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Background: Stillbirth, preterm birth and low birthweight are important indicators of global burden of disease, status of maternal health and healthcare, and predictors of health throughout the life-course. Data are available through Civil Registration and Vital Statistics (CRVS), Health Management Information Systems (HMIS) and household surveys. Comparisons of data by country or over time requires standard definitions and comparable data quality. Data gaps and inconsistencies necessitate adjustments and use of modelled estimates in many settings. Methods: Systematic data searches were undertaken to compile available data on these outcomes for 195 countries. Where no reliable data were available, statistical models were used to generate national estimates. Data quantity and quality were summarised for each outcome, with implications for improvement and research. Results: The estimated burden remains large: 2.6 million stillbirths (2015), 14.9 million preterm births (2010) and 20.5 million low birthweight babies (2015) based on 4,392 data-points from 148 countries. Common data quality challenges include use of non-standard definitions, omission, and misclassification. Targeted data quality assessments are required to detect these. Five data gaps identified to address are: (1) coverage of data systems (2) accurate assessment of vital status at birth, birthweight and gestational age for every birth, (3) accurate recording of these key data elements (4) comparable collation within and across data systems and (5) use of data to inform programmes and policy. Evidence exists across all data platforms of examples of solutions to close these gaps. Systematic data linkage could increase efficiency. Conclusion: Data availability has increased over the last decade, even in the poorest countries. Data quality issues currently hamper the use of these data to improve outcomes in many settings, but could be addressed with political will and targeted investment. Ending preventable deaths among the world's smallest babies requires that these data are accurate, available and used.

Background: Worldwide, 4.4 million stillbirths and neonatal deaths (SB&NND) are estimated to occur annually. Household surveys, notably the Demographic and Health Surveys (DHS), are an important source of SB&NND data. This PhD aimed to review the DHS's evolution for SB&NND data capture and compare the full birth history with additional questions on pregnancy losses (FBH+) and full pregnancy history (FPH) approaches in terms of data quality and potential measurement errors. Methods: A literature and programmatic review of the DHS programme and HDSS pregnancy surveillance system was conducted. A randomised comparison of FBH+ versus FPH modules (the EveryNewborn-INDEPTH (EN-INDEPTH) survey) was undertaken in five health and demographic surveillance sites (HDSS). Reported stillbirth rates (SBR) and neonatal mortality rates (NMR); time for completion of survey modules; evidence for heterogeneity between sites; patterns of corrections between question types and structures, and modules were assessed. Survey data were compared to and HDSS data in four sites. Results: Both FPH and FBH+ modules have been used within the DHS programme, but there is limited evidence concerning their accuracy for SB&NND. The FBH+ was the core model questionnaire between DHS-phases I-VII. A total of 69,176 women consented. 34,805 (50·3%) were randomised to FBH+ and 34,371 (49·7%) to FPH. There was little difference between the average time to administer questions in FBH+ (9.1 minutes) and FPH (10.5 minutes). The SBR was 15.2/1000 and 17.4/1000 total births for FBH+ and FPH, respectively. SBR was 21% (95% CI (-10% - 62%)) higher in FPH than in FBH+ with strong evidence of heterogeneity across the sites (I-squared=80·9% (p < 0.001)). The NMR was similar in FPH (25.1/1000 births) and FBH+ (25.4/1000 births) with no evidence of heterogeneity between the sites (I-squared=0.0% (p=0.48)). Corrections were similar by survey module and occurred in 84% of survey interviews. Single corrections were the most common, multiple-select, and free-text questions increased response time by two minutes on average and had the most corrections. Pregnancy surveillance systems differed between sites (frequency of surveillance visits, main respondent reporting pregnancy outcomes, pregnancy testing, and data capture and surveillance modes). In three of the four sites, both arms of the EN-INDEPTH survey reported more pregnancies than the HDSS Matlab being the exception. Overall, the survey data produced higher estimated SBRs (FBH+: RR=1.13, 95%CI (0.79, 1.63), p=0.519; FPH: RR=1.20, 95%CI (0.76, 1.90), p=0.444) and NMRs (FBH+: RR=1.19, 95%CI (0.98, 1.43), p=0.071; FPH: RR=1.15, 95%CI (1.01, 1.30), p=0.030) than the HDSS data. In Matlab, the HDSS recorded more stillbirths and miscarriages than the survey in the FPH arm only. In Kintampo, the HDSS recorded more stillbirths than the survey, even though the HDSS recorded fewer pregnancies. Conclusions: Evidence from this PhD of improved capture of stillbirths using the FPH influenced the DHS's switch to the FPH module in its eighth phase; however, limited evidence concerning SB&NND data accuracy and quality in standardised surveys is available. Therefore, additional efforts towards improved survey implementation of the FPH approach, including training, interviewer prompts, translations, and developing and testing standard data quality criteria for SB&NND in surveys and routine data, are warranted.

Background: In 2018, 2.5 million newborns died; mainly from prematurity, infections, and intrapartum events. Preventing these deaths requires health systems to provide routine and emergency care at birth, and quality inpatient care for small and sick newborns. Despite high potential impact, inpatient newborn care is not consistently measured. Methods: For this PhD, I conducted a bottleneck analysis using data from 12 national workshops regarding delivery of inpatient newborn care in low- and middle-income countries (LMIC). Using WHO guidelines, grey literature and expert consultation, I mapped the components required to deliver inpatient care and reviewed these against three health facility assessment tools. Finally, I carried out an online survey to elicit global practitioner opinions regarding levels of newborn care, paralleling those used for monitoring emergency obstetric care in LMIC. Results: In 12 high-burden countries in sub-Saharan Africa and Asia, health financing and workforce were identified as the greatest bottlenecks to scaling up quality inpatient care, followed by community ownership. My review identified 654 components required to deliver inpatient care. These are inconsistently measured by existing health facility assessments. The 262 survey respondents agreed on 12 interventions to comprise a package of care for small and sick newborns; selected levels of care varied by clinical background and experience in LMIC. Conclusion: Inpatient newborn care faces multiple health system challenges, particularly to ensure funding and skilled staffing. Standard facility numbers and staffing ratios by defined levels of care are important for countries to benchmark service delivery progress. Due to the large number of components required for delivering quality care, newborn “signal functions” could be selected by level of care to parallel emergency obstetric care indicators. Improved measurement of service readiness requires sustained focus on interoperability of routine measurement systems, and further research to better capture the experience of newborn inpatient care for families.