Cooking or heating with solid fuels increased the all-cause mortality risk among mid-aged and elderly People in China

Abstract

Background
Our study aimed to explore the associations between solid fuels burning for either heating or cooking and all-cause mortality based on 2859 participants from the China Health and Retirement Longitudinal Study during 2011–2018.

Methods
Logistic regression models were performed to estimate the risk for all-cause mortality between different types of fuels in the current longitudinal study. Furthermore, the combined impacts of applying solid fuels for both cooking and heating and the effect among those who switched types of fuels in cooking or heating during follow-up were also analyzed. Interaction and stratification analysis by covariables was applied further to explore the relationship between fuel burning and all-cause mortality.

Results
After full-adjustment, usage of solid fuels was associated with higher all-cause mortality (for heating: OR = 1.93, 95% CI = 1.25, 3.00; for cooking: OR = 1.76, 95% CI = 1.10, 2.82). Using solid fuels for both cooking and heating (OR = 2.36; 95% CI, 1.38, 4.03) was associated with a higher risk of all-cause mortality, while using solid fuels with a single purpose was not (OR = 1.52; 95% CI, 0.90, 2.55). Protective tendencies were detected in switching solid to clean fuel for cooking (OR = 0.62; 95% CI, 0.32, 1.17) and heating (OR = 0.62; 95% CI, 0.35, 1.10).

Conclusion
Either cooking or heating with solid fuels increases the risk of all-cause mortality among Chinese mid-aged and aging people in the urban area of China.

Full text

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Yang et al. Environmental Health (2022) 21:91
https://doi.org/10.1186/s12940-022-00903-6 Environmental Health
†Yuxiang Yang and Liu Yang contributed equally as co-first author.
*Correspondence:
Wenyuan Li
wenyuanli@zju.edu.cn
Zuyun Liu
zuyunliu@zju.edu.cn
Yanan Ma
ynma@cmu.edu.cn
1NHC Key Laboratory of Trace Element Nutrition, National Institute for
Nutrition and Health, Chinese Center for Disease Control and Prevention,
100050 Beijing, China
2Department of Biostatistics and Epidemiology, School of Public Health,
China Medical University, No.77 Puhe Road, Shenyang North New Area,
110122 Shenyang, Liaoning, China
3National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, 100021 Beijing, China
4School of Public Health, Zhejiang University School of Medicine, 866
Yuhangtang Road, 310058 Hangzhou, Zhejiang, China
5Department of Big Data in Health Science School of Public Health,
Center for Clinical Big Data and Analytics Second Affiliated Hospital,
Zhejiang University School of Medicine, 866 Yuhangtang Rd,
310058 Hangzhou, Zhejiang, China
Abstract
Background Our study aimed to explore the associations between solid fuels burning for either heating or cooking
and all-cause mortality based on 2859 participants from the China Health and Retirement Longitudinal Study during
2011–2018.
Methods Logistic regression models were performed to estimate the risk for all-cause mortality between different
types of fuels in the current longitudinal study. Furthermore, the combined impacts of applying solid fuels for
both cooking and heating and the effect among those who switched types of fuels in cooking or heating during
follow-up were also analyzed. Interaction and stratification analysis by covariables was applied further to explore the
relationship between fuel burning and all-cause mortality.
Results After full-adjustment, usage of solid fuels was associated with higher all-cause mortality (for heating:
OR = 1.93, 95% CI = 1.25, 3.00; for cooking: OR = 1.76, 95% CI = 1.10, 2.82). Using solid fuels for both cooking and
heating (OR = 2.36; 95% CI, 1.38, 4.03) was associated with a higher risk of all-cause mortality, while using solid fuels
with a single purpose was not (OR = 1.52; 95% CI, 0.90, 2.55). Protective tendencies were detected in switching solid to
clean fuel for cooking (OR = 0.62; 95% CI, 0.32, 1.17) and heating (OR = 0.62; 95% CI, 0.35, 1.10).
Conclusion Either cooking or heating with solid fuels increases the risk of all-cause mortality among Chinese mid-
aged and aging people in the urban area of China.
Keywords Solid fuels, All-cause mortality, Mid-aged, Elderly
Cooking or heating with solid fuels increased
the all-cause mortality risk among mid-aged
and elderly People in China
Yuxiang Yang1†, Yang Liu2†, Luolan Peng1, Shuai Zhang3, Changzheng Yuan4, Wenyuan Li5*, Zuyun Liu5* and
Yanan Ma2*
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Yang et al. Environmental Health (2022) 21:91
Introduction
Worldwide, around 40% of the population relies on burn-
ing solid fuel (e.g., biomass fuel, coal, and related fuel) for
their household life, and the primary purpose is cook-
ing and heating[1, 2]. Incomplete burning of these kinds
of traditional fuels could release a great number of air
pollutants, such as particulate matter of varying sizes,
formaldehyde, carbon monoxide, nitrogen oxides, etc.,
which could lead to household air pollution (HAP)[3, 4].
