Association between long-term exposure to air pollutant mixture and the risk of Alzheimer’s disease among older people: a prospective cohort study in China – BMC Public Health

Report on the Link Between Air Pollution and Cognitive Decline in an Aging Population

This report outlines the methodology of a prospective cohort study investigating the association between air pollution and Alzheimer’s Disease (AD) among older adults in Zhejiang Province, China. The study’s design and objectives are strongly aligned with several United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 10 (Reduced Inequalities).

Methodological Framework for Assessing Health and Environmental Linkages

Study Design and Participant Cohort: Promoting SDG 3 and SDG 10

The research is founded on the Zhejiang Province Major Public Health Surveillance (ZJMPHS) Program, a large-scale study initiated in 2014 to monitor the health of the elderly, directly contributing to the targets of SDG 3.

• Study Type: Population-based prospective cohort study.
• Sampling Strategy: To ensure a representative population sample and address SDG 10, 11 counties were randomly selected from across Zhejiang Province’s geographical distribution (Northern, Southern, Middle, Eastern, and Western regions).
• Participant Selection:
  1. An initial sample of 14,881 participants aged over 60 years was recruited between 2014 and 2019.
  2. Exclusions were made for participants with missing baseline cognitive data, pre-existing cognitive disorders, no follow-up records, dementia from other causes, or missing pollution or covariate data.
  3. The final analytical cohort consisted of 10,055 eligible participants.
• Ethical Considerations: All participants provided informed written consent prior to data collection.

Follow-up and Assessment of Outcomes

The study’s focus on non-communicable diseases like AD is central to achieving SDG 3.4. Annual follow-ups were conducted until an AD diagnosis, death, loss to follow-up, or December 31, 2021.

• Cognitive Screening: The Chinese version of the Mini-Mental State Examination (MMSE) was used to assess cognitive function, with education-specific cut-off points for impairment.
• Clinical Diagnosis: Participants identified with cognitive impairment were further evaluated by two neurologists for a formal diagnosis of AD, according to the DSM-IV and NINCDS-ADRDA criteria.

Environmental Monitoring and Data Analysis in Support of SDG 11

Estimation of Air Pollutants and Joint Air Pollution Scores

This component of the study directly addresses SDG 11.6, which aims to reduce the adverse environmental impact of cities by improving air quality.

• Data Collection: Daily concentration data for five key air pollutants were sourced from 166 air quality monitoring stations across Zhejiang Province from 2014 to 2019.
  • NO₂ (µg/m³)
  • SO₂ (µg/m³)
  • O₃ (µg/m³)
  • PM_2.5 (µg/m³)
  • PM₁₀ (µg/m³)
• Exposure Estimation: The empirical Bayesian kriging (EBK) spatial interpolation method was used to estimate more precise individual exposure levels. The annual average concentrations were assigned to participants based on their county of residence.
• Joint Air Pollution Score (JAPS): To assess the cumulative impact of air pollutant mixtures, a JAPS was calculated. This composite score weights the concentration of each pollutant by its multivariable-adjusted risk estimate for AD, providing a comprehensive metric for environmental health risk.

Covariate Measurement

To ensure a thorough analysis that accounts for socioeconomic and lifestyle factors, in line with SDG 10, extensive covariate data were collected through standardized questionnaires.

• Demographic and Socioeconomic Data: Age, sex, education level, marital status, family income, and working status.
• Health and Lifestyle Data: Body mass index (BMI), hypertension, hyperlipidemia, diabetes, smoking status, alcohol consumption, physical exercise, and living arrangements (living alone).

Statistical Approach and Sensitivity Analysis

Statistical Analysis

A robust statistical framework was employed to investigate the associations between air pollution and AD, providing critical evidence for public health policies aimed at achieving the SDGs.

