Association between Long-Term Ambient PM2.5 Exposure and under-5 Mortality: A Scoping Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source and Article Search
2.2. Selection of Articles
2.3. Data Extraction and Analysis
3. Results
3.1. Study Characteristics
3.2. Outcomes
3.3. Exposure Assessments
3.4. Adjusted Covariates
3.5. Effect Estimates
3.5.1. Post-Birth Exposure to PM2.5 and under-5 Mortality
3.5.2. Exposure to PM2.5 and Infant Mortality
4. Discussion
Strengths and Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author and Year | Study Area, Country, and Study Period | Study Design and Sample Size | Health Outcome | Cause of Death | Adjusted Covariates | Statistical Model Used | Measures of Association and Selected Results of Percent Change in Excess Risk (95% CI) per 1 µg/m3 Increase in Particles (Unless Otherwise Stated) |
---|---|---|---|---|---|---|---|
Under-5 mortality | |||||||
Liao et al., 2022 [36] | India, 2006–2016 | Cohort 259,627 live-birth children born 5 years preceding the survey from 175,865 women | Neonatal mortality, infant mortality, and child mortality | All-cause | Sex, birth month/year, birth order, location (hospital or not), age of mother, height, marital status, smoking, education level, passive/active smoking, location of house, area, cooking fuel, toilet facilities, wealth index, and monthly rainfall and temperature | Cox proportional hazards regression model | HR 0.13% [95% CI: 0.01%, 0.26%] for post-delivery lifetime exposure; 0.23% [95% CI: 0.09%, 0.38%] for in utero exposure |
Egondi et al., 2018 [37] | Nairobi, Kenya, 2003–2013 | Cohort 21,641 children under age 5 | Under-5 mortality and morbidity | All-cause and respiratory | Sex, age, and socioeconomic status | Logistic and Poisson regression models | IRR Effect size was mentioned considering the exposure level binary (PM2.5 < 25 ≥ µg/m3): 22.00% [95%CI: 8.00%, 39.00%]; for all-cause: 12.00% [95%CI: −12.0%, 42%). |
Lien et al., 2019 [38] | 45 countries in Asia by clustering in 5 regions, 2000–2015 | Ecological | Under-5 and maternal mortality | All-cause and respiratory | Country, year, total population, urban population, female population, employed population, HIV/AIDS-related death, TB death, undernourished population, and temperature | Generalized additive mixed-effects model | IRR 29.00% [95% (CI): 13.11%, 47.13%] for biomass PM2.5; 12.00% [95% (CI): 1.09%, 24.10%] for anthropogenic PM2.5. |
Owili et al., 2020 [39] | Global, 2000–2015 | Ecological | Under-5 and maternal mortality | All-cause | Total number of undernourished, anemic pregnant women, tuberculosis cases, AIDS-related deaths, employed females, population in urban areas, year, country, and annual mean temperature | Generalized linear mixed-effects model | RR 8.90% [95% (CI): 4.11%, 13.91%] increase for biomass PM2.5 and 9.50% [95% (CI): −0.03%, 20.23%] for dust PM2.5 |
Infant mortality | |||||||
Son et al., 2011 [40] | Seoul, South Korea, 2004–2007 | Cohort 359,459 infants | Post-neonatal infant mortality | All-cause, respiratory, and SIDS | Sex, gestational length, season of birth, maternal age and educational level, and heat index | Cox proportional hazards model | HR 14.45% [95% (CI): 6.68%, 22.79%] increase in all-cause mortality for gestational exposure |
Son et al., 2017 [22] | Massachusetts, USA, 2001–2007 | Cohort 465,682 infants | Post-neonatal infant mortality | All-cause, respiratory, and SIDS | Sex, birth weight, length of gestation, mother’s age, educational level, race/ethnicity, marital status, parity, and season of birth | Cox proportional hazards model | HR 112.24% [95% (CI): 71.72%, 162.32%] increase in all-cause infant mortality for post-birth lifetime exposure |
