Spatiotemporal trends and ecological determinants in maternal mortality ratios in 2,205 Chinese counties, 2010–2013: A Bayesian modelling analysis
Autoři:
Junming Li aff001; Juan Liang aff002; Jinfeng Wang aff003; Zhoupeng Ren aff003; Dian Yang aff003; Yanping Wang aff002; Yi Mu aff002; Xiaohong Li aff002; Mingrong Li aff002; Yuming Guo aff005; Jun Zhu aff002
Působiště autorů:
School of Statistics, Shanxi University of Finance and Economics, Taiyuan, Shanxi, China
aff001; National Office for Maternal and Child Health Surveillance of China, Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
aff002; State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
aff003; University of Chinese Academy of Sciences, Beijing, China
aff004; School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
aff005
Vyšlo v časopise:
Spatiotemporal trends and ecological determinants in maternal mortality ratios in 2,205 Chinese counties, 2010–2013: A Bayesian modelling analysis. PLoS Med 17(5): e32767. doi:10.1371/journal.pmed.1003114
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003114
Souhrn
Background
As one of its Millennium Development Goals (MDGs), China has achieved a dramatic reduction in the maternal mortality ratio (MMR), although a distinct spatial heterogeneity still persists. Evidence of the quantitative effects of determinants on MMR in China is limited. A better understanding of the spatiotemporal heterogeneity and quantifying determinants of the MMR would support evidence-based policymaking to sustainably reduce the MMR in China and other developing areas worldwide.
Methods and findings
We used data on MMR collected by the National Maternal and Child Health Surveillance System (NMCHSS) at the county level in China from 2010 to 2013. We employed a Bayesian space–time model to investigate the spatiotemporal trends in the MMR from 2010 to 2013. We used Bayesian multivariable regression and GeoDetector models to address 3 main ecological determinants of the MMR, including per capita income (PCI), the proportion of pregnant women who delivered in hospitals (PPWDH), and the proportion of pregnant women who had at least 5 check-ups (PPWFC). Among the 2,205 counties, there were 925 (42.0%) hotspot counties, located mostly in China’s western and southwestern regions, with a higher MMR, and 764 (34.6%) coldspot counties with a lower MMR than the national level. China’s westernmost regions, including Tibet and western Xinjiang, experienced a weak downward trend over the study period. Nationwide, medical intervention was the major determinant of the change in MMR. The MMR decreased by 1.787 (95% confidence interval [CI]: 1.424–2.142, p < 0.001) per 100,000 live births when PPWDH increased by 1% and decreased by 0.623 (95% CI 0.436–0.798, p < 0.001) per 100,000 live births when PPWFC increased by 1%. The major determinants for the MMR in China’s western and southwestern regions were PCI and PPWFC, while that in China’s eastern and southern coastlands was PCI. The MMR in western and southwestern regions decreased nonsignificantly by 1.111 (95% CI −1.485–3.655, p = 0.20) per 100,000 live births when PCI in these regions increased by 1,000 Chinese Yuan and decreased by 1.686 (95% CI 1.275–2.090, p < 0.001) when PPWFC increased by 1%. Additionally, the western and southwestern regions showed the strongest interactive effects between different factors, in which the corresponding explanatory power of any 2 interacting factors reached up to greater than 80.0% (p < 0.001) for the MMR. Limitations of this study include a relatively short study period and lack of full coverage of eastern coastlands with especially low MMR.
Conclusions
Although China has accomplished a 75% reduction in the MMR, spatial heterogeneity still exists. In this study, we have identified 925 (hotspot) high-risk counties, mostly located in western and southwestern regions, and among which 332 counties are experiencing a slower pace of decrease than the national downward trend. Nationally, medical intervention is the major determinant. The major determinants for the MMR in western and southwestern regions, which are developing areas, are PCI and PPWFC, while that in China’s developed areas is PCI. The interactive influence of any two of the three factors, PCI, PPWDH, and PPWFC, in western and southwestern regions was up to and in excess of 80% (p < 0.001).
Klíčová slova:
Antenatal care – Birth – Death rates – Health education and awareness – China – Labor and delivery – Pregnancy – Tibet
Zdroje
1. Liang J, Li X, Kang C, Wang Y, Kulikoff XR, Coates MM, et al. Maternal mortality ratios in 2852 Chinese counties, 1996–2015, and achievement of Millennium Development Goal 5 in China: a subnational analysis of the Global Burden of Disease Study 2016. Lancet. 2019;393(10168):241–52. doi: 10.1016/S0140-6736(18)31712-4 30554785
2. Alkema L, Chou D, Hogan D, Zhang S, Moller A-B, Gemmill A, et al. Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group. Lancet. 2016;387(10017):462–74. doi: 10.1016/S0140-6736(15)00838-7 26584737
3. Liang J, Dai L, Zhu J, Li X, Zeng W, Wang H, et al. Preventable maternal mortality: geographic/rural-urban differences and associated factors from the population-based Maternal Mortality Surveillance System in China. BMC Public Health. 2011;11(1):243.
4. Hogan MC, Foreman KJ, Naghavi M, Ahn SY, Wang M, Makela SM, et al. Maternal mortality for 181 countries, 1980–2008: a systematic analysis of progress towards Millennium Development Goal 5. Lancet. 2010;375(9726):1609–23. doi: 10.1016/S0140-6736(10)60518-1 20382417
5. Say L, Chou D, Gemmill A, Tunçalp Ö, Moller A-B, Daniels J, et al. Global causes of maternal death: a WHO systematic analysis. Lancet Global Health. 2014;2(6):e323–e33. doi: 10.1016/S2214-109X(14)70227-X 25103301
6. Kassebaum NJ, Bertozzi-Villa A, Coggeshall MS, Shackelford KA, Steiner C, Heuton KR, et al. Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet. 2014;384(9947):980–1004.
