Gestational age data completeness, quality and validity in population-based surveys : EN-INDEPTH study
BACKGROUND: Preterm birth (gestational age (GA) <37 weeks) is the leading cause of child mortality worldwide. However, GA is rarely assessed in population-based surveys, the major data source in low/middle-income countries. We examined the performance of new questions to measure GA in household surveys, a subset of which had linked early pregnancy ultrasound GA data.
METHODS: The EN-INDEPTH population-based survey of 69,176 women was undertaken (2017-2018) in five Health and Demographic Surveillance System sites in Bangladesh, Ethiopia, Ghana, Guinea-Bissau and Uganda. We included questions regarding GA in months (GAm) for all women and GA in weeks (GAw) for a subset; we also asked if the baby was 'born before expected' to estimate preterm birth rates. Survey data were linked to surveillance data in two sites, and to ultrasound pregnancy dating at <24 weeks in one site. We assessed completeness and quality of reported GA. We examined the validity of estimated preterm birth rates by sensitivity and specificity, over/under-reporting of GAw in survey compared to ultrasound by multinomial logistic regression, and explored perceptions about GA and barriers and enablers to its reporting using focus group discussions (n = 29).
RESULTS: GAm questions were almost universally answered, but heaping on 9 months resulted in underestimation of preterm birth rates. Preference for reporting GAw in even numbers was evident, resulting in heaping at 36 weeks; hence, over-estimating preterm birth rates, except in Matlab where the peak was at 38 weeks. Questions regarding 'born before expected' were answered but gave implausibly low preterm birth rates in most sites. Applying ultrasound as the gold standard in Matlab site, sensitivity of survey-GAw for detecting preterm birth (GAw <37) was 60% and specificity was 93%. Focus group findings suggest that women perceive GA to be important, but usually counted in months. Antenatal care attendance, women's education and health cards may improve reporting.
CONCLUSIONS: This is the first published study assessing GA reporting in surveys, compared with the gold standard of ultrasound. Reporting GAw within 5 years' recall is feasible with high completeness, but accuracy is affected by heaping. Compared to ultrasound-GAw, results are reasonably specific, but sensitivity needs to be improved. We propose revised questions based on the study findings for further testing and validation in settings where pregnancy ultrasound data and/or last menstrual period dates/GA recorded in pregnancy are available. Specific training of interviewers is recommended.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
Population health metrics - 19(2021), Suppl 1 vom: 08. Feb., Seite 16 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Haider, M Moinuddin [VerfasserIn] |
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| Links: |
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| Themen: |
Demography |
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| Anmerkungen: |
Date Completed 28.10.2021 Date Revised 30.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12963-020-00230-3 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM321183150 |
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| 100 | 1 | |a Haider, M Moinuddin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Gestational age data completeness, quality and validity in population-based surveys |b EN-INDEPTH study |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Completed 28.10.2021 | ||
