undernutrition and associated risk factors among …

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97

Ntsama et al. World Journal of Pharmacy and Pharmaceutical Sciences

UNDERNUTRITION AND ASSOCIATED RISK FACTORS AMONG

PRESCHOOL AGE CHILDREN IN EVODOULA HEALTH DISTRICT,

CENTRAL REGION OF CAMEROON

Patricia M. Ntsama1,2

*, Anne Christine A. Ndzana1, Véronique J. Essa’a

1, Julie Judith

T. Tsafack1, Gabriel Nama Medoua

1 and Carl M. F. Mbofung

2

1Centre for Food and Nutrition Research, IMPM, P O Box 6163 Yaounde, Cameroon.

2ENSAI, University of Ngaoundere, P.O. Box 455, Ngaoundere, Cameroon.

ABSTRACT

Background: Limited information is available on the risk factors

associated with preschool child undernutrition in Cameroon. This

study describes the prevalence of stunting, wasting and underweight

and their associated factors among pre-school children living in

Evodoula health district, Centre region of Cameroon. Methods: This

was a cross-sectional study that collected anthropometric and socio-

demographic characteristics data in 738 children aged 6 - 59 months.

Weight-for-Height z-score, weight-for-Age z-score and height-for-Age

z-score were based on WHO’s 2006 Child Growth Standards. The

association between undernutrition and associated factors was

determined using multivariate logistic regression analysis. Results: Of the 733 children

included in the analysis, 19.4%, 14.5%, and 8.3%were respectively stunted, underweight and

wasted. Child age was the main factor associated with stunting. Underweight was associated

with child sex, child age, birth weight, family size and mother education. Wasting was

associated with child age, mother age, number of children in household, number of mother’s

children and whether the father had a job. Conclusion: Despite a relatively good food self-

sufficiency situation, undernutrition is prevalent among pre-schoolage children in Evodoula

health district at prevalence above the national prevalence for wasting. Socio-demographic

factors were associated with the different types of undernutrition.

KEYWORDS: Undernutrition, pre-school Children, risk factors, Centre Cameroon.

WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES

SJIF Impact Factor 6.647

Volume 6, Issue 7, 97-111 Research Article ISSN 2278 – 4357

Article Received on

04 May 2017,

Revised on 25 May l 2017,

Accepted on 14 June 2017,

DOI: 10.20959/wjpps20177-9537

*Corresponding Author

Patricia M. Ntsama

Centre for Food and

Nutrition Research,

IMPM, P O Box 6163

Yaounde, Cameroon.


98


INTRODUCTION

Child undernutrition is a major global health problem, contributing to increased morbidity

and mortality, impaired intellectual development and working capacity, and increased risk of

disease in adulthood.[1]

20% of the 7.6 million deaths per year recorded among children under

5 years old can be attributed to undernutrition.[2]

Three indices are commonly used to describe child undernutrition: height-for-age measures

long-term growth retardation (stunting or chronic undernutrition); weight-for-height measures

current or acute undernutrition (wasting) resulting from failure to gain weight or actual

weight loss; and weight-for-age measures the condition of being underweight, it is a

composite measure of stunting and wasting and is recommended as the indicator to assess

changes in the magnitude of undernutrition overtime.[3]

The causes of undernutrition are diverse. The conceptual framework developed by UNICEF

classifies the causes of undernutrition into (a) immediate causes: inadequate dietary intake

and disease, (b) underlying causes: household food insecurity, inadequate care and feeding

practices, unhealthy household environment and inadequate health services, (c) basic causes:

household access to adequate quantity and quality of resources (land, education, employment,

income, technology), inadequate financial, human, physical and social capital, and socio

cultural, economic and political context.[4]

In Cameroon, the various Demographic and Health Surveys [5–8]showed that the nutritional

status of children has deteriorated over the last 20 years. 33% of children under 5 years suffer

from stunting, 15% are underweight, 6% are wasted and 60% suffer from anaemia. However,

there are large disparities among regions; it is estimated that about 50% to 70% of

Cameroonian undernourished children live in the four poorest regions (Far North, North,

Adamawa and East) where health, education and socio-economic indicators are the worst in

the country. If the other six regions present better health, education, and socio-economic

indicators, the nutritional status of children is not always well-known.