In contrast, the combustion products from clean fuels
(e.g., natural gas, electricity, and solar energy) are just
water and carbon dioxide. It’s widely reported that HAP
is currently one of the top ten risk factors for diseases
including pneumonia, COPD, ischemic cardiovascular
and cerebrovascular diseases, lung cancer, and cognitive
decline, which may cause a heap of both social and health
burdens all over the world[4–8].
Previous research was mainly conducted in low- and
middle-income countries (LMICs)[9]. A global Burden of
Disease (GBD) study reported that burning solid fuel for
cooking had led to over 0.8million Chinese premature
death in 2010[10]. e Chinese Longitudinal Healthy
Longevity Survey (CLHLS) showed that participants
who used solid fuel for cooking had a 9% higher mortal-
ity risk than those who used clean fuels[4]. Meanwhile,
researchers also reported that participants who switched
fuels from solid to clean during follow-up didn’t show a
significantly increased risk compared with those who
stably used clean fuels[4]. Another study had estimated
that solid fuel origin HAP had led to over 1million Chi-
nese premature mortalities in 2016[11]. Research from
other regions like South Asia, Nigeria, and sub-Saharan
Africa also showed that using solid cooking fuels could
increase mortality among infants and children[12–14].
us, promoting the transition from solid fuel to clean
fuel is not only a pathway toward improving global pub-
lic health but also about human rights and environmental
protection[15].
Only a few cohort studies have explored the asso-
ciations of solid fuel-burning with all-cause mortality.
A nationwide, large-scale cohort study found that using
solid fuels for either heating or cooking would increase
mortality risk; however, the participants were from only
5 rural areas in China[16, 17]. Although previous studies
well-explored the association between solid fuel burning
and health outcomes, there is still a lack of clear evi-
dence to support the harmful effect caused by household
fuel-burning based on the different HAP exposure pat-
terns between cooking and heating within a nationwide
sample[18], especially in the population of mid-aged or
elderly who were more susceptible to chronic diseases.
Furthermore, little research focused on urban areas
in this field. Although there is a huge gap between
urban and rural under fuel modernization, many urban
residents still use solid fuels in household life[11, 19].
us, we applied samples from China Health and Retire-
ment Longitudinal Study (CHARLS) to explore the asso-
ciation between fuel for either cooking or heating and
the risk of all-cause mortality among mid-aged or elderly
participants in urban China. Meanwhile, we applied the
potentially confounding effects of socioeconomic status
and the house area (which might impact the ventilation
function) and analyzed the interaction between fuels of
different types. Moreover, the combined effect of apply-
ing solid fuels in both cooking and heating and the effect
among those who switched fuels in cooking or heating
during follow-up were also analyzed. Above all, we aim
to give more authentic results about the relationship
between burning solid fuels and all-cause mortality and
may exert additional policies and regulations develop-
ment in this field to achieve the public health goal.
Methods
Study design and participants
CHARLS is a national longitudinal cohort study covering
450 urban communities and rural villages across 28 prov-
inces of China. Participants were all middle-aged and
older adults. e research agenda of CHARLS has been
described elsewhere in detail[20].
In a life history survey (CHARLS 2014), trained inter-
viewers obtained the previous experience of each partici-
pant by a standard life history questionnaire. e survey
offered the past information about the household fuels
(coal, electricity, central heating, or gas) usage of 2011–
2012 (Wave 1) nationally baseline participants.
Baseline survey was launched from 2011 to 2012 and
included 4603 participants ≥ 45 years living in urban with
follow-ups conducted every 2 or 3 years. As shown in
Fig.1, in this study, we excluded those participants who
failed to report the type of household energy in 2011
(n = 665) and those who lost follow-up at the 7.5-year
follow-up in 2018 (n = 1079). Eventually, a total of 2859
participants were included in the present study. e
study protocol was approved by Peking University’s Ethi-
cal Review Committee (IRB 0000105211015). All partici-
pants wrote informed consent before participating in the
study.
Mortality status and follow-up
Data on mortality status were updated in 2011(Wave
1), 2013(Wave 2), 2014(Life history survey), 2015(Wave
3), 2018(Wave 4); the range of the follow-up period was
about 8 years, which survival status via a field investiga-
tion. Trained interviewers investigated baseline survival
status by a cover screen before being recruited in follow-
up, and details of the field investigation are available
online http://charls.pku.edu.cn/en. Database record all
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Yang et al. Environmental Health (2022) 21:91
deaths occurring from April 2011 to March 2019, but the
exact time of deaths were not recorded.
