Introduction

Non-communicable diseases (NCDs), including cardiovascular diseases, respiratory disorders, diabetes, and cancer, are a leading cause of global morbidity and mortality, accounting for over 70% of deaths worldwide.[1] The escalating burden of NCDs represents a significant public health challenge, particularly in low- and middle-income countries where healthcare systems are often ill-equipped to manage chronic conditions.[2] While lifestyle factors such as poor diet, physical inactivity, and tobacco use are well-recognized contributors to NCD risk, environmental determinants, including air pollution, are increasingly being acknowledged as critical drivers of these conditions.[3]

Air pollution, comprising ambient (outdoor) and household (indoor) pollution, is a pervasive environmental health hazard affecting billions of individuals globally.[4] Major pollutants such as Particulate matter (PM2.5 and PM10), Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Ozone (O3), and Carbon monoxide (CO) originate from diverse sources, including vehicular emissions, industrial activities, agricultural practices, and biomass burning.[5] Chronic exposure to these pollutants has been linked to systemic inflammation, oxidative stress, and endothelial dysfunction, pathways that underlie the pathogenesis of multiple NCDs.[6]

The relationship between air pollution and the development of NCDs has garnered significant research attention in recent decades. Epidemiological studies and clinical evidence consistently demonstrate associations between long-term exposure to air pollution and adverse health outcomes, such as hypertension, ischemic heart disease, chronic obstructive pulmonary disease (COPD), type 2 diabetes, and lung cancer.[7] However, the magnitude of these associations, the interplay between different pollutants, and the influence of demographic and socioeconomic factors on susceptibility remain areas of ongoing investigation.[8]

Systematic reviews and meta-analysis provide a powerful approach to synthesizing existing evidence, offering comprehensive insights into the strength and consistency of associations between air pollution and NCDs.[9] By integrating data from diverse studies, these methodologies can identify patterns, highlight research gaps, and inform policy interventions aimed at mitigating air pollution and its health impacts.[10]

This systematic review and meta-analysis aim to explore the role of air pollution in the development of NCDs. Specifically, it seeks to quantify the associations between exposure to key air pollutants and the risk of major NCDs, evaluate the heterogeneity of findings across geographic regions and population subgroups, and assess the quality of evidence to inform public health strategies and policymaking. Understanding these relationships is imperative for designing targeted interventions to curb the dual burden of air pollution and NCDs, thereby promoting global health equity and sustainability.

Material and Methods

This systematic review and meta-analysis (SRMA) followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) standards.[11] The PROSPERO prospectively registered this research study protocol with the registration number CRD42024611293. Because this is a systematic study, meta-analysis was performed to determine that heterogeneity was within acceptable limits.

Eligibility Criteria

Studies were included if they met the following criteria: the study population consisted of adult patients with non-communicable illnesses such as cardiovascular, respiratory, diabetic, or liver diseases; the intervention involved evaluating exposure to air pollutants, including fine particulate matter (PM₂.₅, PM₁₀), ozone (O₃), nitrogen dioxide (NO₂), nitrogen oxide (NOₓ), sulfur dioxide (SO₂), and carbon monoxide (CO); the control group comprised adults without non-communicable diseases; the outcomes focused on identifying strong associations between exposure to these pollutants and the risk of major NCDs such as Parkinson’s disease, liver disease, and heart disease; and the study design included epidemiological approaches such as case-control, cohort, and cross-sectional studies.

Exclusion criteria:

Search Strategy

The literature retrieval approach made use of electronic retrieval tools. A complete and systematic literature review was carried out in the following databases: Google Scholar, PubMed, Embase, Scopus, Web of Science, and Cochrane Library, using a mix of Medical Subject Headings (MeSH) terms and free-text keywords. The search was limited to studies published between January 2015 and December 2024. The primary search phrases were “Air Pollution”, “Non communicable diseases”, “NCD” and “Prevalence”. In addition, appropriate papers were included in the evaluation and analysis following a manual search of the primary trial reference list using the selected subjects.

Study Selection

The search results were entered into Rayyan, an online systematic review application, to pick the studies.[12] A two-stage screening approach was used to select studies. One independent author (R.M.) conducted the literature search and reviewed the titles, abstracts, and keywords of all papers. One author (S.B.) independently screened abstracts and full texts to choose papers that met our review's eligibility criteria. Any conflicts or discordances that arose throughout the selection process were handled either by consensus or consultation with the author (R.M.). If there were disagreements amongst reviewers, the third reviewer (J.F.) moderated a conversation to reach a consensus conclusion.

