reliability and accuracy of anthropometric …
TRANSCRIPT
RELIABILITY AND ACCURACY OF ANTHROPOMETRIC MEASUREMENTS
PERFORMED BY STUDENT NURSES AMONG INFANTS AGED BETWEEN
ZERO TO ONE YEAR IN KILIFI COUNTY HOSPITAL, KENYA
Bogonko Venah Nyakerario
A thesis submitted in partial fulfillment of the requirement for award of a Degree of
Master of Public Health of Pwani University.
May, 2017
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DECLARATION
iii
DEDICATION
This work is dedicated to my husband Deus and my daughter, Gainnah-ameliah Zureil.
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ACKNOWLEDGEMENTS
I would like to express my gratitude to all who have given me support in doing this work
and above all the almighty God. I wish to express deep gratitude and sincere
appreciation to the student nurses, nutritionist, parents/caretakers and infants who
participated in the study.
I wish to specially thank my supervisors Dr M.K Mwangome and Prof. Jameela
Hassanali for continued support and guidance. I appreciate the input of Dr Mwachai
Sophia in data collection and analysis.
Thanks to the lecturers in the School of Health and Human Science for their guidance
and contribution in my study.
Thanks to Dr. Moses Ngari who gave me his input during data analysis and my
classmates (especially Mr.Opiyo) for their support.
I also express my gratitude to Kilifi County, the Management of Kilifi County Hospital
and Maternal Child Health clinic for allowing me to conduct this research at the
hospital.
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ABSTRACT
Anthropometric measurements are inexpensive and non-invasive way of assessing
nutritional status of children. Reliable and accurate anthropometric data is fundamental
in monitoring and improving health status of children. Despite the resources, procedures
and training on growth monitoring, little research has been undertaken to assess the
reliability and accuracy of these measurements among trainee nurses. Unreliability of
these measurements may lead to misclassification and inappropriate interventions.
The objectives of the study were to assess the inter-observer reliability and accuracy of
weight, height, Mid Upper Arm Circumference (MUAC) and their indexes among
infants performed by student nurses in Kilifi County Referral Hospital.
A cross-sectional study design was applied to establish the reliability and accuracy of
anthropometric measurements taken by student nurses. Six students who had already
undergone training in anthropometry and had three months practical experience in
Maternal Child Health Clinic (MCH) were identified and divided into 2 groups. They
were asked to repeatedly measure weight, height/length and MUAC of 320 infants. On
every 5thchild (n=60) an additional measurement was taken by a gold standard
nutritionist to evaluate their accuracy. Intra-Class Correlation (ICC) coefficient was
calculated to evaluate reliability, mean difference and Pitman’s statistic to assess level of
accuracy.
The Intra-Class Correlation (ICC) coefficient among student for all anthropometric
measurements was above 0.9 which indicated near perfect reliability. The student nurses
accurately measured weight, length, Weight-for-Length (WFLz) and Weight-for-Age z
score (WFAz). The mean difference in student nurses measurement compared to expert
for Mid Upper Arm Circumference (MUAC) was -0.096(95% CI -0.141 to -0.050 cms),
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Height-for-Age z score (HFAz) was 0.006 (95%CI-0.014 to 0.025 z scores) and
MUACz was 0.078(95%CI -0.122 to -0.033 z scores). There was evidence that the
variance of paired measurements of MUAC, HFAz and MUACz differed from those of
the expert (pitman statistic <0.05).
Student nurses who had undergone theoretical training but had minimal supervised
practical experience in anthropometry reported high reliability and accuracy scores for
all anthropometric measurements assessed in this study. However, more studies in
different settings would need to be done to enhance generalizability of findings.
Key words: reliability, accuracy, anthropometric measurements, infants and
malnutrition.
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TABLE OF CONTENTS
DECLARATION ............................................................................................................... ii
DEDICATION .................................................................................................................. iii
ACKNOWLEDGEMENTS .............................................................................................. iv
ABSTRACT ....................................................................................................................... v
LIST OF TABLES ............................................................................................................ ix
LIST OF FIGURES ........................................................................................................... x
LIST OF ABBREVIATIONS AND ACRONYMS ......................................................... xi
DEFINITION OF TERMS ............................................................................................. xiii
CHAPTER ONE ................................................................................................................ 1
INTRODUCTION ............................................................................................................. 1
1.1Background Information ........................................................................................... 1
1.2 Problem Statement ................................................................................................... 2
1.3 Justification .............................................................................................................. 4
1.4 Study Objectives ...................................................................................................... 5
1.4.1 General Objective .............................................................................................. 5
1.4.2 Specific objectives ............................................................................................. 5
1.5.1 Alternative/Research Hypothesis (H1) .............................................................. 6
CHAPTER TWO ............................................................................................................... 7
LITERATURE REVIEW .................................................................................................. 7
2.1 Introduction .............................................................................................................. 7
2.2Weight-for-age (WFA) .............................................................................................. 8
2.3 Height-for-age (HFA) .............................................................................................. 9
2.4 Weight-for-height /length (WFH/L) ...................................................................... 10
2.5 Mid - Upper Arm Circumference (MUAC) ........................................................... 12
2.6 Types of measurement errors ................................................................................. 13
CHAPTER THREE ......................................................................................................... 17
MATERIAL AND METHODS ....................................................................................... 17
3.1The study site .......................................................................................................... 17
3.2 Study design ........................................................................................................... 18
3.3 Target population ................................................................................................... 18
3.3.1 Inclusion criteria .............................................................................................. 19
3.3.2 Exclusion criteria ............................................................................................. 19
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3.4 Sampling method.................................................................................................... 20
3.5 Sample size ............................................................................................................. 20
3.6 Data collection procedures ..................................................................................... 21
3.7 Data Management .................................................................................................. 24
3.8 Data Analysis ......................................................................................................... 24
3.9 Ethical Considerations............................................................................................ 25
CHAPTER FOUR ............................................................................................................ 27
RESULTS ........................................................................................................................ 27
4.1 Expert intra observer reliability.............................................................................. 27
4.2 Student Nurses........................................................................................................ 28
4.2.1 Reliability ........................................................................................................ 28
4.2.2 Accuracy .......................................................................................................... 32
CHAPTER FIVE ............................................................................................................. 39
DISCUSSION .................................................................................................................. 39
5.1 Discussion .............................................................................................................. 39
CHAPTER SIX ................................................................................................................ 45
CONCLUSION ................................................................................................................ 45
6.1 Conclusion .............................................................................................................. 45
6.2 Recommendations .................................................................................................. 45
REFERENCES ................................................................................................................ 47
Appendix i- Work Plan ....................................................................................................... i
Appendix ii-Budget ............................................................................................................ ii
Appendix iii (a)-Participant Information Sheet (Parent/Care Taker/Guardian) ............... iii
Appendix iii (b)-Participant Information Sheet (Nutritionist/Student Nurses)................. ix
Appendix iv -Data Collection Tools. ............................................................................... xii
Appendix v- Authorization Letter ................................................................................... xiii
Appendix vi : Ethical Approval .................................................................................... xiv
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LIST OF TABLES
TABLE 2.1: INTERPRETATION OF MID UPPER ARM CIRCUMFERENCE ................................ 13
TABLE 4.1: INTRA-CLASS CORRELATION COEFFICIENT (ICC) MEASURES BY EXPERT
NUTRITIONIST ................................................................................................................... 28
TABLE 4.2: MEAN ANTHROPOMETRIC MEASUREMENTS AND LEVEL OF MALNUTRITION .... 29
TABLE 4.3: ACCURACY OF MEASUREMENTS BY STUDENT NURSES COMPARED TO AN
EXPERT FOR ALL INFANTS ................................................................................................. 33
TABLE 4.4: ACCURACY OF INFANTS MEASUREMENTS BY STUDENT NURSES COMPARED TO
AN EXPERT BY AGE ............................................................................................................ 36
TABLE 4.5: ACCURACY OF INFANTS MEASUREMENTS BY STUDENT NURSES COMPARED TO
AN EXPERT BY NUTRITIONAL STATUS. ............................................................................... 38
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LIST OF FIGURES
FIGURE 3.1: FLOW CHART OF STUDY PROCEDURES ........................................................... 23
FIGURE 4.1: OVERALL INTRA-CLASS CORRELATION COEFFICIENT (ICC) MEASURES BY
STUDENT NURSES (LINE AT ICC=0.6 SHOWS MINIMAL ACCEPTABLE RELIABILITY) ........... 30
FIGURE 4.2: AGE RELATED INTRA-CLASS CORRELATION COEFFICIENT (ICC) MEASURES BY
STUDENT NURSES (LINE AT ICC=0.6 SHOWS MINIMAL ACCEPTABLE RELIABILITY) ........... 31
FIGURE 4.3: BLAND-ALTMAN PLOT OF THE MEAN DIFFERENCES OF MUAC (EXPERT -
STUDENT NURSES) MEASUREMENTS OF OBSERVERS COMPARED TO AVERAGE OF THEIR
PAIRED READINGS ............................................................................................................. 34
FIGURE 4.4: BLAND-ALTMAN PLOT OF THE MEAN DIFFERENCES OF HFAZ (EXPERT -
STUDENT NURSES) MEASUREMENTS OF OBSERVERS COMPARED TO AVERAGE OF THEIR
PAIRED READINGS ............................................................................................................. 34
FIGURE 4. 5: BLAND-ALTMAN PLOT OF THE MEAN DIFFERENCES OF MUACZ (EXPERT -
STUDENT NURSES) MEASUREMENTS OF OBSERVERS COMPARED TO AVERAGE OF THEIR
PAIRED READINGS ............................................................................................................. 35
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LIST OF ABBREVIATIONS AND ACRONYMS
CI - Confidence Interval
CMS - Centimeters
GM-Growth Monitoring
HFA - Height-for-Age
ICC - Intra class correlation coefficient
KDHS -Kenya Demographic and Health Survey
KGS - Kilograms
KMTC-Kenya Medical Training College
LFAz-length for Age z score
MCH - Maternal Child Health
MCH/FP - Maternal Child Health /Family Planning
MDG’S – Millennium Development Goals
MOH – Ministry of Health
MUAC - Mid-Upper Arm Circumference
NCHS- National Center for Health Statistics
R-Coefficient of Reliability
SAM-Severe Acute Malnutrition
SD-Standard Deviation
TEM- Technical Error of Measurement
TEM-Technical Error of Measurement
UNICEF- United Nations Children Fund
WFA -Weight-for-age
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WFAz-weight for Age Z score
WFH -Weight-for-Height
WFLz- Weight for Length Z score
WHO-World Health Organization
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DEFINITION OF TERMS
Accuracy: Extent to which the “true” value of a measurement is attained and mostly
affected by systemic bias; it involves providing a true measurement of the attribute
being measured (Sackett, 1975). In this study accuracy of student nurses will be assessed
by comparing their measurements with those taken by a qualified nutritionist that will
act as the standard.
