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Abstract
Background Anthropometric assessment in the paediatric population is particularly important to assess the child’s general health status, nutritional adequacy, and growth and developmental pattern. However, there are often shortcomings in the quality of anthropometric assessment done in primary healthcare settings despite the presence of established guidelines. In this study, we plan to use the quality improvement (QI) principles to improve the anthropometric assessment of under-5 children attending an urban primary health centre in Delhi, India.
Methods The study was conducted from December 2022 to February 2023. A baseline assessment was conducted to identify the gaps in the anthropometric measurement of under-5 children visiting the outpatient department. A QI team consisting of doctors and key health staff of urban health centre as its members was formed. A root cause analysis of the identified problems was done and changes were planned and implemented in a Plan-Do-Study-Act cycle.
Results There was a marked improvement in the quality of anthropometric measurements, particularly in length measurement for children <24 months of age (0% at baseline vs 81.0% at end-line). However, the improvement in weight measurement of children less than 5 years was lesser (16.2% at baseline vs 44.6% at end-line).
Conclusion Anthropometric assessment of under-5 children can be standardised through the involvement of all stakeholders and capacity building of the concerned healthcare providers, using the QI approach. Repeated assessments are required to ensure the sustainability of the change.
- Quality improvement
- Primary care
- PDSA
- Paediatrics
Data availability statement
Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online supplemental information.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Anthropometric assessment is crucial in assessing a child’s health status, nutrition, and growth, and also for early detection of malnutrition.
WHAT THIS STUDY ADDS
The principles of QI (Quality Improvement) can be applied even in low-resource settings to improve the quality of anthropometric assessment.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
This QI initiative could be replicated in similar primary healthcare settings to improve the quality of anthropometric assessment in under-5 children.
Introduction
Children, especially those under the age of 5 years, are a vulnerable population, as this is the time for significant growth and development. In addition, they have different patterns of illness and are more susceptible to infection than adults. In India, the Sample Registration System reported the under-5, infant and neonatal mortality rates as 32, 28 and 20 per 1000 live births, respectively (SRS Statistical Report, ORGI, 2022).1 Most often, these deaths occur in the background of malnutrition, making children more vulnerable to severe illnesses.2
The triple burden of malnutrition—stunting, wasting and overweight—continues to jeopardise children’s ability to survive and thrive across the globe.3 Anthropometry, particularly in the paediatric population, helps assess the child’s general health status, nutritional adequacy, and growth and developmental pattern.4 As per the standards of the WHO, the nutritional status of the child is determined in terms of the age-standardised and sex-standardised anthropometric z-scores namely, height for age, weight for age and weight for height. The new Joint Child Malnutrition Estimates of 2023 reveal that 31.7% of under-5 children are stunted, 2.8% are overweight and 18.7% are wasted in India.3
A significant proportion of childhood deaths are preventable by simple interventions that can be administered even in resource-constrained settings.5 In India, primary health centres (PHCs) are the first tier of the public healthcare system. They aim to provide comprehensive primary healthcare, with an emphasis on maternal and child health.6 However, there are often shortcomings in the care being provided despite the presence of established guidelines. This is due to limitations in resources and infrastructure, lack of adequate knowledge among healthcare personnel and gaps in effective communication.7 A careful assessment of existing resources and procedures at healthcare facilities can prove beneficial in identifying these gaps and filling them, thus improving the quality of care.8
Quality improvement (QI) by the Institute of Medicine is defined as ‘systematic and continuous actions that lead to measurable improvement in healthcare services and health status of targeted patient groups’.8 It involves ongoing scrutiny for gaps between expected and actual healthcare processes, and efforts to eliminate such gaps. The literature review indicates that many QI initiatives in various aspects ranging from maternal9 and child health10 to non-communicable disease management11 and hand hygiene12 have resulted in successful outcomes in Indian healthcare settings.
All forms of malnutrition are preventable. To stop malnutrition before it starts, children and their families need access to essential services for the early identification of malnutrition by standardised and accurate anthropometric assessment. The healthcare providers who conduct anthropometric assessments in our country have diverse socioeconomic backgrounds and educational levels so their ability to perform anthropometric measurements varies widely. Their skillset in performing anthropometry and whether or not they have been trained for the same are not assessed. The low ability of healthcare providers to perform anthropometric assessment can result in erroneous measurements, leading to misinterpretation of the child’s health status and thereby resulting in a loss of proper interventions. Therefore, QI initiatives are needed to identify the existing gaps and improve on the same so that the capacity of the healthcare providers can be increased and standardisation achieved in anthropometric assessment.