Data extraction and management

The first and co-authors independently retrieved the pertinent research features for the review based on outcome measures from the included studies. A pre-established checklist was used to extract data, which included the last name of the first author, the year the study was published, the study design, the study period, the location, the sample size, the exposure, diagnostic criteria, and the major results. The first author (S.B.) entered data into the program Review Manager (RevMan 5.4).[13] The second author (R.M.) double-checked data input for accuracy by comparing the data given in the review to the data contained in the report.

Outcome measure for the study

The primary outcome was to assess the relationship between the exposure of air pollution and incidence of non-communicable diseases. The secondary outcome of the study is to reveal the strong associations between fine air pollutants, and the risk of major NCDs through the meta-analysis of published studies. The exposure of air pollutants was particulate matter (PM2.5, PM10), Ozone (O3), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Sulfur dioxide (SO2) and, Carbon monoxide (CO). Non-communicable diseases such as Parkinson disease, liver disease, heart disease, dementia, Bell’s palsy and sudden sensorineural hearing loss.

Quality assessment

The selected article’s methodological quality was assessed using the Joanna Briggs Institute (JBI) methodology,[14] as shown in Table 2. The evaluation results were finally expressed as “Green colour” represented “Yes”, which means the study meets the criteria, “Red” represented “No”, which means the study does not meet the criteria, “Yellow” represented “Unclear”, which means there is insufficient information to judge, and “Grey” represented “Not applicable”, which means the criterion is not relevant to the study. It is a method for determining the consistency and validity of study findings. Each article was categorised by JBI except Cheng et al. and Huang et al. studies because these two studies were not eligible for this quality assessment.[15, 16]

Statistical analysis

A comprehensive qualitative analysis was made for air pollutants including PM 2.5, PM10, O3, NO2, NOx, SO2 and CO levels of non-communicable disease (NCD) and without non-communicable disease patients. The data was entered and compiled using Microsoft Excel. A statistical analysis was analysed using Review Manager 5.4 software.[13] Forest plots were used for meta-analysis of PM2.5, PM10, O3, NO2, NOx, SO2 and CO air pollutants of NCD and without NCD patients. The quantitative data and the continuous variables were expressed as mean difference (MD), 95% confidence interval (CI) was used for study specific and overall pooled prevalence. The heterogeneity of the included articles was assessed using I² statistics. Statistically significant heterogeneity was considered if P < 0.05 or I²>50% among the included studies. Sensitivity analysis was done to assess the reliability of the estimate obtained in the meta-analysis.

Results

Study selection and characteristics

A total of 142 studies were initially retrieved from the databases. After removing duplicates, 112 articles remained for abstract screening. Of these, 34 irrelevant studies were excluded, leaving 78 studies for full-text review. During the full-text screening, 23 studies were excluded as they were unsuitable for inclusion in the meta-analysis, and 55 studies proceeded to detailed screening. Following the exclusion of 43 studies due to inappropriate study design or outcomes, 12 studies ultimately met the inclusion criteria and were included in the qualitative and quantitative analysis. Of these, 12 studies specifically examined the association between air pollution and non-communicable diseases.[15-26] included and excluded studies were documented following the PRISMA flowchart of the study selection process, as shown in Figure 1.

Figure 1: PRISMA flow chart of the study selection

Characteristics of the patient

A total of 7,43,083 patients were included in the selected studies which female patients (51.6%) were more affected by non-communicable diseases than male patients (48.6%). Non-communicable diseases affected patients (13.7%) are lesser than controls in this study. All the chosen articles were in hospital settings.

Methodological quality of the included studies

The meta-analysis included 12 articles, focusing on various pollutants: PM2.5 (8 studies), PM10 (9 studies), O3 (5 studies), NO2 (8 studies), NOx (4 studies), SO2 (4 studies), and CO (4 studies).[15-26] These studies evaluated the relationship between the exposure of air pollution and incidents of non-communicable diseases. This study was also determined and compared the association between PM2.5, PM10, O3, NO2, NOx, SO2 and CO air pollutants of NCD and without NCD patients. These studies were published between 2016 and 2024.

Exposure to Air pollutants and NCD risk

The meta-analysis comparing patients with non-communicable diseases (NCDs) and those without NCDs demonstrated a statistically significant overall effect (P < 0.0001), with a pooled odds ratio (OR) of0.05 (95% Confidence Interval [CI]:0.01to 0.21). The 95% CI does not cross the line of no effect, indicating a consistent association. However, considerable heterogeneity was observed among he included studies, as indicated by an I² value of 100% as shown in Figure 2.