Household energy source
e primary exposures were household energy sources,
consisting of cooking and heating fuel types estimated
by the following questions: ‘What is the main source of
cooking fuel? (1) Coal, (2) Natural gas, (3) Marsh gas, (4)
Liquefied petroleum gas, (5) electric, (6) Crop residue/
Wood burning, (7) Other’, ‘Does your residence have
heating? (1) Yes, (2) No’ and ‘What is the main heating
energy source? (1) Solar, (2) Coal, (3) Natural gas, (4) Liq-
uefied petroleum gas, (5) electric, (6) Crop residue/Wood
burning, (7) Other’. erefore, cooking fuels were divided
into clean fuels (Natural gas, Marsh gas, Marsh gas, or
electric) and solid fuels (coal, crop residue, wood burn-
ing). Likewise, heating fuels were categorized as clean
fuels (uniform heating, solar, natural gas, liquefied petro-
leum gas, electric) and solid fuels (coal, crop residue,
wood burning). We also assessed whether the fuel types
were changed during the follow-up.
Covariates
Covariates were selected in 2011 according to previous
study [11]. Venous blood samples were collected and
stored at -80°C by medically trained staff from the Chi-
nese Center for Disease Control and Prevention. Trained
interviewers acquired data on age, sex, ethnicity, house
area, marital status, annual household income, educa-
tional level, smoking status, drinking status, medical
insurance status, and chronic diseases status via standard
questionnaires. An electronic blood pressure monitor
[20] was used by medical staff to measure participant
blood pressure, with the mean of three measurements
taken at 45s intervals being recorded. Hypertension was
defined as systolic blood pressure ≥ 140 mmHg and/or
diastolic blood pressure ≥ 90 mmHg or usage of antihy-
pertensive medicine[21]. Participants with self-reported
diabetes, receiving diabetes treatment, meeting the
American Diabetes Association (ADA) diabetes crite-
ria (fasting plasma glucose ≥ 126 mg/dL or hemoglo-
bin ≥ 6.5%) or participants with physician-diagnosed
diabetes were defined as suffering from diabetes [22].
Body mass index (BMI) was calculated by dividing weight
(kg) by height (m) squared. All but age and BMI were
considered categorical variables in the current study.
All the specific details about covariates above could be
searched from the CHARLS website.
Statistical analyses
Means ± standard deviations (SDs) and numbers (per-
centages) were used to describe continuous and cat-
egorical variables. Meanwhile, one-way ANOVA and
Chi-square test were used to analyze differences between
groups. Logistic regression models were applied to esti-
mate the associations between household fuel types and
all-cause mortality during the 7.5-years follow-up. Uni-
variate and multivariate logistic regression analyses were
utilized to investigate associations of factors (including
types of household fuel, age, gender, ethnicity, BMI, house
area, marital status, household annual income, education
level, smoking status, drinking status, and medical insur-
ance status) with all-cause mortality. Unadjusted and
adjusted odds ratios (ORs) with 95% confidence intervals
Fig. 1 Flow diagram for participants enrolled in the study
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Yang et al. Environmental Health (2022) 21:91
were calculated. Model I was adjusted for age and gender.
Model II was adjusted for ethnicity, marital status, educa-
tion level, annual household income, and medical insur-
ance based on model 1. Model III also included drinking,
smoking, BMI, house area, hypertension, and diabetes.
Furthermore, household fuel use was categorized into
three levels (all clean fuels, mixed-use solid and clean
fuels, and all solid fuels) based on the solid fuels that fre-
quently occurred in cooking and heating. A full model
was established to estimate the association. e self-
reported switch from solid to clean fuels was also con-
sidered might twist the association between solid fuel use
and all-cause mortality. erefore, we further conducted
a fully logistic regression model to check the association
between fuel switch and all-cause mortality.
We then explored whether the associations differ by
age, gender, ethnicity, BMI, house area, marital status,
household annual income, education level, smoking
status, drinking status, and medical insurance status by
adding an interaction term. Age (≤ 60, > 60 years), gen-
der (male, female), BMI (< 23, ≥ 23 kg/m2), house area
(≤ 120, > 120 m2), marital status (live with spouse, live
without spouse), household annual income (≤ 30,000,
> 30,000 yuan), education level (< middle school, ≥mid-
dle school), smoking status (never smoker, ever smoker,
current smoker), drinking status (never drinker, ever
drinker, current drinker) and major chronic disease (no,
yes) modified associations between household fuels and
all-cause mortality. In addition, it was respectively used
cooking fuels and heating fuels as effect modifiers with
the full model to estimate the association between solid
fuel use and deaths. Simultaneously, stratification analy-
sis was also conducted to evaluate the effect of household
fuels on outcomes occurring within 7.5 years follow-up
further to confirm the association between household
fuels and all-cause mortality. e robustness of results
was confirmed after further excluding the participants
who had cancer or CVD [23].
