Effect of birthweight measurement quality improvement on low birthweight prevalence in rural Ethiopia

Population Health Metrics, Volume 19, No. 1, Year 2021

Interpretation

Background: Low birthweight (LBW) (< 2500 g) is a significant determinant of infant morbidity and mortality worldwide. In low-income settings, the quality of birthweight data suffers from measurement and recording errors, inconsistent data reporting systems, and missing data from non-facility births. This paper describes birthweight data quality and the prevalence of LBW before and after implementation of a birthweight quality improvement (QI) initiative in Amhara region, Ethiopia. Methods: A comparative pre-post study was performed in selected rural health facilities located in West Gojjam and South Gondar zones. At baseline, a retrospective review of delivery records from February to May 2018 was performed in 14 health centers to collect birthweight data. A birthweight QI initiative was introduced in August 2019, which included provision of high-quality digital infant weight scales (precision 5 g), routine calibration, training in birth weighing and data recording, and routine field supervision. After the QI implementation, birthweight data were prospectively collected from late August to early September 2019, and December 2019 to June 2020. Data quality, as measured by heaping (weights at exact multiples of 500 g) and rounding to the nearest 100 g, and the prevalence of LBW were calculated before and after QI implementation. Results: We retrospectively reviewed 1383 delivery records before the QI implementation and prospectively measured 1371 newborn weights after QI implementation. Heaping was most frequently observed at 3000 g and declined from 26% pre-initiative to 6.7% post-initiative. Heaping at 2500 g decreased from 5.4% pre-QI to 2.2% post-QI. The percentage of rounding to the nearest 100 g was reduced from 100% pre-initiative to 36.5% post-initiative. Before the QI initiative, the prevalence of recognized LBW was 2.2% (95% confidence interval [CI]: 1.5–3.1) and after the QI initiative increased to 11.7% (95% CI: 10.1–13.5). Conclusions: A QI intervention can improve the quality of birthweight measurements, and data measurement quality may substantially affect estimates of LBW prevalence. A comparative pre-post study was conducted to determine the effect of QI on birthweight data quality and the proportion of newborns with LBW in rural Amhara, Ethiopia. Amhara region is subdivided into 13 administrative zones and 140 districts, and it has an estimated population size of over 21,000,000. Nearly 85% of the population live in rural areas. According to the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) report, in Amhara region, the percentage of institutional delivery and those receiving antenatal care from a skilled provider were 54.2% and 82.6%, respectively [13]. Approximately 23% of women of reproductive age had a body mass index of below 18.5 kg/m2 [14]. A retrospective facility record review was performed in 14 health centers located in seven districts of three zones in Amhara, namely South Gondar, West Gojjam and North Wollo, from February to May 2018 (Additional file 1). The health centers were chosen as prospective sites for the parent ENAT study, and the populations were selected based on high rates of maternal malnutrition, risk of fetal growth restriction, and need for nutrition interventions. Geomorphological maps were used to identify drought and famine-prone districts, and local government and partners were consulted. Two health centers from each district were selected based on the current antenatal care volume, using data from the region’s Health Management and Information System. After selection of the health centers for the parent ENAT study within the zones above (South Gondar, West Gojjam), birthweight QI was implemented in 12 study health centers and birthweight data were prospectively collected from late August to early September 2019, and December 2019 to June 2020. These study health facilities were selected in consultation with the Amhara Regional Health Bureau, Amhara Public Health Institute, and partners, with preference for health centers with higher ANC volume and access to transportation (Additional file 1). The phase 2 study was conducted with the objective of not only improving the quality of anthropometric measurements and recording, but also for testing study