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Effects of Implementation of a care bundle on rates of necrotising enterocolitis and own mother’s milk feeding in the East Midlands: a mixed methods impact and process evaluation study

Safe People

Organisation name

University of Nottingham

Applicant name(s)

Shalini OjhaLisa SzatkowskiJanine Abramson.Ilze Bogdanovica.Research Fellows appointed to projectTBC.

Funders/ Sponsors

NIHR Research for Patient BenefitUniversity Hospitals of Derby and Burton NHS Foundation Trust

DEA accredited researcher?

No

Safe Projects

Project ID

636C-21AE-63AE-61CA-C5F2-790D

Lay summary

Necrotising enterocolitis (NEC) is a life-threatening gut disease in babies who are born early. Preterm birth is the largest cause of death and long term disabilities among newborn babies. The NHS aims to reduce newborn deaths by half by improving safety and effectiveness of neonatal critical care. Prevention of NEC is an important part of this aim. National audits show that NEC rates are higher in the East Midlands compared to the rest of the country. Feeding preterm babies with their own mother’s milk helps prevent NEC but, unfortunately, rates of own mother’s milk feeding in the East Midlands are lower than the national average. The East Midlands Neonatal Operational Delivery Network (EMNODN), a network of the 11 neonatal units in the region, has created a care bundle comprising a set of recommendations to reduce NEC rates and improve mother’s milk feeding among preterm babies. In this study, we will compare information from the 11 EMNODN neonatal units to information from neonatal units in the rest of England and Wales, to find out if NEC and own mother's milk feeding rates are improved by the EMNODN NEC bundle. We will work with the EMNODN parent advisory group and the national charity NEC-UK to conduct the study and present results. We will share the results with neonatal units, parent groups, at neonatal meetings and in medical journals.

Public benefit statement

Use of NNRD data for this study will allow us to better understand health and care needs of preterm infants, specifically to understand feeding practices within neonatal units and factors associated with the development of NEC, and how these vary between geographical areas and over time. If the care bundle being implemented in the East Midlands is found to be effective at improving rates of own mother's milk feeding and reducing rates of NEC, this finding will support the implementation of the bundle nationwide in order to improve health. Thereby, use of NNRD data for this study will help inform future planning of health services and help inform decisions on how to effectively allocate resources according to health needs. Evaluation of the various aspects of the care bundle, using NNRD data alongside qualitative process evaluation, will help elucidate which aspects of the bundle are most effective and/or most completely implemented, informing future research to maximise impacts.

Latest approval date

02/05/2023

Safe Data

Dataset(s) name
Legal basis for provision of data under Article 6

(e) processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller;

Lawful conditions for provision of data under Article 9

(i) processing is necessary for reasons of public interest in the area of public health, such as protecting against serious cross-border threats to health or ensuring high standards of quality and safety of health care and of medicinal products or medical devices, on the basis of Union or Member State law which provides for suitable and specific measures to safeguard the rights and freedoms of the data subject, in particular professional secrecy;

Common Law Duty of Confidentiality

Not applicable

Request frequency

Recurring

Safe Setting

Access type

Release

How has data been processed to enhance privacy?

Before beginning any work with the data we will write a detailed data management and analysis plan, in which we will fully outline the steps that will be taken to ensure that individuals cannot be identified. All data management and analysis will follow this pre-specified plan. We will ensure that descriptive statistics used to summarise the characteristics of the study population are sufficiently aggregated such that identification of individuals is near enough impossible. In presenting our results we will suppress cells with values <5; if necessary we will also suppress values in other cells (i.e., secondary suppression) in order to ensure that any suppressed values cannot be derived by subtractions from published totals.