on normal older adults aged 60 years and above. In terms of depression, recently, several studies have investigated the relationship between air pollution and depression[24], but their results were inconsistent. A cross-sectional study in rural India showed that, compared to a cleaner fuel (liquefied petroleum gas), age-matched women who cooked with biomass fuel had a higher prevalence of depression[25]. Another survey of a total of 4,585 older adults in rural China also showed that those who used solid fuels for cooking were nearly 1.2 times more likely to suffer from depression than those who used clean fuels for cooking[26]. In contrast, a study in the Helsinki region, Finland found no convincing evidence of the effect of long-term exposure to PM2.5 from residential wood combustion on depression[27]. Four European general population cohorts of 70,928 individuals also found no clear evidence for the relationship between air pollution and depressive symptoms[28]. Therefore, more studies based on large national data are needed to demonstrate the relationship between household solid fuels use and depression.
To address the limitations of prior studies, the purpose of this study was to examine the association between SES and depression, SES and sleep quality, as well as the mediating role of household solid fuels in a nationally representative sample of Chinese older adults aged 60 and over. This study helped to understand the mechanisms that influence sleep health and mental health in the older adults, and provided a reference for relevant policy development. Research hypotheses formulated based on this research model are as follows:
H1. People with low SES are more likely to use household solid fuels to cook.
H2. Household solid fuels use has a negative effect on sleep quality.
H3. Household solid fuels use has a positive effect on depression.
This study used the data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). It was a national representative cross-sectional survey of China. The 2018 CLHLS used a multi-stage stratified proportional probability sampling design. It randomly investigated 15847 Chinese older adults from the counties or cities in 22 provinces of mainland China. The data of the older adults was systematically collected through face-to-face interviews by well-trained staff. Additional details on the study design and methodology of CLHLS have been described elsewhere[29].
In total, 9325 older adults over 60 years old were included in this study. 3300 participants were excluded because of missing data on key variables, including education levels, household income, household solid fuels use, sleep quality and depression. 3249 participants excluded because younger than 60 (n = 1), never cooking or use other cooking fuels (n = 347), had incomplete information on depression (n = 1870), answered don’t know on sleep quality (n = 1021), answered don’t know on education level (n = 10). Figure 1 showed the flowchart about the inclusion and exclusion of participants.
SES was a latent variable in this study. Latent variables are those variables which are difficult to measure directly but can be measured through their effect on measured variables. Generally speaking, SES is an overall indicator of an individual's social status relative to others, which is based on a combination of education, occupation and income. However, given that most older adults in this study sample were retired and not formally employed. We finally chose educational level and household income as two measure variables for older adults' SES, which was consistent with the methods used in the published literature[30, 31].
In CLHLS, education and household income were measured by asking two questions" Years of education" and "Your household's total income in the last year". The number of years of education was divided into three categories: 0 years, 1 ~ 5 years and 6 years or more, respectively referred to as no schooling, elementary school and secondary school or above[29]. Given that most older adults in this study were retirees and didn’t have stable pay, we chose household income instead to measure financial status[32]. Household income was divided into three categories: 0 ~ 10000; 10001 ~ 50000; >50000.
The main cooking fuel in participants’ families was the mediator in this study. Household solid fuels use was obtained by asking participants what is the main fuel they used for cooking in their family. Those who answered "never cooked" and "other" were excluded from this study. Energy sources used for cooking were divided into two types: clean fuels and solid fuels. Clean fuels were defined as liquefied natural gas, natural gas and electricity, while solid fuels were defined as coal, charcoal, firewood, wood and animal dung[33].
Sleep quality was measured by the question “how do you rate your sleep quality recently?”, the answers included “very good”, “good”, “fair”, “bad” and “very bad”. To avoid the skewed distribution of sleep quality (for example, many older adults in raw data self-reported good or very good sleep quality). In this study, sleep quality levels of “very good” and “good” were recorded as “high quality”, and other level options were recorded as “low quality”[34].
