Study setting

Our study took place in 14 villages located in peri-urban Beijing (Pinggu County: N40°8′, E117°6′) and Shanxi (Yu County: N38°05′, E113°24′) provinces, representing lower-income areas with energy use practices that are characteristic of northern China. Household use of highly-polluting biomass- and coal-fuelled stoves for cooking and space-heating was common18,28. More information about the study setting is provided elsewhere18.

Study design and participants

The ICP Study is a longitudinal study that was established to identify environmental and nutritional risk factors for chronic disease. In 2015–16 we enrolled 547 adults in Beijing and Shanxi (aged 40–79 at enrolment, 56% female) into the study. Details on the study design and the sampling and recruiting of participants are described elsewhere18. Briefly, most participants (n = 398, aged 60–79 in 2015–16) were previously enrolled in the INTERMAP Study in 1995–97, which randomly sampled households in the study villages and then randomly selected one adult from each household to participate18. The remaining 149 participants (aged 40–59 in 2015–16) were selected at random from village rosters. The ICP Study also enrolled 235 adults in southern China, but cognition was not assessed. The present analysis includes 401 participants without a history of stroke and who completed air pollution and cognitive assessments (inclusion flowchart shown in Supplementary Fig. 1). We obtained written informed consent from participants and ethical approvals from all investigator institutions (McGill University: #A08-M37-16B; Imperial College London: #15IC3095; Peking University: #00,001,052–15,017; Tsinghua University: #20,140,077; Fuwai Cardiovascular Hospital: #2015–650). The protocol involving humans was performed in accordance with institutional guidelines and regulations.

Trained staff implemented the study measurements in Beijing in December 2015 and September 2016 and in Shanxi in August 2015 and November 201518. We conducted two campaigns in all villages to capture the heating and non-heating seasons, which can impact environmental conditions and behaviors including household stove use28. In both campaigns, we administered structured questionnaires and measured air pollution and relevant covariates. In the second campaign, we collected blood samples and assessed cognition.

Personal exposures to air pollution

We measured participants’ 24-h personal exposure to PM2.5 on 2 consecutive days in each campaign (96-h total) using the gold standard gravimetric method. Details about PM2.5 measurement and analysis are summarized here and published elsewhere31.

Participants wore waistpacks with air samplers that collected PM2.5 on Teflon filters. The air samplers consisted of Harvard Personal Exposure Monitors (H-PEMs) (Mesa Labs, Inc., USA) fitted with 37-mm polytetrafluoroethylene (PTFE) filters (Zefluor™; Pall Life Sciences, USA) with 2.0-μm pore size and connected to small pumps (Apex Pro or TUFF™; Casella Waste Systems, Inc., USA) operated at 1.8 L/min32. Pump flow rates were measured at the start and end of each sampling period using a field-calibrated rotameter (mini-BUCK Calibrator M-5, Buck Inc., USA). For quality control and potential contamination assessment, about 7% of field blank filters were placed inside identical H-PEMs, subjected to the same field conditions, and analyzed using the same protocol as the sample filters. Participants were instructed to perform routine daily activities but could place the samplers on an elevated surface within 2 m while sitting, sleeping, and bathing. We added pedometers to a random subsample of waistpacks (70% of 1788 measurements) to assess compliance and deemed participants with < 500 steps in 24-h as potentially non-compliant based on an observed cut-off in the pedometer data.

Filters were analysed for their PM2.5 mass. Before and after air sampling, the PTFE filters were conditioned in a temperature- and humidity-controlled environment for at least 24 h and weighed in duplicate for mass on a high-precision microbalance (MX-5; Mettler-Toledo, USA) at the Wisconsin State Laboratory of Hygiene. If the first two weights differed by > 15 μg, the filter was reweighed until a stable weight was achieved. The average of the closest two weights was used for analysis. The balance’s zero and span were checked after every batch of ten filters. Pre-sampling filter weights were subtracted from the post-sampling weights. We performed blank correction by subtracting season- and site-specific blank values for PM2.5 from the net filter weights and replaced negative blank-corrected mass by randomly assigning a value between 0 and half the limit of detection. We divided PM2.5 mass (μg) by the total volume of air (m3) that passed through the filters during 24-h sampling periods to obtain PM2.5 concentrations (μg/m3).

The filters were also analyzed for black carbon using an aethalometer (SootScan™ OT21 Transmissometer; Magee Scientific, USA)33. Black carbon is a component of PM2.5 emitted during incomplete combustion that has been more strongly associated with some health outcomes than the mass of PM2.534,35. We performed further calibrations31 to equate the optical black carbon measurements to elemental carbon, and performed blank correction by subtracting the season- and site-specific blank values for black carbon from final optical attenuation values. We replaced negative blank-corrected mass loadings by randomly assigning a value between 0 and half the limit of detection. To obtain black carbon concentrations (μg/m3), we multiplied the corrected black carbon mass loadings (μg/cm2) by the area of each filter (9.03 cm2), then divided that mass by the total volume of air (m3) that passed through the filters during sampling.