Evodoula health district is located in the humid forest zone, near the capital and it is among

the most food self-sufficient localities in the country. Most families rely on cocoa and crops

sales to generate cash income. Food consumption provides about 1741 kcal per capita, which

derived mainly from roots and tubers (50%), leguminous (19%) and fat (17%); vegetables are

the main source of protein with 70% of total protein intake, while protein of animal source


99


(fish and meat) provide 30%.[9]

Depending of the season, banana, plum, avocado, orange,

mandarin, mango, papaya, coursop, pineapple and cane-sugar are also wide consumed.[10]

Even if there is relatively enough for people to eat, previous studies[9]

in this zone showed no

direct correlation between food consumption and the nutritional status of population. Other

factors related to the environment, economy, sociology and policy determine the nutritional

status of population.[4]

However, there is limited knowledge of factors associated to

undernutrition in population of Cameroon. Therefore, the present study aimed to describe the

prevalence of undernutrition among pre-school children, and their associated factors in the

Evodoula health district.

METHODS

Study Design

This was a cross-sectional study that assessed the prevalence of undernutrition and associated

factors among children aged 6 – 59 months.

Study Area and Period

The study was conducted during the months of March and April in the health district of

Evodoula (Centre region, Cameroon) located at about 45 km of Yaoundé, the capital, and at

an altitude of 5-600 m above the sea level. The health district of Evodoula is constituted of 6

sub districts: Evodoula, Kalgaha, Ngobo, Nkolassa, Nkolkougda and Nloudou. This health

district is located in the forest area characterized by an equatorial type climate. The seasons

are principally marked by the presence and intensity of rain. From November to March, we

are in the dry season with no or rare rains. The maximum and minimum temperatures are

about 32°C and 18°C respectively, and the relative humidity between 50% and 70%. Cocoa,

peanut, maize, cassava, yams, palm-nut and banana/plantain are the main food crops, while

rice is the main cash crop. Livestock holding in chicken, sheep and pork are relative modest.

Health problem is dominated by infectious and parasitic diseases; malaria is the first cause of

morbidity and mortality, followed by respiratory and gastrointestinal infections.

Study population

The sample size was determined using a single population proportion formula with 36%

prevalence of stunting in Cameroon[8]

, 95% confidence interval and5% marginal of error.

After including 4% non-response rate and multiplying the design effect by 2, 738 children

were included in the study. Source population was all children aged 6-59 months living in the


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health district of Evodoula. Among the 738 children recruited, 222 were living in Evodoula,

118 in Nkolkougda, 66 in Nkolassa, 91 in Nloudou, 156 in Nkalga'a, and 80 in Ngobo.

Data collection

Using a structured questionnaire, caregivers were interviewed on the child’s socio-

demographic characteristics. Data were obtained on whether the child live with his two

parents, the education level of the mother and the father, whether the mother was the primary

caretaker, whether the mother had a job, the family size, the vaccination history of the child

and the occurrence of illness during the previous two weeks, the child’s date of birth, the

child’s birth order, the child’s birth weight, the child’s sex, and the child’s place of delivery.

Anthropometric measurements were made by trained personnel using standard procedures.[11]

The children were weighed without clothes to the nearest 5 g using a portable electronic

infant scale (Seca 416, Hamburg, Germany). Length was measured to the nearest 0.1 cm

using a standardized infant meter (Seca 416, Hamburg, Germany) and standing height was

measured to the nearest 0.1 cm using a portable adult gauge. Anthropometric indices

(Weight-for-Height z-score, Weight-for-Age z-score and Height-for-Age z-score) were based

on the WHO’s 2006[11]

Child Growth Standards, calculated by using WHO Anthro v3.2.2.

