Illustrative analysis of trends in maternal, newborn and child health using routinely collected data in Kenya
1
School of Medicine, Keele University, Keele, United Kingdom
2
Faculty of Business and Technology, University of Sunderland, Sunderland, United Kingdom
3
Tropical Institute of Community Health and Development, Tropical Institute of Community Health and Development, Kisumu, Kenya
Popul. Med. 2026;8(Supplement Supplement 1):A2362
ABSTRACT
BACKGROUND:
Routinely collected maternal, newborn and child health (MNCH) data could enable rapid monitoring during pandemics and other events in low-and-middle-income countries, but this relies on high-quality data. Data quality and trends over time of maternal health were explored in data collected between January 2018 and December 2024 from Kisumu and Siaya counties, Kenya (AUTOMATE study).
METHODS:
Monthly data on indicators of maternal outcome and care from 524 healthcare facilities were analysed as counts and, where denominators were credible, as proportions. Joinpoint regression was implemented to quantify changes (“joinpoints”) in their underlying trends over time, and changes in monthly percentage rates associated with the COVID period (pre-COVID (Jan2018-Feb2020), early-COVID (Mar2020-Sep2020), COVID-continuation (Oct2020-Jun2022), and post-COVID (Jul2022-Dec2024)).
RESULTS:
Data quality was variable with some indicators (e.g., initiating breastfeeding) suffering from incomplete data or unexplained outliers. In MNCH indicators with sufficient data quality, there was no consistent pattern of COVID-19–related disruption. Several adverse outcomes showed sustained improvement across the period, including significant and ongoing reduction in rates of stillbirths and maternal deaths with no identified joinpoints. However, a steady increase in low-birth-weight births was identified. Service delivery patterns changed over time, most notably with a sustained increase in caesarean deliveries. Some indicators showed changes around COVID-19 periods. Maternal referrals had a marked decline during early-COVID, a sharp increase in late COVID-continuation, and subsequent stabilisation at a reduced level compared to pre-COVID. Births to women living with HIV declined, with significant reductions in both the early-COVID period, and early COVID-continuation.
CONCLUSIONS:
This illustrative analysis demonstrates the potential of routinely collected MNCH data to highlight temporal changes and support timely health monitoring when data are complete, standardised, and accessible. Joinpoint regression can identify meaningful shifts in service delivery and outcomes, but reliable interpretation depends on strengthened data quality and integrated digital reporting systems.
Routinely collected maternal, newborn and child health (MNCH) data could enable rapid monitoring during pandemics and other events in low-and-middle-income countries, but this relies on high-quality data. Data quality and trends over time of maternal health were explored in data collected between January 2018 and December 2024 from Kisumu and Siaya counties, Kenya (AUTOMATE study).
METHODS:
Monthly data on indicators of maternal outcome and care from 524 healthcare facilities were analysed as counts and, where denominators were credible, as proportions. Joinpoint regression was implemented to quantify changes (“joinpoints”) in their underlying trends over time, and changes in monthly percentage rates associated with the COVID period (pre-COVID (Jan2018-Feb2020), early-COVID (Mar2020-Sep2020), COVID-continuation (Oct2020-Jun2022), and post-COVID (Jul2022-Dec2024)).
RESULTS:
Data quality was variable with some indicators (e.g., initiating breastfeeding) suffering from incomplete data or unexplained outliers. In MNCH indicators with sufficient data quality, there was no consistent pattern of COVID-19–related disruption. Several adverse outcomes showed sustained improvement across the period, including significant and ongoing reduction in rates of stillbirths and maternal deaths with no identified joinpoints. However, a steady increase in low-birth-weight births was identified. Service delivery patterns changed over time, most notably with a sustained increase in caesarean deliveries. Some indicators showed changes around COVID-19 periods. Maternal referrals had a marked decline during early-COVID, a sharp increase in late COVID-continuation, and subsequent stabilisation at a reduced level compared to pre-COVID. Births to women living with HIV declined, with significant reductions in both the early-COVID period, and early COVID-continuation.
CONCLUSIONS:
This illustrative analysis demonstrates the potential of routinely collected MNCH data to highlight temporal changes and support timely health monitoring when data are complete, standardised, and accessible. Joinpoint regression can identify meaningful shifts in service delivery and outcomes, but reliable interpretation depends on strengthened data quality and integrated digital reporting systems.
| eISSN: | 2654-1459 |
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