Last, we estimated annual mean personal exposures to PM2.5 and black carbon by calculating a weighted average of season-specific exposures based on northern China’s long-established heating (4 months) and non-heating (8 months) seasons.

Current and long-term indoor stove use

An image-based household energy questionnaire28 was used to construct a set of categorical and continuous stove-use variables that characterized current and long-term stove use and intensity of use. Briefly, participants identified all stoves ever used by their household over the past 20 years and reported, for each stove, the type of fuel used, purpose of use, location of use, duration of use in 5-year intervals, and frequency of use. Exclusive use of clean fuels refers to households using only gas or electric appliances. We used this information to construct the following variables: Current indoor stove use pattern for (1) cooking and (2) heating at the time of survey, and intensity of indoor solid fuel use (3) currently (past year) and (4) over the long-term (over the past 20 years). The development of these variables is described below.

Current cooking or heating fuel use (i.e., exclusive use of clean fuels versus use of solid fuel stoves). Each participant was classified into one of the following categories for current stove-use practices at the time of the survey: (1) exclusive use of clean fuel stoves, (2) use of solid fuel stoves indoors, (3) only outdoor use of solid fuel, and (4) no stove (applicable to variable for heating only). Participants who indicated only outdoor (n = 34) or rare use of solid fuel stoves during holidays or when hosting many people (n = 17 and 1 for cooking and heating, respectively) were classified as exclusive clean fuel users. Participants without a heating stove (n = 13) were categorized as exclusive clean fuel users for heating.

Current intensity of solid fuel use (i.e., stove-use days in the past year). For each stove used by the participant, we first collapsed frequency of use from 10 categories listed in the questionnaire28 to five categories: rare, heating season only, non-heating season only, weekly, daily. Then, for each indoor solid fuel stove currently used by the participant, we estimated the average number of stove-use days in the past year as follows:

• Rare (i.e., seldom, holidays, or when hosting many people) → 13 stove-use days per year based on the assumption of half a stove-use day for each statutory day off for a public holiday in China;

• Heating season (i.e., only in colder months) → 121 stove-use days per year based on the number of days in the heating season in northern China (November 15-March 15)36;

• Non-heating season (i.e., only in warmer months) → 244 stove-use days per year based on the number of days in the non-heating season in northern China (March 16-November 14);

• Weekly (i.e., three to four times per week; several days per week) → 182 stove-use days per year based on multiplying 3.5 days per week (i.e., the mid-point of 3 to 4 times per week) by 52 weeks per year

• Daily (i.e., 2–5, 14–16 or 24 h per day or everyday) → 365 stove-use days per year.

We used the same categories to estimate stove use intensity in Beijing and Shanxi, which are neighboring provinces with very similar public holidays, climates, and space heating needs36.

We next calculated current indoor solid fuel stove-use days for each participant as follows:

$$mathop sum limits_{i = 1}^{n} ({text{solid fuel stoves used indoors}}_{{text{i}}} { } times {text{number of days used in the past year}})$$

where i is each solid fuel stove used indoors and n is the total number of solid fuel stoves used indoors. Participants exclusively using clean fuel stoves, using solid fuel stoves outdoors only, or with no stove (applicable to heating stoves only) were assigned a value of 0 solid fuel stove-use days.

Long-term intensity of solid fuel use (i.e., stove-use years during the past 20 years). For each stove used by participants, we collected information on when they started and stopped using it in 5-year intervals. For each indoor solid fuel stove used by the participant, we calculated the years of use since inception of the INTERMAP Study (20 years ago) as follow: midpoint year of time (in 5-year intervals) from when participants reported starting use of a stove to either the midpoint year of time (in 5-year intervals) that participant reported suspension of that stove or 0 if they reported still using it. For example, a participant who started using a stove 15 years ago and suspended use of the stove 5 years ago was assigned a duration of 10 years of use for that stove. Participants who reported starting and suspending use of a stove in the same 5-yr period were assigned a duration of 2.5 years of use for that stove (i.e., the midpoint of the 5-year interval).

Some participants did not report having a solid fuel cookstove during the past 20 years (n = 61), which we attribute to misreporting or data collection error since the original INTERMAP survey conducted in 1996 indicated that all households in the study villages cooked with solid fuel28,37. Informed by field observations and by cross-referencing survey responses with village records28, we assumed that these participants either used solid fuel stoves up until the time period that they reported regularly using clean fuel for cooking (n = 58) or were still using solid fuel stoves if no use of clean fuel stoves was reported (n = 3).

