Nigerian Journal of Paediatrics

Correlation of non-biological factors with anthropometric and haemoglobin measurements of children under 10 years old in southeast Nigeria community-based study

Niger J Paediatr 2019; 46 (1):23 – 29

ORIGINAL

Ughasoro MD

CC – BY

Correlation of non-biological

factors with anthropometric and

haemoglobin measurements of

children under 10 years old in

southeast, Nigeria:

Community-based study

DOI:http://dx.doi.org/10.4314/njp.v46i1.5

Accepted: 28th July 2018

Abstract : Background

eas were more likely to be moder-

Childhood malnutrition also has

ate-severely anaemic (19.7%) and

Ughasoro MD (

)

non-biological determinants and

the difference was statistically

Department of Paediatrics,

little is known about it.

significant ( p = 0.003). Household

University of Nigeria Enugu

Objective: To determine the dif-

size significantly relates to under-

Campus, Enugu, Nigeria.

ferences in children’s height,

weight and childhood anaemia ( p =

Email;maduka.ughasoro@unn.edu.

weight and haemoglobin concen-

0.002 and p = 0.036 respectively).

ng or kakatitis@yahoo.co.uk

tration across different sociode-

Mother’s education were signifi-

mographic characteristics.

cantly related to tunting and child-

Design: The study was commu-

hood anaemia ( p = 0.010 and p =

nity-based study. The weight and

0.001 respectively). Childhood

height of the children were meas-

anaemia was significantly related

ured. The Z-scores were calcu-

to mother’s education ( p = 0.001)

lated. Blood was taken for haemo-

and household ( p = 0.036).

globin estimation.

Regression

Conclusions: Maternal age and

analysis was done to determine

education, household size and

correlates.

place of resident of a child affect

Results:

More rural children

children nutritional status. Im-

(32%) have stunting and the dif-

proved education, and family plan-

ference was statistically signifi-

ning can contribute to the reduc-

cant ( p =0.003). There was no dif-

tion in the burden of malnutrition.

ference in the prevalence of un-

derweight among under urban

Key words: Stunting, Under-

(32.4%) and rural (33.4%) chil-

weight, Children, Nigeria, Non-

dren. Children from the rural ar-

biological determinants.

Introduction

hood anaemia and malnutrition.¹⁵

However, little is

known about the role non-biological determinants like

There are reports of high prevalence of childhood anae-

mothers’ age, maternal education, household size, socio-

6-9

mia , stunted and underweight

1-5

among children espe-

economic status and place of resident play in childhood

cially in sub-Saharan African regions. In Nigeria, anae-

anaemia and malnutrition in Nigeria.

mia contributes to 13.6% of under-five mortality ,

In this study we examined the relationship between

while malnutrition contributes to a much higher mortal-

childhood haemoglobin concentration and anthropomet-

ity. Presently, a lot is known about the biological factors

ric indices like height-/length-for-age and weight-for-

like malaria, intestinal helminths, malignancies e.t.c.

age, with some non-biological determinants among chil-

which are capable to causes of anaemia and malnutrition

dren from two regions in southeast Nigeria.

11-13

. According to reports, the infant and under five

mortality have dropped from 97 per 1000 live births and

158 per 1000 live births to 70 and 120 respectively. But

malnutrition among children under age five has worsen

Materials and Methods

nationwide with highest concerns being in the northern

Study area and population

part of the country. Wasting and stunting have increased

from 24.2% and 34.8% to 31.5% and 43.6% respec-

Abakpa and Ibagwa are an urban and a rural areas in

tively.

A study has shown that beyond biological fac-

Enugu east local government area (LGA) of Enugu

tors, other non-biological factors can contribute to child-

State, while Umuahia and Nkwegwu are an urban and

rural areas in Umuahia North LGA of Abia State. Both

and severely stunted or underweight if their z-score were

Enugu and Abia states are in southeast Nigeria. The

< - 3 SD below the WHO median.

study was a community-based cross-sectional and 298

households were randomly selected from the four study

Haematologic Measures

localities. All the children under 10 years of age living

in the selected households during the period of the study

The haemoglobin estimation of the children was as-

July – August 2015 were selected. Prior to the study an

sessed using the validated HemoCue 301 haemoglobi-

advocacy visits were made to the study areas and the

nometers. The capillary blood was obtained by

approval was obtained from the relevant authorities.

venipuncture of the middle finger using microlance.