All statistical analyses were conducted using R (http://
www.R-project.org; version 3.6.6) and EmpowerStats
software (www.empowerstats.com, X&Y Solutions, Inc.,
Boston, MA, USA). A two-sided P value of < 0.05 was
considered statistically significant.
Results
Basic characteristics of the study sample
e baseline characteristics of the participants recruited
in the follow-up were shown according to the household
fuel used in Tables1 and 2, respectively. In sum, 2859
participants were enrolled in our final analysis. e dis-
tribution of age and gender of all 2859 participants in
the follow-up analysis were 59.32 (10.22) years, and 1520
(53.18%) participants were female. Solid fuel was used
for cooking and heating for around 587 (20.53%) and 998
(35.57%) participants, respectively. Further information
is available in Table S1.
Association between types of household fuel for cooking
or heating and all-cause mortality
Table 3 showed the univariate associations between
cooking fuels, heating fuels, age, gender, ethnicity, mari-
tal status, BMI, house area, household annual income,
education level, smoking, drinking, or medical insurance
Table 1 Baseline characteristics of participants according to
cooking fuels
Characteristic Cooking fuels
Clean fuels Solid fuels P-value
N2272 587
Age(years) 59.10 ± 10.11 60.16 ± 10.61 0.025
BMI (kg/m2)24.77 ± 4.33 24.65 ± 3.86 0.592
Gender, % 0.828
Male 1066 (46.92%) 272 (46.42%)
Female 1206 (53.08%) 314 (53.58%)
Ethnicity, % < 0.001
Other 130 (6.68%) 65 (12.65%)
Han 1816 (93.32%) 449 (87.35%)
House area (m2)0.108
≤ 120 1832 (81.17%) 452 (78.20%)
> 120 425 (18.83%) 126 (21.80%)
Marry status, % 0.302
Live with spouse 1921 (84.70%) 487 (82.96%)
Live without spouse 347 (15.30%) 100 (17.04%)
Household annual
income (yuan)
< 0.001
≤ 30,000 1189 (59.57%) 376 (75.65%)
>30,000 807 (40.43%) 121 (24.35%)
Education level, % < 0.001
<Middle school 838 (37.03%) 363 (61.95%)
≥Middle school 1425 (62.97%) 223 (38.05%)
Medical insurance
status, %
0.150
No 197 (8.71%) 62 (10.63%)
Yes 2065 (91.29%) 521 (89.37%)
Smoking status, % 0.020
Never smoker 1461 (66.35%) 351 (61.69%)
Ever smoker 212 (9.63%) 49 (8.61%)
Current smoker 529 (24.02%) 169 (29.70%)
Drinking status, % 0.519
Never drinker 1360 (60.15%) 362 (61.88%)
Ever drinker 167 (7.39%) 47 (8.03%)
Current drinker 734 (32.46%) 176 (30.09%)
Hypertension, % 0.916
No 1144 (50.35%) 297 (50.60%)
Yes 1128 (49.65%) 290 (49.40%)
Diabetes, % 0.971
No 1983 (87.28%) 512 (87.22%)
Yes 289 (12.72%) 75 (12.78%)
Values were mea ns ± SD or n (percentag es)
Values of poly tomous variables m ay not sum to 100% due to rounding
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Yang et al. Environmental Health (2022) 21:91
status, and all-cause mortality. In univariate analysis, the
association between cooking fuels or heating fuels and
all-cause mortality was significant ([OR]: 1.71; 95% CI,
1.32, 2.22 and [OR]: 1.49; 95% CI, 1.17, 1.88, respectively).
