Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers.
<h4>Objective</h4>Preterm birth is the primary driver of neonatal mortality worldwide, but it is defined by gestational age (GA) which is challenging to accurately assess in low-resource settings. In a commitment to reducing preterm birth while reinforcing and strengthening facility data...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0237656 |
| _version_ | 1819042395259404288 |
|---|---|
| author | Lara Miller Phillip Wanduru Nicole Santos Elizabeth Butrick Peter Waiswa Phelgona Otieno Dilys Walker |
| author_facet | Lara Miller Phillip Wanduru Nicole Santos Elizabeth Butrick Peter Waiswa Phelgona Otieno Dilys Walker |
| author_sort | Lara Miller |
| collection | DOAJ |
| description | <h4>Objective</h4>Preterm birth is the primary driver of neonatal mortality worldwide, but it is defined by gestational age (GA) which is challenging to accurately assess in low-resource settings. In a commitment to reducing preterm birth while reinforcing and strengthening facility data sources, the East Africa Preterm Birth Initiative (PTBi-EA) chose eligibility criteria that combined GA and birth weight. This analysis evaluated the quality of the GA data as recorded in maternity registers in PTBi-EA study facilities and the strength of the PTBi-EA eligibility criteria.<h4>Methods</h4>We conducted a retrospective analysis of maternity register data from March-September 2016. GA data from 23 study facilities in Migori, Kenya and the Busoga Region of Uganda were evaluated for completeness (variable present), consistency (recorded versus calculated GA), and plausibility (falling within the 3rd and 97th birth weight percentiles for GA of the INTERGROWTH-21st Newborn Birth Weight Standards). Preterm birth rates were calculated using: 1) recorded GA <37 weeks, 2) recorded GA <37 weeks, excluding implausible GAs, 3) birth weight <2500g, and 4) PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks.<h4>Results</h4>In both countries, GA was the least recorded variable in the maternity register (77.6%). Recorded and calculated GA (Kenya only) were consistent in 29.5% of births. Implausible GAs accounted for 11.7% of births. The four preterm birth rates were 1) 14.5%, 2) 10.6%, 3) 9.6%, 4) 13.4%.<h4>Conclusions</h4>Maternity register GA data presented quality concerns in PTBi-EA study sites. The PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks accommodated these concerns by using both birth weight and GA, balancing issues of accuracy and completeness with practical applicability. |
| first_indexed | 2024-12-21T09:40:12Z |
| format | Article |
| id | doaj.art-314cf78abe2b4a2e82995e183c7790cd |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T09:40:12Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-314cf78abe2b4a2e82995e183c7790cd2022-12-21T19:08:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01158e023765610.1371/journal.pone.0237656Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers.Lara MillerPhillip WanduruNicole SantosElizabeth ButrickPeter WaiswaPhelgona OtienoDilys Walker<h4>Objective</h4>Preterm birth is the primary driver of neonatal mortality worldwide, but it is defined by gestational age (GA) which is challenging to accurately assess in low-resource settings. In a commitment to reducing preterm birth while reinforcing and strengthening facility data sources, the East Africa Preterm Birth Initiative (PTBi-EA) chose eligibility criteria that combined GA and birth weight. This analysis evaluated the quality of the GA data as recorded in maternity registers in PTBi-EA study facilities and the strength of the PTBi-EA eligibility criteria.<h4>Methods</h4>We conducted a retrospective analysis of maternity register data from March-September 2016. GA data from 23 study facilities in Migori, Kenya and the Busoga Region of Uganda were evaluated for completeness (variable present), consistency (recorded versus calculated GA), and plausibility (falling within the 3rd and 97th birth weight percentiles for GA of the INTERGROWTH-21st Newborn Birth Weight Standards). Preterm birth rates were calculated using: 1) recorded GA <37 weeks, 2) recorded GA <37 weeks, excluding implausible GAs, 3) birth weight <2500g, and 4) PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks.<h4>Results</h4>In both countries, GA was the least recorded variable in the maternity register (77.6%). Recorded and calculated GA (Kenya only) were consistent in 29.5% of births. Implausible GAs accounted for 11.7% of births. The four preterm birth rates were 1) 14.5%, 2) 10.6%, 3) 9.6%, 4) 13.4%.<h4>Conclusions</h4>Maternity register GA data presented quality concerns in PTBi-EA study sites. The PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks accommodated these concerns by using both birth weight and GA, balancing issues of accuracy and completeness with practical applicability.https://doi.org/10.1371/journal.pone.0237656 |
| spellingShingle | Lara Miller Phillip Wanduru Nicole Santos Elizabeth Butrick Peter Waiswa Phelgona Otieno Dilys Walker Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers. PLoS ONE |
| title | Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers. |
| title_full | Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers. |
| title_fullStr | Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers. |
| title_full_unstemmed | Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers. |
| title_short | Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers. |
| title_sort | working with what you have how the east africa preterm birth initiative used gestational age data from facility maternity registers |
| url | https://doi.org/10.1371/journal.pone.0237656 |
| work_keys_str_mv | AT laramiller workingwithwhatyouhavehowtheeastafricapretermbirthinitiativeusedgestationalagedatafromfacilitymaternityregisters AT phillipwanduru workingwithwhatyouhavehowtheeastafricapretermbirthinitiativeusedgestationalagedatafromfacilitymaternityregisters AT nicolesantos workingwithwhatyouhavehowtheeastafricapretermbirthinitiativeusedgestationalagedatafromfacilitymaternityregisters AT elizabethbutrick workingwithwhatyouhavehowtheeastafricapretermbirthinitiativeusedgestationalagedatafromfacilitymaternityregisters AT peterwaiswa workingwithwhatyouhavehowtheeastafricapretermbirthinitiativeusedgestationalagedatafromfacilitymaternityregisters AT phelgonaotieno workingwithwhatyouhavehowtheeastafricapretermbirthinitiativeusedgestationalagedatafromfacilitymaternityregisters AT dilyswalker workingwithwhatyouhavehowtheeastafricapretermbirthinitiativeusedgestationalagedatafromfacilitymaternityregisters |