| 500 | |a Date Revised 30.03.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Preterm birth (gestational age (GA) <37 weeks) is the leading cause of child mortality worldwide. However, GA is rarely assessed in population-based surveys, the major data source in low/middle-income countries. We examined the performance of new questions to measure GA in household surveys, a subset of which had linked early pregnancy ultrasound GA data | ||
| 520 | |a METHODS: The EN-INDEPTH population-based survey of 69,176 women was undertaken (2017-2018) in five Health and Demographic Surveillance System sites in Bangladesh, Ethiopia, Ghana, Guinea-Bissau and Uganda. We included questions regarding GA in months (GAm) for all women and GA in weeks (GAw) for a subset; we also asked if the baby was 'born before expected' to estimate preterm birth rates. Survey data were linked to surveillance data in two sites, and to ultrasound pregnancy dating at <24 weeks in one site. We assessed completeness and quality of reported GA. We examined the validity of estimated preterm birth rates by sensitivity and specificity, over/under-reporting of GAw in survey compared to ultrasound by multinomial logistic regression, and explored perceptions about GA and barriers and enablers to its reporting using focus group discussions (n = 29) | ||
| 520 | |a RESULTS: GAm questions were almost universally answered, but heaping on 9 months resulted in underestimation of preterm birth rates. Preference for reporting GAw in even numbers was evident, resulting in heaping at 36 weeks; hence, over-estimating preterm birth rates, except in Matlab where the peak was at 38 weeks. Questions regarding 'born before expected' were answered but gave implausibly low preterm birth rates in most sites. Applying ultrasound as the gold standard in Matlab site, sensitivity of survey-GAw for detecting preterm birth (GAw <37) was 60% and specificity was 93%. Focus group findings suggest that women perceive GA to be important, but usually counted in months. Antenatal care attendance, women's education and health cards may improve reporting | ||
| 520 | |a CONCLUSIONS: This is the first published study assessing GA reporting in surveys, compared with the gold standard of ultrasound. Reporting GAw within 5 years' recall is feasible with high completeness, but accuracy is affected by heaping. Compared to ultrasound-GAw, results are reasonably specific, but sensitivity needs to be improved. We propose revised questions based on the study findings for further testing and validation in settings where pregnancy ultrasound data and/or last menstrual period dates/GA recorded in pregnancy are available. Specific training of interviewers is recommended | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Demography | |
| 650 | 4 | |a Gestational age | |
| 650 | 4 | |a Household survey | |
| 650 | 4 | |a Last menstrual period | |
| 650 | 4 | |a Preterm birth | |
| 650 | 4 | |a Surveillance | |
| 650 | 4 | |a Ultrasound | |
| 700 | 1 | |a Mahmud, Kaiser |e verfasserin |4 aut | |
| 700 | 1 | |a Blencowe, Hannah |e verfasserin |4 aut | |
| 700 | 1 | |a Ahmed, Tahmeed |e verfasserin |4 aut | |
| 700 | 1 | |a Akuze, Joseph |e verfasserin |4 aut | |
| 700 | 1 | |a Cousens, Simon |e verfasserin |4 aut | |
| 700 | 1 | |a Delwar, Nafisa |e verfasserin |4 aut | |
| 700 | 1 | |a Fisker, Ane B |e verfasserin |4 aut | |
| 700 | 1 | |a Ponce Hardy, Victoria |e verfasserin |4 aut | |
| 700 | 1 | |a Hasan, S M Tafsir |e verfasserin |4 aut | |
| 700 | 1 | |a Imam, Md Ali |e verfasserin |4 aut | |
| 700 | 1 | |a Kajungu, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Khan, Md Alfazal |e verfasserin |4 aut | |