7. United Nations Development Programme (UNDP). UN hails China’s progress in achieving Millennium Development Goals in Final Report Jul 24, 2015. Internet [cited 2020 Jan 1]. https://www.cn.undp.org/content/china/en/home/presscenter/pressreleases/2015/07/-united-nations-hails-chinas-progress-towards-the-millennium-dev.html.
8. Gao Y, Zhou H, Singh NS, Powell-Jackson T, Nash S, Yang M, et al. Progress and challenges in maternal health in western China: a Countdown to 2015 national case study. Lancet Global Health. 2017;5(5):e523–e36. doi: 10.1016/S2214-109X(17)30100-6 28341117
9. Du Q, Nass O, Bergsjo P, Kumar BN. Determinants for high maternal mortality in multiethnic populations in western China. Health Care for Women International. 2009;30(11):957–70. doi: 10.1080/07399330903052137 19809900
10. Li Q, Fottler M. Determinants of maternal mortality in rural China. Health Services Management Research. 1996;9(1):45–54. doi: 10.1177/095148489600900105 10157222
11. Khan KS, Wojdyla D, Say L, Gülmezoglu AM, Van Look PF. WHO analysis of causes of maternal death: a systematic review. Lancet. 2006;367(9516):1066–74. doi: 10.1016/S0140-6736(06)68397-9 16581405
12. Graham WJ, Witter S. Counting what counts for maternal mortality. Lancet. 2014;384(9947):933–5. doi: 10.1016/S0140-6736(14)61604-4 25220960
13. El Arifeen S, Hill K, Ahsan KZ, Jamil K, Nahar Q, Streatfield PK. Maternal mortality in Bangladesh: a Countdown to 2015 country case study. Lancet. 2014;384(9951):1366–74. doi: 10.1016/S0140-6736(14)60955-7 24990814
14. Chowdhury ME, Botlero R, Koblinsky M, Saha SK, Dieltiens G, Ronsmans C. Determinants of reduction in maternal mortality in Matlab, Bangladesh: a 30-year cohort study. Lancet. 2007;370(9595):1320–8. doi: 10.1016/S0140-6736(07)61573-6 17933646
15. Li G, Haining R, Richardson S, Best N. Space–time variability in burglary risk: A Bayesian spatio-temporal modelling approach. Spatial Statistics. 2014;9:180–91.
16. Böhning D, Dietz E, Schlattmann P, Mendonca L, Kirchner U. The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology. Journal of the Royal Statistical Society: Series A (Statistics in Society). 1999;162(2):195–209.
17. Hall DB. Zero-inflated Poisson and binomial regression with random effects: a case study. Biometrics. 2000;56(4):1030–9. doi: 10.1111/j.0006-341x.2000.01030.x 11129458
18. Xie M, He B, Goh T. Zero-inflated Poisson model in statistical process control. Computational Statistics & Data Analysis. 2001;38(2):191–201.
19. Geisser S. Bayesian estimation in multivariate analysis. Annals of Mathematical Statistics. 1965;36(1):150–9.
20. Besag J, York J, Mollié A. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics. 1991;43(1):1–20.
21. Richardson S, Thomson A, Best N, Elliott P. Interpreting posterior relative risk estimates in disease-mapping studies. Environmental Health Perspectives. 2004;112(9):1016–25. doi: 10.1289/ehp.6740 15198922
22. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing. 2000;10(4):325–37.
23. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Statistical Science. 1992;7(4):457–72.
24. Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science. 2010;24(1):107–27.
25. Wang J-F, Zhang T-L, Fu B-J. A measure of spatial stratified heterogeneity. Ecological Indicators. 2016;67:250–6.
26. Li Y, Wei YD. The spatial-temporal hierarchy of regional inequality of China. Applied Geography. 2010;30(3):303–16.
27. Sarker BK, Rahman M, Rahman T, Hossain J, Reichenbach L, Mitra DK. Reasons for preference of home delivery with traditional birth attendants (TBAs) in rural Bangladesh: a qualitative exploration. PLoS ONE. 2016;11(1):e0146161. doi: 10.1371/journal.pone.0146161 26731276
28. Song P, Kang C, Theodoratou E, Rowa-Dewar N, Liu X, An L. Barriers to hospital deliveries among ethnic minority women with religious beliefs in China: a descriptive study using interviews and survey data. International Journal of Environmental Research and Public Health. 2016;13(8):815.
29. Withers M, Kharazmi N, Lim E. Traditional beliefs and practices in pregnancy, childbirth and postpartum: A review of the evidence from Asian countries. Midwifery. 2018;56:158–70. doi: 10.1016/j.midw.2017.10.019 29132060
30. Sychareun V, Hansana V, Somphet V, Xayavong S, Phengsavanh A, Popenoe R. Reasons rural Laotians choose home deliveries over delivery at health facilities: a qualitative study. BMC Regnancy and Childbirth. 2012;12(1):86.
31. Mirowsky J, Ross CE. Education, social status, and health. Newark, New Jersey: Transaction Publishers; 2003.
Článek vyšel v časopise
PLOS Medicine
2020 Číslo 5
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- „Jednohubky“ z klinického výzkumu – 2024/44
- Je libo čepici místo mozkového implantátu?
Nejčtenější v tomto čísle
- National and regional prevalence of posttraumatic stress disorder in sub-Saharan Africa: A systematic review and meta-analysis
- Risk of severe maternal morbidity or death in relation to elevated hemoglobin A1c preconception, and in early pregnancy: A population-based cohort study
- Antibody-based therapies for COVID-19: Can Europe move faster?
- The association of innate and adaptive immunity, subclinical atherosclerosis, and cardiovascular disease in the Rotterdam Study: A prospective cohort study