Assuming the recommended cut-offs for data exclusion[11]

, data were excluded if a child’s

height-for-age z-score (HAZ) was below -6 or above +6, weight-for-age z-score was below -6

or above +5, or weight-for-height z-score (WHZ) was below -5 or above +5, as these extreme

values were most likely a result of errors in measurement or data entry.[11]

Statistical analysis

The main outcomes of interest were stunting defined as height-for-age z-score (HAZ) -2,

underweight defined as weight-for-age z-score (WAZ) -2, moderate wasting defined as

weight-for-height z-score (WHZ) of ≥ -3 and -2, severe wasting defined as WHZ -3.

Statistical analyses were performed using IBM SPSS Statistics 21 software. Chi-square test

was used to test for significant association of the proportion. A P values < 0.05 was

considered to be statistically significant. The association between undernutrition and

associated factors was determined using logistic regression, univariate and multivariate

analyses were performed. With regard to multivariate analysis, only variables found to be

associated to undernutrition with a p value ≤0.25 in univariate analyses were introduced in

the logistical regression model.


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Figure 1: Distribution of wasting according to sub districts of Evodoula health district.

Figure 2: Distribution of underweight according to sub districts of Evodoula health

district

Figure 3: Distribution of stunting according to sub districts of Evodoula health district


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RESULTS

Sample characteristics

738 children were enrolled in the study and after applying the recommended cut-offs for data

exclusion[11]

, a total of 733 children were included in the analysis; their characteristics are

displayed in table 1. Large majority (90%) of children were delivered in health facilities and

received all vaccines prescribed by the national program of immunization (88%). The

majority (77%) of caregivers were living with a partner and most of fathers (87%) as well as

most of mothers (61%) had a job. Regarding the family size, the majority (53%) of household

had less than three children and most of mothers (59%) had one or two children. Concerning

the educational level, 64% of fathers and 75% of mothers had at least secondary education

level.

Table 1: Distribution of the children according to age and gender

Explanatory variables n (%)

Sex of the child Male 374 (51.0)

Female 359 (49.0)

Age of the child (months)

6-11 75 (10.2)

12-23 191 (26.1)

24-35 139 (19.0)

36-47 135 (18.4)

48-59 193 (26.3)

Place of childbirth Health facility 663 (90.4)

Home 70 (9.6)

Birth weight of the child < 3000 g 219 (29.9)

≥ 3000 g 514 (70.1)

Immunization Received all vaccines 644 (87.9)

Did not received all vaccines 89 (12.1)

Marital status of caregiver

live with a partner without being married 422 (57.6)

Married 144 (19.6)

Single 134 (18.3)

Divorced or widow 33 (4.5)

Age of the father < 31 years 380 (51.8)

≥ 31 years 353 (48.2)

Age of the mother < 25 years 375 (51.2)

≥ 25 years 358 (48.8)

Family size < 3 children 389 (53.1)

≥ 3 children 344 (46.9)

Number of mother’s children 1-2 436 (59.5)

> 2 297 (40.5)

The father has a job Yes 641 (87.4)

No 92 (12.6)

The mother has a job Yes 450 (61.4)

No 283(38.6)


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Education level of the father

Primary 265 (36.1)

Secondary 431 (58.8)

University 37 (5.0)

Education level of the mother

Primary 181 (24.7)

Secondary 474 (64.7)

University 78 (10.6)

Wasting

Based on WHZ-score, 61 of the 733 children (8.3%; 95% CI: 5.4% - 12.6%) suffered from

wasting. A total of 8.8% of males (95% CI: 5.4% - 14.1%) and 7.8% of females (95% CI:

5.0% - 12.0%) was affected by wasting. Globally, there was no significant difference (p =

0.619) between the two genders. The prevalence of wasting was highest among the 12 – 23

months age group (13.6%; 95% CI: 8.3% - 21.5%) and least in the 36 – 47 months age group

(3.0%; 95% CI: 1.0% - 9.0%).