Reliability: Repeated measurements giving the same value. It’s also the degree of
replicability among independent measurements of the same true value (Myatt et al,
2006). This study will focus on inter-observer reliability.
Validity: the extent to which a measurement correctly measures what it is supposed to
measure or to which extent the findings of an investigation reflect the truth (Millodot,
2009).
Anthropometric assessment is a nutritional assessment method that involves the use of
measurements of the physical dimensions of the body of an individual and comparing
these dimensions to a group of healthy individuals of similar age and sex in order to
identify deviations and use them to classify nutritional status of the individual (Jelliffe,
1966).
Anthropometric Indices/index: a combination of two anthropometric measurements
for example weight and height, which are necessary for the interpretation and grouping
of measurements (WHO, 1995).
Malnutrition: Individuals are considered malnourished if their diet does not provide
adequate calories and protein for growth and maintenance or they are unable to fully
utilize the food they eat due to illness. They are also malnourished if they consume too
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many calories. Thus, malnutrition is excess (over nutrition) or deficit (under nutrition) of
one or more components of a balanced diet (UNICEF, 2005).
Overweight; is defined as abnormal or excessive fat accumulation that may impair
health (WHO, 2011).
Stunting; it is having a length/height-for-age z score (HFAz), of less than -2 z scores. It
is a chronic form of under nutrition that signifies slowing in skeletal growth rate and
represents accumulated consequences of retarded growth associated with poor growth
(World Health Organisation, 1976).
Underweight; is defined as having a weight-for-age z score (WFAz) of less than -2 z
scores. It indicates a deficit in total body weight compared to that expected for a child of
the same age as a result of either failure to gain weight or loss of weight (World Health
Organisation, 1976).
Wasting; it is low weight-for-length z scores (WFLz) of less than -2 z scores. It is an
acute form of under nutrition that indicates a deficit in lean tissue and fat mass when
compared to that expected for a child of the same height who is normally nourished and
may result from failure to gain weight, or from loss of weight (WHO, 1976).
Zscore (or SD-score); the deviation of an individual’s value from the median value of a
reference population, divided by the standard deviation of the reference population (de
Onis & Blossner, 1997).
1
CHAPTER ONE
INTRODUCTION
This chapter presents; the background of the study, statement of the problem,
significance of the study, study objectives and hypothesis.
1.1Background Information
Malnutrition in all its forms is closely linked directly or indirectly to major causes of
mortality and morbidity. Worldwide, in 2011 about 101 million children under five
years of age were underweight (WHO, 2013). Currently 159 million children below five
years are stunted, while 50 million are wasted. In addition, about 41 million children are
obese or overweight (WHO, UNICEF & World Bank 2015). Malnutrition contributes
significantly to disease and death (Caulfield et al, 2004) therefore, it’s important that
physicians caring for children carry out nutritional screening and assessment to aid in
early diagnosis and timely interventions.
In Kenya, it is estimated that 2.1 million under five children are stunted. This is a serious
development concern as these children will never reach their full mental and physical
potential. As in many other parts of the world, children living in rural areas and those
from poor families are more likely to be malnourished (KDHS, 2008/2009). For Kilifi
county the recently conducted Kenya Demographic and Health Survey (2014/2015)
revealed an underweight rate of 16.9%; the stunting and wasting rates in the county
standing at 39% and 4.1% respectively.
This stunting rate places the county to be the third highest countrywide. This means
nearly two out of every five children below five years are too short for their age, an
irreversible growth phenomenon if not addressed before the age of two years. Stunting is
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a well-established risk indicator of poor child development and if it occurs before the
age of 2 years it predicts poorer cognitive and educational outcomes in later childhood
and adolescence (Michelle & Linda, 1999).
According to United Nations Children Fund (UNICEF) conceptual framework, the
immediate causes of malnutrition are inadequate food intake and infections, while the
underlying causes include household food insecurity, inadequate health services and
poor maternal/child practices (Black et al, 2008).
The prevalence of childhood undernutrition is estimated using anthropometric indices.
Of the three forms of undernutrition, underweight (low weight-for-age) and wasting
(low weight-for-height/Length) are more directly related to acute food shortage ,while
stunting (low height/length-for-age) mostly indicates chronic food shortage (de Onis et
al, 2000). The Mid Upper Arm Circumference (MUAC) is a simple, non-invasive and
inexpensive anthropometric tool used to diagnose Severe Acute Malnutrition (SAM) in
children aged 6 to 59 months and is a better predictor of mortality compared to weight
for age. The cut off point for MUAC that indicate malnutrition is 12.5cm (Myatt et al,
2006, WHO, 2009).
Measurement errors commonly associated with anthropometric measurements include;
inaccuracy and unreliability (Ulijaszek & Kerr, 1999).
Accurate and reliable estimates of undernutrition are required to facilitate policy
evaluation and direct appropriate interventions (Bejon et al, 2008, Black et al, 2008).
1.2 Problem Statement
Of 24 million children born each year in Africa, 4 million (16.6%) will not survive to
see their fifth birthday, even though over 50% (half) of these deaths could be prevented
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through growth monitoring, timely interventions and immunization (Adenike &
Adeyele, 2011).
Kilifi is a rural and poor County in Kenya. According to World Bank (2008) “Kilifi was
the second poorest county in Kenya with an estimated 67% of people living below the
poverty line”. In addition, about 500 cases of Severe Acute Malnutrition (SAM) are
admitted to the Kilifi County Referral Hospital every year (Mwangome et al, 2012).
Inadequate nutrition among children aged below five years is a challenge in Kilifi
County. Undernutrition specifically stunting is highly prevalent in Kilifi (Muinde,
2011). Accordinglyaddressing malnutrition in Kilifi and other similar counties in Kenya
is of great importance in enhancing child development. Anthropometric measurements
are used widely for assessment of nutritional status and diagnosis of malnutrition
(Antwi, 2008). These measurements have to be reliable and accurate.
Growth Monitoring (GM) is a process by which children are routinely
anthropometrically screened with the aim of correctly identifying children that would
benefit from early intervention (Garner et al, 2000). In Kenya; GM activities are mainly
undertaken by nurses in Mother-Child Health (MCH) clinic during vaccination visits.
A study in Bethlehem carried out by Harris et al (1999) to evaluate the relative accuracy
of weight measurements among pediatric patients taken by nurses compared to those
taken by physicians showed that the measurements taken by the nurses and physicians
were significantly unreliable. Another study by Strandgren et al (2001) showed that
measurements of height, weight and head circumference of infants by experienced
nurses had markedly large differences in their individual results hindering appropriate
advice and referral. According to Tilley, (2010), 36% of nurses were unhappy with the
anthropometric training received during training and they attributed this to a lack of
practical training and experience, especially in measuring infants less than six months.
4
Although the results of these studies indicate that nurses may need additional training in
anthropometry and may need to have their skills on anthropometry evaluated before they
qualify to practice, none of these studies was done in Kenya.
It is for this reason that this study aimed to evaluate the accuracy and reliability of
measurements taken by student nurses in 3rd rotation (last rotation) in MCH clinic in
Kilifi county Referral hospital.
The intention of the study therefore was to establish the reliability and accuracy in
nutritional assessments that proceeds timely and evidence based nutrition related
interventions among children.
1.3 Justification
Majority of childhood deaths occur within the first years of life (United Nations, 2001).
Malnutrition is a concern amongst children under five years because it contributes to
increased infant mortality rate (Muller, 2005).
In Kenya, the Ministry of Health (MOH) is the primary health care service provider. It
has a mandate to train mid-level health care workers that include nurses who are the
main health care providers for the greater part of the population. The training includes
standard learning experiences, theory and practice and it takes place in Medical Training
Colleges (KMTC) and health care facilities. Experienced health care providers in these
setting provide needed guidance and supervision to these trainees, (KMTC ACT,
1990/1991).
Anthropometry is one of the key skills applied during the MCH clinic rotation; however,
at the moment, this skill is usually not formally evaluated. It is assumed that the theory
5
and practical lessons undertaken by the students during training are sufficient to result in
reliable and accurate measurement and interpretations.
The study therefore aimed to test this assumption by formally evaluating the reliability
and accuracy of anthropometric measurements taken by student nurses.
The results will be useful in improving practical training of anthropometry during MCH
rotation, and also to identify training gaps in anthropometry for qualified nurses.
1.4 Study Objectives
1.4.1 General Objective
To determine the reliability and the accuracy of anthropometric measures taken by
student nurses among infants in Maternal Child Health Clinic (MCH) in Kilifi County
Referral Hospital (KCRH).
1.4.2 Specific objectives
The objectives of the study were;
1. To determine level of reliability of anthropometric measurements taken by student
nurses in MCH clinic in Kilifi County Referral Hospital (KCRH).
2. To determine level of accuracy of anthropometric measures taken by student nurses
compared to those of a qualified anthropometrist in MCH clinic in Kilifi County
Referral Hospital.
3. To assess the occurrence of anthropometric measurement errors by student nurses and
the effects of these errors on nutritional classification of children in the MCH clinic in
Kilifi County Referral Hospital (KCRH).
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1.5 Null Hypothesis (H0)
Anthropometric measurements taken by student nurses among infants (from birth to one
year) in Kilifi County Referral Hospital are accurate and reliable.
1.5.1 Alternative/Research Hypothesis (H1)
Among infants (from birth to one year) in Kilifi county referral Hospital Measurement
error is higher in anthropometric measurements taken by student nurses.
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CHAPTER TWO
LITERATURE REVIEW
This chapter reviews the literature on anthropometry and anthropometric measurement
errors obtained from published internet Journals and books
2.1 Introduction
Undernutrition contributes 35% of global under-five mortality (WHO, 2013). In Kenya
it remains a priority public health problem that has led to increased loss of labour-output
and death rate. The burden of malnutrition in Kenya is a risk to achieving Sustainable
Development Goals (SDGs) and Vision 2030 that need to be realized through reduction
of mortality rate that includes infant mortality. In addition, malnutrition imposes
significant and long-term economic and human development costs. Reducing
malnutrition in Kenya is not just a health concern/ moral imperative, but also a rational
long-term investment. It requires political goodwill to enhance multi- sector approach
that increase awareness of the important role of nutrition in ensuring a healthy and
productive population (Kenya Nutrition Plan of Action, 2012-2017). According to
KDHS report 2008/2009 “adequate nutrition is significant to child development and the
period from conception to 2 years of age is important for optimal growth and
development.” Thus individuals should be motivated counseled and provided with
health education to embrace good nutrition from birth.