Khadka et al., 2021 [41] | USA, 2011–2013 | Cohort 10,017,357 live births and 58,913 infant deaths | Infant mortality | All-cause | Individual level (age of parents, race, level of education, maternal smoking, marital status, and parity), county level (average temperature, precipitation, and unemployment), pregnancy covariates (method of payment for delivery, child sex, and multiple births), racial composition, poverty rate, median housing value, and number of physicians per 1000 persons | - | No effect size was mentioned. Prenatal PM2.5 exposure was positively associated with infant death in all trimesters; most significant relationship in third trimester; relationship between post-birth PM2.5 exposure and infant mortality was positive but less precisely estimated |
Woodruff et al., 2006 [42] | California, USA, 1999–2001 | Matched case–control 3877 infants | Post-neonatal infant mortality | All-cause, respiratory, and SIDS | Maternal race, education, age, marital status, and parity; confounder: birth weight | Conditional logistic regression model | OR 7.00% [95% (CI): −7.33%, 23.55%] for all-cause mortality; 113.00% [95% (CI): −12.01%, 305.04%] for respiratory mortality |
Jung et al., 2020 [43] | South Korea, 2010–2015 | Case–control 2,501,836 infants | Post-neonatal infant mortality | All-cause and respiratory | Maternal education, season of birth, birth weight (kg), gestational age (weeks), and region | Conditional logistic regression model | OR 2.58% [95% (CI): 0.70%, 4.50%] for infant mortality from gestational exposure 1.75% [95% (CI): 0.27%, 3.26%] and 1.92% [95% (CI): 0.0.42%, 3.45%] for exposure in 1st and 2nd trimesters, respectively |
Heft-Neal et al., 2018 [44] | 30 countries in Sub-Saharan Africa, 2000–2015 | Cross-sectional | Infant mortality | All-cause | Child sex, birth order, age of mother, maternal education, type of cooking fuel, asset-based wealth index, temperature, and seasonal variability | Fixed-effects regression model | RR 0.87% [95% (CI): 0.40%, 1.33%] for infant mortality |
Goyal et al., 2019 [45] | 43 low- and middle-income countries, 1998–2014 | Cross-sectional 534,476 children | Neonatal and post-neonatal mortality and infant mortality | All-cause and respiratory | Child-level (birth order, sex, multiple births, and birth interval), parent-level (age, smoking habit, and education of both parents), and household-level (location, cooking fuel, drinking water, improved sanitation, and wealth quantiles) characteristics | Multivariate logistic regression model | Odds Ratio (OR) 22.0% [95% (CI): 10.62%, 34.54%] increase in neonatal mortality and 13% [95% (CI): 3.90%, 22.89%] increase in infant mortality for anthropogenic PM2.5 |
deSouza et al., 2021 [46] | India, 2015–2016 | Cross-sectional 259,627 children | Neonatal, post-neonatal, and infant mortality | All-cause | Child-level (sex and birth order), mother-level (age of marriage, education, age of giving birth, and smoking habit), and household-level (location, sanitation, fuel use, and safe drinking water access) covariates, including seasonality and long-term trend | Fixed-effects regression model | OR 0.16% [95% (CI): 0.03%, 0.30%] increase in neonatal mortality for exposure in 3rd trimester |
Bachwenkizi et al., 2021 [47] | 15 countries in Africa, 2005–2015 | Cross-sectional 602,863 participants | Infant mortality | All-cause | Mother-level (age, education, and smoking habit), child-level (sex, birth order, vaccination record, and diarrhea), household-level (toilet facilities, drinking water, cooking fuel, and wealth index), and country-level (anemia, health expenditure, child stunting, temperature, and humidity) covariates | Multivariable logistic regression model | OR 3.00% [95% (CI): 0.54, 5.51%] for infant mortality |