The prevalence of wasting was highest in Nloudou sub district and least in Evodoula sub

district (Figure1). The logistic regression analysis showed that children of Evodoula and

Nkalga’a were less likely (p < 0.05) to become wasted than children of Nloudou (OR: 0.42;

95% CI: 0.18-0.99 and OR: 0.43; 95% CI: 0.18-1.01 respectively).

The multivariate logistic regression analysis (Table 2) showed that child age, the birth weight

of the child, mother age, number of children in household, number of mother’s children and

whether the father had a job were significantly associated with wasting. Children of the age

group 12-23 months were 2.16 time more likely to be wasted than children of the group age

48-59 months. Children born with a body weight equal or superior to 3000 g were 0.41 less

likely to be wasted than those born with a body weight inferior to 3000 g. Children of

mothers aged above 25 years old were less likely to be wasted than children of young

mothers (< 25 years old). Children living in households with 5 or more than 5 children were

1.49 times more likely to be wasted than children living in households with less than 5

children. Similarly, children of women with more than two children were 1.94 times more

likely to be wasted than children of women with 1-2 children. Children of fathers having no

job were less likely to be wasted than children of fathers having a job. The sex of the child,

whether the mother has a job, the age of the father and the education level of the mother or

the father were not statically associated with wasting.


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Table 2: Multivariable logistic regression analysis of wasting among pre-school children

in Evodoula health district

Explanatory variables n (%) OR 95% CI p-value

Sex of the child Male 33 (8.8) 1

Female 28 (7.8) 0.77 0.47-1.59 0.647


6-11 7 (9.3) 1.37 0.46-4.39 0.547

12-23 26 (13.6) 2.16 1.03-5.17 0.043

24-35 12 (8.6) 1.23 0.51-3.15 0.616

36-47 4 (3.0) 0.58 0.18-2.06 0.424

48-59 12 (6.2) 1

Birth weight of the child < 3000 g 23 (10.5) 1

≥ 3000 g 38 (7.4) 0.41 0.22-1.76 0.025

Age of the father < 31 years 36 (9.7) 1

≥ 31 years 25 (6.9) 0.56 0.36-1.87 0.225

Age of the mother < 25 years 37 (10.0) 1

≥ 25 years 24 (6.6) 0.62 0.40-0.94 0.023

Number of children in the household < 5 25 (6.9) 1

≥ 5 36 (9.7) 1.49 1.15-3.29 0.017

Number of mother’s children 1-2 32 (8.7) 1

> 2 29 (7.9) 1.94 1.75-5.74 0.036

The father has a job Yes 48 (16.6) 1

No 13 (10.0) 0.35 0.07-0.74 0.032

The mother has a job Yes 25 (6.9) 1

No 36 (9.7) 0.38 0.21-1.73 0.343


Primary 29 (11.0) 1

Secondary 30 (6.9) 0.65 0.39-1.53 0.456

University 2 (5.4) 0.43 0.26-2.32 0.397


Primary 25 (13.9) 1

Secondary 30 (6.3) 1.3 0.77-3.47 0.339

University 6 (7.7) 0.2 0.09-1.53 0.356

Underweight

Based on WAZ-score, 106 of the 733 children (14.5%; 95% CI: 8.7% - 23%) were

underweight. Significantly higher proportion of males (17.9%; 95% CI: 11.7% - 26.5%) than

females (10.9%; 95% CI: 5.3% - 20.9%) were affected by underweight. The prevalence of

underweight was highest among the 12 – 23 months age group (19.9%; 95% CI: 9.4% -

37.2%) and least in the 48 – 59 months age group (10.3%; 95% CI: 6.5% - 15.9%). Figure 2

displays the distribution of underweight according to sub districts. The logistic regression

analysis showed that children of Evodoula and Nkolkougda were less likely (p < 0.05) to

become underweight than children of Nloudou (RR: 0.29; 95% CI: 0.14-0.59 and RR: 0.41;

95% CI: 0.19-0.85 respectively).


