Donat Shamba1, Louise T Day2, Joy E Lawn3, Sojib Bin Zaman4, Avinash K Sunny5, Menna Narcis Tarimo1, Kimberly Peven3,6, Jasmin Khan4, Nishant Thakur5, Md Taqbir Us Samad Talha4, Ashish K C7, Rajib Haider4, Harriet Ruysen3, Tapas Mazumder4, Md Hafizur Rahman4, Md Ziaul Haque Shaikh4, Johan Ivar Sæbø8, Claudia Hanson3,9, Neha S Singh3, Joanna Schellenberg3, Lara M E Vaz10, Jennifer Requejo11. 1. Department of Health Systems, Impact Evaluation and Policy, Ifakara Health Institute, Dar es Salaam, Tanzania. 2. Centre for Maternal, Adolescent, Reproductive & Child Health (MARCH), London School of Hygiene & Tropical Medicine, Keppel St, London, UK. Louise-Tina.Day@lshtm.ac.uk. 3. Centre for Maternal, Adolescent, Reproductive & Child Health (MARCH), London School of Hygiene & Tropical Medicine, Keppel St, London, UK. 4. Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh. 5. Golden Community, Kathmandu, Nepal. 6. Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King's College London, London, UK. 7. International Maternal and Child Health, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden. 8. Department of Informatics, University of Oslo, Oslo, Norway. 9. Global Public Health Karolinska Institutet, Stockholm, Sweden. 10. International Programs, Population Reference Bureau, Washington DC, USA. 11. UNICEF Headquarters, New York, USA.
Abstract
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.
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.
Entities:
Keywords:
Birth; Coverage; Data quality; Facility registers; Health management information systems; Indicators; Maternal; Newborn
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