Ambient Air Pollution and Respiratory Health in Sub-Saharan African Children: A Cross-Sectional Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source and Study Population
2.2. Outcomes and Covariates
2.3. Ambient PM2.5 Data
2.4. Statistical Analyses
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Landrigan, P.J.; Fuller, R.; Acosta, N.J.R.; Adeyi, O.; Arnold, R.; Basu, N.N.; Baldé, A.B.; Bertollini, R.; Bose-O’Reilly, S.; Boufford, J.I.; et al. The Lancet Commission on pollution and health. Lancet 2018, 391, 462–512. [Google Scholar] [CrossRef] [Green Version]
- Lelieveld, J.; Pozzer, A.; Pöschl, U.; Fnais, M.; Haines, A.; Münzel, T. Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovasc. Res. 2020, 116, 1910–1917. [Google Scholar] [CrossRef]
- Shi, T.; McAllister, D.A.; O’Brien, K.L.; Simoes, E.A.F.; Madhi, S.A.; Gessner, B.D.; Polack, F.P.; Balsells, E.; Acacio, S.; Aguayo, C.; et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: A systematic review and modelling study. Lancet 2017, 390, 946–958. [Google Scholar] [CrossRef] [Green Version]
- Sonego, M.; Pellegrin, M.C.; Becker, G.; Lazzerini, M. Risk factors for mortality from acute lower respiratory infections (ALRI) in children under five years of age in low and middle-income countries: A systematic review and meta-analysis of observational studies. PLoS ONE 2015, 10, e0116380. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Horne, B.D.; Joy, E.A.; Hofmann, M.G.; Gesteland, P.H.; Cannon, J.B.; Lefler, J.S.; Blagev, D.P.; Korgenski, E.K.; Torosyan, N.; Hansen, G.I.; et al. Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection. Am. J. Respir. Crit. Care Med. 2018, 198, 759–766. [Google Scholar] [CrossRef] [PubMed]
- Darrow, L.A.; Klein, M.; Flanders, W.D.; Mulholland, J.A.; Tolbert, P.E.; Strickland, M.J. Air pollution and acute respiratory infections among children 0–4 years of age: An 18-year time-series study. Am. J. Epidemiol. 2014, 180, 968–977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- MacIntyre, E.A.; Gehring, U.; Mölter, A.; Fuertes, E.; Klümper, C.; Krämer, U.; Quass, U.; Hoffmann, B.; Gascon, M.; Brunekreef, B.; et al. Air pollution and respiratory infections during early childhood: An analysis of 10 European birth cohorts within the ESCAPE Project. Environ. Health Perspect. 2014, 122, 107–113. [Google Scholar] [CrossRef]
- Nhung, N.T.T.; Schindler, C.; Dien, T.M.; Probst-Hensch, N.; Perez, L.; Künzli, N. Acute effects of ambient air pollution on lower respiratory infections in Hanoi children: An eight-year time series study. Environ. Int. 2018, 110, 139–148. [Google Scholar] [CrossRef] [PubMed]
- Shi, W.; Liu, C.; Annesi-Maesano, I.; Norback, D.; Deng, Q.; Huang, C.; Qian, H.; Zhang, X.; Sun, Y.; Wang, T.; et al. Ambient PM(2.5) and its chemical constituents on lifetime-ever pneumonia in Chinese children: A multi-center study. Environ. Int. 2021, 146, 106176. [Google Scholar] [CrossRef] [PubMed]
- Luong, L.T.M.; Dang, T.N.; Thanh Huong, N.T.; Phung, D.; Tran, L.K.; Van Dung, D.; Thai, P.K. Particulate air pollution in Ho Chi Minh city and risk of hospital admission for acute lower respiratory infection (ALRI) among young children. Environ. Pollut. 2020, 257, 113424. [Google Scholar] [CrossRef]