Growth monitoring mainly entails taking routine measurements to detect abnormal
growth coupled with implementation of an appropriate action plan when abnormalities
are detected (Garner et al, 2000).Growth monitoring is used as part of health promotion,
to discuss feeding practices, hygiene, and other aspects of the child’s health and
behavior (Hall, 1996). Growth monitoring has also been used as an avenue to reassure
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parents/guardians on their child’s development, growth, and convince them of the value
of good nutrition (Garner et al, 2000). Nutritional status can be assessed using different
approaches; by use of clinical signs and symptoms, biochemical indicators, dietary
assessment and anthropometry.
Anthropometry is the study and technique of taking body measurements, especially for
use on a comparison or classification basis (Cogill, 2003). Anthropometry is a source of
important indicators of children’s nutritional status (KDHS, 2014/2015).Anthropometric
indices are used as the main criteria for assessing the nutritional status of children (same
age and sex) by comparing them to a reference growth chart (WHO, 1995).
Anthropometry has a significant advantage over other nutritional indicators; whereas
biochemical and clinical indicators are helpful only at the extremes of malnutrition,
body measurements are sensitive over the various severities of malnutrition. In addition,
anthropometric measurements are inexpensive, relatively easy to obtain and non-
invasive (de Onis et al, 2000).
Its limitations include the length of time needed to take measurements and the level to
which measurement error can influence interpretation that may lead to missed
opportunity of diagnosing malnutrition (Ulijaszek & Kerr, 1999).
Forms of malnutrition that can be assessed anthropometrically include; underweight,
wasting, stunting and overweight (Cogill, 2003).
2.2Weight-for-age (WFA)
Underweight for a specified age and sex is identified through low weight-for-age index.
This index reflects both chronic and acute undernutrition (Cogill, 2003). This means that
an underweight child is almost always also wasted or stunted. Underweight rates are
estimated from the WFA index values.
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The WFA indices are expressed as Z-scores according to both NCHS and WHO
references. The complete analysis is done with the WHO reference (WHO&UNICEF,
2009).
Underweight indicates a deficit in total body weight compared to that expected for a
child of the same age as a result of either failure to gain weight or loss of weight (WHO,
1976). Severe underweight is defined by WFAz of< -3 standard deviation (SD) and
moderate underweight is defined by WFAz of between -2 to -3 SD (WHO&UNICEF,
2009).
It is commonly used to monitor growth and assess change in the magnitude of
malnutrition. It is sensitive to small changes in weight. Growth monitoring charts and
weighing scale are the only tools needed to quickly take the measurements (Cogill,
2003).
In Kenya 11 percent of the children below five years are underweight, while 2 percent
are severely underweight (KDHS, 2014/2015).
2.3 Height-for-age (HFA)
Height- for- age is used for children above 2 years, while those below 2years the term
length -for-age is used. Low length-for-age, resulting from hindered growth and inability
to achieve expected length as compared to a healthy, nourished child of the same age
and sex is called stunting (WHO, 1976).
Stunting rates are estimated from the Height for Age (HFA) index values. The HFA
indices are expressed in Z-scores according to WHO references. The complete analysis
is done with the WHO reference (WHO&UNICEF, 2009).
When a child has Low HFA compared to a child of the same age and sex in the
reference population, it is known as shortness. Height for age is mainly used as a
10
population indicator rather than an indicator for individualized growth monitoring
(Waterlow, 1972).
According to WHO (2009) guidelines for the results expressed in Z-score: Severe
stunting is defined by HFAz< -3 SD and moderate stunting is defined by HFAz between
-2 to -3 SD.
Challenges associated with this measure include: “the age of the child has to be known
and routine growth monitoring does not include height hence stunting is not usually
assessed”.
The length of children less than six months old can be measured with height boards, but
very small infants are difficult to handle and extra care needs to be exercised when
taking their height (Myatt et al, 2006).
2.4 Weight-for-height /length (WFH/L)
Low weight-for-height/length is important to diagnose children affected by acute
undernutrition especially when exact ages are difficult to establish or records are missing
(Cogill, 2003).
Acute malnutrition rates are estimated from the Weight for Height (WFH) index values
combined with the presence of edema. The WFH indices are expressed in both Z-scores
and percentage of the median, according to WHO references (WHO, 2006). The
complete analysis is done with the WHO reference.
Weight-for- height measures body weight in relation to height, and has the advantage of
not requiring age information.
Severe cases of low WFH are known as wasting. Wasting occurs due to: starvation,
diarrhea and chronic conditions/illnesses (Waterlow, 1972).
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Severe malnutrition is defined by WFH z < -3 SD and/or existing bilateral edemas on
the lower limbs. Moderate acute malnutrition is defined by WFHz between -2 to -3 SD
and no edema, while Global acute malnutrition is defined by WFHz< -2 SD and/or
existing bilateral edema (WHO, 2009).
The weight for height index is the most appropriate index to quantify wasting in a
population in emergency situations where acute forms of malnutrition are the
predominant pattern, (WHO, 2009).
When Weight for Height/length index is used to classify children’s nutritional status it
can lead to misclassification where by stunted children with reasonable body proportions
are classified as not malnourished but, children who are short (stunted) and also thin will
be classified as wasted, rather than stunted. Mostly in clinical set up where height
measurements are taken, they are rarely computed on any reference growth chart for
nutritional assessment (WHO, 1995). Myatt et al (2006) suggested that anthropometric
measurements such as weight or height alone are meaningless unless they are interpreted
on a reference chart with respect to age. Weight for height based indicators used alone
(without examination of edema of lower limbs) are poor at detecting cases of
Kwashiorkor, since the weight of retained fluid tend to mask what would otherwise be
low WFH values.
In another study by English et al (2004) to assess pediatric inpatient care in referral level
hospital in Kenya they established that height was hard to measure accurately in children
and also the precision of weight depends on the functioning and calibration of the
weighing scale. Length measurement needs more training and expertise as there is more
chance of error that will result in misclassification of children during growth monitoring
(Nabarro, 1986).
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Four percent of Kenyan children are moderately wasted (WFL of -2 to -3z score), while
one percent of children are severely wasted. The prevalence of wasting is highest among
children 6-11 months (KDHS, 2014/2015).
2.5 Mid - Upper Arm Circumference (MUAC)
Mid-upper arm circumference is simpler to measure and a good predictor of impending
risk of death (Myatt et al, 2006; Berkey et al, 2005) it is also a more sensitive
anthropometric measurement hence it identifies more children who are malnourished
compared to other anthropometric measurements.
Initially, MUAC was used for quick screening of acute malnutrition among children
aged 6-59 month (Cogill, 2003). However, at the moment, the Mid-upper arm
circumference is recommended alongside WFLz for assessing and diagnosis of acute
child under-nutrition in children aged 6 to 59 months old (WHO, 2009). Among children
aged 6 to 59 months, Mid Upper Arm Circumference alone is used to diagnose acute
undernutrition (WHO & UNICEF 2009; Berkley et al, 2005). Health workers especially
in rural / community setting use MUAC in diagnosing of acute undernutrition because
it’s cheap, noninvasive, easy and objective and more acceptable (Pelletier, 1994).
According to WHO& UNICEF (2009) the guidelines for interpretation are shown in
Table 2.1.
13
Table 2.1: interpretation of Mid Upper Arm Circumference
MUAC Readings Degree of Malnutrition Risk of Mortality
<115mm Severe malnutrition High risk of Mortality
>115mm but<125mm Moderate malnutrition Moderate risk of
Mortality
125mm but<135mm Risk of malnutrition Low risk of mortality
>135mm Good Nutritional Status Low risk of morality
Tasmin et al (2012) proposes that at facility level systematic case finding using MUAC
should be used to identify undernourished children and also the readings should be
recorded in millimeters as opposed to centimeters for accuracy purposes.
Mid-upper arm circumference used alone is more sensitive and more specific in
diagnosing malnutrition for children with pedal edema than both WFH and WFA
(Sandiford &Paulin, 1995; Berkley et al, 2005) because the edema will give additional
weight as a result of fluid retention.
In a study carried out to establish the relationship between child anthropometry and
mortality by Pelletier (1994) MUAC was identified as a simple, objective and cost
effective way of assessing nutritional status.
2.6 Types of measurement errors
Mwangome et al (2012) identified that anthropometric measurement errors are common
in routine practice and such errors influence interpretation. They can occur due to faulty
instrument or improper taking of measurement. Availability of well calibrated
instruments that are efficient improves capacity to assess infants accurately. However
the outcome will also depend on the competency of the person taking the measurements
14
(Tilley, 2014). Anthropometric measurement error can’t be avoided but should be
minimized. The measurement errors have a serious effect on population comparison and
growth assessment (Cameron, 1984). Published anthropometric data must be used for
comparative purposes, information on the magnitude of measurement error is important
in interpreting results of statistical tests.
Measurement error has predominantly two types of effect on the quality of the data
collected (Habicht et al, 1979), the effects limit the extent to which measurements depart
from true values and repeated measures give the same value.
Various terms are used to describe anthropometric measurement errors. These include:
unreliability (Habicht et al, 1979) imprecision, undependability and inaccuracy
(Heymsfield et al, 1984). These terms are sometimes used interchangeably although they
statistically carry different meanings.
Unreliability is variability within-subject. Imprecision is the variability of repeated
measurements, and is due to inter- and intra -observer differences in measurement. The
greater the variability between repeated measurements of the same subject or variability
of two or more observers, the lower the precision (Norton &Olds, 1996). Accuracy is the
extent to which the ‘true’ value of a measurement is attained (Mueller & Martorell,
1988).
Researchers have used various ways to assess reliability of measurements. In
determining accuracy and reliability of measurements Mueller & Martoller (1988)
identified Technical Error of Measurement (TEM) as the commonly used measure of
variability. Technical Error of Measurement is the square root of measurement error
variance. Smaller values of TEM imply better replicability of measurements. The use of
15
coefficient of reliability(R) is another method of determining reliability. The coefficient
of reliability ranges from 0 to 1.The closer the R value is to one the lesser the variability
between observers. Reliability measures can also be presented using Intra class
correlation coefficient (ICC). It ranges from 0 to 1, where ICC of 0 indicates no
reliability,> 0 but < 0.2; slight reliability,> 2 < 0.4; fair reliability, > 0.4< 0.6; moderate
reliability, > 0.6 > 0.8 indicates substantial reliability and ICC = 1 shows almost perfect
reliability, (MUSC, 2006).When comparing measurements of the same subject the closer
the ICC value is to 1 the better the reliability.
Inaccuracy is systematic bias, and may be due to errors of measurement technique or
instrument error. Validity is the extent to which a measurement actually measures a
given characteristic (Norton & Olds, 1996).It is difficult to assess accuracy of
measurements taken by an individual because the true value is unknown. Hence, to
assess accuracy it entails comparing the values taken by a well-trained supervisor and
the observer being evaluated (Mueller & Mortorell, 1988). However, experience doesn’t
guarantee accuracy and may result in biasness.