Sample size The minimum sample size was calculated

A child was categorized as anaemic if their Hb was ≤

based on sensitivity and specificity of clinical pallor of

11g/dl and non-anaemic if the Hb was > 11g/dl accord-

70% respectively , 95% confidence interval and anae-

ing to internationally-recognized classification criteria

mia prevalence of 42%. This gave minimum sample size

(15). All children categorized as anaemic were further

of 374 children, but 588 children were finally enrolled.

classified as mild anaemic (Hb ≥ 10 g/dl -- ≤ 11g/dl),

Patient recruitment A simple random selection was to

moderately anaemic (Hb > 7 g/dl – 9.9g/dl), severely

one local government area (LGA) each from the list of

anaemic (Hb < 7 g/dl). All anaemic children were given

LGAs in each of the study states. The wards in the se-

haematinics on site and referred to hospital for further

lected LGA were grouped into rural and urban. A simple

evaluation.

random selection was used to select one rural ward and

one urban ward. The wards were grouped in clusters

Data analysis

according to geographical locations and one cluster was

selected from each ward. A cluster comprises streets in

The data were entered and analyzed using SPSS version

the urban area and hermits in the rural areas. Individual

20 for windows statistical software package (SPSS Inc.,

houses of households with children under 10 years were

Chicago, IL, USA). Means and standard deviations were

identified and numbered. The households were system-

calculated. Correlation between anaemia or anthropom-

atically selected in an alternate of two, and selected

etric indices with non- biological determinants; mother’s

households were informed about the study and those that

age, education, household size, socioeconomic status

showed willingness to participate were invited to the

and place of resident. The mothers were categorized into

recruitment centers (health centers)

three groups based on age: ≤ 24 years, 25 – 34 years

and ≥ 35 years, and categorized into four groups based

Household questionnaire

on highest educational level attained: no formal educa-

tion, primary, secondary and tertiary. The children were

An interviewer-administered questionnaire was used to

categorized into 5 socioeconomic groups based parents’

education and occupation according to Oyedeji , group

collection information on mother’s age, mother’s and

fathers highest educational level attained, mother’s and

1 & 2, group 3 and group 4 & 5 as high, middle and low

father’s occupation, household size, child’s age.

socioeconomic status. The children were also catego-

rized into four group based on household sizes (number

Anthropometry

of children in a household): 1 child, 2 children, 3 chil-

dren and ≥4 children.

The World Health Organization (WHO) recommended

method was used in the measurement of recumbent

For multivariate analysis, certain determinants were

length and height to the nearest 0.1cm ( ). For a child

dichotomized into two groups. Based on age, mother

under 2 years or if he/she was not to stand unassisted,

were categorized into two age groups, ≤ 30 years as

his/her length was measured using measuring mat. The

young mothers and ≥ 31 years as older mothers. Based

height was measured using stadiometer with moveable

on education, mothers were categorized into two groups;

bar and steel calibrated pole. Validated electronic

those with no formal education or primary education

weighing scale was to the measure the weight of the

were grouped as poorly-educated, while those with sec-

children to the nearest 0.01kg. A standard 10kg weight

ondary or tertiary education were grouped as well-

was used to frequently check the scale during the study.

educated. The household size was categorized into two

The children were weighed in bare feet with minimal

groups; households with ≤ 3 children as small household

clothing. The length/height and weight were independ-

and those with ≥ 4 children as large household.

ently measured by two clinicians (resident doctors and

The statistical significant was p value ≤ 0.05.

nurses) and the mean were used. The standard WHO

growth standards for boys and girls were the references

Ethics

used in the calculation of the Z-scores.

