Article Text

Original research
Association between indoor air pollution and depression: a systematic review and meta-analysis of cohort studies
  1. Xiaona Zhang1,
  2. Linlin Ding2,
  3. Fen Yang2,
  4. Guiyuan Qiao2,
  5. Xiaolian Gao2,
  6. Zhenfang Xiong3,
  7. Xinhong Zhu2
  1. 1 Hainan General Hospital, Haikou, China
  2. 2 School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
  3. 3 School of Nursing and Health Management, Wuhan Donghu University, Wuhan, China
  1. Correspondence to Dr Xinhong Zhu; zxh88{at}hbtcm.edu.cn; Dr Zhenfang Xiong; 825286722{at}qq.com

Abstract

Objectives Incomplete combustion of solid fuel and exposure to secondhand smoke (SHS) are the primary causes of indoor air pollution (IAP), potentially leading to detrimental effects on individual mental health. However, current evidence regarding the association between IAP and depression remains inconclusive. This study aims to systematically investigate the evidence regarding the association between IAP and the risk of depression.

Design Systematic review and meta-analysis of cohort studies.

Data sources Two independent reviewers searched PubMed, the Cochrane Library, Web of Science and EMBASE for available studies published up to 13 January 2024.

Eligibility criteria We included all cohort studies published in English that aimed to explore the relationship between IAP from solid fuel use and SHS exposure and the risk of depression.

Data extraction and synthesis Two independent reviewers extracted data and assessed the risk of bias. The association between IAP and depression was calculated using pooled relative risk (RR) with 95% CIs. Heterogeneity was assessed using the I2 value, and the effect estimates were pooled using fixed-effects or random-effects models depending on the results of homogeneity analysis.

Results We included 12 articles with data from 61 217 participants. The overall findings demonstrated a significant association between IAP exposure and depression (RR=1.22, 95% CI: 1.13 to 1.31), although with substantial heterogeneity (I2=75%). Subgroup analyses based on pollutant type revealed that IAP from solid fuel use was associated with a higher risk of depression (RR=1.20, 95% CI: 1.13 to 1.26; I2=62%; 5 studies, 36 768 participants) than that from SHS exposure (RR=1.11, 95% CI: 0.87 to 1.41; I2=80%; 7 studies, 24 449 participants). In terms of fuel use, the use of solid fuel for cooking (RR: 1.23, 95% CI: 1.16 to 1.31; I2=58%; 4 studies, 34 044 participants) and heating (RR 1.15, 95% CI: 1.04 to 1.27; I2=65%; 3 studies, 24 874 participants) was associated with increased depression risk.

Conclusions The findings from this systematic review and meta-analysis of cohort studies indicated an association between exposure to IAP and depression.

PROSPERO registration number CRD42022383285.

  • public health
  • depression & mood disorders
  • public health
  • mental health

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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STRENGTHS AND LIMITATIONS OF THIS STUDY

  • This systematic review and meta-analysis originated from an extensive search encompassing numerous cohort studies, offering a thorough evaluation of the link between indoor air pollution (IAP) and depression risk.

  • The analysis incorporates data covering diverse IAP sources such as solid fuel use and secondhand smoke (SHS) exposure.

  • The Newcastle–Ottawa Scale was used to evaluate the quality of the included studies.

  • Solid fuel use and SHS exposure were assessed as binary exposures (exposed or unexposed), so we were unable to perform dose–response analyses.

  • There was substantial heterogeneity observed in the included data.

Introduction

Air pollution is a major contributor to the global burden of disease, with 6.70 million deaths attributable to outdoor and indoor air pollution (IAP), comprising 12% of total mortality, as indicated in the 2019 Global Burden of Disease report.1 In less developed countries, polluting fuels (such as coal, wood, agricultural residue and animal dung) continue to serve as the primary sources of heating, cooking and lighting. Presently, one-third of the global population relies on these polluting fuels for cooking, heating or lighting.2 These fuels are commonly used in open fires or simple stoves, which often leads to incomplete combustion and the release of a significant amount of IAP when the smoke is not properly vented.2 Another notable form of IAP is caused by secondhand smoke (SHS), known as passive smoking or environmental tobacco smoke.3 As the most frequent IAP in the world, SHS is the leading contributor to indoor particulate matter (PM2.5), nitrogen dioxide (NO2) and many volatile organic compounds (VOCs).4