Approaches to minimize measurement errors include; appropriate staff training on
anthropometric procedures, following measuring protocol and routine checking of
instruments/equipment like for proper calibration are important.
To obtain accurate measures of weight, children aged less than six months should be
weighed on specialist pediatrics scales that are graduated in units of ten grams rather
than on conventional hanging scales that are graduated in units of hundred grams
because children under 6 months of age weigh only a few kilograms (Myatt et al, 2006).
According to Feeney, 2004, majority of errors were made in recording Mid Upper Arm
Circumference (MUAC) values rather than in deciding whether MUAC values fell
below or above a threshold value. Thus, to minimize these errors enough time should be
16
given for documentation. Tasmin et al (2012) recommended that MUAC should be
recorded in millimeters for accuracy purposes. In a study carried out by Tilley (2014) to
investigate anthropometric training by Non-governmental Organizations (NGOs) she
identified that measurement error can affect reliability and accuracy but can be
minimized by training and standardization.
In the assessment of reliability of anthropometry performed by community drawn
anthropometrist in rural Ethiopia Ayele et al (2012) concluded that measurements of
height and weight were more reliable than those of MUAC. In addition, reliability was
better in older children compared to younger ones. Another study carried out in
Malaysia identified that public health nurses less reliably and accurately took length
measurements compared to weight among children less than two years old (Jamaiyah et
al, 2010).
Reliability and accuracy of student nurses’ anthropometry is a key skill that needs to be
formally evaluated to test the assumption that theory and practical sessions on
anthropometry as per the curriculum results to accurate, reliable measurements and
interpretation. Most of the anthropometric measurement studies have been done on older
children and assessed other cadres of health workers hence, measurement errors
obtained in these studies may not be applicable to a similar study done on infants thus
the need for this study.
17
CHAPTER THREE
MATERIAL AND METHODS
3.1The study site
This study was carried out in Kilifi County Referral Hospital. Kilifi Country was formed
in 2010 as a result of a merger of Kilifi, Malindi, Ganze and Kaloleni Districts. It
borders the counties of Tana River to the North, Taita Taveta to the West, Mombasa and
Kwale to the South and the Indian Ocean to the East. Based on the 2009 Kenya
Population and Housing Census, the county had about 200 000 households and is home
to 1,109,735 people which accounted for 2.9 percent of the total Kenyan population
(Kenya Census, 2009). The population growth rate is reported to be 3.05 %. The total
fertility rate for the county was at 5.1 % (KDHS, 2014). Children aged 0-14 years make
the majority (47%) of Kilifi population. The rest 15-34 years (33 %), 35-64 years (17 %)
over 65 years (4 %) (Kenya Census, 2009). The main communities within Kilifi town
and county include Mijikenda, Swahili, Bajuni, Indians and Arabs. The weather is
generally warm throughout the year (above 25 degrees) with two seasons of moderate
rainfall (about 800-1000mm). Long periods of rain start around March and last into July,
while the short rains start around October and last until December. Economic activities
include industrial investments, tourism, small scale farming, fishing and business
(Wekesa et al, 2006). Kilifi town is the country headquarters of the Kilifi County and
has a population of 122,899 (Kenya Census, 2009). Kilifi County hospital located within
Kilifi town is a referral center for most health centers and dispensaries in the county.
The MCH clinic within KCRH provides maternal child health services that include
family planning services, antenatal services and child health services. The child health
services include: growth monitoring and/nutritional assessment for under-five children,
18
immunization, outpatient management of childhood illnesses, nutritional counseling,
health education and referral of children with malnutrition and severe illnesses for
further management in the wards. The flow of clients in child wellness clinic starts at the
health education/waiting bay to the weighing area then data room at the entry point to
the nutritional counseling room where vitamin A supplements and anthelminthic drugs
are given after which the clients move to the immunization room and they exit through
exit data entry room and nutritional review for those who are malnourished. Each room
is managed by one qualified nurse and student nurses on duty, while the nutrition review
area is managed by a nurse, nutritionist and students (nurses and nutritionist) .The
average number of children seen daily in the clinic is 100.
3.2 Study design
A cross-sectional survey study design was used in this study. The data was collected
within a period of 2weeks that sought to describe the level of reliability and accuracy of
anthropometric measurements carried out by student nurses among infants in Kilifi
County Referral Hospital.
3.3 Target population
The study targeted student nurses on their 3rd year of training, who were on placement at
MCH during the data collection period, a qualified practicing nutritionist with at least
5years minimum experience in taking anthropometric measurements in different
scenarios to be the gold standard to which the student’s measurements were to be
compared in determining accuracy and reliability. The target also involved
parents/caretakers with children aged less than one year who visited MCH clinic during
the data collection period.
19
3.3.1 Inclusion criteria
Infants
Clinically stable/non-sick infants, who were accompanied by consenting parents
/guardians and had attended MCH clinic for growth monitoring during the data
collection period.
Student nurses
Student nurses had to be in their third year of training, be on their 3rd rotation in MCH
clinic and had to give consent to participate in the study.
Qualified Nutritionist
A consenting qualified nutritionist working in MCH, with at least 5years experience in
anthropometry screening in all ages.
3.3.2 Exclusion criteria
Infants
Sick infants, children more than 12 months and those whose parents/guardian declined
to give consent.
Student nurses
Students not completed the level of training specified in the inclusion criteria
Student nurses who declined to give consent.
Qualified Nutritionist
Nutritionist less than 5years experience in anthropometry.
Refusal to consent to participating in the study.
20
3.4 Sampling method
All student nurses undergoing their 3rd MCH clinic placement were eligible for
participation. Simple random sampling method was used to select the 6 student nurses
who participated in the study. Purposive sampling was used in selecting the qualified
nutritionist (where only one nutritionist who met the inclusion criteria was selected).
The infant samples were selected using convenient sampling method from parents/care-
givers attending MCH clinic.
3.5 Sample size
In 1998, Walter et al, (1998) developed a formula to calculate the required number of
subjects (k) when assessing reliability of continuous measures using the intra class
correlation coefficient (ICC). The ICC is a number between 0 and 1.An ICC closer to 1
signifies perfect reliability, while that below 0.2 signifies poor strength of agreement and
hence unreliable. The sample size calculation method described by Walter et al., was
used to determine the sample size of the infants who were recruited for the study. An
ICC of less than 0.4 was defined to be of insufficient reliability (po) and the expected an
ICC of above 0.6. Choosing an alpha value of 1.64 (one sided at 5 % significance; p
value of 0.05) and beta value of 1.28 (power = 90%).In the study the number of
observations was 3(n=3).
Sample size for infants below 6 months and those above 6 months was calculated
separately using the formula.
k = 1 +2(Uα + Uβ) 2 n ………….Equation 1.
(1nC0)2(n-1)
21
Where; - Po = as ICC of 0.4(fair agreement); P1 = 0.60 ICC (expected ICC value
moderate agreement), since null hypothesis (H0) =alternative hypothesis (H1) at 95%
confidence interval; Uα=1.6449;Uβ= Power of 90% (1.28); 1nC0 = 0.54 derived from a
previous equation 12(Walter et al, 1998).
Fitting the components into the formulae; k = 1 + (2 (1.64+1.28)2 x 3)/ (natural log
0.54)2 x (3-1)
A total number of 71 infants in each age group were sufficient for 3 observers to show
that an ICC of 0.6 (expected) or greater was truly different from that of 0.4(unreliable).
To allow for a possible drop out, the study aimed to recruit 80 infants in each age group
(<6 months and>6 months). The study considered 2 groups of observers (3 observers in
each group) and 2 groups of infants (80 in each group) hence the sample size was 160
infants for each group of 3 observers making a total of 320 infants.
3.6 Data collection procedures
One day refresher training for the nutritionist (mainly revision on the theory and
discussions on how to minimize error) was undertaken by a highly qualified, well
trained (at least 7 years of active field experience in anthropometric assessment)
nutritionist. The student nurses were taken through a day session to familiarize them on
the study procedures, consenting process, safe handling of infants, managing maternal
anxiety and nutritional counseling.
In order to establish that the nutritionist was an ideal gold standard, his intra-observer
variation was assessed. In this process, the nutritionist repeatedly took weight, length
and MUAC measurements for 15 infants as pretest. The nutritionist data had to be
analyzed before the main data collection could commence.
22
During the main data collection involving student nurses data was collected between
8.00 a.m to 4.30p.m daily for 2 weeks. At the MCH clinic, health education on
malnutrition and importance of visiting maternal child health clinic was given to visiting
mothers/caregiver. Information about the study was shared and mothers were informed
that they could choose not to participate. Measuring equipment routinely used for
measuring infants in MCH clinics in Kenya were used and were checked every morning
for calibration using certified Kenya Bureau of Standards stones to ensure they were in
good working condition. The weight of the child was measured using hanging scale
(Salter Model), weight measurements were taken to the nearest0.1kgs. The length was
measured using head to heel method in supine position on a height measuring board.
The length measurements were taken to the nearest 0.1cms using an infantometer
(SECA model).Mid upper arm circumference was measured midway between the
shoulder and the elbow using an arm circumference tape(TALC Insertion Tape) applied
horizontally and recorded in millimeters. The measurements were taken following the
procedure in the United Nations guide (United Nations, 1986).
The student nurses were divided into two groups of three students each. In order to
assess the inter-observer reliability each of the 3 students was to independently measure
MUAC, weight and length of 160 infants only once. For every 5th child measured by
students the qualified nutritionist who had undergone one day refresher training took
additional measurements that acted as the gold standard measurement in determining
accuracy.
In this way, each child was measured 3 times by the student nurses and a sub-set of the
children received an additional measure by the qualified nutritionist.
23
A pair of anthropometric measurements (from nutritionist and students) was obtained to
make a dataset. Measures were blinded from each other by providing different recording
sheets for each student and qualified nutritionist for each child. Details of child: date of
birth (this helped in calculating the child’s age) and sex were recorded as reflected on
the mother and child booklet.
The researcher supervised the exercise at all times and when absent the nutritionist took
charge.