Those whose weight-for-age were < -2 SDs from the

The study protocol received ethical approval from the

standard mean were categorized as underweight. The

Ethics Committee of the University of Nigeria Teaching

children whose length-/height-for-age were < -2 SDs

Hospital Ituku/Ozalla, Enugu, Nigeria and Ethics Com-

from the standard mean were categorized as stunted. A

mittee of Federal Medical Centre, Umuahia, Abia State

child was moderately stunted or underweight if his/her z

The study was explained to the caregivers/parents of the

-score of height-for-age or weight-for-age were < -2

children and written consent was obtained before partici-

standard deviations below the WHO median but ≥ -3SD,

pating in the study.

Results

Fig 3: Height-for-age z-score for the children, blue is rural,

yellow is urban.

A total of 552 households were identified to participate

in the study and 298 households presented for the study,

while 254 households did not turn out, giving coopera-

tion rate of 54%. Out of the 298 households that re-

ported, 602 children were recruited in the study. How-

ever, 14 questionnaire were excluded from the analysis,

essentially due to missing data. Thus 588 questionnaires

were finally analyzed ( Fig 1).

Fig 1: The selection chart

Households identified to

participate (n = 552)

Parents that failed to present at the

stipulated study centres (Health

centers) and those that came when

the study has been concluded (n =

Households that

254)

The distribution of mothers’ age, educational status,

presented for the

household size, socioeconomic status of the subjects

study and gave their

were presented in Table 1. Majority (Abakpa 54.0%,

consent for their

Ibagwa 56.5%, Umuahia 60.2% and Nkwegwu 60.0%)

children to partici-

of the mothers in all localities were the age range of 25 –

pate (n=298; 54%

34 years. Majority (Abakpa 63.8%, Ibagwa 50.0%,

cooperation rate).

Umuahia 44.1% and Nkwegwu 75.4%) of the mothers in

602 children were

all localities except Umuahia has at least secondary

reruited from the

298 households

school education. The mean household size (Abakpa

3.53 children, and Ibagwa 3.51children) in the two lo-

calities in Enugu state were higher than the mean house-

14 children were excluded

from the study due to

hold size (Umuahia 2.5 children, and 2.64 children) in

missing data. (n=14)

the two localities in Abia state.

Majority (Abakpa

52.6%, Ibagwa 51.8%, Umuahia 44.6% and Nkwegwu

588 children were included in

62.1%) of the children belong to the middle (group 3)

the final analysis. (n=588)

socioeconomic group in all localities except in Umuahia.

Most (Abakpa 52.2%, Ibagwa 63.1%, Umuahia 49.0%

and Nkwegwu 55.0%) of the children involved in this

Fig 2: Weight-for-age z-score for the children, blue is rural,

study were male except in Umuahia.

yellow is urban

The mean age, height-for-age, weight-for-age, haemo-

globin concentration and z-scores of the children re-

cruited in this study is presented in Table 2. The mean

age was 46.43 months in the urban and 46.55 month in

the rural. The mean height-for-age z-score was -0.64

(urban) and -0.88(rural). The percentage of children that

were moderately stunted in the urban was 12.3% and in

the rural was 16.9% and the difference was not statisti-

cally significant ( p = 0.116). The percentage of children

that were severely stunted in the urban was 6.6% and in

the rural was 15.2% and the difference was statistically

significant ( p = 0.003). The mean weight-for-age z-score

was -0.52 (urban) and -0.63 (rural). The percentage of

children that were moderately underweight in the urban

was 17.7% and in the rural was 18.69% and the differ-

ence was not statistically significant ( p = 0.697). The

percentage of children that were severely underweight in

the urban was 12.7% and in the rural was 14.8% and the

difference was not statistically significant ( p = 0.859).

The mean haemoglobin concentration in urban was

10.94g/dl, and in the rural was 10.16g/dl. The preva-

lence of moderate anaemia in urban was 9.7% and in the

rural was 18.8%. The difference was statistically signifi

significant ( p = 0.003). The prevalence of severe anae-

mia in urban was 1.7% and in the rural was 0.9%. The

difference was not statistically significant ( p = 0.394).