Hence, this study was planned to use the QI principles to improve the anthropometric assessment of under-5 children attending an urban PHC in Delhi, India. The SMART aim statement was to increase the proportion of under-5 children presenting to the outpatient department (OPD) at urban health centre (UHC) Dakshinpuri, whose weight and height were measured according to the standard steps,13–15 from the baseline to at least 80%, within 6 weeks.
Methodology
Background of the study setting
The site of the study was the UHC, a PHC located in Dakshinpuri Extension, an urban resettlement colony in South Delhi since 2002, catering to a population of approximately 38 000 people. This area has been adopted as an urban field practice area of a tertiary-level medical college. The centre provides outpatient services on all weekdays. Antenatal care and immunisation services are provided twice a week. The UHC is run by a team that includes different cadres of staff such as doctors (senior residents and junior residents), OPD registration staff, a nursing officer, a pharmacist and a medical social service officer and supervised by the faculty of the medical college.
A pilot study was done 1 year before this study to assess the quality of child-care services in the UHC; it was observed that there were a few gaps in the services provided to children under 5 years of age in the OPD. The gaps were particularly observed in the measurement of the weight and height of children for growth monitoring in the UHC. Hence, using the QI principles, we planned to improve and align the anthropometric assessment in our UHC with standard guidelines.13–15
The study was carried out between December 2022 and February 2023.
A baseline assessment was conducted to identify the gaps in the anthropometric measurement of under-5 children visiting the OPD. A team consisting of doctors (faculty members, senior residents and junior residents) and key health staff of UHC (ie, OPD registration staff who are also responsible for taking the anthropometric measurements of the patients, nursing officer, pharmacists, medical social service officer, health educators and health assistants) as its members for this QI was identified. As a first step, a root cause analysis of the identified problems was done. The change was planned and implemented in a Plan-Do-Study-Act (PDSA) cycle (online supplemental figure 1).
Supplemental material
Details of the QI initiative
A tool for data collection on mobile devices was developed in Epicollect5 (Centre for Genomic Pathogen Surveillance, 5 Cambridgeshire, UK). These forms were designed to capture a predefined set of quality indicators, adapted from existing guidelines and tools.13–15 The baseline assessment was carried out for about 2 weeks, from 26 December 2022 to 9 January 2023. Children under 5 years of age attending the OPD during this period were included in the assessment. After registration, the children’s anthropometric measurements (length, height and weight) were taken by the health workers and recorded in the OPD card. The method of measurement was observed by trained staff and a standard checklist was used to compare against the standard process.13–15 A process flow chart was drawn to understand the microprocesses from registration to dispensing of medication to the children availing OPD services (online supplemental figure 2).
The baseline results were consolidated, and a root cause analysis was done by brainstorming with the team to determine the reasons for gaps found in the anthropometry of under-5 children in the UHC. Lack of sufficient training in the measurement of weight and height for the staff, the broken headboard of the stadiometer and no system in place for quality monitoring of anthropometry were some of the reasons identified (figure 1).
Root cause analysis of inadequacies in anthropometry. UHC, urban health centre. *WFH/L: weight for height/length.
The first PDSA cycle was conducted over a total period of 6 weeks. In the ‘plan’ phase, the gaps identified from the baseline assessment and the root cause analysis were prioritised and simple solutions were identified for some of the major gaps; for example, the broken stadiometer was replaced with a new one.
In the ‘do’ phase (12 January 2023–16 January 2023), after brainstorming and group discussions with the team, the following change ideas were implemented such as providing a new stadiometer, calibration of weighing scales, training and capacity-building of staff. The health staff member appointed for measuring the height/length and weight of children at the registration counter was trained by the QI team members (junior residents) on the standard procedures for anthropometry. The method of training included the demonstration of height/weight measurement by the QI team members (junior residents) as per the standard guidelines13–15 and clarification of all related doubts from the staff regarding the same was done. This was followed by ‘on-the-job’, real-time feedback to registration staff, by QI team members. In the ‘do’ phase, all the planned changes such as following the standard steps for anthropometric measurement and real-time assessment of the same by QI members (junior residents under the supervision of a senior resident) were introduced for all children under 5 years who attended the health centre, irrespective of the reason for the clinic visit, which could be for immunisation or any illness.
Weight measurement was done using an infant weighing scale for children <24 months of age and by a digital weighing scale with the child standing for those between 24 and 59 months.