• Primary Analysis: Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), with adjustments for all measured covariates.
• Temporal Analysis: The time-exposure relationship between JAPS and AD risk was explored using cumulative exposure concentrations and different exposure windows (early, middle, late stage).
• Stratified Analysis: To identify vulnerable subgroups and inform targeted interventions under SDG 10, analyses were stratified by lifestyle factors (smoking, alcohol, exercise), age, and sex.

Sensitivity Analysis

To confirm the stability and reliability of the findings, several sensitivity analyses were performed:

1. Using only air pollution data from a single year (2015).
2. Excluding participants diagnosed with AD within the first two years of follow-up.
3. Restricting the analysis to a subset of five counties with large populations.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 3: Good Health and Well-being

  The article’s primary focus is on health, specifically investigating the link between air pollution and the incidence of Alzheimer’s disease (AD) and cognitive impairment in an elderly population. It directly addresses health outcomes and the determinants of health, which is the core of SDG 3.

• SDG 11: Sustainable Cities and Communities

  The study measures and analyzes the impact of urban and suburban air quality on residents. By examining pollutants like PM2.5, PM10, NO₂, SO₂, and O₃ in various counties and districts, the article connects directly to the environmental quality aspect of sustainable cities.

• SDG 17: Partnerships for the Goals (specifically Data, Monitoring and Accountability)

  The research is based on the “Zhejiang Province Major Public Health Surveillance (ZJMPHS) Program,” a large-scale, long-term data collection effort. This program exemplifies the kind of robust data gathering, monitoring, and analysis needed to track progress on health and environmental goals, which is a key component of SDG 17.

2. What specific targets under those SDGs can be identified based on the article’s content?

• SDG 3: Good Health and Well-being

  • Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being. The article contributes to this target by researching a major non-communicable disease (Alzheimer’s) and identifying a key preventable risk factor (air pollution), which is crucial for developing prevention strategies.
  • Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination. The study directly investigates illnesses (cognitive impairment and AD) resulting from exposure to air pollution, aligning perfectly with this target’s objective.

• SDG 11: Sustainable Cities and Communities

  • Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality. The article’s methodology is centered on measuring ambient air pollutants (PM2.5, PM10, etc.) in populated areas and assessing their adverse health impacts, directly addressing the need to monitor and improve urban air quality.

• SDG 17: Partnerships for the Goals

  • Target 17.18: By 2020, enhance capacity-building support to developing countries… to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age… and other characteristics relevant in national contexts. The ZJMPHS program described in the article is a practical example of this target in action. It collects and analyzes high-quality health data disaggregated by numerous covariates such as age, sex, education, and family income to understand a complex public health issue.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• For Target 3.4 and 3.9 (Good Health and Well-being)

  • Indicator (Implied): Incidence rate of Alzheimer’s Disease. The primary outcome of the study is the “diagnosis of AD,” which serves as a direct measure of the burden of this non-communicable disease linked to pollution.
  • Indicator (Implied): Prevalence of cognitive impairment. The study uses the “Chinese version of the Mini-Mental State Examination (MMSE)” to assess cognitive function, providing a measurable indicator of brain health and well-being in the population.

• For Target 11.6 (Sustainable Cities and Communities)

  • Indicator (Directly Mentioned): Annual mean levels of fine particulate matter and other pollutants. The article explicitly states that “Daily concentrations of air pollutants, including NO₂ (µg/m³), SO₂ (µg/m³), O₃ (µg/m³), PM₂.₅ (µg/m³) and PM₁₀ (µg/m³), were measured.” This directly corresponds to the official SDG indicator 11.6.2, which tracks the annual mean levels of PM2.5 and PM10 in cities.

• For Target 17.18 (Partnerships for the Goals)

  • Indicator (Implied): Availability of disaggregated health and demographic data. The article’s “Covariate measurement” section lists numerous variables collected, including “age, sex, education, marital status, family income, working status,” demonstrating the existence and use of a statistical framework that produces data disaggregated by characteristics relevant to national contexts.

4. Summary Table of SDGs, Targets, and Indicators

Source: bmcpublichealth.biomedcentral.com