Table 1: Mothers’ age, Educational status, Household size, Socio -economic status, Children age, and gender

Enugu State

Abia State

Variable

Abakpa (Urban)

Ibagwa (Rural)

Umuahia (Urban)

Nkwegwu

n (%)

(Rural)

n (%)

Mothers’ Age (years) (n 1 =83; n 2 = 62; n 3 =93; n 4 = 60 )

≤ 24

17 (20.6)

12 (19.3)

9 (9.7)

14 (23.3)

25 – 34

45 (54.0)

35 (56.5)

56 (60.2)

36 (60.0)

≥ 35

21 (25.4)

15 (24.2)

28 (30.1)

10 (16.7)

Mothers’ Education (n 1 =81; n 2 = 62; n 3 = n 4 =57)

No formal education

0 (0.0)

1 (1.6)

1 (1.8)

Primary

11 (13.8)

20 (32.3)

0 (0.0)

6 (10.5)

Secondary

52 (63.8)

31 (50.0)

2 (2.2)

43 (75.4)

Tertiary

18 (22.4)

10 (16.1)

41 (44.1)

7 (12.3)

Household Size (number of children)

50 (53.8)

(n 1 =83; n 2 = 62; n 3 = 92 n 4 =58)

2.64

Mean size

3.53

3.51

2.5

18 (31.0)

1 child

17 (20.0)

4 (6.7)

27 (29.4)

13 (22.4)

2 children

18 (22.2)

17 (26.6)

22 (23.9)

12 (20.7)

3 children

6 (6.7)

11 (17.8)

23 (25.0)

15 (25.9)

≥4 children

42 (51.1)

30 (48.9)

20 (21.7)

Socioeconomic Status (n 1 =79; n 2 = 56; n 3 =92; n 4 =58)

4 (6.9)

1 & 2

13 (16.4)

6 (10.7)

44 (47.8)

36 (62.1)

42 (52.6)

29 (51.8)

41 (44.6)

18 (31.0)

4 & 5

24 (31.0)

21 (37.5)

7 (7.6)

Child’s Age (mnths) (n 1 =176; n 2 =141;n 3 =151; n 4 =120)

60 (50.0)

≤24

53 (30.1)

42 (29.8)

83 (55.0)

27 (22.5)

25 – 59

52 (29.4)

48 (34.0)

24 (15.9)

33 (27.5)

≥60

71 (40.5)

51 (36.2)

44 (29.1)

Child’s Gender(n 1 =176; n 2 =141; n 3 =151; n 4 =120)

66 (55.0)

Male

92 (52.2)

89 (63.1)

74 (49.0)

54 (45.0)

Female

74 (47.8)

52 (36.9)

77 (51.0)

n 1 = Abakpa; n 2 = Ibagwa; n 3 = Umuahia; n 4 = Nkwegwu

Table 2: The prevalence of different levels of stunted (height-for-age z-score ≤< -2), underweight (weight-for-age z-score < -2) and

anaemia (haemoglobin estimation ≤ 11g/dl)

Variable

Urban

Rural

p -value

n=323

n=259

Age (months)

Mean (SD)

46.43 (32.36)

46.55 (28.42)

Mean Height-for-Age (kg) (SD)

72.0 (40.27)

87.3 (24.68)

Mean z-score (SD)

-0.64 (2.9)

-0.88 (2.43)

%moderately stunted (z-score <-2 ≥ -3)

40(12.3)

44(16.9)

13.7

0.0003

% severely stunted (z-score < -3)

21(6.6)

39(15.2)

Mean percentage Weight-for-Age % (SD)

44.6 (20.1)

35.5 (17.6)

Mean z-score (SD)

-0.52 (1.76)

-0.63 (1.55)

%moderately underweight (z-score <-2 ≥ -3)

64 (19.7)

48 (18.6)

% severely underweight (z-score < -3)

41 (12.7)

38 (14.8)

0.032

0.859

Haemoglobin estimation (g/dl)

Mean (SD)

10.94 (1.86)

10.16 (2.62)

%moderately anaemic (Hb 10g/dl ->7g/dl)

31 (9.7)

49 (18.8)

8.87

0.003

% severely anaemic (Hb ≤ 7g/dl)

5 (1.7)

2 (0.9)

SD, standard deviation; n, number of children.