Table 1: Characteristics of included studies

Figure 2: Forest plot of NCD and without NCD

Relationship between NCDs and without NCDs patient’s Air pollutants

A meta-analysis was conducted to evaluate the relationship of air pollutants, specifically Particular matters (PM2.5, PM10) and Nitrogen dioxide (NO2), between non-communicable diseases (NCDs) compared to those without NCDs. The pooled effect size estimate revealed a statistically significant positive relationship between two groups, [Mean Difference (MD) 0.35 (95% CI: 0.11 – 0.59), P = 0.005], [MD: 0.66 (95% CI: 0.17 – 1.16), P = 0.008] and [MD: 0.56 (95% CI: 0.15 – 0.97), P = 0.007]. The 95% CI does not cross the line of no effect and heterogeneity among studies was considerable, as indicated by an I² value of 98%, 99% and 94%, suggesting substantial variability across the included studies as shown in the Figure 3. In meta-analysis, the relationship of Ozone (O3) air pollutants exposure between non-communicable diseases (NCDs) compared to those without NCDs were determined. The pooled effect size estimate revealed not statistically significant, MD = -0.22 (95% CI: -0.53 to 0.10), (P = 0.18), which indicating no clear association based on the available evidence. The 95% CI touches the line of no effect, and the presence of heterogeneity was extremely high with I² value of 94% which indicates considerable variability in the observed effects (Figure 3).

Figure 3: Forest plot of PM2.5, PM10, O3 and NO2 air pollutants of NCD patients

The meta-analysis demonstrated statistically not significant difference in exposure to air pollutants, specifically Nitrogen oxides (NOx) and Sulfur dioxide (SO2) between patients with non-communicable diseases (NCDs) and those without NCDs. For NOx exposure, the pooled effect size was Mean Difference (MD): 0.28 (95% CI: -0.17 to 0.73, P = 0.22), with moderate heterogeneity (I² = 38%). For SO2 exposure, the pooled effect size was MD: -0.11 (95% CI: -0.76 to 0.54, P = 0.75), with substantial heterogeneity (I² = 99%). In both exposures, the 95% confidence intervals included the line of no effect, indicating that the differences observed were not statistically significant (Figure 4).

Figure 4: Forest plot of NOx, SO2 and CO air pollutants of NCD patients

The pooled analysis revealed a statistically significant correlation between carbon monoxide (CO) exposure and the difference in effects between patients with and without NCDs, with a correlation coefficient of 4.13 (95% CI: 1.25 to 7.01, P= 0.005). The 95% confidence interval does not cross the line of no effect, indicating a meaningful association in the exposure. The heterogeneity among the included studies was moderate, as evidenced by an I² value of 34%, suggesting some variability in the observed effects across studies as shown in the Figure 4.

Publication bias

Publication bias and heterogeneity was assessed using funnel plot among included studies. The funnel plot (Figure 5) visually suggests asymmetry, indicating a potential for publication bias, particularly in studies evaluating the association between air pollution exposure and non-communicable diseases (NCDs). An x-axis represents the effect size of Odds Ratio (OR), ranging from 0.001 to 1000. A y-axis measures the precision of the effect size of standard error of log odds ratio (SE [log (OR)]). Each circles represents an individual study included in the meta-analysis. The plot displays asymmetry, suggesting a possible publication bias or heterogeneity among the included studies. The lack of symmetry may indicate the small-study effects, where smaller studies with non-significant results might be underreported. Further analysis, such as Egger’s test, Begg’s test, or Trim-and-fill method, should be performed to assess and adjust for potential bias. To minimize the risk of bias, a comprehensive literature search was conducted. However, the presence of heterogeneity (I² ranging from 34% to 100%) suggests that other factors, such as methodological differences and variations in study populations, may have contributed to the observed asymmetry.

Figure 5: Funnel Plot of NCD and without NCD

Table 2: Risk of bias analysis by JBI approach

Discussion

The present study aimed to assess the relationship between exposure to various air pollutants and the incidence of non-communicable diseases (NCDs). The results indicate a significant association between certain air pollutants (PM2.5, PM10, NO2, CO) and NCDs, with varying levels of heterogeneity across studies. However, no clear association was observed with pollutants such as O3, NOx, and SO2. This contrasts with some previous studies, while aligning with others, providing insights into the complexity of the relationship between air pollution and NCD risk.