| 700 | 1 | |a Martins, Justiniano S D |e verfasserin |4 aut | |
| 700 | 1 | |a Nahar, Quamrun |e verfasserin |4 aut | |
| 700 | 1 | |a Nettey, Obed Ernest A |e verfasserin |4 aut | |
| 700 | 1 | |a Tesega, Adane Kebede |e verfasserin |4 aut | |
| 700 | 1 | |a Yargawa, Judith |e verfasserin |4 aut | |
| 700 | 1 | |a Alam, Nurul |e verfasserin |4 aut | |
| 700 | 1 | |a Lawn, Joy E |e verfasserin |4 aut | |
| 700 | 0 | |a Every Newborn-INDEPTH Study Collaborative Group |e verfasserin |4 aut | |
| 700 | 1 | |a Byass, Peter |e investigator |4 oth | |
| 700 | 1 | |a Lawn, Joy E |e investigator |4 oth | |
| 700 | 1 | |a Waiswa, Peter |e investigator |4 oth | |
| 700 | 1 | |a Blencowe, Hannah |e investigator |4 oth | |
| 700 | 1 | |a Yargawa, Judith |e investigator |4 oth | |
| 700 | 1 | |a Akuze, Joseph |e investigator |4 oth | |
| 700 | 1 | |a Fisker, Ane B |e investigator |4 oth | |
| 700 | 1 | |a Martins, Justiniano S D |e investigator |4 oth | |
| 700 | 1 | |a Rodrigues, Amabelia |e investigator |4 oth | |
| 700 | 1 | |a Thysen, Sanne M |e investigator |4 oth | |
| 700 | 1 | |a Biks, Gashaw Andargie |e investigator |4 oth | |
| 700 | 1 | |a Abebe, Solomon Mokonnen |e investigator |4 oth | |
| 700 | 1 | |a Ayele, Tadesse Awoke |e investigator |4 oth | |
| 700 | 1 | |a Bisetegn, Telake Azale |e investigator |4 oth | |
| 700 | 1 | |a Delele, Tadess Guadu |e investigator |4 oth | |
| 700 | 1 | |a Gelaye, Kassahun Alemu |e investigator |4 oth | |
| 700 | 1 | |a Geremew, Bisrat Misganaw |e investigator |4 oth | |
| 700 | 1 | |a Gezie, Lemma Derseh |e investigator |4 oth | |
| 700 | 1 | |a Melese, Tesfahun |e investigator |4 oth | |
| 700 | 1 | |a Mengistu, Mezgebu Yitayal |e investigator |4 oth | |
| 700 | 1 | |a Tesega, Adane Kebede |e investigator |4 oth | |
| 700 | 1 | |a Yitayew, Temesgen Azmeraw |e investigator |4 oth | |
| 700 | 1 | |a Kasasa, Simon |e investigator |4 oth | |
| 700 | 1 | |a Galigawango, Edward |e investigator |4 oth | |
| 700 | 1 | |a Gyezaho, Collins |e investigator |4 oth | |
| 700 | 1 | |a Kaija, Judith |e investigator |4 oth | |
| 700 | 1 | |a Kajungu, Dan |e investigator |4 oth | |
| 700 | 1 | |a Nareeba, Tryphena |e investigator |4 oth | |
| 700 | 1 | |a Natukwatsa, Davis |e investigator |4 oth | |
| 700 | 1 | |a Tusubira, Valerie |e investigator |4 oth | |
| 700 | 1 | |a Enuameh, Yeetey A K |e investigator |4 oth | |
| 700 | 1 | |a Asante, Kwaku P |e investigator |4 oth | |
| 700 | 1 | |a Dzabeng, Francis |e investigator |4 oth | |
| 700 | 1 | |a Etego, Seeba Amenga |e investigator |4 oth | |
| 700 | 1 | |a Manu, Alexander A |e investigator |4 oth | |
| 700 | 1 | |a Manu, Grace |e investigator |4 oth | |
| 700 | 1 | |a Nettey, Obed Ernest |e investigator |4 oth | |
| 700 | 1 | |a Newton, Sam K |e investigator |4 oth | |
| 700 | 1 | |a Owusu-Agyei, Seth |e investigator |4 oth | |
| 700 | 1 | |a Tawiah, Charlotte |e investigator |4 oth | |
| 700 | 1 | |a Zandoh, Charles |e investigator |4 oth | |
| 700 | 1 | |a Alam, Nurul |e investigator |4 oth | |
| 700 | 1 | |a Delwar, Nafisa |e investigator |4 oth | |
| 700 | 1 | |a Haider, M Moinuddin |e investigator |4 oth | |
| 700 | 1 | |a Imam, Md Ali |e investigator |4 oth | |
| 700 | 1 | |a Mahmud, Kaiser |e investigator |4 oth | |
| 700 | 1 | |a Baschieri, Angela |e investigator |4 oth | |
| 700 | 1 | |a Cousens, Simon |e investigator |4 oth | |
| 700 | 1 | |a Gordeev, Vladimir S |e investigator |4 oth | |
| 700 | 1 | |a Hardy, Victoria Ponce |e investigator |4 oth | |
| 700 | 1 | |a Kwesiga, Doris |e investigator |4 oth | |
| 700 | 1 | |a Machiyama, Kazuyo |e investigator |4 oth | |
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