The student nurses’ datasheets were collected immediately after recording to avoid
change or copying from each other. At the end of the day, all data sheets were checked
for clarity and completeness. Below is a flow diagram illustrating the study procedures
(Figure 3.1)
Figure 3.1: Flow chart of study procedures
Phase 1
Expert intra-observer
variation/reliability
Phase 2b
Expert accuracy test (N=
60 infants)
Phase 2a
6 student nurses inter
observer reliability
(N=320 infants)
3 student nurses in each group
reliability
160 infants, 80 below 6 months and 80
above six months
24
3.7 Data Management
Data was checked for completeness at the end of each day, cleaned and organized in a
file. The data sheets were coded /numbered then the data was entered into an access data
base. Data was entered separately for each observer group i.e. group 1 had observers
number 1, 2 and 3 while group 2 had observer number 4, 5 and 6. The three
measurements by one observer for each child were recorded in one line such that for
each group, 9columns were generated. Microsoft Access was used for data entry; in
Access data entry errors were limited by designing forms with controls to limit data
entry choices.
Data was exported to ‘R’ for analysis. ‘R’ is comprehensive statistical software with
standard statistical tests, model and analysis. It has very good graphical capabilities, is
open source and free and has a cross-platform that can run on many operating systems.
However, incomplete data was excluded during analysis.
3.8 Data Analysis
The data collected by the nutritionist was described: number of infants measured, age
distribution and sex. The median value of each of the anthropometric measurements and
inter quartile ranges Z-scores were calculated that included MUAC Z scores for
children more than 90 days, weight for length Z- scores weight for age Z-scores and
length for age Z-scores using WHO growth references standards .
Then the prevalence of wasting (WFL), underweight (WFA) and stunting (LFA) was
established using WHO growth references standards version 3.2.2(WHO,2011).
25
The average ICC between the first and second anthropometric measurement of the
nutritionist data for each infant was also determined. The difference in intra-observer
reliability of infants (males and females) was established.
The Intra-class correlations (ICC) were also computed for measurements by the student
nurses. The inter-observer bias was considered because the measurements were done by
different people but on the same child. First, the level of agreement on the absolute
measurements of weight, length and MUAC was tested. Later the level of agreement for
the Z scores derived from these measurements was also tested. Accuracy was estimated
by calculating the mean differences (95%CI) and the Pitman’s test. The Bland Altman
Plots were used to reveal the relationship between differences and average to look for
any systematic bias and identify possible outliers (Bland, 1986).
3.9 Ethical Considerations
Ethical approval was sought from the Pwani University ethical committee. Formal
permission to collect data was obtained from the Kilifi County Research office and from
the hospital in-charge.
Informed written Consent was obtained from the qualified nutritionist in MCH and the
student nurses, who participated in the study.
Given that the measurements taken in this study were routine measurements that
involved anon-invasive procedure and written consent was not common in MCH, the
researcher followed advice from the MCH in-charge and oral consent was given by the
mothers/caregivers who voluntarily accepted to have their babies participate in the study
(Appendix iii; a and b).
The participants were informed that their participation in the research survey was purely
voluntary and there wasn’t any punitive measure taken against those who declined to
26
participate. The participants were informed about the risks and benefit that may accrue
from the study (Appendix iii a and b).
All information obtained in the course of this study was treated with utmost
confidentiality and was not used outside the scope of the study. This was done in
compliance with the requirement for confidentiality, which seek to protect the identity of
research subject against potential abuse/stigmatization. More so, there was no conflict of
interest to declare from the researcher.
27
CHAPTER FOUR
RESULTS
4.1 Expert intra observer reliability
This phase of the study enrolled 15 infants to measure the intra-observer reliability of
the expert nutritionist. Seven (7) males and eight (8) female infants were measured,
taking weight, length and MUAC measurements. The median and inter quartile ranges
for MUAC were 13.3 cms (12.075-14.575), for weight 6.4 kgs (5.4-7.8) and for length
67cm (60.9-69). The mean and SD for LFAz was -2.26(2.383); 8 (53%) infants stunted
(LFAz < -2), for WFAz was -2.200(2.026) of the 15 infants; 9 (60 %) infants were
underweight (WFAz <- 2). The mean and SD WFLz was -1.092 (1.572); 4 (27 %)
infants were wasted (WFLz <- 2), for MUACz mean (SD) for the 15 infants was-
1.07(1.576) with 5(33%) children having <- 2 MUACz.
The ICC for weight was 1.00(95%CI 1.00-1.00), MUAC was 0.99 (95 % CI 0.97-1.00)
a, length 1.00 (0.97-1.00), MUACz 0.99(95 % CI 0.97-1.00), WFAz 1.00 (95 % CI
1.00-1.00), WFLz 0.98 (95% CI 0.94-1.00) and LFAz 1.00 (1.00-1.00), Table 4.1.
28
Table 4.1: Intra-class Correlation Coefficient (ICC) measures by Expert Nutritionist
4.2 Student Nurses
4.2.1 Reliability
In this phase of the study 320 infants were approached to participate. However, due to
missing information on age and recording errors, data for 14 infants were excluded from
the final analysis. Data analysis was therefore carried out for 306 infants, 151 of whom
were aged less than6 months, while 155 infants were aged greater than 6 months. Within
this sample, 142(46.4%) were males, while 164 (53.6%) were females. The mean (SD)
for MUAC was 13.8cms (SD 1.662), for weight7Kgs (SD 2.029) and length 64cms (SD
7.267).The mean(SD) of WFAz was -0.53(5.15), LFAz was -0.81(2.78) and WFLz was
Anthropometric measure Intra-Class Correlation
coefficient
Lower
CI
Upper
CI
MUAC 0.99 0.99 1.00
Female 0.98 0.95 1.00
Male 0.99 0.95 1.00
Weight 1.00 0.99 1.00
Female 1.00 1.00 1.00
Male 1.00 1.00 1.00
Length 1.00 1.00 1.00
Female 1.00 0.98 1.00
Male 1.00 0.99 1.00
MUACz 1.00 1.00 1.00
Female 0.99 0.95 1.00
Male 0.99 0.97 1.00
WFAz 1.00 0.99 1.00
Female 1.00 1.00 1.00
Male 1.00 1.00 1.00
WFLz 0.98 0.92 1.00
Female 1.00 1.00 1.00
Male 0.98 0.94 1.00
LFAZ 1.00 1.00 1.00
Female 1.00 0.99 1.00
Male 1.00 1.00 1.00
29
0.20(1.41).Twenty seven infants (8.8%) had MUAC <11.5cm, 15 (4.9%) were wasted
(WFLz<-2), 69(22.5%) stunted (LFAz<-2), 58(19%) were underweight (WFAz<-2).
Infant MUACz could only be calculated for infants above 3 months. In this study there
were 255 infants above 3 months, 25(9.8%) had MUAC Z scores of <-2 (Table 4.2)
Table 4.2: Mean anthropometric measurements and level of malnutrition
The ICC was calculated first for the 2 groups of observers; Group 1 had measured 149
infants aged 0 to 12 months, while Group 2 had measured 157 infants of the same age
(Figure 4.1). Secondly, data was split by age and ICC was calculated for infants below
and above 6 months (Figure 4.2). Lastly, the overall ICC for all the infants above and
below six months by all 6 observers was pooled and reported (Figure 4.2).
The Intra class correlation coefficient for group one for MUAC was 0.99(95%CI 0.98-
0.99), WFLz 0.90 ( 95 % CI 0.87-0.92) and MUACz for 125 infants above 90 days was
0.99 (95%CI 0.98-0.99). Group two ICC for MUAC was 0.95(95%CI 0.93-0.96), WFLz
Measurement Total Mean(SD)
N = 306
Moderately
Malnourished (%)
Severely
Malnourished
(%)
MUAC 13.8(1.662) 24(7.8%) 27(8.8%)
WEGHT 7.0(2.029) - -
LENGTH 64.0(7.268) - -
-2to-3z score: N
(%)
>-3z score
WFAz -0.5277(2.154) 17(5.6%) 41(13.4%)
LFAz -0.8063(2.779) 16(5.2%) 53(17.3%)
WFLz 0.2007(1.408) 6(2%) 9(2.9%)
MUACz(N=255) -0.0631(1.511) 11(4.3%) 14(5.5%)
30
0.98(95%CI 0.98-0.99) and MUACz for 130 infants above 90 days was 0.96(95% CI
0.95-0.97), Figure 4.1.
Figure 4.1: Overall Intra-class Correlation Coefficient (ICC) measures by student nurses
(Line at ICC=0.6shows minimal acceptable reliability)
Among the student nurses the pooled ICC (95%CI) per age for MUAC was 0.94(0.91-
0.98) for infants <6months and 0.99(0.98-0.99) for those above six months. The level of
agreement of WFLz was 0.85(0.79-0.90) for infants<6months and 0.99(0.99-0.99) for
those >6 months, Figure 4.2. The overall level of agreement for MUAC was
.
.
.
.
.
.
.
muac
1
2
Subtotal (I-squared = 98.5%, p = 0.000)
WEIGHT
1
2
Subtotal (I-squared = 99.6%, p = 0.000)
LENGTH
1
2
Subtotal (I-squared = 97.5%, p = 0.000)
WFAZ
1
2
Subtotal (I-squared = 99.5%, p = 0.000)
LFAZ
1
2
Subtotal (I-squared = 95.0%, p = 0.000)
WFLZ
1
2
Subtotal (I-squared = 99.7%, p = 0.000)
MUACZ
1
2
Subtotal (I-squared = 97.4%, p = 0.000)
ID
Study
0.99 (0.98, 0.99)
0.95 (0.93, 0.96)
0.97 (0.96, 0.97)
0.99 (0.99, 0.99)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
1.00 (1.00, 1.00)
0.99 (0.98, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 1.00)
0.99 (0.99, 1.00)
0.90 (0.87, 0.92)
0.98 (0.98, 0.99)
0.94 (0.93, 0.96)
0.99 (0.98, 0.99)
0.96 (0.95, 0.97)
0.97 (0.97, 0.98)
ES (95% CI)
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
49.02
50.98
100.00
Weight
%
0.99 (0.98, 0.99)
0.95 (0.93, 0.96)
0.97 (0.96, 0.97)
0.99 (0.99, 0.99)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
1.00 (1.00, 1.00)
0.99 (0.98, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 1.00)
0.99 (0.99, 1.00)
0.90 (0.87, 0.92)
0.98 (0.98, 0.99)
0.94 (0.93, 0.96)
0.99 (0.98, 0.99)
0.96 (0.95, 0.97)
0.97 (0.97, 0.98)
ES (95% CI)
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
48.69
51.31
100.00
49.02
50.98
100.00
Weight
%
.60 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Student nurses ICC per group
31
0.96(95%CI 0.95-0.97), WFLz 0.92 (95%CI 0.90-0.95) and MUACz for 255 infants
above 90 days was 0.97(95%CI 0.96-0.98) as illustrated in Figure 4.2.
Figure 4.2: Age related Intra-class Correlation Coefficient (ICC) measures by student
nurses (Line at ICC=0.6 shows minimal acceptable reliability)
.
.
.
.
.
.
.