Table 4 shows the independent associations between
solid fuel use for cooking and heating and all-cause
mortality in the cohort. Solid (vs. clean) in cooking fuel
users, the association remained robust after adjustment
for age and gender in model I ( [OR]: 1.64; 95% CI, 1.23,
2.20), and further adjustment for ethnicity, marital status,
education level, household annual income and medical
insurance in model II ( [OR]: 1.62; 95% CI, 1.10, 2.40), as
well as model III additionally adjustment for BMI, drink-
ing status, smoking status, house area, hypertension, and
Table 2 Baseline characteristics of participants according to
heating fuels
Characteristic Heating fuels
Clean fuels Solid fuels P-value
N1848 1011
Age(years) 59.34 ± 10.27 59.29 ± 10.13 0.902
BMI (kg/m2)24.85 ± 4.44 24.58 ± 3.89 0.147
Gender, % 0.928
Male 864 (46.75%) 474 (46.93%)
Female 984 (53.25%) 536 (53.07%)
Ethnicity, % < 0.001
Other 88 (5.64%) 107 (11.88%)
Han 1471 (94.36%) 794 (88.12%)
House area (m2)0.042
≤ 120 1502 (81.67%) 782 (78.51%)
> 120 337 (18.33%) 214 (21.49%)
Marry status, % 0.77
Live with spouse 1558 (84.49%) 850 (84.08%)
Live without spouse 286 (15.51%) 161 (15.92%)
Household annual
income (yuan)
< 0.001
≤ 30,000 956 (58.12%) 609 (71.82%)
>30,000 689 (41.88%) 239 (28.18%)
Education level, % < 0.001
<Middle school 641 (34.86%) 560 (55.45%)
≥Middle school 1198 (65.14%) 450 (44.55%)
Medical insurance
status, %
< 0.001
No 134 (7.29%) 125 (12.41%)
Yes 1704 (92.71%) 882 (87.59%)
Smoking status, % 0.005
Never smoker 1200 (67.08%) 612 (62.32%)
Ever smoker 174 (9.73%) 87 (8.86%)
Current smoker 415 (23.20%) 283 (28.82%)
Drinking status, % 0.119
Never drinker 1102 (59.99%) 620 (61.45%)
Ever drinker 152 (8.27%) 62 (6.14%)
Current drinker 583 (31.74%) 327 (32.41%)
Hypertension, % 0.965
No 932 (50.43%) 509 (50.35%)
Yes 916 (49.57%) 502 (49.65%)
Diabetes, % 0.228
No 1623 (87.82%) 872 (86.25%)
Yes 225 (12.18%) 139 (13.75%)
Values were mea ns ± SD or n (percentag es)
Values of poly tomous variables m ay not sum to 100% due to rounding
Table 3 Univariate analysis between characteristics of
participants with all-cause mortality
Statistics All-cause
mortality
Age(years) 59.32 ± 10.22 1.12 (1.10, 1.13)
BMI (kg/m2)24.74 ± 4.23 0.93 (0.90, 0.97)
Cooking fuels, %
Clean fuels 2272 (79.47%) 1.0 (Reference)
Solid fuels 587 (20.53%) 1.71 (1.32, 2.22)
Heating fuels, %
Clean fuels 1848 (64.64%) 1.0 (Reference)
Solid fuels 1011 (35.36%) 1.49 (1.17, 1.88)
Gender, %
Male 1338 (46.82%) 1.0 (Reference)
Female 1520 (53.18%) 0.50 (0.40, 0.64)
Ethnicity, %
Han 195 (7.93%) 1.0 (Reference)
Other 2265 (92.07%) 1.42 (0.78, 2.59)
House area (m2)
≤ 120 2284 (80.56%) 1.0 (Reference)
> 120 551 (19.44%) 0.82 (0.60, 1.12)
Marital status, %
Live with spouse 2408 (84.34%) 1.0 (Reference)
Live without spouse 447 (15.66%) 2.43 (1.86, 3.18)
Household annual income (yuan)
≤ 30,000 1565 (62.78%) 1.0 (Reference)
>30,000 928 (37.22%) 0.82 (0.62, 1.07)
Education level, %
<Middle school 1201 (42.16%) 1.0 (Reference)
≥Middle school 1648 (57.84%) 0.46 (0.37, 0.59)
Medical insurance status, %
No 259 (9.10%) 1.0 (Reference)
Yes 2586 (90.90%) 0.81 (0.55, 1.18)
Smoking status, %
Never smoker 1812 (65.39%) 1.0 (Reference)
Ever smoker 261 (9.42%) 2.70 (1.91, 3.81)
Current smoker 698 (25.19%) 1.61 (1.22, 2.12)
Drinking status, %
Never drinker 1722 (60.51%) 1.0 (Reference)
Ever drinker 214 (7.52%) 2.19 (1.52, 3.17)
Current drinker 910 (31.97%) 0.98 (0.75, 1.28)
Hypertension, %
No 1441 (50.40%) 1.0 (Reference)
Yes 1418 (49.60%) 2.52 (1.96, 3.23)
Diabetes, %
No 2495 (87.27%) 1.0 (Reference)
Yes 364 (12.73%) 2.21 (1.65, 2.95)
Values were mea ns ± SD or n (percentag es)
Values of poly tomous variables m ay not sum to 100% due to rounding
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Yang et al. Environmental Health (2022) 21:91
diabetes [OR]: 1.76; 95% CI, 1.10, 2.82), while for heating,
the ORs were [OR]: 1.64; 95% CI, 1.26, 2.13, [OR]: 1.52;
95% CI, 1.07, 2.17 and [OR]: 1.93; 95% CI, 1.25, 3.00 in
Model I–III, respectively.
Table5 found that double exposure to solid fuels for
cooking and heating ([OR]: 2.36; 95% CI, 1.38, 4.03) was
associated with a higher participants’ risk of all-cause
mortality. Furthermore, using solid fuels for cooking or
heating was not ([OR]: 1.52; 95% CI, 0.90, 2.55). Mean-
while, in Table6 there was no significant difference in
both switched solid fuel to clean fuel for cooking ([OR:
0.62; 95% CI, 0.32, 1.17) and heating ([OR]: 0.62; 95%
CI,0.35, 1.10) but the same protective tendencies were
detected in both.