- Khilnani, G.C.; Tiwari, P. Air pollution in India and related adverse respiratory health effects: Past, present, and future directions. Curr. Opin. Pulm. Med. 2018, 24, 108–116. [Google Scholar] [CrossRef] [PubMed]
- Brugha, R.; Grigg, J. Urban air pollution and respiratory infections. Paediatr. Respir. Rev. 2014, 15, 194–199. [Google Scholar] [CrossRef]
- Quarato, M.; De Maria, L.; Gatti, M.F.; Caputi, A.; Mansi, F.; Lorusso, P.; Birtolo, F.; Vimercati, L. Air Pollution and Public Health: A PRISMA-Compliant Systematic Review. Atmosphere 2017, 8, 183. [Google Scholar] [CrossRef] [Green Version]
- Nhung, N.T.T.; Amini, H.; Schindler, C.; Kutlar Joss, M.; Dien, T.M.; Probst-Hensch, N.; Perez, L.; Künzli, N. Short-term association between ambient air pollution and pneumonia in children: A systematic review and meta-analysis of time-series and case-crossover studies. Environ. Pollut. 2017, 230, 1000–1008. [Google Scholar] [CrossRef]
- Katoto, P.D.M.C.; Byamungu, L.; Brand, A.S.; Mokaya, J.; Strijdom, H.; Goswami, N.; De Boever, P.; Nawrot, T.S.; Nemery, B. Ambient air pollution and health in Sub-Saharan Africa: Current evidence, perspectives and a call to action. Environ. Res. 2019, 173, 174–188. [Google Scholar] [CrossRef]
- Masekela, R.; Vanker, A. Lung Health in Children in Sub-Saharan Africa: Addressing the Need for Cleaner Air. Int. J. Environ. Res. Public Health 2020, 17, 6178. [Google Scholar] [CrossRef]
- Amegah, A.K.; Agyei-Mensah, S. Urban air pollution in Sub-Saharan Africa: Time for action. Environ. Pollut. 2017, 220, 738–743. [Google Scholar] [CrossRef] [PubMed]
- Tusting, L.S.; Gething, P.W.; Gibson, H.S.; Greenwood, B.; Knudsen, J.; Lindsay, S.W.; Bhatt, S. Housing and child health in sub-Saharan Africa: A cross-sectional analysis. PLoS Med. 2020, 17, e1003055. [Google Scholar] [CrossRef] [Green Version]
- Rutstein, S.O. Steps to Constructing the New DHS Wealth Index. 2015. Available online: https://dhsprogram.com/programming/wealth%20index/Steps_to_constructing_the_new_DHS_Wealth_Index.pdf (accessed on 14 September 2021).
- Hammer, M.S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A.M.; Hsu, N.C.; Levy, R.C.; Garay, M.J.; Kalashnikova, O.V.; Kahn, R.A.; et al. Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018). Environ. Sci. Technol. 2020, 54, 7879–7890. [Google Scholar] [CrossRef] [PubMed]
- Buchard, V.; Randles, C.A.; da Silva, A.M.; Darmenov, A.; Colarco, P.R.; Govindaraju, R.; Ferrare, R.; Hair, J.; Beyersdorf, A.J.; Ziemba, L.D.; et al. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies. J. Clim. 2017, 30, 6851–6872. [Google Scholar] [CrossRef]
- Gao, Y.; Zhang, L.; Kc, A.; Wang, Y.; Zou, S.; Chen, C.; Huang, Y.; Mi, X.; Zhou, H. Housing environment and early childhood development in sub-Saharan Africa: A cross-sectional analysis. PLoS Med. 2021, 18, e1003578. [Google Scholar] [CrossRef]
- Roser, M.; Human Development Index (HDI). Our World in Data. 2014. Available online: https://ourworldindata.org/human-development-index (accessed on 14 September 2021).
- DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 1986, 7, 177–188. [Google Scholar] [CrossRef]
- Okomo, U.; Idoko, O.T.; Kampmann, B. The burden of viral respiratory infections in young children in low-resource settings. Lancet. Glob. Health 2020, 8, e454–e455. [Google Scholar] [CrossRef]
- Zhang, F.; Wang, J.; Ichoku, C.; Hyer, E.J.; Yang, Z.; Ge, C.; Su, S.; Zhang, X.; Kondragunta, S.; Kaiser, J.W.; et al. Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: A case study in northern sub-Saharan African region. Environ. Res. Lett. 2014, 9, 75002. [Google Scholar] [CrossRef]
- Liu, H.; Zhang, X.; Zhang, H.; Yao, X.; Zhou, M.; Wang, J.; He, Z.; Zhang, H.; Lou, L.; Mao, W.; et al. Effect of air pollution on the total bacteria and pathogenic bacteria in different sizes of particulate matter. Environ. Pollut. 2018, 233, 483–493. [Google Scholar] [CrossRef]
- Vargas Buonfiglio, L.G.; Comellas, A.P. Mechanism of ambient particulate matter and respiratory infections. J. Thorac. Dis. 2020, 12, 134–136. [Google Scholar] [CrossRef]
- Mushtaq, N.; Ezzati, M.; Hall, L.; Dickson, I.; Kirwan, M.; Png, K.M.Y.; Mudway, I.S.; Grigg, J. Adhesion of Streptococcus pneumoniae to human airway epithelial cells exposed to urban particulate matter. J. Allergy Clin. Immunol. 2011, 127, 1236–1242.e2. [Google Scholar] [CrossRef] [PubMed]
- Grigg, J. Air Pollution and Respiratory Infection: An Emerging and Troubling Association. Am. J. Respir. Crit. Care Med. 2018, 198, 700–701. [Google Scholar] [CrossRef]
- Heft-Neal, S.; Burney, J.; Bendavid, E.; Burke, M. Robust relationship between air quality and infant mortality in Africa. Nature 2018, 559, 254–258. [Google Scholar] [CrossRef]
- Mustapha, B.A.; Blangiardo, M.; Briggs, D.J.; Hansell, A.L. Traffic air pollution and other risk factors for respiratory illness in schoolchildren in the niger-delta region of Nigeria. Environ. Health Perspect. 2011, 119, 1478–1482. [Google Scholar] [CrossRef]
- Shirinde, J.; Wichmann, J.; Voyi, K. Association between wheeze and selected air pollution sources in an air pollution priority area in South Africa: A cross-sectional study. Environ. Health 2014, 13, 32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shirinde, J.; Wichmann, J.; Voyi, K. Allergic rhinitis, rhinoconjunctivitis and hayfever symptoms among children are associated with frequency of truck traffic near residences: A cross sectional study. Environ. Health 2015, 14, 84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Olutola, B.G.; Claassen, N.; Wichmann, J.; Voyi, K. Factors associated with parent-reported wheeze and cough in children living in an industrial area of Gauteng, South Africa. Environ. Sci. Pollut. Res. Int. 2018, 25, 33455–33463. [Google Scholar] [CrossRef] [Green Version]