MUAC
<6 months
>6 months
Subtotal (I-squared = 98.4%, p = 0.000)
WEIGHT
<6 months
>6 months
Subtotal (I-squared = 99.8%, p = 0.000)
LENGTH
<6 months
>6 months
Subtotal (I-squared = 94.3%, p = 0.000)
WFAZ
<6 months
>6 months
Subtotal (I-squared = 99.9%, p = 0.000)
LFAZ
<6 months
>6 months
Subtotal (I-squared = 96.2%, p = 0.000)
WFLZ
<6 months
>6 months
Subtotal (I-squared = 99.9%, p = 0.000)
MUACZ
<6 months
>6 months
Subtotal (I-squared = 93.0%, p = 0.000)
ID
Study
0.94 (0.91, 0.96)
0.99 (0.98, 0.99)
0.96 (0.95, 0.97)
0.99 (0.98, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 1.00)
0.99 (0.99, 1.00)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
0.98 (0.98, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 0.99)
0.99 (0.99, 1.00)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
0.85 (0.79, 0.90)
0.99 (0.99, 0.99)
0.92 (0.90, 0.95)
0.96 (0.93, 0.97)
0.99 (0.98, 0.99)
0.97 (0.96, 0.98)
ES (95% CI)
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
39.22
60.78
100.00
Weight
%
0.94 (0.91, 0.96)
0.99 (0.98, 0.99)
0.96 (0.95, 0.97)
0.99 (0.98, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 1.00)
0.99 (0.99, 1.00)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
0.98 (0.98, 0.99)
1.00 (1.00, 1.00)
0.99 (0.99, 0.99)
0.99 (0.99, 1.00)
1.00 (1.00, 1.00)
1.00 (0.99, 1.00)
0.85 (0.79, 0.90)
0.99 (0.99, 0.99)
0.92 (0.90, 0.95)
0.96 (0.93, 0.97)
0.99 (0.98, 0.99)
0.97 (0.96, 0.98)
ES (95% CI)
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
49.35
50.65
100.00
39.22
60.78
100.00
Weight
%
.60 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
student nurses ICC
32
4.2.2 Accuracy
There were 2 groups made up of 3 student nurses each. For accuracy, each group of
student nurses repeatedly measured MUAC, weight and length of 30 infants. Each of
these measures was paired to those of the nutritionist for the same child. This means that
among student nurses, a total of 90 measures were collected (30x3) for each group.
Between the 2 groups, a total of 180 paired measurements of MUAC, weight and length
were taken. The mean differences (95% CI) and pitman test in student nurses
measurement compared to those of the expert were calculated. The mean difference was
calculated in two steps; first, for each paired child, the observer’s measurement was
subtracted from the expert measurement. The mean of the differences for all the children
was then obtained. A positive difference means that the student nurse had lower
measurements than the expert (under estimating), while a negative difference means that
the student nurse is over estimating the measurement.
Overall, the mean difference in weight was - 0.001 (95%CI-0.011 to 0.010 Kgs), MUAC
was -0.096(95% CI -0.141 to -0.050 cms), HFAz was 0.006(95% CI-0.014 to 0.025 z
scores) and MUACz was 0.078(95% CI -0.122 to -0.033 Z scores), Table 4.3.
33
Table 4.3: Accuracy of measurements by student nurses compared to an expert for all
infants
There was evidence that the variance of paired measurements of MUAC, HFAz and
MUACz differed from those of the expert (pitman statistic <0.05). These data has been
illustrated in a Bland Altman plot by plotting the difference against the average (Figures
4.3, 4.4& 4.5).
Anthropometric
measure
Number
of pairs
Overall
Mean
difference
Lower CI Upper CI Pitman’
s test
MUAC (cm) 180 -0.096 -0.141 -0.050 0.012
Weight 180 -0.001 -0.011 0.010 0.825
Length 180 0.014 -0.030 0.059 0.119
WFAz 180 -0.001 -0.014 0.013 0.622
HFAz 180 0.006 -0.014 0.025 0.024
WFLz 180 -0.012 -0.042 0.020 0.280
MUACz 147 0.078 -0.122 -0.033 0.000
34
Figure 4.3 shows that, as the average increases the MUAC differences seem to decrease.
Figure 4.3: Bland-Altman plot of the mean differences of MUAC (expert - student
nurses) measurements of observers compared to average of their paired readings.
Figure 4.4, it is evident that none of the actual differences was greater than + or -1 Z
score. There is no clear trend on the differences by average.
Figure 4.4: Bland-Altman plot of the mean differences of HFAz (expert - student nurses)
measurements of observers compared to average of their paired readings
Diff
eren
ce
Average9.8 17.35
-.900001
1.5D
iffer
ence
Average-7.95 5.02
-.32
.88
35
From Figure 4.5, reveal that although positive differences were less than +1 Z score,
there were observed negative difference of greater than -1 Z score, mainly within the
lower averages. This would indicate that more differences were observed either among
thinner/wasted or younger infants.
Figure 4. 5: Bland-Altman plot of the mean differences of MUACz (expert - student
nurses) measurements of observers compared to average of their paired readings
The study further explored age related differences in measurements between student
nurses and expert nutritionist. Mean difference (95% CI) for infants <6months for
MUAC was;-0.1396 (-0.212 to-0.068) and MUACz was - 0.0814 (-0.136 to-0.027) as
shown in Table 4.4.
Diffe
ren
ce
Average-4.35 2.52
-1.63
.82
36
Table 4.4: Accuracy of infants measurements by student nurses compared to an
expert by age
Among infants aged above 6 months the mean difference (95% CI) for MUAC was-
0.044(-0.095 to 0.007cm), WFLz was -0.045(-0.097 to0.008 z scores) and MUACz was -
0.043 (-0.090 to 0.005z scores), as reeled in Table 4.4.
In general, there was statistical evidence that measurements taken by student nurses
differed from those of the expert nutritionist among infants under 6 months (p value <0.05
for MUAC and MUACz) but not for infants above 6 months. (P value>0.05 for all
measurements).
Mean differences among younger infants aged below 6 months
Anthropometric
measure
Number
of pairs
Mean
difference
Lower CI Upper CI Pitman’s
test
MUAC (cm) 96 -0.140 -0.212 -0.068 0.000
Weight 96 0.005 0.008 0.018 0.438
Length 96 -0.008 -0.067 0.050 0.779
WFAz 96 0.007 -0.011 0.026 0.423
HFAz 96 -0.005 -0.032 0.023 0.728
WFLz 96 0.018 -0.019 0.054 0.342
MUACz 63 -0.081 -0.136 -0.027 0.004
Mean differences among older infants aged above 6 months
Anthropometric
measure
Number
of pairs
Mean
difference
Lower CI Upper CI Pitman’s
test
MUAC 84 -0.044 -0.095 0.007 0.092
Weight 84 -0.007 -0.024 0.010 0.409
Height 84 0.040 -0.028 0.109 0.246
WFAz 84 -0.010 -0.030 0.011 0.350
HFAz 84 0.017 -0.012 0.047 0.246
WFLz 84 -0.044 -0.097 0.008 0.094
MUACz 84 -0.043 -0.090 0.005 0.078
37
The researcher also calculated mean differences between student nurses and expert
nutritionist on wasted (WFLZ<-2) and non-wasted (WFLZ>-2) infants. Mean difference
(95% CI) for infants who were wasted for length was; 0.004(-0.079 to 0.088cm) and
HFAz was 0.002 (-0.036 to0.04z scores).Among infants who were not wasted the mean
difference (95% CI) for MUAC was-0.104 (-0.156 to -0.053cm), WFLz was -0.003 (-
0.037 to0.031 z scores) and MUACz was -0.081(-0.132 to -0.03), as shown inTable 4.5.
Altogether, there was statistical evidence that length and HFAz measurements taken by
student nurses differed from those of the expert nutritionist among wasted infants (p
value <0.05 for length and HFAz).Similarly MUAC,WFLz and MUACz among non-
wasted infants also had a significant difference(p value <0.05 for MUAC,WFLz and
MUACz).
38
Table 4.5: Accuracy of infants measurements by student nurses compared to an
expert by nutritional status.
Mean differences among wasted infants (WFLZ<-2)
Measures Number
of pairs
Mean
difference
Lower
CI
Upper CI Pitmans
test
MUAC
(cm)
45 -0.069 -0.166 0.028 0.271
weight
(kg)
45 -0.011 -0.034 0.012 0.758
Length
(cm)
45 0.004 -0.079 0.088 0.036
WFAZ 45 -0.016 -0.047 0.016 0.379
HFAZ 45 0.002 -0.036 0.04 0.048
WFLZ 45 -0.037 -0.546 0.471 0.095
MUACZ 39 -0.068 -0.161 0.026 0.111
Mean differences among non-wasted infants (WFLZ>-2)
Measures Number
of pairs
Mean
difference
Lower
CI
Upper CI Pitman’s
test
MUAC
(cm)
135 -0.104 -0.156 -0.053 0.004
weight
(kg)
135 0.003 -0.009 0.015 0.86
Length
(cm)
135 0.018 -0.035 0.071 0.434
WFAZ 135 0.005 -0.01 0.02 0.625
HFAZ 135 0.007 -0.017 0.03 0.153
WFLZ 135 -0.003 -0.037 0.031 0.021
MUACZ 108 -0.081 -0.132 -0.03 0.001
39
CHAPTER FIVE
DISCUSSION
5.1 Discussion
This study aimed to assess the reliability and accuracy of anthropometric measures taken
by student nurses among infants aged 0-12 months. The overall reliability on all
anthropometric measures among all the student nurses was high; ICC>0.9. The least
reliable anthropometric measure was WFLz; ICC =0.92. We investigated further
differences in reliability by age and by student nurses group. The reliability of all
anthropometric measures taken among infants aged below 6 months was good; ICC>0.8.
WFLz was the least reliable; ICC=0.85. Among infants aged above 6 months, reliability
of all anthropometric measures was very high; ICC>0.9.
On accuracy, we found statistically significant differences between student nurses and
the expert in MUAC, HFAz and MUACz (p<0.05). However, the differences were too
small to be of any clinical relevance; the difference in MUAC was 1mm, HFAz was
0.006, while MUACz was 0.08. The researcher further investigated differences by age
and nutritional status; we found significant differences in MUAC and MUACz in
younger infants and length and HFA in wasted children. Similarly, these differences
were too small to have any clinical relevance.
