Assessment of the potential of hospital birth records to estimate the number of births: A case study of Germiston and Nkomazi Local Municipalities
Bibliographic record
Abstract
The advantage of a well-developed health information system is the significant role played by records produced by such a system beyond recording medical history of individuals.They are the foundation for birth registrations which when fully complete is an important tool for acquiring data necessary for planning and monitoring child and maternal health in a country.This study aimed to investigate the potential of hospital birth records to estimate the number of births in the country and supplement birth registrations data.Data was abstracted from public facilities where births occur in two municipalities; Germiston in Gauteng and Nkomazi in Mpumalanga for the period 2014 to 2016.Modified version of the BORN Data Quality Framework (BORN-DQF) of the Ontario Agency for Health Protection and Promotion (2016) was used to assess the contents and quality of hospital birth records.Four dimensions of framework were employed to test the relevance, usability, comparability and accuracy of the data.Hospital records provided evidence of their potential as source of birth data, and for providing detailed information on maternal and child health conditions at birth currently unavailable in the birth register data.However, challenges observed in relation to lack of adherence to documented record management policies and guidelines particularly at lower levels of care, cast doubt on accessibility of these records for research purposes.For a number of key data items, data was moderately complete with space for improvement.Marked differences were found in quality of recording between the two study areas.Poor quality of data for indicators associated with health of the mother (parity and gestational age) and health of the child (birthweight) may be attributable to lack of awareness of the importance of capturing these data by hospital personnel as these are important indicators for health facilities.Linked hospital records and birth register data rates obtained point to limited common data items from both sources and questionable quality of reporting as main weaknesses.An assessment of the level of agreement between hospital records and birth register data undertaken showed high agreement and sensitivity for a number of variables, pointing to high quality of matched data.Exception was made for death, fewer numbers available for this data item suggested pervasive misreporting in hospital records.This indicates inability of hospital records in their current state http://etd.uwc.ac.za/ ii to provide solution to underreporting of child deaths observed in vital registrations data.The challenge is to impress on facilities managers the importance of completing data items important to monitor their own performance and for other stakeholders and to appreciate benefits of birth and other vital data generated within facilities.For hospital records to be the source of improvements for birth register, quality of recording must improve, this will enable birth register data to serve a wider range of stakeholders including researchers.Interventions and various interim measures such as service level agreements and memoranda of understanding between key role players to improve efficiency of the system are encouraged, however long term legislative reforms needed to improve efficiency of the system must be prioritised by key entities involved for the benefit of the country.A broader study incorporating births in private health facilities will provide clarity on the influence of socio economic status on matching rates observed in this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".