Finally, we combined information on duration of use and intensity of use (based on number of stove-use days per year categories described), and calculated total indoor solid fuel stove-use years during the past 20 years for each participant as follows:

$$frac{{begin{aligned} &{mathop sum nolimits_{i = 1}^{n} ({text{solid fuel stove used indoors}}_{{text{i}}}} \ & quad times {{text{number of stove – use days per year for that stove}}} \ & quad times {{text{number of years used}})} \ end{aligned} }}{{365 ,{text{days per year}}}}$$

where i is each solid fuel stove used indoors and n is the total number of solid fuel stoves used indoors.

Assessment of cognitive function

Trained staff assessed cognition using the MoCA (https://www.mocatest.org/), a screening tool developed to detect mild cognitive impairment in middle-aged and older adults with high sensitivity and specificity16,17. MoCA evaluates seven individual cognitive domains: visuospatial/executive, naming, attention, language, abstraction, delayed recall, and orientation that yield domain-specific and overall scores (see Table 2 for task description and point system). Our pilot study identified four questions in the MoCA-Beijing survey that were linguistically or culturally inappropriate for our participants, reflecting issues previously observed17. We thus modified the questions using text from the Singapore and Changsha (China) versions of MoCA (changes shown in Supplementary Fig. 3).

Covariates

The ICP Study administered household and individual questionnaires to collect information on household demographics, socioeconomic status, and chronic disease risk factors including alcohol consumption, tobacco use, secondhand smoking, physical activity, medical history, and past food shortage experiences18. Serum concentrations of triglycerides and high- and low-density lipoprotein cholesterol were analyzed using standard methods18.

Outdoor air pollution was assessed by inverse distance weighting the hourly PM2.5 data from government air monitoring stations (http://beijingair.sinaapp.com) within 50 km of each village and calculating 24-h averages that corresponded with personal exposure measurements. These estimates were highly correlated with village-level outdoor gravimetric PM2.5 measurements collected by the ICP Study on 24 study-days (Pearson r = 0.91)31.

Statistical analysis

We summarized participants’ sociodemographic and health characteristics by current cooking fuel use. Mixed effects regression models with restricted maximum likelihood were used to estimate the cognitive associations with exposures to HAP. We specified a random effect at the village level and assumed a compound symmetry correlation structure given the relatively large number of participants clustered within villages and that the number of participants per village varied considerably (range: 1 to 74, median: 26)38. The general regression equation for the models is provided in the supplementary information (Supplementary Eq. 1).

For continuous exposures, we assessed the response function for overall raw cognitive score using natural cubic spline models with two to four degrees of freedom. All of these functions were deemed consistent with linearity through visual inspection (Supplementary Fig. 4). For cognitive outcomes, we z-standardized the domain-specific and overall raw MoCA scores (mean = 0; standard deviation = 1) to facilitate comparison of results across domains, but also maintained the raw score for overall cognition to allow for interpretation against the original survey.

Using directed acyclic graphs, we a priori identified known or suspected risk factors for cognitive impairment that were also plausibly associated with HAP without being on the causal pathway. The multivariable models were adjusted for age, gender, educational attainment, occupation, annual household income, exposure to tobacco smoking, frequency of exercising, frequency of farming, self-reported health status, frequency of drinking alcohol, marital status and number of household occupants as proxy measures of social contact1,39, total cholesterol and past experience with food shortage as proxy measures of diet and nutrition (cooking fuel models only)1,24, and province of residence (variable categories shown in Table 1). We additionally adjusted for outdoor PM2.5 in a second set of models with measured personal exposures to better isolate the exposure contribution of household stove use. Missing data for income (dichotomized as < or ≥ Renminbi 20,000; n = 49) and cholesterol (continuous; n = 23) were handled with multiple imputation as described elsewhere28.

We investigated potential effect modification by province, gender, and education based on findings in previous studies8,9,11,12,15. We also conducted multiple sensitivity analyses, including (1) adjusting for potential confounders that could also be along the causal pathway including co-morbid conditions (i.e., physician-diagnosed diabetes, heart disease, or hypertension) and body mass index1,24, (2) replacing the binary annual household income variable with a more resolved six-category income variable to examine potential residual confounding by income; (3) adjusting for heating fuel (for cooking fuel models) and cooking fuel (for heating fuel models); (4) combining current cooking and heating fuel use into a single exposure variable (i.e., any use of solid fuel stoves); (5) excluding the 61 participants who reported no history of solid fuel use during the past 20 years, which we believe was reporting error, from the analysis with ‘long-term intensity of use’ as the exposure.

All analyses were conducted in R version 4.0.3, using the “nlme”, “splines”, and “MICE” packages for mixed models, natural cubic splines, and multiple imputations, respectively.

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