Robust estimation of infant feeding indicators by data quality assessment of longitudinal electronic health records from birth up to 18 months of life

•Infant feeding repositories from EHR routinely collected data are a valuable data source for monitoring and research.•Routinely clinical data must be analyzed to assure its validity and reliability before reuse.•A DQ assurance approach by indicators allows to increase the tolerance level to DQ erro...

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Veröffentlicht in:Computer methods and programs in biomedicine 2021-08, Vol.207, p.106147-106147, Article 106147
Hauptverfasser: García-de-León-Chocano, Ricardo, Sáez, Carlos, Muñoz-Soler, Verónica, Oliver-Roig, Antonio, García-de-León-González, Ricardo, García-Gómez, Juan Miguel
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container_title Computer methods and programs in biomedicine
container_volume 207
creator García-de-León-Chocano, Ricardo
Sáez, Carlos
Muñoz-Soler, Verónica
Oliver-Roig, Antonio
García-de-León-González, Ricardo
García-Gómez, Juan Miguel
description •Infant feeding repositories from EHR routinely collected data are a valuable data source for monitoring and research.•Routinely clinical data must be analyzed to assure its validity and reliability before reuse.•A DQ assurance approach by indicators allows to increase the tolerance level to DQ errors and with it, decrease the probability of selection bias for its use in outcomes monitoring.•The availability of DQ infant feeding repositories facilitates the implementation of the BFHI. The Baby-Friendly Hospital Initiative (BFHI) is an international strategy aimed at improving breastfeeding practices in health care services. Regular monitoring of indicators is key for BFHI implementation and maintenance. Currently, routine data collected from electronic health records (EHR) is an excellent source for infant feeding monitoring, however data quality (DQ) assessment should be undertaken. The aim of this research is to enable robust estimations of infant feeding indicators through DQ assessment of routine EHR data. We use the longitudinal series of healthcare contacts belonging to 6427 children born from 2009 to 2018 in the Health Area V of Murcia (Spain). Longitudinal data came from EHR at hospital discharge and community infant health reviews up to 18 months. The data of each healthcare contact contained a 24-h recall of infant feeding. We perform a DQ process in three phases: (1) an assessment of each-single-contact and the definition of their infant feeding status; (2) a longitudinal DQ assessment of completeness and consistency of the series of contacts to obtain meta-information that guides the duration calculus, for each case, of the different types of breastfeeding: exclusive breastfeeding (EBF), full breastfeeding (FBF) and any breastfeeding (ABF); and finally (3) a robust estimation of indicators and description of DQ of each indicator. We found deficiencies of DQ in 30.42% of single contacts for EBF, 19.02% for FBF and 22.50% for ABF that were used to establish the infant feeding status. However, after longitudinal DQ assessment, we obtained valid and reliable data rates for most indicators such as “median duration of breastfeeding” nearly 90%, both for FBF and ABF, not so for EBF. Despite the DQ deficiencies found in raw data, the DQ assurance approach by indicators proposed in this work, allowed us to obtain a robust estimation of indicators with a significant percentage of subjects with valid information for ABF and FBF monitoring. The estimations
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The Baby-Friendly Hospital Initiative (BFHI) is an international strategy aimed at improving breastfeeding practices in health care services. Regular monitoring of indicators is key for BFHI implementation and maintenance. Currently, routine data collected from electronic health records (EHR) is an excellent source for infant feeding monitoring, however data quality (DQ) assessment should be undertaken. The aim of this research is to enable robust estimations of infant feeding indicators through DQ assessment of routine EHR data. We use the longitudinal series of healthcare contacts belonging to 6427 children born from 2009 to 2018 in the Health Area V of Murcia (Spain). Longitudinal data came from EHR at hospital discharge and community infant health reviews up to 18 months. The data of each healthcare contact contained a 24-h recall of infant feeding. We perform a DQ process in three phases: (1) an assessment of each-single-contact and the definition of their infant feeding status; (2) a longitudinal DQ assessment of completeness and consistency of the series of contacts to obtain meta-information that guides the duration calculus, for each case, of the different types of breastfeeding: exclusive breastfeeding (EBF), full breastfeeding (FBF) and any breastfeeding (ABF); and finally (3) a robust estimation of indicators and description of DQ of each indicator. We found deficiencies of DQ in 30.42% of single contacts for EBF, 19.02% for FBF and 22.50% for ABF that were used to establish the infant feeding status. However, after longitudinal DQ assessment, we obtained valid and reliable data rates for most indicators such as “median duration of breastfeeding” nearly 90%, both for FBF and ABF, not so for EBF. Despite the DQ deficiencies found in raw data, the DQ assurance approach by indicators proposed in this work, allowed us to obtain a robust estimation of indicators with a significant percentage of subjects with valid information for ABF and FBF monitoring. The estimations were consistent with results previously published. 