Predictive Modelling and Community Health System Factors for Reducing Preventable Neonatal Deaths in Mashonaland West Province, Zimbabwe, 2020-2021

In 2020, an average of 6,700 newborn babies died every day globally within the first 28 days of life and majority of these deaths were preventable. Current evidence shows that a continuum of institutional and community health system initiatives will not only save the lives of neonates but also prevent disability. This study modelled the predictive effect of institutionalizing community health system on reducing neonatal deaths. The study used an embedded sequential mixed methods design (QUAN (qual)), utilizing a two-arm parallel groups cluster randomized controlled trial in Mashonaland West, Zimbabwe. The intervention group participated in modified women’s and men’s participatory learning and action groups and received community health initiatives focused on training village health workers in essential newborn care and strengthening community governance. The control group received standard Ministry of Health and Child Care initiatives. Data was collected through structured questionnaires and analyzed using SPSS, Stata, and machine learning techniques, with a significance level of p < 0.05. Results indicated that the timing of neonatal death (χ² = 973, p < 0.0001), low birth weight less than 2,500g (χ² = 129.6, p < 0.0001), and women's literacy (χ² = 99.63, p < 0.0001) were significantly associated with neonatal deaths. Fully functional community governance structures were linked with a reduced likelihood of neonatal death (ARR = 0.327, 95% CI: 0.21-0.44) and Area Under the Curve (AUC) of 0.84. Predictive models achieved high accuracy rates, with random forest demonstrating exceptional results strengthening the argument that the community health system interventions influenced reducing neonatal deaths. In conclusion, prioritizing initiatives that enhance early antenatal care booking and strengthening community health systems can effectively reduce preventable neonatal deaths.

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Publication Details

Journal
East African Scholars Multidisciplinary Bulletin
Published
2025-04-12
DOI
https://doi.org/10.36349/easjmb.2025.v08i02.003
Primary Topic
Global Maternal and Child Health
Type
article
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article

Predictive Modelling and Community Health System Factors for Reducing Preventable Neonatal Deaths in Mashonaland West Province, Zimbabwe, 2020-2021

Eustarckio Kazonga, Meggie Gabida, Kasonde Bowa, Panashe Chiurunge et al.
East African Scholars Multidisciplinary Bulletin
Global Maternal and Child Health
article

Predictive Modelling and Community Health System Factors for Reducing Preventable Neonatal Deaths in Mashonaland West Province, Zimbabwe, 2020-2021

Eustarckio Kazonga, Meggie Gabida, Kasonde Bowa, Panashe Chiurunge, Celestino Dhege
article en

Abstract

In 2020, an average of 6,700 newborn babies died every day globally within the first 28 days of life and majority of these deaths were preventable. Current evidence shows that a continuum of institutional and community health system initiatives will not only save the lives of neonates but also prevent disability. This study modelled the predictive effect of institutionalizing community health system on reducing neonatal deaths. The study used an embedded sequential mixed methods design (QUAN (qual)), utilizing a two-arm parallel groups cluster randomized controlled trial in Mashonaland West, Zimbabwe. The intervention group participated in modified women’s and men’s participatory learning and action groups and received community health initiatives focused on training village health workers in essential newborn care and strengthening community governance. The control group received standard Ministry of Health and Child Care initiatives. Data was collected through structured questionnaires and analyzed using SPSS, Stata, and machine learning techniques, with a significance level of p < 0.05. Results indicated that the timing of neonatal death (χ² = 973, p < 0.0001), low birth weight less than 2,500g (χ² = 129.6, p < 0.0001), and women's literacy (χ² = 99.63, p < 0.0001) were significantly associated with neonatal deaths. Fully functional community governance structures were linked with a reduced likelihood of neonatal death (ARR = 0.327, 95% CI: 0.21-0.44) and Area Under the Curve (AUC) of 0.84. Predictive models achieved high accuracy rates, with random forest demonstrating exceptional results strengthening the argument that the community health system interventions influenced reducing neonatal deaths. In conclusion, prioritizing initiatives that enhance early antenatal care booking and strengthening community health systems can effectively reduce preventable neonatal deaths.

East African Scholars Multidisciplinary BulletinVol. 8(02)
Openalex Percentile: Top 90%
Global Maternal and Child Health
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Predictive Modelling and Community Health System Factors for Reducing Preventable Neonatal Deaths in Mashonaland West Province, Zimbabwe, 2020-2021 — Eustarckio Kazonga, Meggie Gabida, et al. · East African Scholars Multidisciplinary Bulletin (2025) | TGRS Research Map | TGRS