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The multivariate logistic regression analysis (Table 3) showed that child sex, child age, birth

weight of the child, number of children in household, and education level of the mother were

significantly (p < 0.05) associated with underweight. Female children were less likely to be

underweight than male children. Children of the age group 12-23 months were 2.44 times

more likely to be underweight than children of the group age 48-59 months. Children born

with less than 3000 g were 2.13 more likely to be underweight than children born with more

than 3000 g. Children living in households with 5 or more than 5 children were 1.25 times

more likely to be underweight than children living in households with less than 5 children.

Children of mothers with secondary or university level were less likely to be underweight

than children of mothers with primary education level. The age of the father, the age of the

mother, the number of children of the mother, whether the father or the mother has a job and

the education level of the father were not statically associated with underweight.

Table 3: Multivariable logistic regression analysis of underweight among preschool

children in Evodoula health district



Female 39 (10.9) 0.38 0.23-0.89 0.026


6-11 12 (16.0) 1.58 0.66-3.78 0.305

12-23 38 (19.9) 2.44 1.26-4.73 0.008

24-35 22 (15.8) 1.54 0.73-3.25 0.259

36-47 14 (10.4) 1.24 0.56-2.72 0.599

48-59 20 (10.4) 1

Birth weight of the child < 3000 g 48 (21.9) 2.13 1.34-3.38 0.026

≥ 3000 g 55(10.7) 1


≥ 31 years 39 (10.6) 1.57 0.53-3.21 0.361


≥ 25 years 41 (11.2) 1.80 0.87-4,34 0.275


≥ 5 67 (18.2) 1.25 1.04 -2.87 0.019


> 2 49 (13.4) 0.85 0.67-1.83 0.296

The father has a job Yes 73 (19.9) 1

No 33 (8.9) 0.43 0.21-1.64 0.379


No 79 (17.1) 0.73 0.60-1.87 0.343


Primary 49 (18.6) 1

Secondary 55 (12.7) 1.05 0.83-2.25 0.344

University 2 (5.4) 1.67 0.26-1.92 0.375


Primary 67 (18.2) 1

Secondary 39 (10.6) 0.65 0.17-0.82 0.026

University 0 (0.00) 0.30 0.21-0.79 0.018


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Stunting

Based on HAZ-score, 142 of the 733 children (19.4%; 95% CI: 14.7% - 25.1%) were stunted.

Significantly higher proportion of males (22.7%; 95% CI: 16.3% - 30.8%) than females

(15.9%; 95% CI: 11.4% - 21.6%) were affected by stunting. The prevalence of stunting was

highest among the 12 – 23 months age group (24.1%; 95% CI: 19% - 30%) and least in the

48 – 59 months age group (14.4%; 95% CI: 11.4% - 18.1%). Figure 3 displays the

distribution of stunting according to sub districts. Prevalence of stunting was relatively

highest in Nkolassa sub district and least in Nkolkougda sub district. However, the logistic

regression analysis showed no significant (p < 0.05) association between stunting and sub

districts.

The association between stunting and some socio-economic characteristics of children is

presented in Table 4. The age of the child and the birth weight were the only factors

significantly (p < 0.05) associated with stunting. Children of the age groups 12-23 and 24-35

months were respectively 2.16 and 2.03 times more likely to be stunted than children of the

group age 48-59 months, and children born with a body weight inferior to 3000 g were more

likely to be stunted than those born with a body weight equal or superior to 3000 g.