tools and procedures of the ENAT parent study. Birthweight measurement quality improvement initiative package A high-quality digital infant scale (ADE-M112600, Germany) (precision 5 g) was provided to each study health center for measuring the newborn weight at birth. Adequate weighing stations were set up within all health centers; scales were placed on a clean flat surface with the display clearly visible and calibrated every morning with the 1000 g and 2000 g standard weights. If the weight did not read between ± 10 g of the standardized weights, proper adjustments with the placement of scales were made until the correct measures were displayed. Health center staff (n = 1 to 4 midwives per health center) were trained on how to accurately measure birthweight using digital weight scales. Methods to weigh the baby with clothing using the tare function were developed, based on methods proposed by the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) [15]. Where weight measurement of a naked newborn was not possible, a scale was first tared with a blanket or cloth, and weight of the baby with the clothing or blanket was taken. Training on recording the full precision of birthweight values, without any rounding to the nearest number was given to all personnel involved in measuring anthropometry. We developed job aids demonstrating how to properly measure and document birth weight using step-by-step pictorial procedures and posted them on the delivery room walls (Additional file 2). Ongoing field supervision was performed in all health centers by the field team (study physicians and field coordinators) throughout the post-QI phase. To ensure the standard operating procedures were followed, the field team observed the midwives measuring and recording the newborns’ weight, checked facility birthweight records (labor/delivery registers) for heaping and rounding, and provided feedback when appropriate. In addition, a review meeting with all health center midwives and directors was held after 2 weeks of implementing the initiative (phase 2 study). Quality of birthweight data extracted from health center records, lessons learnt and ways to forward were discussed. Birthweight data were extracted from delivery registers by trained research staff using the Survey Solutions® electronic data collection software (version 20.10, World Bank, Washington DC, USA). Routine field supervision was conducted in all sites, and collected data were reviewed and cleaned by the data management team daily. During phase 1, about 1400 health center delivery registers were retrospectively reviewed. All labor/delivery registers recorded from February to May 2018 (n ~ 100 records from each health center) were included. Birthweight was measured using fully and semi-functional analog/spring weighing scales available in the health center (Fig. 2), with routine delivery room measuring practices and record keeping. Birthweight scales used before and after initiative. A) Birthweight scale commonly used pre-QI. B) Birthweight scale used post-QI In phase 2, health center midwives measured the weight of newborns at birth (n ~ 100 per health center) using precise digital infant weight scales (Fig. 2). In line with the routine practice, health center midwives weighed the newborn only once and documented on labor/delivery registers. Birthweight data quality was assessed for implausibility, heaping and rounding. The presence of implausible values defined as extreme or unlikely birthweight values, i.e., < 350 g or > 6000 g, was checked. Percentages of heaping exactly at multiples of 500 g (e.g., 1500 g, 2000 g, 2500 g, 3000 g, 3500 g, etc.) were calculated. Heaping index (HI) was also computed by dividing the number of exact weight values (e.g., 3000 g) by all weights within the adjacent 250 g brackets, excluding the exact values (e.g., 2750–2999 + 3001–3249) [16]. Proportions of birthweights rounded to the nearest 100 g increments were calculated. Histograms were constructed to visually inspect the birthweight distribution. To calculate the prevalence of LBW, the number of live births with a birthweight of less than 2500 g was divided by the total number of liveborn babies with reported birthweights. To examine the effect of birthweight heaping, prevalence of LBW was computed by including 50% of newborns who had exactly 2500 g as LBW [16, 17]. Data analysis was done using STATA v.15 (StataCorp LLC, College Station, TX, USA).