From the research literature in Chapter Two, we found that there is scarcity of published
literature reporting reliability and accuracy of routine anthropometry performed by
student nurses. In a study on reliability of mid upper arm circumference measurements
taken by 48 student nurses among 5 children aged 2 to 5 years in Ghana (Saeed et al,
2015). Inter-observer reliability was reported using ICC making it possible to compare
with the currentfindings. The student nurses’ reliability in this study was generally better
40
than the study in Ghana that indicated almost no reliability among the 5 student nurses
in MUAC measurement with overall ICC of 0.042. This comparison should be viewed
with an understanding that the number of children measured in the Ghana study was
much smaller (n=5)than in our study (n=306), the age in the Ghana study was much
higher (2-5 years) than in our study(0-12months),the observers-child ratio was
high(48:1) and in our study the ratio was(3:1).
However, there are a lot more studies published on reliability of anthropometry among
other health workers. A reliability study carried out in Ethiopia (Ayele, 2012) involving
594 pre-school children including infants measured by six community drawn
anthropometrist reported more reliable measurements of height (ICC=0.997) and weight
(ICC=0.998) than MUAC that had an ICC of 0.954. In that study, just like in this one,
measurements of length and weight were more reliable compared to those of MUAC. In
this study, the overall ICC for length was= 1, weight was=0.99 and MUAC
was=0.96.The Ethiopian study had a larger sample size (n=594) compared to our study
(n=306), it involved both younger (below 24 months) and older children (24 to 59
months) and the measurements were collected in a community setting. In another similar
study five minimally trained health workers in Guatemala measured height, weight and
arm circumference among 127 children aged 12 to 60 months under field conditions.
They exhibited better reliability (ICC=0.8881) in taking MUAC compared to weight for
age and weight for height (Velzerboer et al, 1983). The correlation between the
measurements of the health workers and that of the anthropometrist for arm
circumference was=0.8881, WFA=0.8756 and WFH=0.7588. In that study, just like in
this study, compared to WFA and HFA, WFL index presented the lowest reliability.
In literature search we found most reliability studies reported reliability of absolute
measures of weight, MUAC and height /length only, (WHO, 2006; Ayele, 2012).
41
Reliability of indices; WFLz, WFAz, HFAz and MUACz that are used as indicators for
undernutrition (WHO, 2009) are usually not presented. In this study a low WFLz ICC
score (0.85) among infants aged less than 6 months was found. This was the lowest ICC
recorded for that age group. This is an interesting finding especially because the ICC for
absolute weight and length measurements was near perfect (0.9).
The current findings are comparable to what others found. Mwangome et al (2012)
assessed reliability and accuracy of anthropometric measurements taken by community
health workers in Kenya while in Guatemala, Velzerboer et al (1983) assessed
minimally trained health promoters and they both found absolute measures of weight
and length more reliable than WFLz. Studies have explained that; the individual
variation/error of weight and length are compounded when they are presented as z scores
and these results to lower reliability for WFL. Low WFL index could also result from
the sensitivity of the index to individual changes in the absolute measures of weight and
length.
Well trained observers in the WHO multicenter growth reference study group measured
MUAC, weight and Length among children aged 0-59 months in 5 different countries
(WHO, 2006). Inter-observer reliability results were reported using Technical Error of
Measurement (TEM). They reported TEM of above 95 % for all measurements taken
which is interpreted as a very high reliability. This finding is similar to what we found
where the inter-observer reliability for all measurements in our study was an ICC of
>0.90, which is interpreted as very high reliability. However, the WHO study did not
involve student nurses and there was through training on anthropometry and monitoring
of data collection procedures. The level of agreement between observers was presented
using the TEM. Such a study, though related to this will be difficult to sufficiently
compare findings with.
42
Accuracy
There were few published studies on accuracy of anthropometry. None of these studies
examined student nurses. In this study, when the readings of the student nurses were
compared to those of the expert, the student nurses were more precise in taking weight
and length measurements than MUAC measurements, (Table 4.3). The mean difference
in MUAC measurement was -0.096 cms, Weight was -0.001Kgs and Length was
0.014cms.
These differences were statistically significant but were too small to surpass the
acceptable difference for a given measurement and hence were not of any clinical
relevance.
The allowable difference in MUAC is 5mm, weight =100g and length 7mm (de Onis et
al, 2004) hence these differences would not lead to any misclassification of the infants’
nutritional status. In a related study Mwangome et al (2012) also found small differences
within a similar group of children but using community health workers who measured
infants 0 to 6 months instead of student nurses. The mean difference in MUAC was
0.65mm and WFL was -0.03.There was no significant difference when the
measurements were compared to those of their trainers. These findings indicated that the
community health workers were more accurate in taking MUAC measurements, which
is slightly different from what we found with student nurses within the same population.
The student nurses were less accurate and they tended to overestimated MUAC
measurements and there was a significant difference when their measurements were
compared with those of the expert.
In support of this finding a review of anthropometric measurement errors and how the
errors influence measurements and interpretation UliJaszek&Kerr (1999) acknowledged
43
that different anthropometric measures had distinctive levels of accuracy. Weight and
height measurements were identified as the most precisely measured. To assess accuracy
in Ayele’s study involving community drawn anthropometrist it was recognized that the
inter-anthropometrist error was greater in smaller children (those below 24 moths)
compared to larger children (those above 24 months) especially for length measurements
(Ayele, 2012). This study showed less accuracy in younger children in MUAC and not
length as indicated in Ayele’s study. These observations would need to be validated
using data from other similar but more controlled accuracy studies.
In this study, we observed differences in MUAC and MUACz measurements among
younger infants and length and HFA among wasted infants. These differences could
have resulted from difficulties related with fully stretching infants and keeping them still
when taking measurements. Handling soft tissue, especially in measurement of MUAC
could also result to measurement errors (WHO, 2006). In addition, this shows increased
probability of occurrence of measurement error among younger children because they
are likely to be uncooperative when measurements are being taken compared to older
children (Walker et al, 2013). The findings are similar with practical experience in the
clinical area where small/younger children are less cooperative, posing a challenge in
taking measurements. In view of the findings, student nurses may need additional
training and practice that entail techniques to enhance accurate measurements of infants.
This includes; involving care takers /parents in taking measurements so that they can
calm the child and improvement of speed in performing measurements.
The study used equipment’s as per the WHO recommendation especially the infant
meter which is usually not available in many clinical setting. The use of this
equipment’s may have influenced the accuracy of measurements compared to use of
improvised height boards and rarely standardized weight scales in most maternal child
44
health clinics in Kenya. Only health infants were involved in this study however, the
student nurses can apply the same skills in measuring infants in the different setting. To
enhance accuracy, measurements should be taken twice and the average of the two
values reported (Martorell, 1975) however, this was not done in this study.
45
CHAPTER SIX
CONCLUSION
6.1 Conclusion
Student nurses who had undergone theoretical and practical experience in anthropometry
reported high reliability and accuracy scores for all anthropometric measurements
assessed in this study under close supervision.
The results of this study may not be applicable to measurements done under routine
growth monitoring activities and in other settings. Thus, supervision of anthropometric
measurements during clinical practice could be beneficial.
More studies in different settings would need to be done to enhance generalizability of
findings.
6.2 Recommendations
In literature, there is a variation in the way reliability scores of anthropometric measures
are reported. Studies have used different statistical analysis methods of reliability
including reporting technical error of measurement (TEM), the coefficient of reliability
(R) or the intra-class correlation coefficient (ICC). This has made it difficult for findings
from reliability studies in general to be comparable across different settings. The lack of
similar way of reporting reliability data needs to be addressed if studies are to be
compared and a common understanding on reliability of anthropometry reached
(Martorell et al, 1975; Moreno et al, 2003).
Limited reference was available for the study hence more controlled studies evaluating
reliability and accuracy of trainee nurses should be carried out to enhance generalization
of findings.
46
Most studies on reliability and accuracy of anthropometric measures present data on
absolute measures only, as identified in this study high weight and length reliability and
accuracy does not necessarily result to equivalent reliability and accuracy of their z
scores (WFLz, WFAz, HFAz).Upcoming studies on reliability and accuracy of
anthropometric measures should present data of indexes (HFA, WFA and WFL) to
support their use, especially because it is the anthropometric indexes and not the
absolute measures that are used as indicators of nutritional status and for directing
interventions (WHO&UNICEF, 2009).
In this study, we observed small but significant difference in the measuring of HFAz
between the student nurses and the expert anthropometrist. In Kilifi the rate of stunting
is higher (39%) than the national average, which is at 26 % (KDHS, 2014/2015)
therefore, more supervised practical growth monitoring sessions for student nurses are
recommended to improve on reliability and accuracy of MUAC and HFA.
47
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Walker C.L., et al. (2013). Global burden of childhood pneumonia and diarrhoea.Lancet
381, 1405–1416.
Walter, S., et al. (1998). Sample size and optimal designs for reliability studies.
Statistics in Medicine,17,101 – 110.
Waterlow, J. (1972).Classification and definition of protein-calorie malnutrition. British
medicaljournal,3:566-569.
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Lowlands of Kenya. CIMMYT. p. 6. ISBN 978-970-648-099-6.
WHO& UNICEF. (2009).WHO child growth standards and the identification of severe
acute malnutrition in infants and children. A joint statement by the World Health
Organization and the United Nations.
WHO, UNICEF&World Bank. (2015).Levels and trends in child malnutrition UNICEF
WHO – World Bank Group joint child malnutrition estimates Key findings of the
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WHO.(1976).Methodology of Nutrition Surveillance .Twenty-Seventh Repot of a Joint
FAO/UNICEF/WHO Expert committee .World Health Organization Tech Rep; 593
WHO. (1995). Physical Status the use and Interpretation of Anthropometry. Report of
the WHO Expert Committee.World Health Organization Tech Rep,854
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46.
World Bank.(2008). Kenya Poverty and Inequality Assessment [Online].Poverty
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i
Appendix i- Work Plan
Activity Month
1.Preparation of Proposal, review of literature,
Preparation of tools.
June/August,2014
2. Ethical review and approval September/
November,2014
3Pilot study December,2014
4.Data collection December2014/
January,2015
5. Data ,editing, cleaning, entry and analysis February,2015
6: Report writing March to May,2015
7. Dissemination of research findings
May ,2015
8. Final reporting of the project June,2015
ii
Appendix ii-Budget
The rates in the budget are estimated by the researcher.
ACTIVITIES Duration/Quantity KSH
Instruments and equipment
Data storage equipment (external hard
disk)
3500 x 1 3,500
Memory stick/ flash disk 1 2 GB 2,880
Half of Dozen CD’s RW 200 x 6 1,200
6 lever Files 6x 235 1,410
Supplies
Paper/stationery 4,000
Photocopy, printing 10,000
Communication(airtime and internet) 7,000
20 pens 20 x 20 400
6 clip boards 6 x 200 1,200
6 note books short hand 6 x 100 600
OTHERS
Meals 14×6×500 42,000
Transport 100×14 1,400
Secretarial services 20,000
Others miscellaneous 12,559
TOTAL 108,149
iii
Appendix iii (a)-Participant Information Sheet (Parent/Care Taker/Guardian)
Title: Reliability and Accuracy of Anthropometry Performed by Student Nurses among
Infants in Kilifi County Hospital
I am a Post graduate student (public health) at Pwani University and am conducting a
research study on reliability and accuracy of anthropometry performed by student nurses
among infants under 12 months in Kilifi county hospital.