In Table S2 and Table S3, there were no interactions
between cooking fuels and heating fuels (P for interac-
tion > 0.05). e results revealed that although the inter-
action of solid fuels from cooking and heating was not
modified the association between solid fuels and all-cause
mortality, the strengthen hazard effect was detected.
When we further excluded the participants who had
malignant cancer or CVD, the association between solid
fuels and death remained in Table S4. In addition, inter-
action tests and stratified results for characteristics were
shown in Table S5 and Table S6.
Discussion
e current longitudinal study indicated that using
solid fuels for heating or cooking was positively associ-
ated with all-cause mortality after multiple adjustments.
Afterwards, the association between burning solid fuels
and the risk of all-cause mortality by different usage cat-
egories (all clean fuels, mixed-use solid and clean fuels,
and all solid fuels) was also evaluated. It showed that
compared with clean fuels’ users, using solid fuels for
both heating and cooking had a higher risk of all-cause
mortality. Furthermore, regarding the condition of
changing fuel’s type, protective tendencies of all-cause
mortality were observed among those who switched the
Table 4 Relationship between household fuels and risk of all-
cause mortality in different models
Exposure OR (95% CI)
Model IaModel IIbModel IIIc
Cooking fuels
Clean fuels 1.0
(Reference)
1.0
(Reference)
1.0 (Refer-
ence)
Solid fuels 1.64 (1.23,
2.20)
1.62 (1.10,
2.40)
1.76 (1.10,
2.82)
Heating fuels
Clean fuels 1.0
(Reference)
1.0
(Reference)
1.0 (Refer-
ence)
Solid fuels 1.64 (1.26,
2.13)
1.52 (1.07,
2.17)
1.93 (1.25.
3.00)
Abbreviati ons: OR, odd ratio; CI, co ndence interval
a Adjust for Age ( years) and Gender (Male, Fem ale)
b Adjust for Age (years), Gender (Male, Female), Ethnicity (Other, Han), Marital
status (Live w ith spouse, Live witho ut spouse), Education leve l (< Middle school,
≥Middle school), Household annual income (≤ 30,000, > 30,000) and Medical
insurance (No, Yes)
c Adjust for Age (years), BMI (kg/m2), Gender (Male, Female), Ethnicity (Other,
Han), House area (≤ 12 0, > 12 0 m2), Ma rital status (Live with s pouse, Live without
spouse), Household annual income (≤ 30,000, > 30,000), Medical insurance (No,
Yes), Education level (< Middle school, ≥Middle school), Smoking (Never smoker,
Ever smoker, Current smoker), Drinking (Never drinker, Ever drinker, Current
drinker), Hype rtension (No, Yes), and Diabetes (N o, Yes)
Table 5 Adjusted Odd Ratio for all-cause mortality according to
solid fuel use for cooking and heating
Exposure OR (95% CI)
Model IaModel IIbModel IIIc
All clean fuels 1.0
(Reference)
1.0
(Reference)
1.0 (Refer-
ence)
Mixed-use solid and clean
fuels
1.31 (0.95,
1.80)
1.28 (0.84,
1.95)
1.52 (0.90,
2.55)
All solid fuels 1.96 (1.42,
2.71)
1.88 (1.21,
2.93)
2.36 (1.38,
4.03)
Abbreviati ons: OR, odd ratio; CI, co ndence interval
a Adjust for Age ( years) and Gender (Male, Fem ale)
b Adjust for Age (years), Gender (Male, Female), Ethnicity (Other, Han), Marital
status (Live w ith spouse, Live witho ut spouse), Education leve l (< Middle school,
≥Middle school), Household annual income (≤ 30,000, > 30,000) and Medical
insurance (No, Yes)
c Adjust for Age (years), BMI (kg/m2), Gender (Male, Female), Ethnicity (Other,
Han), House area (≤ 12 0, > 12 0 m2), Ma rital status (Live with s pouse, Live without
spouse), Household annual income (≤ 30,000, > 30,000), Medical insurance (No,
Yes), Education level (< Middle school, ≥Middle school), Smoking (Never smoker,
Ever smoker, Current smoker), Drinking (Never drinker, Ever drinker, Current
drinker), Hype rtension (No, Yes), and Diabetes (N o, Yes)
Table 6 Adjusted Odd Ratio for all-cause mortality in association
with previous switch from solid to clean fuels
Exposure OR (95% CI)
Model IaModel IIbModel IIIc
Previous switch from solid
to clean fuels
Cooking
Solid fuel use 1.0
(Reference)
1.0
(Reference)
1.0 (Refer-
ence)
Solid to clean fuel use 0.53 (0.33,
0.84)
0.59 (0.35,
1.01)
0.62 (0.32,
1.17)
Heating
Solid fuel use 1.0
(Reference)
1.0
(Reference)
1.0 (Refer-
ence)
Solid to clean fuel use 0.53 (0.36,
0.79)
0.59 (0.37,
0.94)
0.62 (0.35,
1.10)
Abbreviati ons: OR, odd ratio; CI, co ndence interval
a Adjust for Age ( years) and Gender (Male, Fem ale)
b Adjust for Age (years), Gender (Male, Female), Ethnicity (Other, Han), Marital
status (Live w ith spouse, Live witho ut spouse), Education leve l (< Middle school,
≥Middle school), Household annual income (≤ 30,000, > 30,000) and Medical
insurance (No, Yes)
c Adjust for Age (years), BMI (kg/m2), Gender (Male, Female), Ethnicity (Other,
Han), House area (≤ 12 0, > 12 0 m2), Ma rital status (Live with s pouse, Live without
spouse), Household annual income (≤ 30,000, > 30,000), Medical insurance (No,
Yes), Education level (< Middle school, ≥Middle school), Smoking (Never smoker,
Ever smoker, Current smoker), Drinking (Never drinker, Ever drinker, Current
drinker), Hype rtension (No, Yes), and Diabetes (N o, Yes)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

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Yang et al. Environmental Health (2022) 21:91
fuels from solid to clean compared with those stable solid
fuels’ users.