The Center for Epidemiologic Studies Short Depression Scale (CES-D) was used to measure depressive symptoms in CLHLS. CES-D has shown good validity and reliability in the Chinese population[35]. All questions on the scale included five response categories: always, often, sometimes, rarely and never. Of the ten questions, seven are positive, three are negative which required reverse scoring. Positive questions answers of "always" received 3 points, "often" received 2 points, "sometimes" received 1 point, “rarely" and "never" received 0 points. Finally, a total score was calculated for the ten questions. Those who scored 10 and below were defined as normal, and those who scored 11 to 30 were defined as having depressive symptoms[29].
We selected several confounding variables to adjust for in the model based on the review of the literature. It included socio-demographic characteristics variables, behavior factors variables, and physical health status variables. Socio-demographic characteristics variables included age (continuous variable), gender (male or female), residential area (urban or rural) and marital status (married and lived with spouse or else). Behavior factors variables included smoking status (yes or no), alcohol drinking (yes or no) and exercise status (yes or no). Physical health status is mainly concerned with the prevalence of chronic diseases (no chronic condition or one chronic condition or multimorbidity physical health).
All data analyses in this paper were performed with Stata 16.0. Firstly, to show the basic characteristics of the participants, we used mean and standard deviation for continuous variables, frequency and percentage for categorical variables. Secondly, we used Pearson's correlation analysis to examine the relationships among key variables. Thirdly, SEM provided a framework for assessing the association between SES, household solid fuels use, sleep quality and depression. SES was identified by two indicators: household income and education levels. The SEM model in this study was controlled for age, sex, marital status, residential area, smoking status, alcohol drinking, exercise status, and chronic disease.
SEM can be used to test the direct and indirect effects of a set of explanatory and mediator variables on the outcome variable, and it is one of the methods for testing complex data. The criteria for a well-fitting model were defined as follows: comparative fit index (CFI) > 0.90[36], root mean square error of approximation (RMSEA) < 0.08, standardized root mean square residual (SRMR) < 0.08[37]. Maximum likelihood estimation was used for model estimation. Standardized coefficients were used to calculate estimates of path coefficients, they represented the strength of the path between two variables, and p-values < 0.05 was considered significant.
A total of 9325 older adults (4320 male and 5005 female) were included in this study. The basic characteristics of all participants were shown in Table 1. The mean age of all participants was 82.85 years old, 57.6% of the study participants were from rural areas, 47.4% of participants were married and live with spouse. 83.4% of participants did not smoke, 84.4% of participants did not drink, 64.6% of participants did not do exercise. 40.6% of older adults self-reported they had multimorbidity. 43.2% of participants never attended school. 30.6% of participants had an annual household income less than RMB 10,000, which indicates that a large proportion of participants were low SES population.28.7% of respondents used solid fuels for cooking at home, the other (71.3%) used clean fuels. Almost half of the participants (46.8%) considered that their sleep quality was not good. About 10.2% (952/9325) of the older adults in this study had a depression score of more than 10. That was to say, nearly about one in ten older adults was at risk of having depressive symptoms.