AI Digest

Study Justification:

The study aimed to assess the impact of a birthweight quality improvement (QI) initiative on the prevalence of low birthweight (LBW) in rural Ethiopia. LBW is a significant contributor to infant morbidity and mortality globally, particularly in low-income settings. However, birthweight data in these settings often suffer from measurement errors, inconsistent reporting systems, and missing data. This study aimed to address these issues and improve the quality of birthweight measurements to obtain more accurate estimates of LBW prevalence.

Highlights:

1. The study conducted a comparative pre-post analysis in selected rural health facilities in Amhara region, Ethiopia.
2. Before the QI initiative, birthweight data were retrospectively reviewed from February to May 2018, and after the QI implementation, birthweight data were prospectively collected from late August to early September 2019 and December 2019 to June 2020.
3. The QI initiative included the provision of high-quality digital infant weight scales, routine calibration, training in birth weighing and data recording, and field supervision.
4. The study found that the QI intervention significantly improved the quality of birthweight measurements, reducing heaping and rounding errors.
5. Before the QI initiative, the prevalence of recognized LBW was 2.2%, which increased to 11.7% after the QI initiative.
6. The findings highlight the importance of accurate birthweight measurements in estimating LBW prevalence and the potential impact of QI interventions in improving data quality.

Recommendations:

1. Implement birthweight QI initiatives in other health facilities and regions in Ethiopia to improve the accuracy of birthweight measurements and obtain more reliable LBW prevalence estimates.
2. Provide high-quality digital infant weight scales and ensure routine calibration in all health centers to maintain measurement accuracy.
3. Conduct regular training sessions for health center staff on birth weighing and data recording techniques to minimize errors.
4. Establish a system of ongoing field supervision to ensure adherence to standard operating procedures and provide feedback to improve data quality.
5. Promote the use of job aids and visual aids in health centers to guide staff in accurately measuring and documenting birth weight.

Key Role Players:

1. Amhara Regional Health Bureau
2. Amhara Public Health Institute
3. Health center directors and midwives
4. Study physicians and field coordinators
5. Trained research staff
6. Data management team

Cost Items for Planning Recommendations:

1. High-quality digital infant weight scales
2. Calibration equipment
3. Training materials and resources
4. Field supervision expenses (transportation, accommodation, etc.)
5. Job aids and visual aids for health centers
6. Data collection and management software (Survey Solutions®)
7. Research staff salaries and allowances

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is a comparative pre-post study, which provides valuable information. The authors collected data before and after the implementation of a birthweight quality improvement initiative in rural Ethiopia. They measured the quality of birthweight data by assessing heaping and rounding, and calculated the prevalence of low birthweight (LBW) before and after the intervention. The results show a significant improvement in birthweight measurement quality and an increase in the prevalence of recognized LBW after the intervention. The study provides specific details about the intervention, including the provision of high-quality digital infant weight scales, training in birth weighing and data recording, and routine field supervision. However, the abstract lacks information about the sample size and characteristics of the study population, which could be important for assessing the generalizability of the findings. Additionally, the abstract does not mention any limitations of the study or potential sources of bias. To improve the evidence, the authors could include more information about the study population and address any limitations or potential sources of bias in the abstract.