- Naidoo, R.N.; Robins, T.G.; Batterman, S.; Mentz, G.; Jack, C. Ambient pollution and respiratory outcomes among schoolchildren in Durban, South Africa. SAJCH 2013, 7, 127–134. [Google Scholar] [CrossRef]
- Olaniyan, T.; Jeebhay, M.; Röösli, M.; Naidoo, R.N.; Künzli, N.; de Hoogh, K.; Saucy, A.; Badpa, M.; Baatjies, R.; Parker, B.; et al. The association between ambient NO(2) and PM(2.5) with the respiratory health of school children residing in informal settlements: A prospective cohort study. Environ. Res. 2020, 186, 109606. [Google Scholar] [CrossRef]
- Pieterse, E.; Parnell, S.; Haysom, G. African dreams: Locating urban infrastructure in the 2030 sustainable developmental agenda. Area Dev. Policy 2018, 3, 149–169. [Google Scholar] [CrossRef] [Green Version]
- Abera, A.; Friberg, J.; Isaxon, C.; Jerrett, M.; Malmqvist, E.; Sjöström, C.; Taj, T.; Vargas, A.M. Air Quality in Africa: Public Health Implications. Annu. Rev. Public Health 2021, 42, 193–210. [Google Scholar] [CrossRef]
- Kinney, P.L.; Gichuru, M.G.; Volavka-Close, N.; Ngo, N.; Ndiba, P.K.; Law, A.; Gachanja, A.; Gaita, S.M.; Chillrud, S.N.; Sclar, E. Traffic Impacts on PM(2.5) Air Quality in Nairobi, Kenya. Environ. Sci. Policy 2011, 14, 369–378. [Google Scholar] [CrossRef] [Green Version]
- Alli, A.S.; Clark, S.N.; Hughes, A.; Nimo, J.; Bedford-Moses, J.; Baah, S.; Wang, J.; Vallarino, J.; Agyemang, E.; Barratt, B.; et al. Spatial-temporal patterns of ambient fine particulate matter ({PM}2.5) and black carbon ({BC}) pollution in Accra. Environ. Res. Lett. 2021, 16, 74013. [Google Scholar] [CrossRef] [PubMed]
- Akinlade, G.O.; Olaniyi, H.B.; Olise, F.S.; Owoade, O.K.; Almeida, S.M.; Almeida-Silva, M.; Hopke, P.K. Spatial and temporal variations of the particulate size distribution and chemical composition over Ibadan, Nigeria. Environ. Monit. Assess. 2015, 187, 544. [Google Scholar] [CrossRef]
- Liu, N.M.; Grigg, J. Diesel, children and respiratory disease. BMJ Paediatr. Open 2018, 2, e000210. [Google Scholar] [CrossRef] [PubMed]