Characteristics | Frequency | |
---|---|---|
Study area | LMIC [38,39,45] | 3 |
USA [22,41,42] | 3 | |
South Korea [40,43] | 2 | |
India [36,46] | 2 | |
Africa [37,44,47] | 3 | |
Study design | Cohort [22,36,37,40,41] | 5 |
Cross-sectional [44,45,46,47] | 4 | |
Case–control [42,43] | 2 | |
Ecological [38,39] | 2 | |
Outcomes | Under-5 mortality [36,37,38,39] | 4 |
Post-neonatal mortality [22,40,42,43] | 4 | |
Neonatal/post-neonatal/infant mortality [45,46] | 2 | |
Infant mortality [41,44,47] | 3 | |
Cause of death | All-cause mortality [22,36,37,38,39,40,41,42,43,44,45,46,47] | 13 |
Respiratory mortality [22,37,40,42] | 4 | |
Sudden infant death syndrome [22,40,42] | 3 |
Author and Year | Exposure Assessment | Source of PM2.5 and Constituents | Exposure Window | |
---|---|---|---|---|
Post-Birth/Lifetime Exposure | In Utero/Prenatal and Post-Birth Exposure | |||
Lien et al., 2019 [38] | Aerosol optical depth retrieved from MODIS | PM2.5 (biomass burning, anthropogenic pollutant, mineral dust, biomass/dust mixture, anthropogenic/dust mixture, and biomass/anthropogenic mixture) | ■ | |
Owili et al., 2020 [39] | Aerosol optical depth retrieved from MODIS | PM2.5 (anthropogenic, biomass, and dust) | ■ | |
Egondi et al., 2018 [37] | Ground-based monitoring station data | PM2.5 | ■ | |
Goyal et al., 2019 [45] | High-resolution calibrated satellite data | Dust and sea salt from other carbonaceous and manmade PM | ○ | |
Son et al., 2017 [22] | From PM2.5 prediction models based on satellite imagery | Ambient PM2.5 | ■ | |
Son et al., 2011 [40] | Ground-based monitoring station data | PM2.5 among other pollutants | ○ | |
Jung et al., 2020 [43] | CMAQ model | Ambient PM2.5 | ○ | |
Woodruff et al., 2006 [42] | Ground-based monitoring station data | Ambient PM2.5 | ■ | |
Liao et al., 2022 [36] | Combination of monitoring station data, satellite-based AOD data, and random forest model | Ambient PM2.5 | ○ | |
Heft-Neal et al., 2018 [44] | Data from satellite-based estimates of PM2.5 | Ambient PM2.5 | ○ | |
Khadka et al., 2021 [41] | Combination of ground-based monitoring and CMAQ model | Ambient PM2.5 | ○ | |
deSouza et al., 2021 [46] | Satellite-derived PM2.5 concentrations | Ambient PM2.5 | ○ | |
Bachwenkizi et al., 2021 [47] | Satellite-derived PM2.5 for mass and chemical transport model (GEOS-Chem) | Ambient PM2.5, including constituents such as organic matter (OM), black carbon (BC), sulfate, nitrate (NO3, NH4), and soil dust (DUST). | ■ |
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Anita, W.M.; Ueda, K.; Uttajug, A.; Seposo, X.T.; Takano, H. Association between Long-Term Ambient PM2.5 Exposure and under-5 Mortality: A Scoping Review. Int. J. Environ. Res. Public Health 2023, 20, 3270. https://doi.org/10.3390/ijerph20043270
Anita WM, Ueda K, Uttajug A, Seposo XT, Takano H. Association between Long-Term Ambient PM2.5 Exposure and under-5 Mortality: A Scoping Review. International Journal of Environmental Research and Public Health. 2023; 20(4):3270. https://doi.org/10.3390/ijerph20043270
Chicago/Turabian StyleAnita, Wahida Musarrat, Kayo Ueda, Athicha Uttajug, Xerxes Tesoro Seposo, and Hirohisa Takano. 2023. "Association between Long-Term Ambient PM2.5 Exposure and under-5 Mortality: A Scoping Review" International Journal of Environmental Research and Public Health 20, no. 4: 3270. https://doi.org/10.3390/ijerph20043270