Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360
Authors: Cross James H. , Bohne Christine , Ngwala Samuel K. , Shabani Josephine , Wainaina John , Dosunmu Olabisi , Kassim Irabi , Penzias Rebecca E. , Tillya Robert , Gathara David , Zimba Evelyn , Ezeaka Veronica Chinyere , Odedere Opeyemi , Chiume Msandeni , Salim Nahya , Kawaza Kondwani , Lufesi Norman , Irimu Grace , Tongo Olukemi O. , Malla Lucas , Paton Chris , Day Louise T. , Oden Maria , Richards-Kortum Rebecca , Molyneux Elizabeth M. , Ohuma Eric O. , Lawn Joy E. ,   with NEST360 Neonatal Inpatient Dataset Learning Group, Asibon Aba , Adudans Steve , Otiangala Dickson , Mchoma Christina , Yosefe Simeon , Balogun Adeleke , Omoke Sylvia , Rashid Ekran , Masanja Honorati , English Mike , Hagel Christiane 
Published: November 2023

Background

Every Newborn Action Plan (ENAP) coverage target 4 necessitates national scale-up of Level-2 Small and Sick Newborn Care (SSNC) (with Continuous Positive Airway Pressure (CPAP)) in 80% of districts by 2025. Routine neonatal inpatient data is important for improving quality of care, targeting equity gaps, and enabling data-driven decision-making at individual, district, and national-levels. Existing neonatal inpatient datasets vary in purpose, size, definitions, and collection processes. We describe the co-design and operationalisation of a core inpatient dataset for use to track outcomes and improve quality of care for small and sick newborns in high-mortality settings.

Methods

A three-step systematic framework was used to review, co-design, and operationalise this novel neonatal inpatient dataset in four countries (Malawi, Kenya, Tanzania, and Nigeria) implementing with the Newborn Essential Solutions and Technologies (NEST360) Alliance. Existing global and national datasets were identified, and variables were mapped according to categories. A priori considerations for variable inclusion were determined by clinicians and policymakers from the four African governments by facilitated group discussions. These included prioritising clinical care and newborn outcomes data, a parsimonious variable list, and electronic data entry. The tool was designed and refined by > 40 implementers and policymakers during a multi-stakeholder workshop and online interactions.

Results

Identified national and international datasets (n = 6) contained a median of 89 (IQR:61–154) variables, with many relating to research-specific initiatives. Maternal antenatal/intrapartum history was the largest variable category (21, 23.3%). The Neonatal Inpatient Dataset (NID) includes 60 core variables organised in six categories: (1) birth details/maternal history; (2) admission details/identifiers; (3) clinical complications/observations; (4) interventions/investigations; (5) discharge outcomes; and (6) diagnosis/cause-of-death. Categories were informed through the mapping process. The NID has been implemented at 69 neonatal units in four African countries and links to a facility-level quality improvement (QI) dashboard used in real-time by facility staff.

Conclusion

The NEST360 NID is a novel, parsimonious tool for use in routine information systems to inform inpatient SSNC quality. Available on the NEST360/United Nations Children's Fund (UNICEF) Implementation Toolkit for SSNC, this adaptable tool enables facility and country-level comparisons to accelerate progress toward ENAP targets. Additional linked modules could include neonatal at-risk follow-up, retinopathy of prematurity, and Level-3 intensive care.