The significant morbidity and mortalities from, and the recurrent outbreaks of, the vaccine-preventable infectious diseases (VPDs) of childhood could be due not only to non-receipt of recommended vaccinations but also to untimely receipt, which impairs the validity of immunisation coverage and protection against VPDs. This study explored the determinants of untimely receipt and non-receipt of routine childhood immunisation and made recommendations for policy and practice.

This qualitative study was based on the Adapted Omale INDEPT FORCIS Framework – Determinants of Routine Childhood Vaccination Receipt Conceptual Framework.

From 22 August 2022 to 9 September 2022, 15 semi-structured, face-to-face focus group discussions were conducted in English, pidgin English and the local language with 127 purposively selected consenting parents of infants aged 0–2 months (with other key community members) and primary healthcare workers involved in the provision of routine childhood immunisation in Ebonyi state, Nigeria.

Data analysis involved deductive (and some inductive) thematic analysis.

There were many underlying determinants of untimely receipt and non-receipt of routine childhood immunisation, which included individual-related factors (mothers’ unfavourable experiences and perceptions and lack of knowledge about childhood diseases, vaccines/vaccinations and the vaccination system, relocation/travel from place of residence, children and/or mothers being ill); childhood disease-related, vaccination-related and the vaccination system-related factors (diseases not always severe/fatal, vaccinations causing side-effects, vaccination system constraints in availability, accessibility, affordability and acceptability); family and other individual-related factors; and broader context-related sociopolitical, cultural, economic, infrastructural, historic and health system factors.

The evidence demonstrates many specific and fundamental complex and interrelated determinants of untimely receipt and non-receipt of routine childhood immunisation and emphasises the need for multifaceted and innovative actions in dealing with the determinants in the drive to address the high morbidity and mortalities and recurring outbreaks of VPDs in Ebonyi state, Nigeria, and similar settings.

Intermittent physiological monitoring and early warning scores (EWS) are limited in their ability to detect deteriorating patients in a timely manner. Wearable physiological sensors allow continuous remote monitoring and may be more timely and accurate in the identification of those at risk, compared with manual collection. This study aims to determine if wearable physiological sensors can be used for the early detection of postoperative deterioration, while being acceptable to patients and healthcare staff.

This is a prospective observational cohort study that will recruit adults undergoing major surgery in Benin, India, Ghana, Guatemala, Mexico, Nigeria, Rwanda and the UK. Participants will wear wearable physiological chest and limb sensors before, during and after surgery for up to 10 days or until discharge. In this ‘shadow-mode’ study, continuous physiological observations collected using the devices will not be made available to clinical teams. No changes in participant care will result. Standard of care clinical data will be collected contemporaneously. Continuous sensor data will be used to design algorithms to predict deterioration and specific complications in this population. Usability and feasibility testing, through focus groups, interviews and questionnaires, will be undertaken with healthcare professionals and people undergoing surgery.

Our stakeholder panel are directly involved in all aspects of this study, which will be conducted in accordance with the principles of the International Conference on Harmonisation Tripartite Guideline for Good Clinical Practice (ICH GCP) in addition to the principles of the ethics committee(s)/Institutional Review Boards (IRBs) who have reviewed and approved this study. Artificial intelligence (AI) prediction models will be reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis+Artificial Intelligence (TRIPOD+AI) and Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI) reporting guidelines frameworks.

NCT06565559.