ey were accompanied by the rapid development of
the national economy and power infrastructure, more
and more people selected clean fuels as an alternative
energy source[19, 24]. However, as the biggest developing
country globally and one of the LMICs, China remains
heavily dependent on solid fuels [11, 19]. Besides, it’s
known that people are more vulnerable to air pollutants
with age increasing[25]. Moreover, there is a tendency
that nowadays, more and more Chinese people are will-
ing to move to the countryside after retirement or under
poor health conditions, which indicates a significant
intervention point to raise the awareness among this
group of people to utilize clean instead of solid fuels[4,
26]. Our findings may provide a beneficial pathway for
improving health conditions and prolonging life.
is study found that burning solid fuel was positively
associated with the risk of all-cause mortality. e results
also showed that those who switched from solid fuels to
cleaning might not have a higher risk than those who
stably used clean fuels. Simultaneously using solid fuels,
cooking, and heating would further increase the risk of
all-cause mortality compared to those who use clean
fuels.
e same results were obtained in previous studies.
A prospective study conducted in China among 13,528
non-smokers to explore the relationship between heating
fuel types and all-cause mortality showed that solid fuel
heating could indicate a 55% higher risk of all-cause mor-
tality compared with participants who used clean fuels. It
also increased the risk of stroke among non-smokers[27].
Another research by Liu et al. showed that indoor solid
fuel combustion for heating might increase the risk of
cervical cancer death after full adjustment[28]. ere was
another report that, unlike those using clean fuels for
heating, solid fuels were positively associated with car-
diovascular mortality and all-cause mortality. Meanwhile,
participants who reported having previously switched to
clean fuels for cooking had a lower cardiovascular and
all-cause mortality[17].
As for studies focused on the relationship between
cooking fuels and mortality, our study results were con-
sistent with previous research. For instance, a national
retrospective longitudinal survey among Chinese showed
that when analyzing the relationship between cooking
fuels and the risk for all-cause mortality, those using solid
fuels had 1.09 times higher risk of all-cause mortality
than those using clean fuels. Also, significantly increased
risk was not observed in those who switched from solid
fuels to clean ones during the follow-up period, which
indicated the need to promote fuel reform[4]. Findings
from urban participants enrolled in China Kadoorie Bio-
bank (CKB) showed that persistent solid fuels’ users had
19% higher risks of all-cause mortality. Besides, com-
pared with the participants who used clean cooking fuels,
solid ones also had a higher risk of 24% and 43% mortal-
ity caused by cardiovascular diseases and respiratory dis-
eases, respectively[16]. In another study, using solid fuels
for cooking was associated with a greater risk (over 10%
for both). Participants who transferred to clean fuels had
a lower risk for both mortalities than persistent solid fuel
users[17].
is study also examined the joint effects of using solid
fuels for cooking and heating. To our best knowledge, no
previous research had focused on this part. Results indi-
cated that using solid fuels for both purposes would over
double the risk of all-cause mortality compared to using
clean fuels for cooking and heating. It may be explained
that the former would increase the exposing time of
HAP and was always accompanied by poor ventilation
in those who use solid fuels in the household[6]. More-
over, we discussed the effect among those who switched
the household burning fuels from solid to clean one,
and it showed that there was no significant difference in
both switched solid fuels to clean fuels for either cook-
ing or heating, but the same protective tendencies were
detected in both.