Prior research has demonstrated that exposure to IAP from solid fuel use or SHS exposure is linked to negative health effects, including chronic obstructive pulmonary disease, lung cancer, tuberculosis and respiratory diseases.5 6 Moreover, research has indicated that elevated levels of IAP may have adverse effects on people’s mental health (such as anxiety and depressive disorders).7 8

Depression is mainly manifested as decreased interest and poor mood, with an estimated frequency of 540.5 per 100 000 individuals. It ranks as the third-largest burden of non-fatal diseases globally.9 It has an estimated lifetime prevalence of about 11%,10 with increasing trends in the general population.11 Many factors can contribute to the onset of depression, such as genetic factors, demographic factors,12–14 lifestyle habits (eg, drinking and smoking)15 16 and environmental characteristics (such as air pollution).17 18 Numerous studies have indicated a positive correlation between short-term and long-term exposure to outdoor air pollution and depression.19 However, the association between IAP and depression remains unclear. Recent studies have reported an association between IAP exposure and depression, but with inconsistent and contradictory results.20 21 Findings from a study involving individuals aged 45 years and older across 28 Chinese provinces revealed a correlation between indoor solid fuel use and a higher prevalence of depression.22 Furthermore, a study of 1280 middle-aged non-smokers revealed a positive correlation between SHS exposure and depression, as well as a dose–response interaction.23 However, a study by Bot et al 24 of 2845 people from two separate Dutch non-smoking samples found no correlation between SHS exposure and depression and/or anxiety, which was analogue to the study by Lam et al. 25

We aimed to conduct a systematic review and meta-analysis of cohort studies examining the association between IAP exposure and depression.

Methods

Data sources and search strategy

This study has been registered in PROSPERO (CRD42022383285). We used the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines to inform the reporting (online supplemental table 1).26 We searched four databases (PubMed, Web of Science, the Cochrane Library and EMBASE), using search terms related to IAP (air pollution, indoor air pollution, household air pollution, IAP, solid fuel, secondhand smoke, SHS, tobacco smoke pollution, passive smoke, environmental tobacco smoke), depression (depression, depressive, depressive disorder, depressive symptoms, emotional depression) and cohort studies (Cohort Studies, Cohort, Cohort Analysis, Cohort Analyses, longitudinal, prospective, retrospective, follow up, Observational Studies). The detailed search strategy is shown in online supplemental table 2. All English-language publications published up to 13 January 2024 were considered for review. We also checked through the reference list of the included studies to attempt to identify any other potentially related studies.

Supplemental material

Study selection

Inclusion criteria: (1) participants were human; (2) original research; (3) reported the correlation between IAP from solid fuel combustion or SHS and depression; (4) reported risk ratio (RR), OR or HR and 95% CIs or provided original data to calculate; (5) cohort studies; (6) English-language articles. Depression is diagnosed as follows: (1) doctor’s diagnosis; (2) currently taking antidepressants or in antidepressant treatment; (3) assessed through various scales. We defined exposure to IAP as: (1) reporting household use of solid fuel (eg, coal, charcoal, crop residue and wood burning) for cooking, heating and/or lighting; or (2) exposure to SHS.

Exclusion criteria: (1) studies on outdoor air pollution; (2) conference papers or review articles; (3) letters to the editor or case reports; (4) animal studies; (5) studies with inadequate information. We engaged in a two-step selection procedure to ensure that eligible studies were properly identified: first, the titles and abstracts of the relevant papers were reviewed; second, the full text was reviewed against the eligibility criteria (figure 1).

Figure 1

Flow chart of study inclusion.

Data extraction

Two coauthors (XZha and LD) independently searched and extracted the following information: first author’s name, country and year of publication, study type, participants, sample size, diagnosis of depression, IAP category and assessment measures, Newcastle–Ottawa Scale (NOS) score, RR (or other measures of effect size) and 95% CI. Furthermore, for studies reporting multiple stratified risk estimates, each stratified effect value is treated as a separate set of data. OR, HR, or RR and 95% CI adjusted for major confounding factors in the original study were preferentially extracted. If the OR, HR, or RR and 95% CI were not provided in the original literature, the original data were extracted (the number of exposed/unexposed and cases/non-cases groups).