This Subject information sheet will be explained to you in a language you understand
best (kigiriama/Kiswahili/English). Adequate time will be given for you to decide
whether or not you want to take part. You will be expected to give a written consent if
you agree your child to take part.
You are invited to volunteer for a study/research. This information sheet will assist you
in deciding if you would like to participate, before you agree to take part in this study
you should fully understand what is involved. If you have any questions which are not
fully explained in this leaflet, do not hesitate to ask the researcher.
The aim of this study is to assess the consistency and accuracy of measurements (length,
weight and mid upper arm circumference) among infants less than one year taken by
student nurses in Maternal Child Health clinic (MCH), Kilifi county hospital.
As a parent or guardian, we request for your permission to take these measurements on
your child. If you agree, we may ask that you assist in taking these measurements by
holding the child during measurement and thereafter listen to health education.
During the study only body measurements of infants will be taken to include; length,
weight and mid upper arm circumference (MUAC) hence minimal discomfort is
expected. The time required is approximately 10 to 15 minutes per child. NO payments
to the child’s mother/caretaker will be made.
iv
The information that will be collected during this study will be used to determine how
differently student nurses measure and if the measurements are similar and valid. All the
information which will be collected during the course of the research will be kept
confidential, your identity will not be revealed at any instance as No individual identities
will be used in any report from the study .The data will not be accessible to any
individual not part of the research team. The findings of the study will be shared with
the county medical officer of health, Hospital nursing officer and Kenya medical
training college.
Participation in research is voluntary. This means that you are free to withdraw anytime
without penalty. If you agree to participate, you will receive no direct payment from
taking part in this study however there are some benefits. The immediate benefit to
participating is that if a child is found to be malnourished, he/she will be referred to the
outpatient clinic for further investigation and management and the mother will receive
nutritional counseling.
If you have any questions about this study you can contact the researcher who is
responsible on the following address.
Bogonko Venah, P.O BOX 95-80108, Kilifi.
Cell no. 0725868032
v
CONSENT FORM
I have read the information sheet and understood it. The purpose and procedures
involved in this research have been explained to me satisfactorily by the researcher. I
understand that I can change my mind at any stage.
Yes (I agree to participate)
No (I do not agree to participate)
Signature…………………………... …………….Date…………………………
Researcher’s signature…………………………….Date………………Time……
vi
FOMU YA MAELEZO YA MSHIRIKI (Mzazi/Mlezi/Msimamizi)
Kichwa: Kuaminika na uhalali wa kupima maumbile ya mwili wa watoto wachanga,
unaofanywa na wanafunzi wauguzi katika Hospital ya Kilifi County.
Mimi ni mwanafunzi wa chuo kikuu cha Pwani, kitengo cha afya ya uma, nikiwa
nafanya uchunguzi kuhusu kuaminika na uhalali wa kupima maumbile ya mwili wa
watoto wachanga wa umri wa chini ya miezi kumi na miwili, unaofanywa na wanafunzi
wauguzi katika Hospital ya Kilifi County.
Maelezo yaliyomo kwenye fomu hii yameandikwa kwa lugha ya Kiswahili. Utapewa
muda wakutosha wakufanya uamuziwa ikiwa utajiunga na uchunguzi huu ama la.
Ukikubali kuhusika, utahitajika kutia sahihi kujiunga na uchunguzi huu.
Unakaribishwa kujitolea kuhusika na uchunguzi. Fomu hii ya maelezo ya kushiriki
itakusaidia kufanya uamuzi ikiwa utakubali kujiunga na uchunguzi huu. Kabla ya
kukubali kujiunga unahitajika kuelewa kwa ukamilifu yanayohusu uchunguzi huu.
Kama utakuwa na maswali ambayo hayajajibika katika maelezo haya, unatakikana
kumuuliza muhusika wa uchunguzi huu.
Madhumuni ya utafiti huu ni kutaka kujua vile vipimo vya mtoto kama vile uzito, unene
wa mkono na urefu vinavyochukuliwa na wanafunzi wauguzi hapa Kilifi vinatofautiana
mmoja kwa mwengine. Hii ni kwa sababu vipimo hivi hutumika kwa kupimia ukuaji wa
mtoto. Katika autafiti huu, tungependa kuwaalika kina mama au walezi wa watoto
wasiozidi mwaka mmoja hapa kliniki ya watoto ya Kilifi.
Kama mzazi/mlezi wa mtoto huyu, ningependa kukuimiza ya kwamba ukituruhusu
tumpime vipimo hivi kazi yako itakuwa ni kusaidiana na yule anayepima mtoto kwa
vii
kumushika mtoto ili apimwe kwa urahisi ,baadaye utapokea mafundisho kuusu lishe
bora.
Kuhusika kwako ni kwa hiyari. Uko huru kukataa kuhusika kwenye utafiti huu. Wewe
na mtoto wako mtahudumiwa sawa bila kujali kama unahusika au kutohusika na utafiti
huu.
Iwapo utakubali kuhusika unayo ruhusa kubadili nia wakati wowote. Faida moja ya
kukubali kujiunga na utafiti huu ni kwamba ikiwa mtoto atakapatikana kuwa na
ugonjwa wa utapia mulo, basi ataelekezwa kwa kliniki kwa uchunguzi zaidi, pia
mamake mtoto atapatiwa mafunzo juu ya lishe bora.
Wakati wa uchunguzi huu, ni vipimo vya mwili peke yake vitakavyochukuliwa kwa
mtoto mchanga, kama vile; urefu, unene wa mkono na urefu. Vipimo hivi huchukuliwa
kwa kawaida hivyo basi mtoto hatapata usumbufu wowote. Kuchukua vipimo hivi kwa
mtoto itachukua kama dakika kumi hivi.Hakutakuwa na malipo yoyote yatakayofanywa
kwa mzazi ama mlezi wa mtoto.
Habari zote zitakazochukuliwa kutokana na uchunguzi huu zitakuwa siri, na majina ya
watakaohusika hayatajulishwa kwa mtu yoyote asiyehusika na utafiti ,wala
hayatatumika kwa ripoti yoyote ile. Mambo tutakayojifunza kutoka kwa utafiti huu
yatasaidia katika ukufunzi wa wanafunzi wauuguzi. Majibu ya uchunguzi huu
yatajulishwa kwa mkuu wa afya wa County ya Kilifi na tasisi (College) ya afya ya
Kenya. Kujiunga kwako kwa uchunguzi huu ni hiari yako na uko na uhuru wa kujiondoa
wakati wowote bila ya tuzozwa faini.
Kama uko na maswali yoyote kuhusiana na uchunguzi huu, unaweza kuwasiliana na
anayesimamia uchunguzi kupitia anwani ifuatayo:
viii
Bogonko Venah, S.L.P. 95-80108, Kilifi.
Namba ya simu: 0725868032
Fomu Ya Makubaliano
Nimesoma habari hizi (nimesomewa habari hizi) na nimezielewa vyema. Nimeelezwa
kusudi (lengo) na mahitaji yanayohusika katika uchunguzi huu na mchunguzi, na
nimekubali kushiriki kwa hiari.
Mhusika anayejiunga (sahihi): …………………tarehe…………………………….
Mchunguzi (sahihi): …………………………tarehe………………Wakati……….
ix
Appendix iii (b)-Participant Information Sheet (Nutritionist/Student Nurses)
Title: Reliability and accuracy of Anthropometry Performed by Student Nurses among
Infants in Kilifi County Hospital.
I am a post graduate student (Public Health) at Pwani University and am conducting a
research study on reliability and accuracy of anthropometry performed by student nurses
among infants in Kilifi county hospital.
You are invited to volunteer for a study/research. This information sheet will assist you
in deciding if you would like to participate, before you agree to take part in this study
you should fully understand what is involved. I am approaching a nutritionist, student
nurses and mothers of infants less than 12 months for their voluntary participation in this
study.
This study intends to learn more about the possibility of different people getting the
same or different measurements for weight, height/ length and mid upper arm
circumference (MUAC). This is because the measures are used to assess growth of
children under five in maternal child health clinic. The study includes approaching
mothers/ caretakers of children 0-12 months in the community who are attending mother
child health clinic in Kilifi county hospital. The nutritionist will undergo refresher
training on how to take accurate measures height/ length, weight and MUAC of children
under one year. All participants will be acquainted with the study procedures. You will
be expected to convey health education to mothers attending MCH clinic on nutrition,
importance of growth monitoring and visiting MCH clinic.
Then you will identify, inform and consent those with children 0-12 months to
participate in the study with the help of the researcher. You will take single measures of
height/length, weight and MUAC of these children and record in the provided sheets,
and then give basic nutrition counseling to participating mothers especially those with
x
abnormal measures using the growth curve (the nutritionist will take measurements of
every 5th child measured by the student nurses).
The data collected during the study will be analyzed to see how differently different
people measure. This process will involve comparison of your measurements with those
of others.
The immediate benefit to you includes acquirement and enhancement of skills in taking
weight, height and MUAC measurements of infants under one year. In addition you will
acquire information and skills on nutrition counseling and in giving health talks to the
mothers and this will be beneficial to you individually.
The information about the measurements you take won’t be shared with anyone else not
in research team. The information will be used for the purpose intended for in the
research. The information will be shared with county medical officer of health, Hospital
nursing officer and Kenya medical training college without revealing individual’s
identities of those who participated.
Your participation in this study is voluntary and you are free to withdraw anytime
without penalty.
Your job/study as a nutritionist/a student will not be affected whether you take part or
not. If you have any questions about this study you can contact the researcher who is
responsible on the following address.
Bogonko Venah, P.O BOX 95-80108, Kilifi.
Cell no. 0725868032.
xi
Consent form for student nurses /qualified nutritionist.
I have read the information sheet and understood it. The purpose and procedures
involved in this research have been explained to me satisfactorily by the researcher. I
understand that I can change my mind at any stage (and this will not affect my work
/studies today or in the future].
Yes (I agree to participate)
No (I do not agree to participate)
Signature…………………………... …………….Date…………………………
Researcher’s signature…………………………….Date……………………
xii
Appendix iv -Data Collection Tools.
Ch
ild
nu
mb
er
Date
of
bir
th
Date
seen
Sex
MU
AC
in
mil
lim
et
ers
Wei
gh
t
in k
gs
Len
gth
in c
m
001
002
003
xiii
Appendix v- Authorization Letter
xiv
Appendix vi : Ethical Approval