Measurement of weight involves six steps. Length for children aged <24 months was measured using an infantometer. The technique for length measurement was observed for 15 children at baseline and 21 children at the end-line (after 2 weeks). Measurement of length for children <24 months involved eight steps. Height measurement for children aged 24–59 months was done using a stadiometer. Measurement of height for children 24–59 months involved 12 steps.
The ‘study’ phase was initiated after 2 weeks of intervention. All the parameters measured during baseline were reassessed over 14 working days (1 February 2023–18 February 2023). The proportion of under-5 children visiting the UHC whose weight, height or length were recorded as per standard steps13–15 was determined. The results were compiled and the extent of improvement in the anthropometric assessment of children <5 years of age was analysed to understand what went right and to identify the scope for improvement.
Statistical analysis
We tabulated the numbers and proportions of children for whom specific steps of anthropometry were done correctly at baseline and end-line, as well as the numbers and proportions of children for whom all steps were done correctly. We compared the baseline and end-line performance for weight and height measurements using the χ2 test. For length measurement, considering the smaller sample size, we used the Fisher’s exact test instead.
Since testing multiple hypotheses could create problems of false discoveries, we aimed to control the false discovery rate to 0.05. Thus, rather than using p<0.05 as a cut-off for statistical significance, we first computed an appropriate cut-off using the method described by Benjamini and Hochberg.16 We considered the difference between the baseline and end-line as statistically significant if the p value was less than or equal to this cut-off.
Patients/the public were not directly involved in the design, conduct, reporting or dissemination plan of this work as it was a QI study focusing on system improvement.
Results
The anthropometry measurement technique was observed for a total of 70 children at baseline and 66 children at the end of the first PDSA cycle. 44.3% of the children were less than 24 months of age and 55.7% were 24–59 months of age at baseline. The proportion of children less than 24 months was 39.4% and children between 24 and 59 months were 60.6% at the end-line (table 1). For comparison of baseline and end-line anthropometry performance, the Benjamini-Hochberg procedure gave 0.005 as an appropriate cut-off, that is, p values less than or equal to 0.005 were considered statistically significant.
Age distribution of children whose anthropometry was observed
Weight measurement
The proportion of children for whom all six steps were followed correctly improved from 16.2% at baseline to 44.6% at the end of the first PDSA cycle (table 2). In over 90% of children, the weighing scale was placed on a flat, firm surface, zero reading was ensured prior to weighing, weight was noted when the reading was constant and the value recorded matched the scale to the nearest 0.1 kg; at both baseline and end-line. The most common error, at both baseline (72.1%) and end-line (47.7%), was weighing the child with excessive clothing. Statistically significant improvements were seen in weighing children with little or no clothing (p=0.004), as well as following all steps correctly (p<0.001).
Accuracy of weight measurement steps during baseline and end-line
Length measurement for children <24 months
None of the children at baseline were measured correctly using all eight steps. This improved to 81% at the end of the first PDSA cycle (table 3). The most common error was not removing the child’s cap or hood before measuring length, but this improved from 15.4% at baseline to 85.7% at the end of the first PDSA cycle. Statistically significant improvements were seen in removing the child’s cap or hood before measurement (p<0.001), placing the soles flat against the adjustable board (p=0.005) and following all steps correctly (p<0.001).
Accuracy of length measurement steps during baseline and end-line
Height measurement for children 24–59 months
The technique for height measurement was observed for 23 children aged 24–59 months at baseline and 35 children at the end-line (table 4). None of the children at baseline were measured correctly using all the twelve steps. This improved to 57.1% at the end of the first PDSA cycle (table 4). The most common error at baseline was measuring without the child’s feet close together. At the end-line, the most common error was the failure to have the child’s occiput, upper back, buttocks and heels touch the bar. Statistically significant improvements were seen in having a vertical stadiometer bar (p=0.004), having an intact headboard perpendicular to the vertical bar (p<0.001), removing the child’s footwear before measurement (p<0.001), removing the child’s cap or hood before measurement (p<0.001), having the back of head, upper back, buttocks and heel of the child touch the stadiometer bar (p<0.001), having the child place their feet close together (p<0.001), making the headboard touch the top of the child’s head (p<0.001) and following all steps correctly (p<0.001).
Accuracy of height measurement steps during baseline and end-line
Discussion
Key findings
This QI initiative focused on strengthening anthropometric assessment based on standard steps13–15 for children under the age of 5 years at an urban PHC in Delhi, through root cause analysis, followed by a PDSA cycle. The baseline assessment helped find the gaps in anthropometry, and the QI team then intervened to fill those gaps and improved the quality of care, by running one PDSA cycle over 6 weeks.