Several previous studies explained the adverse effects
caused by burning solid fuels. Air pollutants, such as car-
bon monoxide, sulfur dioxide, nitrogen oxide, polycyclic
aromatic hydrocarbons, particulate matter, and heavy
metals, are always released by an uncompleted combus-
tion process[29, 30]. ese pollutants link to activate
various physiological processes, i.e., the production of
reactive oxygen species (ROS), oxidative stress, and sys-
temic inflammation[31], as well as causing further dam-
age by disrupting human proteins, esters, and DNA and
causing oxidative damage[32]. Afterward, the above
process with long-term exposure might initiate several
health impairments and related diseases on cardiovas-
cular, cognitive, respiratory, and other human structures
and functions[5, 33–35].
Based on the current results, although the government
has made great efforts to promote fuel revolution in the
past decades[36], there is still a neglectable proportion of
urban residents who were still using solid fuels in their
household life, which did lie a huge burden on health and
well-being. It could partly be explained that there are a
lot of “urban villages” currently in China, people who live
here tend to have a poor environment and public health
safety[37]. us, future fuel improvement policies should
still pay more attention to people who live in urban areas,
especially in urban villages, not only in rural areas. More-
over, except changing the types of fuels, techniques for
making advanced solid fuels and indoor-ventilate could
also be considered to address and assist in solving the
problem of HAP. An interesting report showed that,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 8 of 9
Yang et al. Environmental Health (2022) 21:91
in high-income countries like countries in Europe and
North America, although more and more people chose
solid fuels for heating, they experienced less HAP due
to sophisticated fuel tech and pollutant discharge[38]. In
summary, under the remarkable aging process in China,
there is an urgent need to take various measures includ-
ing those mentioned above to release the burden on the
health system. Decreasing the exposure to HAP by burn-
ing solid fuels could be applied as one of the crucial parts
and could be validated by longitudinal or interventional
studies in the future research if it’s possible.
Apart from the study design, our study also has sev-
eral other strengths. Except for long-term follow-up, our
study explored the adverse effects caused by either cook-
ing fuels or heating fuels, fitting multiple covariates (i.e.,
ethnic, house area) that hadn’t been broadly considered
in the previous studies into multiple adjustments. e
results might provide more solid evidence of the rela-
tionship between burning fuels and all-cause mortality.
However, our study did have some limitations. Firstly,
due to the inherent limitations of the prospective study,
the loss of follow-up was inevitable, and some changes
like the shifting type of fuels in household life may hap-
pen during the long-term follow-up intervals. However,
this study had a relatively intensive frequency of follow-
up. Secondly, the condition of household ventilation and
stoves that may impact our outcomes was unavailable
due to the characteristics of the database[17]. irdly, our
study didn’t adjust the effect of outdoor pollutants, physi-
cal activity, and confounding covariates, so the possibility
of residual confounding, unmeasured confounding, and
measurement error persists. ese limitations should be
mended in further research if it’s available.
Conclusion
Our current study found that burning solid fuel was
positively associated with the risk of all-cause mor-
tality regardless of the purpose for either cooking or
heating. Plus, using solid fuels for cooking and heat-
ing would double the risk of all-cause mortality com-
pared to those who singly use clean fuels. The above
results may also serve as a direction for detecting spe-
cific intervention groups contributing to more accu-
rate prevention services. Further research may target
to potential effects of using different household fuels.
Abbreviations
BMI Body mass index
CHARLS China health and retirement longitudinal study
CI Confidence interval
CLHLS Chinese longitudinal healthy longevity Sur vey
COPD Chronic obstructive pulmonary diseases
GBD Global burden of disease
HAP Household air pollution
LMICs Low- and middle-income countries
OR Odds ratio
ROS Reactive oxygen species
SD Standard deviation
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12940-022-00903-6.
Supplementary Material 1
Acknowledgements
We would like to thank all the participants, investigators, and researchers in
the whole project.
Author contributions
Study concept and design: Y.Y., Y.L., and Y.M. Acquisition, analysis, or
interpretation of data: All authors. Drafting of the manuscript: Y.Y., Y.L., and
Y.M. Critical revision of the manuscript for important intellectual content: All
authors. Statistical analysis: Y.L. and Y.M. Administrative, technical, or material
support: W.L., Z.L., and Y.M. Study supervision: W.L., Z.L., and Y.M. Y.Y. and Y.L.
contributed equally as co-first authors. W.L., Z.L., and Y.M. contributed equally
as corresponding co-authors. All authors reviewed the manuscript.
Funding/Support
None.
Availability of data and materials
All the data and materials were based on China Health and Retirement
Longitudinal Study conducted from 2011 to 2018. Available: http://charls.pku.
edu.cn/.
Declarations
Ethics approval and consent to participate
The study protocol was approved by Peking University’s Ethical Review
Committee (IRB 0000105211015). All participants wrote informed consent
before participating in the study.
Consent for publication
Not applicable.
Competing interests
The authors disclose no conflicts.
Competing interests
Not applicable.
Received: 30 April 2022 / Accepted: 22 September 2022
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