| variables | N | % |
|---|---|---|
| Socio-demographic Characteristics | ||
| Age | 82.85 ± 11.39 | |
| Sex | ||
| male | 4320 | 46.3 |
| female | 5005 | 53.7 |
| Residential area | ||
| urban | 5375 | 57.6 |
| rural | 3950 | 42.4 |
| Marital status | ||
| Married and live with spouse | 4424 | 47.4 |
| else | 4901 | 52.6 |
| Behavior factors | ||
| Smoking status | ||
| No | 7780 | 83.4 |
| Yes | 1545 | 16.6 |
| Alcohol drinking | ||
| No | 7866 | 84.4 |
| Yes | 1459 | 15.6 |
| Exercise status | ||
| No | 6026 | 64.6 |
| Yes | 3299 | 35.4 |
| Physical health status | ||
| Chronic disease | ||
| No chronic condition | 2646 | 28.4 |
| One chronic condition | 2889 | 31.0 |
| Multimorbidity | 3790 | 40.6 |
| socioeconomic status | ||
| Education levels | ||
| 0 years | 4026 | 43.2 |
| 1∼5 years | 2266 | 24.3 |
| 6 years or more | 3033 | 32.5 |
| Household income | ||
| < 10000 | 2854 | 30.6 |
| 10000 ~ 50000 | 2982 | 32.0 |
| > 50000 | 3489 | 37.4 |
| Mediator | ||
| Household solid fuels use | ||
| Clean fuels | 6648 | 71.3 |
| Solid fuels | 2677 | 28.7 |
| Outcome variables | ||
| Sleep quality | ||
| Low sleep quality | 4363 | 46.8) |
| High sleep quality | 4962 | 53.2 |
| Depression | ||
| No | 8373 | 89.7 |
| Yes | 952 | 10.2 |
The correlation coefficients among key variables are shown in Table 2. The results showed that depression was significantly correlated with education level (r = -0.076, p < 0.01), household income (r = -0.062, p < 0.01), household solid fuels use (r = 0.062, p < 0.01), and sleep quality (r = -0.238, p < 0.01). Sleep quality was also significantly correlated with education level (r = 0.064, p < 0.01), household income (r = 0.056, p < 0.01), household solid fuels use (r = -0.044, p < 0.01). It suggested that it made sense to include the above variables in the SEM analysis of the association between SES, household solid fuels use, sleep quality and depression.
| Variables | Education level | Household income | Household solid fuels use | Sleep quality | Depression |
|---|---|---|---|---|---|
| Education level | 1.000 | ||||
| Household income | 0.229** | 1.000 | |||
| Household solid fuels use | -0.199** | -0.331** | 1.000 | ||
| Sleep quality | 0.064** | 0.056** | -0.044** | 1.000 | |
| Depression | -0.076** | -0.062** | 0.062** | -0.238** | 1.000 |
| **correlation coefficients were statistically significant at the 0.01 level | |||||
The mediating effect of household solid fuels use between low SES and sleep quality, low SES and depression
After controlling for age, sex, marital status, residential area, smoking status, alcohol drinking, exercise status, and chronic disease, the results of SEM were shown in Fig. 2 and Table 3. Path analysis was applied to evaluate the relationships between low SES and depression, low SES and sleep quality, mediated by household solid fuels use. The model fitted the data well (CFI = 0.941, SRMR = 0.025, RMSEA = 0.042). The result showed that SES was negatively correlated with household solid fuels use (β= -0.569, p < 0.001), and household solid fuels use was positively related to depression (β = 0.060, p < 0.001), negative related to sleep quality (β= -0.044, p < 0.001). The estimates regarding the indirect effects of SES on both depression (β=-0.034, p < 0.001) and sleep quality (β = 0.025, p < 0.001) were statistically significant. Additionally, there was a bidirectional relationship between sleep quality and depression (β=-0.224, p < 0.001). These findings indicated that household solid fuels use partially mediated the relationship between SES and depression, it also played a partial mediating role in the link between SES and sleep quality, thereby supporting H1, H2 and H3.
| Direct and indirect model | ||
|---|---|---|
| β | p | |
| Direct Path | ||
| SES →Household solid fuels use | -0.569 | < 0.001*** |
| Household solid fuels use → Sleep quality | -0.044 | < 0.001*** |
| Household solid fuels use → Depression | 0.060 | < 0.001*** |
| Indirect Paths | ||
| SES → Depression | -0.034 | < 0.001*** |
| SES → Sleep quality | 0.025 | < 0.001*** |
β, standardized coefficient; p, two-tailed p values. SES, socioeconomic status. *Significant at p < 0.05 level, ** significant at p < 0.01 level, *** significant at p < 0.001 level
In the present study, we used the data from a large field survey in China to analyze the associations between SES, household solid fuels use, sleep quality and depression in the older adults. In this study, 10.2% older adults had depressive symptoms and 46.8% older adults self-reported that they had bad sleep quality. As we hypothesized, the older adults with lower SES were more likely to use solid fuels for cooking, and household solid fuels use was a predictor of poor sleep quality and depression, even after considering a wide range of covariates, including demographic characteristics, behavior factors, and chronic disease status.