We observed marked improvement in the quality of anthropometric measurements, particularly in length measurement for children <24 months of age. However, the improvement in weight measurement of children less than 5 years was lesser (16.2% at baseline vs 44.6% at end-line).
Findings in the context of other literature
Across the globe, efforts to improve healthcare have shifted from focusing on access to healthcare to improving the quality of healthcare.17 We observed some gaps in outpatient care provided to children, similar to those seen in other settings.
Regarding anthropometry, previous literature has shown that trained staff can perform measurements on children with sufficient quality in primary settings. For example, excellent correlations were noted between clinic staff and gold-standard measurements at Special Supplemental Nutrition Programme for Women, Infants and Children clinics in Southern California18 Similarly, in a study among 16 primary care teams in Toronto, Canada, minimal technical errors were noted in anthropometry of children 0–18 years of age. However, length measurement for children <2 years was particularly prone to error.19
Training programmes for anthropometry have had varying degrees of success. Gupta et al measured the quality of anthropometry for under-5 children in a tertiary care hospital in Atlanta, Georgia. Their outcome measures were ‘implausible’ values of Z-scores, digit preference and dispersion of Z-score results. They observed minimal to no impact of hospital staff training on these indicators. Hospital staff cited bulky measurement equipment and lack of time and space as barriers to accurate measurement.20 Training of community health volunteers (CHVs) in Indonesia through the presentation of the correct method and demonstration of models was found to improve CHV skills and reduce the perceived difficulty in anthropometric measurement.21
Strengths and limitations
This was a one-of-its-kind initiative where QI for under-5 care was implemented at a primary healthcare facility in India. We used a team approach, incorporating suggestions and feedback from various cadres of staff to understand and address problems. We tried to capture information on all eligible children visiting the facility, to minimise selection bias. And, the improvements in our indicators do suggest an improvement in staff competency as well.
Our work has some limitations too. The improvement in indicators postintervention could be partly explained by observer bias, as the data collectors were part of the QI team and were aware of the initiative. Also, there was a lower number of children who had length/height measurements, as compared with those who had weight measurements. This is because, as per the OPD protocol, length and height measurements were not done for children who had already been measured in the past week while weight measurements were done for children in every visit irrespective of the time interval with their previous visit. The frequency of repeat child visits to our facility, therefore, explains the disparity between length/height and weight measurement numbers.
QI is an iterative process, often involving multiple PDSA cycles. Given time and other practical constraints, we could do only one PDSA cycle. However, we have established a system in place for continuous quality monitoring of the anthropometry of all under-5 children coming to the UHC as a mechanism to ensure the sustainability of the intervention. This is being carried out by modes of weekly audits (done by the junior resident posted at the UHC, under the supervision of the senior resident) of the anthropometric measurements done at the UHC and also by providing refresher training to the staff once every 3 months. Further, issues if any related to the QI of all services provided at the UHC are addressed in the UHC monthly meeting held once every month in the department. In the future, we plan to continue the quality assessment of other services provided as well including clinical case management and immunisation services and to address the gaps, as required.
Implications
The information yielded during this initiative would be useful for primary care facilities caring for under-5 children. The facilitators and barriers that we identified would help implement further QI initiatives in similar settings. The assessment form prepared by us can be incorporated into the training of health workers.
Data availability statement
Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online supplemental information.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants and was approved by Institute Ethics Committee, All India Institute of Medical Sciences, New Delhi (vide letter number IEC-957/13.01.2023). Participants gave informed consent to participate in the study before taking part.
Acknowledgments
We hereby acknowledge all the staff of Urban Health Centre, Dakshinpuri for their valuable support to our quality improvement project.
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
X @dr_dsandeep
Contributors AKS: study development, background literature review, oversight of study, collection of data, interpretation of data, preparation of manuscript for publication. SVM: study development, background literature review, oversight of the study, collection of data, interpretation of data, preparation of the manuscript for publication. SD, SS, RMM, RB, PS & PG: study development, background literature review, oversight of the study, collection of data, interpretation of data. RK & BN: provided guidance to all other authors throughout this project from developing the study to PDSA cycle design, to the preparation of the manuscript for publication. The senior authors (RK & BN) accepts complete accountability for the completed work, encompassing the study's execution, data accessibility, and the determination to proceed with publication.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.