Air Pollutants and NCDs: A Complex Association

Air pollution, especially fine particulate matter and gases like nitrogen dioxide, is well-established as a risk factor for various health issues, including cardiovascular disease, respiratory disorders, and cancer. In this study, PM2.5, PM10, NO2, and CO were found to have statistically significant associations with NCDs. These results corroborate findings from previous meta-analyses and cohort studies which have consistently linked particulate matter (PM2.5 and PM10) and nitrogen dioxide (NO2) exposure with increased risks of developing NCDs.[27,28] Fine particulate matter (PM2.5) is particularly concerning due to its ability to penetrate deep into the lungs and enter the bloodstream, causing systemic inflammation and exacerbating existing health conditions.[29] Similarly, NO2 exposure has been associated with respiratory diseases and cardiovascular risk, as it is a byproduct of traffic and industrial activities, which frequently affect urban populations.[30]

In this meta-analysis, the relationship between air pollution and NCDs was strongest for PM2.5 and PM10, with significant pooled effect sizes (MD 0.35, 0.66, and 0.56, respectively). The heterogeneity among studies was high (I² = 98%, 99%, and 94%), which is a common challenge in meta-analyses involving environmental exposures. This variability may be due to differences in geographical locations, study designs, population demographics, and air quality levels across studies. A similar level of heterogeneity was observed in a study by Miller et al. (2020), which also examined the impact of PM2.5 and NO2 on cardiovascular disease risk and reported considerable variation across studies depending on factors like location and control for confounding variables.[31]

Ozone and NCDs: Lack of Clear Association

Interestingly, the present analysis did not find a statistically significant relationship between O3 exposure and NCDs (MD = -0.22, P = 0.18). This finding differs from other studies that have reported a link between ground-level ozone and respiratory diseases, particularly in sensitive populations such as the elderly and children.[32] One potential explanation for this discrepancy could be the methodological differences between studies, including variations in how exposure to ozone was measured and the different health outcomes studied. Ozone is a secondary pollutant that forms when sunlight reacts with pollutants like nitrogen oxides and volatile organic compounds, and its effects may be more context-dependent, varying with weather patterns, seasonal fluctuations, and urbanization levels. The heterogeneity in our study (I² = 94%) further suggests that contextual factors such as geographical location and air quality monitoring methods may have influenced the observed effects, making it difficult to establish a consistent association between ozone exposure and NCD risk.

Nitrogen Oxides and Sulfur Dioxide: Limited Evidence for NCDs

The analysis revealed no significant association between NOx and SO2 exposure and NCD risk (NOx: MD = 0.28, P = 0.22; SO2: MD = -0.11, P = 0.75). While both NOx and SO2 are common air pollutants, particularly in industrial and urban areas, the limited evidence for their impact on NCDs in this meta-analysis suggests that the effects of these pollutants may not be as pronounced as those of fine particulate matter or NO2. These findings are in line with some studies that have reported weak or inconsistent associations between NOx and NCD outcomes.[33] NOx primarily contributes to the formation of ground-level ozone and particulate matter, and its health effects may be indirect, depending on its interaction with other pollutants. Similarly, SO2 has been more strongly linked to respiratory issues, but its role in NCDs like cardiovascular disease remains less clear.[34] The high heterogeneity observed in our study (I² = 99%) may further explain the lack of consistent findings across studies, as different study designs and regions could have led to varying results.

Carbon Monoxide: A Significant Risk Factor

A noteworthy finding in this study was the significant association between carbon monoxide (CO) exposure and NCDs, with a pooled effect size of 4.13 (95% CI: 1.25 to 7.01, P = 0.005). This is consistent with previous research that has linked CO exposure to cardiovascular risk, particularly in populations exposed to high levels of traffic-related pollution.[35] CO is a colorless, odorless gas that can interfere with oxygen transport in the bloodstream, leading to various health complications, particularly in individuals with pre-existing cardiovascular conditions. Although the heterogeneity in the studies was moderate (I² = 34%), the consistent positive association between CO exposure and NCD risk suggests that CO is a significant risk factor that warrants attention in future research and policy-making efforts.

Implications for Public Health and Future Research

The findings of this meta-analysis highlight the substantial health risks posed by exposure to air pollutants, particularly particulate matter (PM2.5, PM10), NO2, and CO. The evidence suggests that reducing exposure to these pollutants could help mitigate the burden of NCDs, especially in urban and industrial areas with high levels of air pollution. Public health initiatives aimed at improving air quality, such as stricter regulations on industrial emissions and promoting cleaner transportation options, could play a critical role in reducing the incidence of NCDs.

However, the high levels of heterogeneity observed in several analyses suggest the need for further research to better understand the complex relationship between air pollution and NCDs. Future studies should focus on standardizing exposure assessment methods, accounting for potential confounders, and exploring the effects of multiple pollutants in combination. Longitudinal cohort studies that track individuals over time may provide further insights into the long-term impacts of air pollution on health.

Conclusion

This meta-analysis provides strong evidence for a significant association between exposure to certain air pollutants, particularly PM2.5, PM10, NO2, and CO, and the increased risk of non-communicable diseases. While certain pollutants like O3, NOx, and SO2 showed weaker or no significant associations, the findings emphasize the need for continued efforts to reduce air pollution and protect public health. The considerable heterogeneity observed across studies highlights the complexity of this issue and the importance of context in understanding the health impacts of air pollution.

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