Notably, the choice of cooking fuels (solid fuels or clean fuels) was closely linked to people’s SES characteristics, such as household income and education level. These results were consistent with other studies. A prospective cohort study in Sichuan, China found that household solid fuels users for cooking were older and more likely to be less educated and poorer, compared with those who used clean fuels for cooking[38]. Possible explanations for this result were as follows. On the one hand, because of low household income, the poorer and more vulnerable older adults are inability to pay the price of clean fuels and relevant cooking facilities, so they are preferring to use solid fuels such as dung, straw and wood for cooking and heating. On the other hand, people of better educated are more concerned about their health, and have better ability and greater willingness to increase investment in clean fuels. Therefore, the negative effect of household solid fuels use is more likely to occur in the older adults with low SES.
Depression and poor sleep quality are the most common problems among the older adults and often occur simultaneously. Most of the current studies continued to support the bidirectional relationship between poor sleep quality and depression[3, 39]. Poor sleep quality was a risk factor for the onset and recurrent depression. Conversely, depression induced poor sleep quality. Discover the common risk factors for sleep quality and depression were helpful to improve the health status of the older adults. Household solid fuels use was determined to be a factor that both affect sleep health and mental health in the older adults in this study.
Our results indicate that household solid fuels use was significantly associated with poor sleep quality in the older adults. It was in line with other studies based on middle-aged Chinese adults[21] and older adults aged over 80[23]. Our study confirmed the negative impact of household solid fuels use on sleep quality in the older adults aged 60 and above. The mechanism underlying the association between indoor air pollution and poor sleep quality remains unclear, but some studies have suggested some possible mechanisms. One possible explanation is related to the respiratory system. Studies have suggested when people are exposed to air pollution, particles from air pollutants will deposit in the airways, increase the risk of respiratory tract inflammation, and ultimately affect sleep quality[40]. The second potential mechanism is related to the central nervous system. Exposure to air pollutants will induce local and systemic inflammation, producing inflammatory biomarkers capable of reaching the brain, causing neuro-toxicity and neuro-inflammation which have adverse effects on sleep quality[41, 42]. The third possible mechanism is related to brain damage. Pollutants from the use of solid fuels can cause a range of damage to the brain, such as alterations of the blood-brain barrier, degenerating cortical neurons, and nonneurotic plaques. These will harm the brain's movement and sleep quality.
This study also supported that there is an association between household solid fuels and depression. The results of previous studies are inconsistent on the relationship between household solid fuels use and depressive symptoms[11, 27, 28, 43, 44]. Possible reasons for the inconsistent findings on the association between depression and solid fuels maybe they have different methods to identify depression[45, 46], different research subjects and different sample sizes. Our research was based on large survey data in China. Additionally, our study used the CES-D scale to screen for depressed patients, compared to studies based on the diagnosis of doctors or the use of antidepressant medications[47], it was more useful in identifying patients who were unaware of their depressive symptoms or who were not seeking medication help, especially those with mild and moderate depression symptoms. Therefore, our study can further uncover the unobserved effects of household solid fuels use on depression. At the same time, in the present study, we observed a significant bivariate correlation between sleep quality and depression. Thus, poor sleep quality due to household solid fuels use will also ultimately increases the incidence of depression. Therefore, this study argued that the effects of solid fuels on depression should rise more concern. Regarding the possible mechanisms between air pollution and depression, a great number of studies have suggested that pollutants from air pollution harm the central nervous system[48–50]. Some studies have suggested that pollutants could enter the systemic circulation, reach the brain, and cause oxidative stress and inflammation in the central nervous system[51, 52]. More researches are needed in the future to discover the mechanisms between solid fuels and depression.