- Wetsman, N. Air-pollution trackers seek to fill Africa’s data gap. Nature 2018, 556, 284. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Country, Year | No. of Under-5 Children | No. of Under-5 Children with Data on Cough | % with Cough | No. of Under-5 Children with Data on ALRI | % with ALRI * | % Urban Clusters | Mean Child’s Age in Months | % Girl | % with Smaller Birth Size than Average * | % Poorer & Poorest Households | Mean Maternal Age in Years | % No. Formal Education |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Angola, 2016 | 17,367 | 16,409 | 10.5 | 16,374 | 4.1 | 55.3 | 29.6 | 50.7 | 9.7 | 47.7 | 28.0 | 35.0 |
Burkina Faso, 2010 | 17,249 | 15,628 | 10.8 | 1680 | - | 27.3 | 42.7 | 49.5 | 13.0 | 37.1 | 29.3 | 80.4 |
Burundi, 2017 | 16,949 | 16,074 | 37.7 | 16,072 | 12.2 | 18.9 | 30.4 | 49.7 | 16.1 | 43.8 | 30.5 | 42.7 |
Burundi, 2010 | 9403 | 8737 | 37.6 | 3280 | - | 19.6 | 42.8 | 48.9 | 16.7 | 37.5 | 30.3 | 47.9 |
Cameroon, 2018 | 13,102 | 12,250 | 18.6 | 12,225 | 3.8 | 52.4 | 30.2 | 50.2 | 13.5 | 36.1 | 28.5 | 19.8 |
CDR, 2014 | 20,309 | 18,470 | 30.4 | 5591 | - | 29.7 | 44.0 | 50.3 | 12.1 | 49.0 | 29.1 | 21.0 |
Ethiopia, 2016 | 18,324 | 17,353 | 15.5 | 17,331 | 7.2 | 31.0 | 30.3 | 48.4 | 25.6 | 49.6 | 29.2 | 54.0 |
Ethiopia, 2005 | 15,033 | 13,681 | 15.1 | 2048 | - | 26.5 | 37.6 | 48.2 | 27.5 | 38.0 | 28.9 | 67.9 |
Ghana, 2014 | 12,934 | 12,149 | 13.8 | 1615 | - | 50.2 | 44.2 | 48.4 | 17.6 | 42.8 | 30.7 | 27.9 |
Ghana, 2008 | 12,233 | 11,313 | 21.4 | 2420 | - | 43.9 | 41.9 | 51.3 | 14.7 | 41.2 | 30.2 | 28.7 |
Guinea, 2018 | 9170 | 8361 | 12.3 | 8356 | 4.6 | 31.3 | 29.1 | 48.8 | 11.1 | 45.1 | 29.2 | 75.5 |
Kenya, 2014 | 39,985 | 38,108 | 36.3 | 13,782 | - | 38.6 | 44.2 | 49.7 | - | 46.3 | 28.6 | 16.2 |
Kenya, 2008 | 10,174 | 9486 | 23.2 | 2185 | - | 32.5 | 42.0 | 50.0 | 16.1 | 35.2 | 28.6 | 15.7 |
Malawi, 2016 | 27,594 | 25,933 | 23.6 | 25,879 | 10.1 | 18.8 | 30.7 | 50.7 | 15.1 | 44.3 | 28.0 | 11.7 |
Malawi, 2010 | 27,516 | 25,189 | 28.0 | 7027 | - | 11.5 | 42.6 | 50.6 | 14.8 | 43.9 | 28.3 | 15.8 |
Mali, 2018 | 11,269 | 10,470 | 9.0 | 10,468 | 3.1 | 28.6 | 29.6 | 48.7 | 14.6 | 43.1 | 28.6 | 73.2 |
Mali, 2012 | 11,723 | 10,787 | 8.0 | 850 | - | 26.6 | 43.6 | 48.7 | 14.0 | 38.1 | 28.4 | 81.5 |
Mozambique, 2011 | 14,510 | 13,322 | 10.9 | 1434 | - | 35.8 | 43.1 | 50.0 | 13.6 | 34.5 | 28.3 | 32.3 |
Nigeria, 2018 | 46,230 | 42,199 | 15.5 | 42,196 | 4.9 | 39.0 | 30.8 | 49.1 | 12.5 | 37.9 | 29.9 | 36.2 |
Rwanda, 2015 | 12,834 | 12,213 | 25.2 | 3074 | - | 23.0 | 44.4 | 48.0 | 15.0 | 44.2 | 30.2 | 14.2 |
Rwanda, 2010 | 12,897 | 12,029 | 23.3 | 2800 | - | 15.9 | 42.8 | 47.8 | 16.3 | 43.3 | 30.7 | 18.0 |