Quality assessment

Using the NOS, the following three main features were considered: the selection of study groups, group comparability, and the ascertainment of exposure or outcome in case-crossover or cohort studies. The total score on the scale was 9, with individual scores of 4 for selection, 2 for comparability and 3 for exposure/outcome confirmation. Studies scoring ≥7 on the NOS are categorised as high quality, while those scoring ≤3 are considered low quality. Two coauthors (XZha and LD) independently assessed all included NOS scores and compared examination results until an agreement was reached. XZha was responsible for disagreements.

Statistical analysis

Meta-analysis was conducted to estimate the difference in risk between the exposed/unexposed and cases/non-cases groups. We used RR as a measure of association across all studies. The extracted original data were adopted to calculate pooled RR and 95% CI to evaluate the relationship between IAP and depression risk. We performed subgroup analyses including by location, diagnosis of depression, sex, fuel use and participants to explore the effects of different subgroups on the study. The heterogeneity among studies is described by I2 (the I2 values of 25%, 50% and 75% represent low, medium and high heterogeneity, respectively).27 If p<0.05 and/or I2>50%, indicating heterogeneity, a random-effects model was used to aggregate effect sizes, and subgroup analysis was carried out to explore the origins of heterogeneity. If p>0.10 and I2≤50%, homogeneity was satisfied and a meta-analysis was performed with a fixed-effects model. Whether a study had a substantial impact on the results was explored by sensitivity analysis. The funnel plot was adopted to evaluate qualitatively publication bias and Egger’s test was adopted to evaluate quantitatively publication bias when ≥10 studies were included.28 Each study’s effect sizes were calculated using the RevMan package V.5.4 and STATA V.18. A two-tailed p<0.05 was defined as a statistically significant difference.

Study variables

Outcome assessment of depression was performed by the following scales: (1) the Center for Epidemiologic Studies Depression Scale (CES-D), (2) the Edinburgh Postnatal Depression Scale (EPDS) and (3) the CES-D-10. IAP exposure was defined by a variety of methods such as self-report, biological testing or unstructured questions. Depending on the type of fuel, there are clean fuels (electricity, liquefied gas, natural gas, etc) and solid fuels (coal, solid charcoal, wood, etc). SHS exposure was evaluated using a binary classification method (non-exposure and exposure).

Patient and public involvement

None.

Results

Description of studies

The database search revealed 1579 records, with 223 duplicates being deleted. After a critical review of titles and abstracts, 1301 articles were removed because they were unrelated to our research questions, and after a full-text review of the remaining 55 studies, the final 12 articles containing 25 available data met our inclusion criteria and were used in the systematic review (figure 1).22 29–39

Online supplemental table 3 presents the major characteristics of the included studies as well as the outcomes of the NOS quality evaluation. Among these studies, nine studies were conducted in China, two in America and one in France. All studies used a cohort study design. Five studies found an association between IAP from solid fuel use and depression, and seven studies found an association between IAP from SHS exposure and depression, with sample sizes ranging from 178 to 8637 participants. We reported an RR value for each association between IAP exposure and depression and combined all effect values to report the total RR of 95% CI. For data on IAP exposure, eight studies used questionnaires and four were self-reported. The most common instruments used in the assessment of depression were the CES-D-10 (n=3) and EPDS (n=3), followed by the CES-D (n=2), the Depressive Symptoms Scale (n=1) and the Composite International Diagnostic Interview Short Form diagnoses (n=1). For diagnosis of depression, 10 articles were based on the depression scale, and 2 articles were based on self-report. The overall quality score ranged from 7 to 8 (online supplemental table 4). All studies received a high-quality score (NOS ≥7). There were no low-quality studies, and the average quality score was 7.4.

Overall association between IAP exposure and depression

The overall pooled RR of 25 available data using a random-effects model indicated a significant relationship between exposure to IAP and depression risk (RR=1.22, 95% CI: 1.13, 1.31, I2=75%). The main result is shown in figure 2.

Figure 2

Forest plot of the association between indoor air pollution and depression risk.

