Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings

Shivaprasad S. Goudar, Kristen B. Stolka, Marion Koso-Thomas, Narayan V. Honnungar, Shivanand C. Mastiholi, Umesh Y. Ramadurg, Sangappa M. Dhaded, Omrana Pasha, Archana Patel, Fabian Esamai, Elwyn Chomba, Ana Garces, Fernando Althabe, Waldemar A. Carlo, Robert L. Goldenberg, Patricia L. Hibberd, Edward A. Liechty, Nancy F. Krebs, Michael K. Hambidge, Janet L. MooreDennis D. Wallace, Richard J. Derman, Kodkany S. Bhalachandra, Carl L. Bose

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Background: To describe quantitative data quality monitoring and performance metrics adopted by the Global Network's (GN) Maternal Newborn Health Registry (MNHR), a maternal and perinatal population-based registry (MPPBR) based in low and middle income countries (LMICs). Methods: Ongoing prospective, population-based data on all pregnancy outcomes within defined geographical locations participating in the GN have been collected since 2008. Data quality metrics were defined and are implemented at the cluster, site and the central level to ensure data quality. Quantitative performance metrics are described for data collected between 2010 and 2013. Results: Delivery outcome rates over 95% illustrate that all sites are successful in following patients from pregnancy through delivery. Examples of specific performance metric reports illustrate how both the metrics and reporting process are used to identify cluster-level and site-level quality issues and illustrate how those metrics track over time. Other summary reports (e.g. the increasing proportion of measured birth weight compared to estimated and missing birth weight) illustrate how a site has improved quality over time. Conclusion: High quality MPPBRs such as the MNHR provide key information on pregnancy outcomes to local and international health officials where civil registration systems are lacking. The MNHR has measures in place to monitor data collection procedures and improve the quality of data collected. Sites have increasingly achieved acceptable values of performance metrics over time, indicating improvements in data quality, but the quality control program must continue to evolve to optimize the use of the MNHR to assess the impact of community interventions in research protocols in pregnancy and perinatal health.

Original languageEnglish (US)
Article numberS2
JournalReproductive Health
Volume12
Issue number2
DOIs
StatePublished - Jun 8 2015

Fingerprint

Observational Studies
Registries
Population
Pregnancy Outcome
Birth Weight
Pregnancy
Health
Quality Control
Mothers
Data Accuracy
Infant Health
Maternal Health
Research

Keywords

  • Data monitoring
  • Data quality
  • Low-income countries
  • Maternal health
  • Metrics
  • Newborn health
  • Perinatal registry

ASJC Scopus subject areas

  • Obstetrics and Gynecology
  • Reproductive Medicine

Cite this

Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings. / Goudar, Shivaprasad S.; Stolka, Kristen B.; Koso-Thomas, Marion; Honnungar, Narayan V.; Mastiholi, Shivanand C.; Ramadurg, Umesh Y.; Dhaded, Sangappa M.; Pasha, Omrana; Patel, Archana; Esamai, Fabian; Chomba, Elwyn; Garces, Ana; Althabe, Fernando; Carlo, Waldemar A.; Goldenberg, Robert L.; Hibberd, Patricia L.; Liechty, Edward A.; Krebs, Nancy F.; Hambidge, Michael K.; Moore, Janet L.; Wallace, Dennis D.; Derman, Richard J.; Bhalachandra, Kodkany S.; Bose, Carl L.

In: Reproductive Health, Vol. 12, No. 2, S2, 08.06.2015.

