Barriers and enablers to routine register data collection for newborns and mothers: EN-BIRTH multi-country validation study.

BACKGROUND: Policymakers need regular high-quality coverage data on care around the time of birth to accelerate progress for ending preventable maternal and newborn deaths and stillbirths. With increasing facility births, routine Health Management Information System (HMIS) data have potential to track coverage. Identifying barriers and enablers faced by frontline health workers recording HMIS source data in registers is important to improve data for use. METHODS: The EN-BIRTH study was a mixed-methods observational study in five hospitals in Bangladesh, Nepal and Tanzania to assess measurement validity for selected Every Newborn coverage indicators. We described data elements required in labour ward registers to track these indicators. To evaluate barriers and enablers for correct recording of data in registers, we designed three interview tools: a) semi-structured in-depth interview (IDI) guide b) semi-structured focus group discussion (FGD) guide, and c) checklist assessing care-to-documentation. We interviewed two groups of respondents (January 2018-March 2019): hospital nurse-midwives and doctors who fill ward registers after birth (n = 40 IDI and n = 5 FGD); and data collectors (n = 65). Qualitative data were analysed thematically by categorising pre-identified codes. Common emerging themes of barriers or enablers across all five hospitals were identified relating to three conceptual framework categories. RESULTS: Similar themes emerged as both barriers and enablers. First, register design was recognised as crucial, yet perceived as complex, and not always standardised for necessary data elements. Second, register filling was performed by over-stretched nurse-midwives with variable training, limited supervision, and availability of logistical resources. Documentation complexity across parallel documents was time-consuming and delayed because of low staff numbers. Complete data were valued more than correct data. Third, use of register data included clinical handover and monthly reporting, but little feedback was given from data users. CONCLUSION: Health workers invest major time recording register data for maternal and newborn core health indicators. Improving data quality requires standardised register designs streamlined to capture only necessary data elements. Consistent implementation processes are also needed. Two-way feedback between HMIS levels is critical to improve performance and accurately track progress towards agreed health goals

What is meant by validity in maternal and newborn health measurement? A conceptual framework for understanding indicator validation

BACKGROUND: Rigorous monitoring supports progress in achieving maternal and newborn mortality and morbidity reductions. Recent work to strengthen measurement for maternal and newborn health highlights the existence of a large number of indicators being used for this purpose. The definitions and data sources used to produce indicator estimates vary and challenges exist with completeness, accuracy, transparency, and timeliness of data. The objective of this study is to create a conceptual overview of how indicator validity is defined and understood by those who develop and use maternal and newborn health indicators. METHODS: A conceptual framework of validity was developed using mixed methods. We were guided by principles for conceptual frameworks and by a review of the literature and key maternal and newborn health indicator guidance documents. We also conducted qualitative semi-structured interviews with 32 key informants chosen through purposive sampling. RESULTS: We categorised indicator validity into three main types: criterion, convergent, and construct. Criterion or diagnostic validity, comparing a measure with a gold standard, has predominantly been used to assess indicators of care coverage and content. Studies assessing convergent validity quantify the extent to which two or more indicator measurement approaches, none of which is a gold-standard, relate. Key informants considered construct validity, or the accuracy of the operationalisation of a concept or phenomenon, a critical part of the overall assessment of indicator validity. CONCLUSION: Given concerns about the large number of maternal and newborn health indicators currently in use, a more consistent understanding of validity can help guide prioritization of key indicators and inform development of new indicators. All three types of validity are relevant for evaluating the performance of maternal and newborn health indicators. We highlight the need to establish a common language and understanding of indicator validity among the various global and local stakeholders working within maternal and newborn health

A call for standardised age-disaggregated health data

The 2030 Sustainable Development Goals agenda calls for health data to be disaggregated by age. However, age groupings used to record and report health data vary greatly, hindering the harmonisation, comparability, and usefulness of these data, within and across countries. This variability has become especially evident during the COVID-19 pandemic, when there was an urgent need for rapid cross-country analyses of epidemiological patterns by age to direct public health action, but such analyses were limited by the lack of standard age categories. In this Personal View, we propose a recommended set of age groupings to address this issue. These groupings are informed by age-specific patterns of morbidity, mortality, and health risks, and by opportunities for prevention and disease intervention. We recommend age groupings of 5 years for all health data, except for those younger than 5 years, during which time there are rapid biological and physiological changes that justify a finer disaggregation. Although the focus of this Personal View is on the standardisation of the analysis and display of age groups, we also outline the challenges faced in collecting data on exact age, especially for health facilities and surveillance data. The proposed age disaggregation should facilitate targeted, age-specific policies and actions for health care and disease management