Abstract

A comparative pre-post study was conducted to determine the effect of QI on birthweight data quality and the proportion of newborns with LBW in rural Amhara, Ethiopia. Amhara region is subdivided into 13 administrative zones and 140 districts, and it has an estimated population size of over 21,000,000. Nearly 85% of the population live in rural areas. According to the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) report, in Amhara region, the percentage of institutional delivery and those receiving antenatal care from a skilled provider were 54.2% and 82.6%, respectively [13]. Approximately 23% of women of reproductive age had a body mass index of below 18.5 kg/m2 [14]. A retrospective facility record review was performed in 14 health centers located in seven districts of three zones in Amhara, namely South Gondar, West Gojjam and North Wollo, from February to May 2018 (Additional file 1). The health centers were chosen as prospective sites for the parent ENAT study, and the populations were selected based on high rates of maternal malnutrition, risk of fetal growth restriction, and need for nutrition interventions. Geomorphological maps were used to identify drought and famine-prone districts, and local government and partners were consulted. Two health centers from each district were selected based on the current antenatal care volume, using data from the region’s Health Management and Information System. After selection of the health centers for the parent ENAT study within the zones above (South Gondar, West Gojjam), birthweight QI was implemented in 12 study health centers and birthweight data were prospectively collected from late August to early September 2019, and December 2019 to June 2020. These study health facilities were selected in consultation with the Amhara Regional Health Bureau, Amhara Public Health Institute, and partners, with preference for health centers with higher ANC volume and access to transportation (Additional file 1). The phase 2 study was conducted with the objective of not only improving the quality of anthropometric measurements and recording, but also for testing study tools and procedures of the ENAT parent study. Birthweight measurement quality improvement initiative package A high-quality digital infant scale (ADE-M112600, Germany) (precision 5 g) was provided to each study health center for measuring the newborn weight at birth. Adequate weighing stations were set up within all health centers; scales were placed on a clean flat surface with the display clearly visible and calibrated every morning with the 1000 g and 2000 g standard weights. If the weight did not read between ± 10 g of the standardized weights, proper adjustments with the placement of scales were made until the correct measures were displayed. Health center staff (n = 1 to 4 midwives per health center) were trained on how to accurately measure birthweight using digital weight scales. Methods to weigh the baby with clothing using the tare function were developed, based on methods proposed by the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) [15]. Where weight measurement of a naked newborn was not possible, a scale was first tared with a blanket or cloth, and weight of the baby with the clothing or blanket was taken. Training on recording the full precision of birthweight values, without any rounding to the nearest number was given to all personnel involved in measuring anthropometry. We developed job aids demonstrating how to properly measure and document birth weight using step-by-step pictorial procedures and posted them on the delivery room walls (Additional file 2). Ongoing field supervision was performed in all health centers by the field team (study physicians and field coordinators) throughout the post-QI phase. To ensure the standard operating procedures were followed, the field team observed the midwives measuring and recording the newborns’ weight, checked facility birthweight records (labor/delivery registers) for heaping and rounding, and provided feedback when appropriate. In addition, a review meeting with all health center midwives and directors was held after 2 weeks of implementing the initiative (phase 2 study). Quality of birthweight data extracted from health center records, lessons learnt and ways to forward were discussed. Birthweight data were extracted from delivery registers by trained research staff using the Survey Solutions® electronic data collection software (version 20.10, World Bank, Washington DC, USA). Routine field supervision was conducted in all sites, and collected data were reviewed and cleaned by the data management team daily. During phase 1, about 1400 health center delivery registers were retrospectively reviewed. All labor/delivery registers recorded from February to May 2018 (n ~ 100 records from each health center) were included. Birthweight was measured using fully and semi-functional analog/spring weighing scales available in the health center (Fig. 2), with routine delivery room measuring practices and record keeping. Birthweight scales used before and after initiative. A) Birthweight scale commonly used pre-QI. B) Birthweight scale used post-QI In phase 2, health center midwives measured the weight of newborns at birth (n ~ 100 per health center) using precise digital infant weight scales (Fig. 2). In line with the routine practice, health center midwives weighed the newborn only once and documented on labor/delivery registers. Birthweight data quality was assessed for implausibility, heaping and rounding. The presence of implausible values defined as extreme or unlikely birthweight values, i.e., 6000 g, was checked. Percentages of heaping exactly at multiples of 500 g (e.g., 1500 g, 2000 g, 2500 g, 3000 g, 3500 g, etc.) were calculated. Heaping index (HI) was also computed by dividing the number of exact weight values (e.g., 3000 g) by all weights within the adjacent 250 g brackets, excluding the exact values (e.g., 2750–2999 + 3001–3249) [16]. Proportions of birthweights rounded to the nearest 100 g increments were calculated. Histograms were constructed to visually inspect the birthweight distribution. To calculate the prevalence of LBW, the number of live births with a birthweight of less than 2500 g was divided by the total number of liveborn babies with reported birthweights. To examine the effect of birthweight heaping, prevalence of LBW was computed by including 50% of newborns who had exactly 2500 g as LBW [16, 17]. Data analysis was done using STATA v.15 (StataCorp LLC, College Station, TX, USA).

Materials

Innovations

The innovation described in the study is a birthweight quality improvement (QI) initiative implemented in rural health facilities in Amhara region, Ethiopia. The initiative aimed to improve the quality of birthweight measurements and data recording to accurately determine the prevalence of low birthweight (LBW) infants. The following innovations were implemented as part of the QI initiative:

1. Provision of high-quality digital infant weight scales: Each study health center was provided with a high-quality digital infant scale with a precision of 5 grams. These scales were used to measure the newborn’s weight at birth.

2. Routine calibration of scales: The scales were calibrated every morning using standardized weights of 1000 grams and 2000 grams to ensure accurate measurements.

3. Training in birth weighing and data recording: Health center staff, including midwives, were trained on how to accurately measure birthweight using the digital weight scales. They were also trained on recording the full precision of birthweight values without rounding to the nearest number.

4. Routine field supervision: Ongoing field supervision was conducted by the study physicians and field coordinators to ensure that the standard operating procedures were followed. They observed the midwives measuring and recording the newborns’ weight, checked facility birthweight records for heaping and rounding, and provided feedback when necessary.

5. Review meetings and feedback: A review meeting was held after 2 weeks of implementing the initiative to discuss the quality of birthweight data extracted from health center records, share lessons learned, and identify ways to improve.