Research output: Contribution to journalArticle

Goudar, SS, Stolka, KB, Koso-Thomas, M, Honnungar, NV, Mastiholi, SC, Ramadurg, UY, Dhaded, SM, Pasha, O, Patel, A, Esamai, F, Chomba, E, Garces, A, Althabe, F, Carlo, WA, Goldenberg, RL, Hibberd, PL, Liechty, EA, Krebs, NF, Hambidge, MK, Moore, JL, Wallace, DD, Derman, RJ, Bhalachandra, KS & Bose, CL 2015, 'Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings', Reproductive Health, vol. 12, no. 2, S2. https://doi.org/10.1186/1742-4755-12-S2-S2
Goudar, Shivaprasad S. ; Stolka, Kristen B. ; Koso-Thomas, Marion ; Honnungar, Narayan V. ; Mastiholi, Shivanand C. ; Ramadurg, Umesh Y. ; Dhaded, Sangappa M. ; Pasha, Omrana ; Patel, Archana ; Esamai, Fabian ; Chomba, Elwyn ; Garces, Ana ; Althabe, Fernando ; Carlo, Waldemar A. ; Goldenberg, Robert L. ; Hibberd, Patricia L. ; Liechty, Edward A. ; Krebs, Nancy F. ; Hambidge, Michael K. ; Moore, Janet L. ; Wallace, Dennis D. ; Derman, Richard J. ; Bhalachandra, Kodkany S. ; Bose, Carl L. / Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings. In: Reproductive Health. 2015 ; Vol. 12, No. 2.
@article{fdd3c21ab3b447b3a5bb7ee1a6227a65,
title = "Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings",
abstract = "Background: To describe quantitative data quality monitoring and performance metrics adopted by the Global Network's (GN) Maternal Newborn Health Registry (MNHR), a maternal and perinatal population-based registry (MPPBR) based in low and middle income countries (LMICs). Methods: Ongoing prospective, population-based data on all pregnancy outcomes within defined geographical locations participating in the GN have been collected since 2008. Data quality metrics were defined and are implemented at the cluster, site and the central level to ensure data quality. Quantitative performance metrics are described for data collected between 2010 and 2013. Results: Delivery outcome rates over 95{\%} illustrate that all sites are successful in following patients from pregnancy through delivery. Examples of specific performance metric reports illustrate how both the metrics and reporting process are used to identify cluster-level and site-level quality issues and illustrate how those metrics track over time. Other summary reports (e.g. the increasing proportion of measured birth weight compared to estimated and missing birth weight) illustrate how a site has improved quality over time. Conclusion: High quality MPPBRs such as the MNHR provide key information on pregnancy outcomes to local and international health officials where civil registration systems are lacking. The MNHR has measures in place to monitor data collection procedures and improve the quality of data collected. Sites have increasingly achieved acceptable values of performance metrics over time, indicating improvements in data quality, but the quality control program must continue to evolve to optimize the use of the MNHR to assess the impact of community interventions in research protocols in pregnancy and perinatal health.",
keywords = "Data monitoring, Data quality, Low-income countries, Maternal health, Metrics, Newborn health, Perinatal registry",
author = "Goudar, {Shivaprasad S.} and Stolka, {Kristen B.} and Marion Koso-Thomas and Honnungar, {Narayan V.} and Mastiholi, {Shivanand C.} and Ramadurg, {Umesh Y.} and Dhaded, {Sangappa M.} and Omrana Pasha and Archana Patel and Fabian Esamai and Elwyn Chomba and Ana Garces and Fernando Althabe and Carlo, {Waldemar A.} and Goldenberg, {Robert L.} and Hibberd, {Patricia L.} and Liechty, {Edward A.} and Krebs, {Nancy F.} and Hambidge, {Michael K.} and Moore, {Janet L.} and Wallace, {Dennis D.} and Derman, {Richard J.} and Bhalachandra, {Kodkany S.} and Bose, {Carl L.}",
year = "2015",
month = "6",
day = "8",
doi = "10.1186/1742-4755-12-S2-S2",
language = "English (US)",
volume = "12",
journal = "Reproductive Health",
issn = "1742-4755",
publisher = "BioMed Central",
number = "2",

}

TY - JOUR

T1 - Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings

AU - Goudar, Shivaprasad S.

AU - Stolka, Kristen B.

AU - Koso-Thomas, Marion

AU - Honnungar, Narayan V.

AU - Mastiholi, Shivanand C.

AU - Ramadurg, Umesh Y.

AU - Dhaded, Sangappa M.