Senegal, 2014 | 7063 | 6515 | 7.1 | 466 | - | 31.7 | 44.2 | 50.4 | 32.7 | 60.1 | 29.5 | 70.7 |
Senegal, 2015 | 7286 | 6679 | 10.9 | 727 | - | 29.6 | 44.6 | 50.0 | 31.8 | 56.7 | 29.5 | 69.0 |
Senegal, 2016 | 7147 | 6610 | 9.6 | 634 | - | 30.0 | 45.0 | 48.2 | 30.9 | 61.0 | 29.5 | 67.8 |
South Africa, 2016 | 13,792 | 12,854 | 25.3 | 12,800 | 4.6 | 60.0 | 32.0 | 45.4 | 13.8 | 41.5 | 28.9 | 1.0 |
Tanzania, 2016 | 14,449 | 13,405 | 16.7 | 13,388 | 4.9 | 27.9 | 29.6 | 49.0 | 10.2 | 37.1 | 29.2 | 19.2 |
Togo, 2014 | 10,384 | 9626 | 24.7 | 2381 | - | 37.0 | 44.0 | 50.1 | 16.4 | 41.1 | 30.2 | 39.6 |
Uganda, 2016 | 21,356 | 19,957 | 39.3 | 19,930 | 14.4 | 22.8 | 30.7 | 48.9 | 19.5 | 42.7 | 28.7 | 12.0 |
Zambia, 2018 | 13,905 | 12,983 | 20.8 | 12,981 | 2.1 | 35.6 | 29.4 | 50.0 | 12.0 | 46.6 | 28.4 | 9.5 |
Zimbabwe, 2015 | 11,202 | 10,512 | 35.7 | 10,461 | 7.8 | 41.2 | 30.7 | 48.5 | 14.1 | 44.8 | 28.6 | 1.2 |
Zimbabwe, 2005 | 10,784 | 9725 | 18.2 | 1763 | - | 32.0 | 40.8 | 50.5 | 14.2 | 40.2 | 27.8 | 4.0 |
Average | - | - | 20.5 | - | 6.4 | 32.4 | 37.7 | 49.3 | 16.3 | 43.6 | 29.1 | 35.8 |
Country, Year | Number of Under-5 Children | Number with Data on PM2.5 | Previous-Month PM2.5 | Annual-PM2.5 | Spearman R * | ||||
---|---|---|---|---|---|---|---|---|---|
Mean (SD) | IQR | Range | Mean (SD) | IQR | Range | ||||
Angola, 2016 | 17,367 | 16,363 | 15.5 (8.7) | 9.4 | (5.7–42.5) | 18.6 (5.1) | 5.9 | (10.8–36.2) | 0.31 |
Burkina Faso, 2010 | 17,249 | 16,238 | 38.9 (23.6) | 23.4 | (14.8–147.5) | 61.0 (5.6) | 8.9 | (48.4–72.6) | 0.49 |
Burundi, 2017 | 16,949 | 16,497 | 21.2 (6.3) | 8.3 | (11.8–46.9) | 24.4 (2.8) | 3.3 | (19.3–35.2) | 0.27 |
Burundi, 2010 | 9403 | 9138 | 25.7 (7.5) | 11.7 | (10.1–42.8) | 20.0 (3.0) | 3.6 | (15.2–30.6) | 0.09 |
Cameroon, 2018 | 13,102 | 12,937 | 19.5 (9.7) | 13.8 | (6.0–65.7) | 45.3 (8.8) | 11.6 | (24.5–70.2) | 0.48 |
CDR, 2014 | 20,309 | 18,442 | 21.2 (15.4) | 7.3 | (7.1–70.1) | 34.0 (7.4) | 9.2 | (14.5–51.3) | 0.22 |
Ethiopia, 2016 | 18,324 | 17,720 | 23.0 (6.2) | 6.6 | (5.6–43.3) | 22.1 (4.6) | 4.3 | (9.3–34.4) | 0.53 |
Ethiopia, 2005 | 15,033 | 14,830 | 19.3 (7.1) | 8.0 | (4.7–42.6) | 15.9 (3.9) | 5.9 | (6.0–25.3) | 0.81 |
Ghana, 2014 | 12,934 | 12,180 | 19.5 (6.5) | 7.1 | (9.9–37.8) | 46.1 (2.7) | 3.7 | (39.1–54.7) | 0.23 |
Ghana, 2008 | 12,233 | 11,543 | 21.6 (7.5) | 5.4 | (12.3–58.9) | 73.9 (6.2) | 7.1 | (58.5–86.9) | −0.04 |
Guinea, 2018 | 9170 | 8647 | 56.1 (4.9) | 5.8 | (43.4–77.4) | 49.7 (3.6) | 5.1 | (43.0–60.5) | 0.48 |