Subgroup analysis

We carried out subgroup analysis to explore sources of heterogeneity. Table 1 reports subgroup analysis by IAP source, location, diagnosis of depression, sex, fuel use and participants, adjusting for confounding. For the source of IAP, we observed a positive association between IAP from solid fuel (OR=1.20, 95% CI=1. 13, 1.26; I2=62%) and depression. In terms of location where the study was conducted, the pooled RR using a random-effects model for China was 1.22 (95% CI=1.16, 1.29; I2=58%). For the diagnosis of depression, we found that IAP was positively correlated with depression assessed through scales (RR=1.21, 95% CI=1.11, 1.31; I2=76%) and self-report (RR=1.27, 95% CI=1.04, 1.55; I2=79%). When performing subgroup analysis by sex, a statistically significant difference between IAP and depression was found in the female study (RR=1.19, 95% CI=1.03, 1.37; I2=70%). For fuel use type, IAP from solid fuels used for heating was significantly associated with depression (RR=1.15, 95% CI=1.04, 1.27; I2=65%). For the subgroup of participants, the following results were found: ≥45 years old (RR=1.22, 95% CI=1.16, 1.29; I2=65%), pregnant women (RR=1.16, 95% CI=0.91, 1.47; I2=41%) and children (RR=0.18, 95% CI=0.03, 1.19; I2=93%). The detailed subgroup analysis results are presented in table 1.

Table 1

Subgroup analysis

Figure 3 presents a forest plot of the study results.

Figure 3

Forest plot of the relationship between exposure to IAP from solid fuel use and SHS exposure and the risk of depression. IAP, indoor air pollution; SHS, secondhand smoke.

Sensitivity analysis and publication bias

Based on the random-effects model, sensitivity analysis was performed using the one-by-one exclusion method, and the results showed that the combined RR values before and after the exclusion of a particular study in the study of IAP and the risk of depression were generally consistent, suggesting that the stability of the meta-analysis results was good (online supplemental figure 1).

Supplemental material

Egger’s tests (online supplemental figure 2) showed that publication bias was present only for SHS exposure (p=0.020), males (p=0.012) and pregnant women (p=0.021). The funnel plot was asymmetrical (online supplemental figure 3).

Supplemental material

Supplemental material

Discussion

Major findings

In this systematic review and meta-analysis, we synthesised data from 12 studies from three countries to comprehensively evaluate the association between IAP exposure and depression. The study considered a broad spectrum of IAP sources, encompassing solid fuel and SHS. The results showed a significant association between IAP exposure and depression (RR=1.22, 95% CI: 1.13, 1.31; I2=75%). Subgroup analyses based on pollutant type demonstrated that IAP from solid fuel was associated with a higher risk of depression (RR=1.20, 95% CI: 1.13 to 1.26; I2=62%, 36 768 participants, 5 studies) than that from SHS exposure (RR=1.11, 95% CI: 0.87 to 1.41; I2=80%, 24 449 participants, 7 studies).

IAP from solid fuel and depression

IAP from incomplete fuel combustion is positively associated with risk of depression22 and other neurobehavioural problems, such as cognitive impairment,40 anxiety symptoms7 and poor sleep quality.41 After pooled analysis of the included studies, our results showed an increased risk estimate between IAP from solid fuel and depression. Our results are consistent with a recent systematic review conducted by Li et al 42 that found a positive association between exposure to IAP from solid fuel and depression. The study by Li et al encompassed nine studies, with only one being a cohort study, while the remainder were cross-sectional studies. In our study, all studies included were cohort studies, which were more reliable than cross-sectional studies. Solid fuel combustion generates a vast amount of air pollutants (such as PM, sulfur dioxide, NO2, carbon monoxide and VOCs),43 particularly in inadequately ventilated homes.44 Compared with ambient air pollution, the use of indoor solid fuels may result in higher levels of PM10.45 According to mechanistic studies, inhaling air pollutants can cause neuroinflammation, oxidative stress, cerebrovascular damage and dopaminergic neurotoxicity,46 all of which are linked to depressive episodes resulting from neurotransmitters and hormonal dysregulation.47 A well-known aetiological mechanism of depression is that serotonin and norepinephrine are unbalanced in the central nervous system.48 Hence, it is reasonable to propose that air pollution may play a role in depression. Research indicated that transitioning to clean fuels such as natural gas and electricity can decrease IAP by close to 96%.49 Thus, the application of upgraded stoves could serve to minimise household air pollution in undeveloped countries.50 In the future, it is crucial to reduce IAP by limiting the use of solid fuels and promoting efficient ventilation to minimise further adverse health impacts.

