Scholarlink

Background <p>Underreporting of neonatal admissions and deaths – particularly among &lt; 1000&#xa0;g newborns – compromises the plausibility of in-patient neonatal mortality rate (iNMR) estimates. This limits progress tracking, since 2.3&#xa0;million neonatal deaths occur annually, and 65 countries are projected to miss the Sustainable Development Goal (SDG) 3.2 target of fewer than 12 deaths per 1000 live births by 2030. Reliable iNMR estimates are essential for national and hospital-level monitoring. This study assessed birthweight-specific adjustment curves developed using simulated data, and extended the approach to routine data from 172,809 neonatal admissions across four African countries in the NEST360 network.</p> Methods <p>Simulated data were generated using birthweight intervals of 500&#xa0;g from 500 to 5000&#xa0;g, with 1000 values per category representing 1000 hospitals. Admissions were drawn from a negative binomial distribution and mortality rates from a beta distribution based on published birthweight-specific iNMRs. Adjustment functions were developed using data from 500 hospitals without underreporting and tested on 500 with induced underreporting. This approach was then extended to real data from 65 NEST360 partnering hospitals. The adjustment functions were derived from 26 hospitals with plausible reporting (iNMR ≥ 700 per 1000 for neonates &lt; 1000&#xa0;g).</p> Results <p>In simulated data, adjusted iNMRs closely matched original values, with median differences ranging from − 4 to 9 deaths per 1000 across birthweight categories. In NEST360 dataset, unadjusted iNMRs were 121 (range 13–272) in Nigeria, 128 (60–214) in Tanzania, 132 (42–234) in Malawi, and 135 (60–250) in Kenya per 1000 admissions. After adjustment, estimates increased to 162 (127–333), 183 (128–233), 174 (120–268), and 172 (123–261), respectively. Variability across hospitals decreased, with standard deviation reducing from 13 to 5 deaths per 1000.</p> Conclusions <p>Birthweight specific adjustment functions provide a practical and scalable approach to improving the plausibility of iNMR estimates where underreporting is common. These methods can be integrated into routine systems (such as Electronic Medical Record Systems and DHIS2) where key data are available, with modest computational demands. Their use can strengthen mortality surveillance and support more reliable policy and planning, while contributing to progress toward SDG 3.2, contingent on continued improvements in data quality and system capacity.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Neonatal admissions and mortality: a statistical adjustment approach using birthweight-specific curves to address bias due to underreporting in 65 hospitals in four African countries

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

查看全文
  • Lucas Malla,
  • Siu Nam Wong,
  • Samuel Ngwala,
  • Evelyn Zimba,
  • Msandeni Chiume,
  • Norman Lufesi,
  • Kondwani Kawaza,
  • Honorati Masanja,
  • Josephine Shabani,
  • Prosper Mshana,
  • Robert Tillya,
  • Nahya Salim,
  • John Wainaina,
  • Julius Thomas,
  • Junwei Chen,
  • Franklin Okech,
  • George Okello,
  • William Macharia,
  • Olabisi Dosunmu,
  • Chinyere Ezeaka,
  • Opeyemi Odedere,
  • Morris Ogero,
  • James H. Cross,
  • Rebecca E. Penzias,
  • Christine Bohne,
  • Rebecca Richards-Kortum,
  • Maria Oden,
  • Eric O. Ohuma,
  • Joy E. Lawn

摘要

Background

Underreporting of neonatal admissions and deaths – particularly among < 1000 g newborns – compromises the plausibility of in-patient neonatal mortality rate (iNMR) estimates. This limits progress tracking, since 2.3 million neonatal deaths occur annually, and 65 countries are projected to miss the Sustainable Development Goal (SDG) 3.2 target of fewer than 12 deaths per 1000 live births by 2030. Reliable iNMR estimates are essential for national and hospital-level monitoring. This study assessed birthweight-specific adjustment curves developed using simulated data, and extended the approach to routine data from 172,809 neonatal admissions across four African countries in the NEST360 network.

Methods

Simulated data were generated using birthweight intervals of 500 g from 500 to 5000 g, with 1000 values per category representing 1000 hospitals. Admissions were drawn from a negative binomial distribution and mortality rates from a beta distribution based on published birthweight-specific iNMRs. Adjustment functions were developed using data from 500 hospitals without underreporting and tested on 500 with induced underreporting. This approach was then extended to real data from 65 NEST360 partnering hospitals. The adjustment functions were derived from 26 hospitals with plausible reporting (iNMR ≥ 700 per 1000 for neonates < 1000 g).

Results

In simulated data, adjusted iNMRs closely matched original values, with median differences ranging from − 4 to 9 deaths per 1000 across birthweight categories. In NEST360 dataset, unadjusted iNMRs were 121 (range 13–272) in Nigeria, 128 (60–214) in Tanzania, 132 (42–234) in Malawi, and 135 (60–250) in Kenya per 1000 admissions. After adjustment, estimates increased to 162 (127–333), 183 (128–233), 174 (120–268), and 172 (123–261), respectively. Variability across hospitals decreased, with standard deviation reducing from 13 to 5 deaths per 1000.

Conclusions

Birthweight specific adjustment functions provide a practical and scalable approach to improving the plausibility of iNMR estimates where underreporting is common. These methods can be integrated into routine systems (such as Electronic Medical Record Systems and DHIS2) where key data are available, with modest computational demands. Their use can strengthen mortality surveillance and support more reliable policy and planning, while contributing to progress toward SDG 3.2, contingent on continued improvements in data quality and system capacity.