Kenya, 2014 | 39,985 | 38,935 | 10.1 (3.4) | 5.2 | (3.9–34.8) | 13.1 (2.1) | 2.9 | (7.3–18.3) | 0.45 |
Kenya, 2008 | 10,174 | 9871 | 8.9 (3.3) | 3.7 | (3.7–21.6) | 8.9 (2.4) | 3.9 | (5.8–16.0) | 0.67 |
Malawi, 2016 | 27,594 | 26,243 | 21.9 (8.4) | 15.3 | (8.2–42.3) | 13.7 (1.1) | 1.3 | (11.4–17.4) | 0.37 |
Malawi, 2010 | 27,516 | 26,381 | 6.4 (1.9) | 1.2 | (2.7–14.3) | 11.9 (0.6) | 0.8 | (10.2–13.7) | −0.03 |
Mali, 2018 | 11,269 | 10,665 | 33.6 (11.3) | 14.3 | (13.5–74.5) | 59.4 (3.6) | 5.1 | (52.6–68.5) | 0.42 |
Mali, 2012 | 11,723 | 11,723 | 44.5 (15.0) | 8.2 | (20.9–91.9) | 59.9 (4.3) | 5.8 | (50.4–72.4) | 0.31 |
Mozambique, 2011 | 14,510 | 13,715 | 9.9 (6.2) | 6.2 | (3.5–35.6) | 9.7 (2.8) | 4.1 | (4.4–16.9) | 0.67 |
Nigeria, 2018 | 46,230 | 44,896 | 32.0 (12.7) | 18.9 | (11.4–104.5) | 78.8 (9.9) | 15.9 | (47.0–101.0) | 0.27 |
Rwanda, 2015 | 12,834 | 12,339 | 25.8 (9.5) | 12.7 | (12.4–46.6) | 23.8 (2.1) | 2.7 | (17.4–28.6) | 0.14 |
Rwanda, 2010 | 12,897 | 12,402 | 25.8 (9.4) | 12.5 | (12.4–46.6) | 23.8 (2.1) | 2.7 | (17.4–28.6) | 0.14 |
Senegal, 2014 | 7063 | 6715 | 49.2 (17.0) | 25.3 | (17.0–90.7) | 42.3 (3.1) | 4.6 | (36.1–51.4) | 0.04 |
Senegal, 2015 | 7286 | 6939 | 61.5 (27.5) | 52.6 | (16.6–112.9) | 51.4 (4.6) | 6.8 | (39.2–61.7) | −0.13 |
Senegal, 2016 | 7147 | 6788 | 48.4 (26.1) | 45.2 | (16.8–107.1) | 53.6 (4.4) | 6.2 | (42.7–64.3) | 0.49 |
South Africa, 2016 | 13,792 | 13,712 | 11.2 (4.5) | 7.5 | (2.3–33.6) | 11.2 (3.8) | 6.0 | (3.4–21.5) | 0.94 |
Tanzania, 2016 | 14,449 | 13,549 | 13.2 (6.6) | 5.6 | (5.8–43.2) | 13.6 (3.7) | 4.9 | (7.9–27.3) | 0.73 |
Togo, 2014 | 10,384 | 10,204 | 64.6 (30.8) | 54.3 | (21.0–132.2) | 52.7 (5.6) | 9.7 | (42.9–76.2) | −0.25 |
Uganda, 2016 | 21,356 | 20,112 | 19.8 (7.3) | 7.6 | (10.3–43.9) | 24.9 (7.0) | 11.7 | (12.5–39.9) | 0.71 |
Zambia, 2018 | 13,905 | 13,229 | 18.6 (11.8) | 21.1 | (4.0–52.4) | 12.6 (2.1) | 3.1 | (8.7–20.1) | 0.25 |
Zimbabwe, 2015 | 11,202 | 11,174 | 15.6 (7.2) | 14.1 | (5.2–35.9) | 11.4 (1.4) | 2.5 | (8.8–15.6) | 0.37 |
Zimbabwe, 2005 | 10,784 | 10,728 | 11.9 (7.5) | 11.6 | (3.3–35.8) | 8.5 (0.9) | 1.1 | (6.7–12.4) | 0.38 |
Variable | Cough | ALRI | ||||||
---|---|---|---|---|---|---|---|---|
N | OR [95%CI] | I2 (%) | Phet | N | OR [95%CI] | I2 (%) | Phet | |
All | 368,366 | 1.000 [0.981, 1.009] | 50.4 | 0.001 | 109,644 | 0.975 [0.941, 1.010] | 67.4 | <0.001 |
Cluster type | ||||||||
Urban | 111,729 | 0.999 [0.980, 1.018] | 71.3 | <0.001 | 30,582 | 0.971 [0.910, 1.037] | 62.5 | 0.001 |