30 years and 19.2% in mothers ≥ 31 years) was statisti-

The distribution of stunted, underweight and anaemia of

cally significant ( p = 0.032). The difference in number

the children according to mothers’ age, educational

of children that were anaemicin two age categories of

status, household size and place of resident are presented

mothers’ (66.5% in mothers ≤ 30 years and 65.6% in

in Table 3. About equal percentage of children (30.6%

mothers ≥ 31 years) was not statistically significant ( p =

mothers ≤ 30 years and 30.4% for mothers ≥31 years)

0.802).

were stunted in two age categories of mothers’ age.The

The difference in number of children that were stunted

difference in number of children that were underweight

in two educational categories of mothers’ (21.8% in

in two age categories of mothers’ (12.7% in mothers ≤

poorly-educated mothers and 35.2% in well-educated

mothers) was statistically significant ( p = 0.010). The

nificant ( p = 0.627). The difference in number of chil-

difference in number of children that were underweight

dren that were underweight in two household categories

in two age categories of mothers’ (12.7% in poorly -

(23.6% in small household and 13.1% in large house-

educated mothers and 19.4% in well-educated mothers)

hold) was statistically significant ( p = 0.002. The differ-

was not statistically significant ( p = 0.096).The differ-

ence in number of children that were anaemic in two

ence in number of children that were anaemicin two age

household categories (69.1% in small household and

categories of mothers’ (47.3% in poorly -educated moth-

60.7% in large household) was statistically significant ( p

ers and 65.2% in well-educated mothers) was statisti-

= 0.036).

cally significant ( p = 0.01).

There was no statistical difference in the prevalence of

The difference in number of children that were stunted

stunted ( p = 0.191), underweight ( p = 0.917) or anaemia

in two household categories (35.6% in small household

( p = 0.463) between urban and rural resident children.

and 33.6% in large household) was not statistically sig-

Table 3: Factors that may affect stunted (height-for-age z-score <-2), underweight (weight-for-age z-score < -2) and anaemia

(haemoglobin estimation ≤ 11g/dl) using multivariate analysis.

Stunted

Underweight

Anaemia

n (%)

variables

n (%)

Adjust OR

p value

(p-value)

95%CI

Mothers’ age

≤ 30 (n=338)

103 (30.6)

0.001

43 (12.7)

4.637

225 (66.5 )

0.063

0.611

≥ 31(n=244)

74(30.4)

(0.970)

47 (19.2)

(0.032)

160 ( 65.6 )

(0.802)

(0.39-0.96)

Mothers’ education

No formal education & Primary

23 (21.8)

6.579

13 (12.7)

2.775

50 (47.3)

10.741

0.52

0.49

(n=104)

168 (35.2)

(0.010)

93 (19.4)

(0.096)

312 (65.2)

(0.001)

(0.32-0.86)

(0.32-0.76)

Secondary & Tertiary (n=478)

Household size

133 (35.6 )

0.236

88 (23.6 )

9.472

258 (69.1)

4.399

2.07

1.46

≤ 3 children (n=373)

70 (33.6 )

(0.627)

27 (13.1 )

(0.002)

126 (60.7)

(0.036)

(1.29-3.31)

(1.02-2.08)

≥4 children (n=208)

Place of resident

78 (23.0)

1.712

113 (33.3)

0.011

226 (66.7)

0.539

Urban (n=339)

45 (18.5)

(0.191)

82 (33.9)

(0.917)

169 (69.4)

(0.463)

Rural (n=243)

The distribution of weight-for-age for both rural and

high with no inter-district variation between urban and

urban children is represented in Figure 2. There was an

rural. Studies among Kazakhstan and Indonesia children

reported lower prevalence respectively

18,21

uneven distribution with most of the population skewed

. This could

towards the left side of the mean in both urban and rural.

be attributed to seasonal variation with farming seasons

A greater proportion of the both urban and rural children

in the rural and nutritional deprivation when schools

had weight that was below the standard weight for age.

session in the urban.

