{
  "abstract": "Introduction In China, the increase in high-risk pregnancies along with rising maternal age and complications has underscored the need for the development of maternal and newborn risk management programmes. The Chinese National Maternal and Newborn Safety Action Plan (CNMNSAP) was initiated in 2017. Given that neonatal mortality is a key indicator of healthcare quality, we evaluate the real-world effects of CNMNSAP against neonatal mortality among pregnant women with high-risk conditions.Methods In this retrospective, matched, population-based cohort study, we collected information on all pregnant women with clinically diagnosed conditions from electronic medical records in Chengdu, China, between July 2014 and December 2019. Individual-level data, covering all healthcare services and testing records in public hospitals, were obtained and categorised into two groups based on the timing of CNMNSAP implementation (pre-CNMNSAP vs post-CNMNSAP). After 1:1 propensity score matching, we calculated the annual percentage change (APC) of neonatal mortality within 7 days post-delivery and compared outcomes between two groups of pregnant women with conditions. We then employed multivariate log-binomial regression models to examine the association between the CNMNSAP implementation and temporal changes in neonatal mortality.Results During the 5-year study period, a total of 241 343 women with high-risk conditions delivered prior to CNMNSAP and 163 367 after its implementation. After 1:1 propensity score matching, 299 190 mothers were included for analysis. We estimated that the APC changed from 10.0% (95% CI −0.4% to 21.5%) prior to the maternal risk management programme to −28.5% (95% CI –44.2% to –8.4%) after its implementation, with an attributed risk reduction of 1.29 neonatal deaths per 1000 deliveries annually. In subgroup analysis, we found a significant reduction in neonatal mortality after policy implementation among mothers aged 18–34 years, those with a normal body mass index and those having a history of abortion.Conclusions The CNMNSAP was found to be associated with a significant annual reduction in early neonatal mortality risk among pregnant women with high-risk conditions in Chengdu, China. The maternal risk management programme effectively improved outcomes for high-risk pregnancies, highlighting the importance of maternal risk classification and management throughout pregnancy.",
  "authors": [
    {
      "affiliations": [
        "School of Public Health, Tianjin Medical University, Tianjin, China"
      ],
      "name": "Nan Zhang"
    },
    {
      "affiliations": [
        "Sichuan Provincial Women’s and Children’s Hospital, The Affiliated Women’s and Children’s Hospital of Chengdu Medical College, Chengdu, Sichuan, China",
        "Jintang County Chinese Medical Hospital, Chengdu, Sichuan, China"
      ],
      "name": "Chunrong Li"
    },
    {
      "affiliations": [
        "Centre for Health Systems and Policy Research, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Zihao Guo"
    },
    {
      "affiliations": [
        "School of Public Health, Tianjin Medical University, Tianjin, China",
        "Centre for Health Systems and Policy Research, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Dorothy Yingxuan Wang"
    },
    {
      "affiliations": [
        "School of Public Health, Tianjin Medical University, Tianjin, China"
      ],
      "name": "Yue Du"
    },
    {
      "affiliations": [
        "Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, Xinjiang, China"
      ],
      "name": "Kai Wang"
    },
    {
      "affiliations": [
        "Centre for Health Systems and Policy Research, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Qiaoge Chi"
    },
    {
      "affiliations": [
        "Centre for Health Systems and Policy Research, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Ka-Chun Chong"
    },
    {
      "affiliations": [
        "School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China"
      ],
      "name": "Mu He"
    },
    {
      "affiliations": [
        "School of Public Health, Capital Medical University, Beijing, China"
      ],
      "name": "Shengzhi Sun"
    },
    {
      "affiliations": [
        "School of Health Professions, University of Southern Mississippi, Hattiesburg, Mississippi, USA"
      ],
      "name": "Yang Ge"
    },
    {
      "affiliations": [
        "Health Commission of Chengdu, Chengdu, Sichuan, China"
      ],
      "name": "Wei Song"
    },
    {
      "affiliations": [
        "Centre for Health Systems and Policy Research, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Kailu Wang"
    },
    {
      "affiliations": [
        "School of Public Health, Peking University, Beijing, China"
      ],
      "name": "Wangnan Cao"
    },
    {
      "affiliations": [
        "School of Public Health, Peking University, Beijing, China",
        "Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, Beijing, China"
      ],
      "name": "Yuantao Hao"
    },
    {
      "affiliations": [
        "School of Public Health, Tianjin Medical University, Tianjin, China",
        "Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin, China",
        "MoE Key Laboratory of Prevention and Control of Major Diseases in the Population, Tianjin Medical University, Tianjin, China"
      ],
      "name": "Shi Zhao"
    }
  ],
  "title": "Real-world evaluation of Chinese National Maternal and Newborn Safety Action Plan for reducing neonatal mortality among pregnant women with conditions: a retrospective, matched, population-based cohort study",
  "uid": "4b89cbc3-113b-5103-a066-2880a6d5aa57"
}