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The Baby-Friendly Hospital Initiative (BFHI) is an international strategy aimed at improving breastfeeding practices in health care services. Regular monitoring of indicators is key for BFHI implementation and maintenance. Currently, routine data collected from electronic health records (EHR) is an excellent source for infant feeding monitoring, however data quality (DQ) assessment should be undertaken. The aim of this research is to enable robust estimations of infant feeding indicators through DQ assessment of routine EHR data. We use the longitudinal series of healthcare contacts belonging to 6427 children born from 2009 to 2018 in the Health Area V of Murcia (Spain). Longitudinal data came from EHR at hospital discharge and community infant health reviews up to 18 months. The data of each healthcare contact contained a 24-h recall of infant feeding. We perform a DQ process in three phases: (1) an assessment of each-single-contact and the definition of their infant feeding status; (2) a longitudinal DQ assessment of completeness and consistency of the series of contacts to obtain meta-information that guides the duration calculus, for each case, of the different types of breastfeeding: exclusive breastfeeding (EBF), full breastfeeding (FBF) and any breastfeeding (ABF); and finally (3) a robust estimation of indicators and description of DQ of each indicator. We found deficiencies of DQ in 30.42% of single contacts for EBF, 19.02% for FBF and 22.50% for ABF that were used to establish the infant feeding status. However, after longitudinal DQ assessment, we obtained valid and reliable data rates for most indicators such as “median duration of breastfeeding” nearly 90%, both for FBF and ABF, not so for EBF. Despite the DQ deficiencies found in raw data, the DQ assurance approach by indicators proposed in this work, allowed us to obtain a robust estimation of indicators with a significant percentage of subjects with valid information for ABF and FBF monitoring. The estimations were consistent with results previously published. 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The Baby-Friendly Hospital Initiative (BFHI) is an international strategy aimed at improving breastfeeding practices in health care services. Regular monitoring of indicators is key for BFHI implementation and maintenance. Currently, routine data collected from electronic health records (EHR) is an excellent source for infant feeding monitoring, however data quality (DQ) assessment should be undertaken. The aim of this research is to enable robust estimations of infant feeding indicators through DQ assessment of routine EHR data. We use the longitudinal series of healthcare contacts belonging to 6427 children born from 2009 to 2018 in the Health Area V of Murcia (Spain). Longitudinal data came from EHR at hospital discharge and community infant health reviews up to 18 months. The data of each healthcare contact contained a 24-h recall of infant feeding. We perform a DQ process in three phases: (1) an assessment of each-single-contact and the definition of their infant feeding status; (2) a longitudinal DQ assessment of completeness and consistency of the series of contacts to obtain meta-information that guides the duration calculus, for each case, of the different types of breastfeeding: exclusive breastfeeding (EBF), full breastfeeding (FBF) and any breastfeeding (ABF); and finally (3) a robust estimation of indicators and description of DQ of each indicator. We found deficiencies of DQ in 30.42% of single contacts for EBF, 19.02% for FBF and 22.50% for ABF that were used to establish the infant feeding status. However, after longitudinal DQ assessment, we obtained valid and reliable data rates for most indicators such as “median duration of breastfeeding” nearly 90%, both for FBF and ABF, not so for EBF. Despite the DQ deficiencies found in raw data, the DQ assurance approach by indicators proposed in this work, allowed us to obtain a robust estimation of indicators with a significant percentage of subjects with valid information for ABF and FBF monitoring. The estimations were consistent with results previously published. The methodology provided with this study allows a continuous and reliable population monitoring of infant feeding indicators of BFHI from routine EHR data.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.cmpb.2021.106147</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-9171-820X</orcidid><orcidid>https://orcid.org/0000-0002-1747-9287</orcidid><oa>free_for_read</oa></addata></record>
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subjects Baby-friendly hospital initiative
Data quality
Electronic health record
Indicator
Infant feeding
Monitoring
title Robust estimation of infant feeding indicators by data quality assessment of longitudinal electronic health records from birth up to 18 months of life
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