These innovations aimed to improve the accuracy of birthweight measurements, reduce heaping (weights at exact multiples of 500 grams), and minimize rounding to the nearest 100 grams. The study found that the QI initiative led to a significant improvement in birthweight data quality and an increase in the prevalence of recognized LBW infants.

Overall, the QI initiative demonstrated that implementing innovative approaches, such as using high-quality digital scales, providing training, and ensuring routine supervision, can improve access to accurate birthweight data and contribute to better maternal and infant health outcomes.

AI Innovations Description

The recommendation to improve access to maternal health based on the study is to implement a birthweight quality improvement (QI) initiative. This initiative includes the following components:

1. Provision of high-quality digital infant weight scales: Provide health centers with precision digital weight scales (with a precision of 5g) to accurately measure newborn birthweights.

2. Routine calibration and training: Ensure that the weight scales are calibrated regularly to maintain accuracy. Train health center staff, particularly midwives, on how to accurately measure birthweights using the digital weight scales.

3. Data recording and supervision: Provide training on recording the full precision of birthweight values without rounding to the nearest number. Develop job aids and post them in delivery rooms to guide personnel in measuring and documenting birthweights accurately. Conduct ongoing field supervision to observe and provide feedback on the measurement and recording of birthweights.

4. Data quality assessment: Regularly assess the quality of birthweight data by checking for implausible values, heaping (weights at exact multiples of 500g), and rounding to the nearest 100g. Calculate heaping index to measure the extent of heaping. Use histograms to visually inspect the birthweight distribution.

5. Collaboration and review: Collaborate with relevant stakeholders, such as the Amhara Regional Health Bureau and Amhara Public Health Institute, to ensure the implementation of the QI initiative. Conduct review meetings with health center midwives and directors to discuss data quality, lessons learned, and ways to improve.

By implementing this birthweight QI initiative, the study found that the quality of birthweight measurements improved, resulting in a decrease in heaping and rounding errors. This improvement in data quality can have a significant impact on estimating the prevalence of low birthweight (LBW). Therefore, implementing similar QI initiatives in other rural areas can help improve access to accurate maternal health data and inform targeted interventions to address LBW and its associated risks.

AI Innovations Methodology

The study described in the provided text focuses on the effect of a birthweight quality improvement (QI) initiative on the prevalence of low birthweight (LBW) in rural Ethiopia. The methodology used in this study can be summarized as follows:

1. Study Design: A comparative pre-post study design was employed to assess the impact of the QI initiative. Birthweight data were collected both before and after the implementation of the initiative.

2. Study Setting: The study was conducted in selected rural health facilities located in the Amhara region of Ethiopia. The region has a large rural population and is characterized by low institutional delivery rates and high rates of maternal malnutrition.

3. Data Collection: Retrospective data collection was performed by reviewing delivery records from February to May 2018 in 14 health centers. After the QI initiative was implemented, birthweight data were prospectively collected from late August to early September 2019, and December 2019 to June 2020.

4. QI Initiative: The birthweight QI initiative included several components aimed at improving the quality of birthweight measurements. These components included providing high-quality digital infant weight scales with a precision of 5g, routine calibration of the scales, training in birth weighing and data recording, and ongoing field supervision.

5. Data Quality Assessment: The quality of birthweight data was assessed by measuring heaping (weights at exact multiples of 500g) and rounding to the nearest 100g. The presence of implausible values was also checked. Histograms were constructed to visually inspect the birthweight distribution.

6. LBW Prevalence Calculation: The prevalence of LBW was calculated by dividing the number of live births with a birthweight of less than 2500g by the total number of liveborn babies with reported birthweights. To account for birthweight heaping, 50% of newborns with exactly 2500g were included in the LBW category.

7. Data Analysis: Data analysis was performed using STATA software. Prevalence rates and statistical measures were calculated to assess the impact of the QI initiative on birthweight data quality and LBW prevalence.

In summary, this study utilized a comparative pre-post study design to evaluate the impact of a birthweight QI initiative on birthweight data quality and LBW prevalence in rural Ethiopia. The study involved data collection, implementation of the QI initiative, data quality assessment, and data analysis using statistical software.

Author

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.1186/s12963-021-00265-0

Research Areas:

Community Interventions, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female