Thus there is a higher tendency for a child to be under-

weight than have normal weight or being overweight.

The proportion of children that were anaemic was high

and are well documented: Assefa et al , Ughasoro et

al , and Dangour et al , in their studies among Ethio-

The distribution of height-for-age for both rural and

urban children is represented in Figure 3. There was an

pian, Nigeria and Kazakhstan children respectively re-

uneven distribution with most of the population skewed

ported high prevalence of moderate-severely anaemic in

towards the right side of the mean in both urban and

children. In this study, the prevalence in the rural was

rural. Greater proportion of both urban and rural chil-

higher compared to the children in the urban and the

dren had normal height. Thus there is higher tendency

difference was statistically significant. Could it be that

for a child to have normal height than to be stunted.

most children in the rural tend to consume non-heme

iron as their main source of dietary iron.

Lack of at-

tention to the nutritional content of food children feed

on could be contributory.

The children of well-

Discussion

educated mothers have more tendency to be stunted and

anaemic compared to the children of poor educated

mothers. This is not in accord with expected variation

The prevalence of stunted growth was low among chil-

dren in both urban and rural areas. This was similar to

since low maternal education is expected to affect chil-

15% that Dangour et al

reported but contrast to what

dren nutritional status adversely

. There is no clear

was reported by Fernando et al.

The prevalence was

hypothesis that can explain this. Could it be that most

higher among rural school children and similar to what

enlightened mother practice exclusive breastfeed for a

has been reported. The overall relatively equal preva-

shorter period before introducing cereal-based feeds

lence obtained in the two localities could be due to var-

early and often given their children iron supplements?

ied dynamics that exist in these different environment.

Studies have shown that phytates in some cereal feeds

The proportion of children that were underweight was

chelate calcium and reduce its absorption. Also presence

of high iron content in the intestine reduces the absorp-

bin genotype. Information on these biological determi-

tion of calcium, with resultant nutritional rickets and

nants will give insight into the complexity of factors that

reduced linear length of the bones and height in general.

determine the nutritional status of a child. Another limi-

According to the WHO malnutrition classification, when

tation is the use of Oyedeji an old model for categoriz-

the prevalence of stunting, and wasting are ≥ 40% and ≥

ing socio-economic class. A more recent tools like use

15%, it is considered as very high and serious if the

of household assets and household financial income to

range of 30 -39.9% and 10 – 14.9% in the community

generate quartile or quintile groups, would have been

respectively.

In this study, the prevalence of stunting

more appropriate. Unfortunately, the data collected

in the study ranged from 18.9% in the urban to 32.1% in

could provide the necessary indices require for such

the rural. Therefore, can be regarded as a serious public

calculation.

health problem in the rural, while efforts should be to

keep it down in the urban.

The prevalence of underweight and anaemia among chil-

Conclusion

dren in large households were significantly higher and

this is similar to what have been reported.

27,28

This could

The prevalence of stunted, underweight and aneamia in

be attributed to increased concentration of food con-

southeast Nigeria was high. There are multiple causes of

sumption of resources in the family.

It stand to reason

malnutrition in Nigeria children, but rural dwelling,

that increase in family size lead to stretch on family in-

large household size, and low mother’s educational level

come with resultant reduce food availability and under-

are prominent among the non-biological factors. There-

nutrition, But there are various cofounding variables:

fore any intervention aimed to improve on the nutri-

household income, and maternal education, that have to

tional status of children in a given community should

be considered before drawing any conclusion. One ma-

make every effort to improve the overall maternal edu-

jor limitation of the study was none inclusion of biologi-

cation, improve standard of living in the rural communi-

cal determinants like malaria, intestinal helminths, bac-

ties and improved family planning strategy.

teremia, human immunodeficiency virus and haemoglo-

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