The results of this study suggested that household solid fuels use mediates the relationship between both SES and depression, SES and sleep quality. The negative effect of household solid fuels use on depression and sleep quality depends to a large extent on the social vulnerability and health vulnerability of different groups. Older adults with low SES, as a vulnerable group in terms of SES and health, they are more likely to be exposed to indoor air pollution from household solid fuels use which has negative effects on their health. However, the negative impact of household solid fuels use on mental health and sleep health has not received sufficient attention in the older adults with low SES. In primary health care services for the older adults, more attention needs to be paid to the older adults with low SES, increasing their access to clean fuels for cooking and improving their living environment to reduce the occurrence of poor sleep quality and depression in the older adults.
Strengths in our research lay in the following aspects. Firstly, this study involved a large population of over 9325 older adults from 500 urban and rural communities in China. It controlled for possible confounding variables such as demographic characteristics, behavior factors and chronic disease status. Secondly, our study provided possible explanatory factors for the differences between depression and sleep quality in older adults with different SES. Household solid fuels use mediated the relationship between both SES and depression, SES and sleep quality.
We acknowledge there are several potential limitations existed in this study. Firstly, given the cross-sectional study, causal inferences were not possible to make. Future longitudinal studies could be continued to validate the association between SES, household solid fuels use, sleep quality and depression in the older adults. Secondly, sleep quality was obtained through self-evaluation of participants, there may had recall bias. In addition, different studies used different methods to investigate sleep quality, which limited comparability with other studies to some extent. Thirdly, we did not have information on the frequency of cooking, usage of cooking fuels, and whether they had indoor ventilation. The extent of indoor air pollution exposure can vary from home to home. Fourthly, the level of outdoor air pollution varies from region to region, we cannot fully control the effects of outdoor air pollution. Given the confinement of indoor spaces, indoor air pollution levels were generally higher than outdoor air pollution level when older adults used solid fuels in house. Therefore, the negative effect of indoor air pollution form household solid fuels use was higher than outdoor air pollution on the older adults who often stay at home.
In this study, based on national survey data, we confirmed that household solid fuels use was a risk factor for poor sleep quality and depressive symptoms in the older adults. Higher costs of clean fuels and low health awareness may be barriers to use clean fuels for cooking among the older adults with low SES. Therefore, relevant policies should increase the accessibility of clean fuels and work to reduce the use of solid fuels in house, especially for the older adults with low SES.
socioeconomic status
Chinese Longitudinal Healthy Longevity Survey
The Center for Epidemiologic Studies Short Depression Scale
comparative fit index
root mean square error of approximation
standardized root means square residual
structural equation model.
Acknowledgements
We thank the Chinese Longitudinal Healthy Longevity Survey, which provided the data in this research.
Authors ‘contributions
WL analyzed the data, wrote the draft of the paper. QY contributed to the design of the paper and gave advice on statistical methodology. YC checked the draft of the paper and gave advice on statistical methodology. GZ provided suggestions and supervision of the work. All authors read and approved the final version.
Funding
This study was supported by the National Natural Science Foundation of China (Grant No.71774102). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
Availability of data and materials
Data for this study were sourced from Chinese Longitudinal Healthy Longevity Survey (CLHLS) and available here: https://doi.org/10.18170/DVN/WBO7LK
Ethics approval and consent to participate
The CLHLS study was approved by the Research Ethics Committee of Peking University (IRB00001052–13074), and all participants or their proxy respondents provided written informed consent. The research was performed in accordance with the Declaration of Helsinki. All procedures were performed in accordance with relevant guidelines.
Consent for publication
Not applicable.
Competing of interests
The authors declare that they have no competing interests.
Author details
1Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine Shandong University, 44 Wen-hua-xi Road, Jinan 250012, Shandong, China. 2NHC Key Laboratory of Health Economics and Policy Research, Shandong University, 44 Wen-hua-xi Road, Jinan 250012, Shandong, China.