Rural | 256,637 | 0.999 [0.987,1.011] | 67.9 | <0.001 | 76,612 | 0.975 [0.957,0.993] | 43.6 | 0.053 |
Wealth index | ||||||||
Q1–Q2 | 93,114 | 1.006 [0.987, 1.024] | 65.0 | <0.001 | 23,461 | 0.940 [0.913, 0.969] | 16.1 | 0.291 |
Q3–Q5 | 101,236 | 0.991 [0.975, 1.008] | 59.1 | <0.001 | 23,611 | 0.974 [0.939, 1.011] | 48.7 | 0.034 |
Stunting | ||||||||
Stunted | 44,197 | 0.991 [0.972, 1.012] | 40.0 | 0.015 | 9249 | 0.966 [0.851, 1.096] | 79.1 | <0.001 |
Non-stunted | 280,787 | 1.001 [0.992, 1.011] | 45.8 | 0.003 | 73,734 | 0.993 [0.963, 1.025] | 57.6 | 0.009 |
Sex | ||||||||
Boys | 159,179 | 1.000 [0.988, 1.013] | 53.0 | <0.001 | 38,660 | 0.983 [0.934, 1.036] | 58.2 | 0.008 |
Girls | 156,223 | 1.001 [0.993, 1.008] | 26.9 | 0.086 | 36,517 | 0.976 [0.942, 1.010] | 12.6 | 0.327 |
Age | ||||||||
<24 months | 60,622 | 0.996 [0.964, 1.029] | 54.4 | 0.010 | 32,020 | 0.976 [0.935, 1.018] | 46.1 | 0.046 |
24–59 months | 88,685 | 0.993 [0.977, 1.010] | 4.4 | 0.403 | 43,220 | 0.987 [0.947, 1.029] | 54.3 | 0.016 |
Location | ||||||||
West Africa | 115,221 | 1.002 [0.989, 1.015] | 65.4 | 0.001 | 30,493 | 0.945 [0.861, 1.037] | 80.6 | 0.001 |
Rest of Africa | 253,145 | 0.996 [0.989, 1.004] | 33.0 | 0.082 | 79,151 | 0.983 [0.946, 1.021] | 59.4 | 0.012 |
HDI index | ||||||||
Medium-to-high | 81,517 | 1.022 [0.982, 1.064] | 63.3 | 0.018 | 17,201 | 1.018 [0.975, 1.064] | 0 | 0.609 |
low | 286,849 | 0.998 [0.989, 1.006] | 45.4 | 0.008 | 92,443 | 0.958 [0.915, 1.002] | 75.3 | <0.001 |
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Cai, Y.S.; Gibson, H.; Ramakrishnan, R.; Mamouei, M.; Rahimi, K. Ambient Air Pollution and Respiratory Health in Sub-Saharan African Children: A Cross-Sectional Analysis. Int. J. Environ. Res. Public Health 2021, 18, 9729. https://doi.org/10.3390/ijerph18189729
Cai YS, Gibson H, Ramakrishnan R, Mamouei M, Rahimi K. Ambient Air Pollution and Respiratory Health in Sub-Saharan African Children: A Cross-Sectional Analysis. International Journal of Environmental Research and Public Health. 2021; 18(18):9729. https://doi.org/10.3390/ijerph18189729
Chicago/Turabian StyleCai, Yutong Samuel, Harry Gibson, Rema Ramakrishnan, Mohammad Mamouei, and Kazem Rahimi. 2021. "Ambient Air Pollution and Respiratory Health in Sub-Saharan African Children: A Cross-Sectional Analysis" International Journal of Environmental Research and Public Health 18, no. 18: 9729. https://doi.org/10.3390/ijerph18189729