AU - Pasha, Omrana

AU - Patel, Archana

AU - Esamai, Fabian

AU - Chomba, Elwyn

AU - Garces, Ana

AU - Althabe, Fernando

AU - Carlo, Waldemar A.

AU - Goldenberg, Robert L.

AU - Hibberd, Patricia L.

AU - Liechty, Edward A.

AU - Krebs, Nancy F.

AU - Hambidge, Michael K.

AU - Moore, Janet L.

AU - Wallace, Dennis D.

AU - Derman, Richard J.

AU - Bhalachandra, Kodkany S.

AU - Bose, Carl L.

PY - 2015/6/8

Y1 - 2015/6/8

N2 - Background: To describe quantitative data quality monitoring and performance metrics adopted by the Global Network's (GN) Maternal Newborn Health Registry (MNHR), a maternal and perinatal population-based registry (MPPBR) based in low and middle income countries (LMICs). Methods: Ongoing prospective, population-based data on all pregnancy outcomes within defined geographical locations participating in the GN have been collected since 2008. Data quality metrics were defined and are implemented at the cluster, site and the central level to ensure data quality. Quantitative performance metrics are described for data collected between 2010 and 2013. Results: Delivery outcome rates over 95% illustrate that all sites are successful in following patients from pregnancy through delivery. Examples of specific performance metric reports illustrate how both the metrics and reporting process are used to identify cluster-level and site-level quality issues and illustrate how those metrics track over time. Other summary reports (e.g. the increasing proportion of measured birth weight compared to estimated and missing birth weight) illustrate how a site has improved quality over time. Conclusion: High quality MPPBRs such as the MNHR provide key information on pregnancy outcomes to local and international health officials where civil registration systems are lacking. The MNHR has measures in place to monitor data collection procedures and improve the quality of data collected. Sites have increasingly achieved acceptable values of performance metrics over time, indicating improvements in data quality, but the quality control program must continue to evolve to optimize the use of the MNHR to assess the impact of community interventions in research protocols in pregnancy and perinatal health.

AB - Background: To describe quantitative data quality monitoring and performance metrics adopted by the Global Network's (GN) Maternal Newborn Health Registry (MNHR), a maternal and perinatal population-based registry (MPPBR) based in low and middle income countries (LMICs). Methods: Ongoing prospective, population-based data on all pregnancy outcomes within defined geographical locations participating in the GN have been collected since 2008. Data quality metrics were defined and are implemented at the cluster, site and the central level to ensure data quality. Quantitative performance metrics are described for data collected between 2010 and 2013. Results: Delivery outcome rates over 95% illustrate that all sites are successful in following patients from pregnancy through delivery. Examples of specific performance metric reports illustrate how both the metrics and reporting process are used to identify cluster-level and site-level quality issues and illustrate how those metrics track over time. Other summary reports (e.g. the increasing proportion of measured birth weight compared to estimated and missing birth weight) illustrate how a site has improved quality over time. Conclusion: High quality MPPBRs such as the MNHR provide key information on pregnancy outcomes to local and international health officials where civil registration systems are lacking. The MNHR has measures in place to monitor data collection procedures and improve the quality of data collected. Sites have increasingly achieved acceptable values of performance metrics over time, indicating improvements in data quality, but the quality control program must continue to evolve to optimize the use of the MNHR to assess the impact of community interventions in research protocols in pregnancy and perinatal health.

KW - Data monitoring

KW - Data quality

KW - Low-income countries

KW - Maternal health

KW - Metrics

KW - Newborn health

KW - Perinatal registry

UR - http://www.scopus.com/inward/record.url?scp=84977493600&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84977493600&partnerID=8YFLogxK

U2 - 10.1186/1742-4755-12-S2-S2

DO - 10.1186/1742-4755-12-S2-S2

M3 - Article

C2 - 26062714

AN - SCOPUS:84977493600

VL - 12

JO - Reproductive Health

JF - Reproductive Health

SN - 1742-4755

IS - 2

M1 - S2

ER -