IAP from SHS and depression

Increasing epidemiological studies have focused on studying the potential association between IAP from SHS exposure and depression, but the findings are inconsistent. Some evidence reported that exposure to IAP from SHS is positively associated with the risk of depression,24 30 51 while other studies have found a no significant relationship.24 25 Evidence from this updated meta-analysis of cohort studies revealed that an overall 11% increase in the risk of depression was observed for SHS exposure (RR=1.11, 95% CI: 0.87, 1.41). This finding is consistent with previous meta-analyses,52 which included seven observational studies and indicated a 60% increase in the prevalence of depression after exposure to SHS (OR=1.60, 95% CI: 1.35, 1.90). Recently, another meta-analysis of evidence from 24 observational studies revealed a 32% overall increase in the likelihood of depression after exposure to SHS (OR=1.32, 95% CI: 1.25, 1.39) and a similar dose–response relationship was observed (OR=1.57, 95% CI: 1.26, 1.87).53 However, the study by Han et al only included 2 cohort studies, with the remaining 22 being cross-sectional studies, which may potentially compromise their reliability. In our meta-analysis, all seven articles assessing the association between SHS and depression were cohort studies. Exposure to SHS is associated with inflammation-related mechanisms, which may lead to the production of inflammatory cytokines in the body, closely linked to the onset of depression.54 55 However, a recent study conducted by Bot et al 24 found no significant increase in the prevalence of depression among non-smokers exposed to SHS (OR=0.96, 95% CI: 0.73, 1.27). The inconsistent results could be attributed to the selection of non-smokers and the assessment methodologies employed. Bot et al 24 used plasma cotinine levels to evaluate SHS exposure status in the Netherlands, whereas our study used questionnaires to evaluate SHS exposure levels among the majority of individuals from China. Furthermore, Bot et al 24 assessed depression using the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV), while the majority of the studies we included used the CES-D-10. The DSM-IV assesses patients’ depression using a comprehensive assessment measure, whereas the CES-D-10 assesses patients by investigating their mood status in the past 7 days. Different assessment methods may explain this conflicting result.

Limitations

This study has some important limitations. First, there was substantial between-study heterogeneity observed across most of the associations examined. Even though subgroup analyses were performed for factors believed to contribute to this heterogeneity, heterogeneity remained apparent, hindering our comprehensive exploration of potential sources of heterogeneity. Second, this meta-analysis relies on self-reported questionnaires to assess IAP exposure instead of using a more precise biological measure. This approach could lead to an underestimation or overestimation of the actual correlation, potentially introducing bias. Third, our study reported IAP exposure as binary exposure (exposed or unexposed), thereby precluding the possibility of conducting a dose–response analysis. Moreover, the studies included were mostly from China and the USA. Therefore, the outcomes of this study are not necessarily applicable to other regions, and further studies in more regions and on a larger scale are needed in the future.

Conclusions

In conclusion, the current systematic review and meta-analysis suggested that exposure to IAP from solid fuel and SHS is associated with an increased risk of depression. The association between solid fuel IAP exposure and risk of depression was larger than that between SHS exposure and depression. Assuming some causal role of IAP exposure, it is critical to mitigate IAP from solid fuel by transitioning to cleaner fuels or employing improved biomass stoves and to implement effective policies to reduce indoor smoking.

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information.

Ethics statements

Patient consent for publication

Ethics approval

Not applicable.

References

Supplementary materials

Footnotes

  • Contributors Conceptualisation—XZHu, FY and ZX. Methodology—XZha. Literature search—XZha and LD. Formal data analysis and investigation—XZha. Writing (original draft preparation)—XZha, LD and XZHu. Writing (critically revised the work)—XZha, LD, XZHu, FY, ZX, GQ and XG. Funding acquisition—ZX. XZHu and ZX are responsible for the overall content as guarantor.

  • Funding This study was supported by the Hubei Provincial Association of Higher Education (grant number 2022ZA25) and Hubei Province Education Science Plan 2022 annual special funding key projects (grant numbers 2022ZA25) to ZX.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.