Table 4: Multivariable logistic regression analysis of stunting among preschool children

in Evodoula health district



Female 57 (15.9) 0.89 0.64-1.23 0.062

Age of the child (months) 6-11 7 (9.3) 0.58 0.21-1.89 0.330

12-23 46 (24.1) 2.16 1.12-4.23 0.012

24-35 32 (23.0) 2.03 1.23-4.09 0.021

36-47 29 (21.5) 1.86 1.09-3.66 0.051

48-59 28 (14.5) 1

Birth weight of the child < 3000 g 66 (30.1) 1.43 0.48-2.89 0.017

≥ 3000 g 72 (14.0) 1


≥ 31 years 59 (7.99) 1.29 0.93-1.81 0.247


≥ 25 years 62 (8.40) 1.44 0.78-3.07 0.183


≥ 5 46 (6.23) 0.82 0.72-1.17 0.158


> 2 69 (9.34) 1.35 0.91-1.98 0.196

The father has a job Yes 105(14.23) 1

No 33 (4.46) 0.83 0.69-0.98 0.122


No 82 (11.1) 1.07 0.69-1.64 0.369


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Education level of the father Primary 91 (6.23) 1

Secondary 77 (12.46) 0.85 0.69-1.93 0.374

University 0 (0.00) 0.43 0.26-1.82 0.312

Education level of the mother Primary 76 (10.29) 1

Secondary 62 (8.40) 0.91 0.77-3.47 0.263

University 0 (0.00) 0.30 0.21-1.53 0.281

DISCUSSION

Pre-school children living in Evodoula Health District were affected by undernutrition in

different proportions; the prevalence of stunting, underweight and wasting was 19.4%, 14.5%

and 8.3% respectively.

Although in the same WHO classification range (medium) for severity of undernutrition, the

prevalence of global wasting (8.3%) in our study was relatively higher than the prevalence

reported at national level (6%).[8]

This prevalence was even higher than the prevalence

reported for the Adamawa (6.4%) and the East (5.9%) regions[8]

which are among the regions

presenting the worst socio-economic indicator in the country. In some way, this could be

explain by the fact that our study was conducted by the end of the dry season, where food

availability is likely to be reduced, as several studies showed that the index of wasting

(WHZ) was sensitive to seasonal changes particularly to the end of dry season.[12-14]

However, Evodoula health district is located in the humid forest, and contrary to savannah,

seasonal variations have little impact on the availability of food in the forest.[15]

Our study

period (March – April) corresponds to the peak period for farm activities with a high physical

implication of women[15]

. This involvement of women in farm activities reduced their time to

take proper care of children, and this is more likely to explain the high proportion of wasted

children in our study. We observed that most children did not have their mothers as primary

caretaker during day times; most of the time, the mothers were occupied by farm activities,

living the children without proper care and almost without food as the food left were not

always eaten by designated children. The child age, the birth weight of the child, the mother

age, the number of children in household, the number of mother’s children and whether the

father had a job were factors associated with wasting. Positive association between wasting

and the number of children in household and the number of mother’s children, suggest that

larger family size expose children at higher risk of wasting, which could be due to the

imbalance between family size and resources.[16]

Interestingly, children of fathers with no job

were less likely to be wasted than children of fathers having a job; this could be explain in a

way that fathers having no job had more time to take care of their children. In this regards, a


108


study conducted by Tran[17]

showed that father’s involvement in child care significantly

reduce the chance of children to become malnourish.

The prevalence of stunting in our study (19.4%) was lower than the prevalence (36%) found

in 1996 in the same area, in the same group of age.[9]

This difference could be explained by

improvement of local conditions. Besides, the results of this study revealed that the

prevalence of stunting in Evodoula Health District was lower than the prevalence noted by

EDS-MICS[8]

in the 10 regions of Cameroon except Douala and Yaoundé where a prevalence

of 12.9% and 12.8% was respectively observed. The lower incidence of stunting in Evodoula

Health District compare to the other regions could be a reflection of the fact that this area is

among the most food self-sufficient localities in the country. Regarding the factors associated

with chronic undernutrition, only the age and the birth weight of the child presented

significant association, suggesting these factors should be most considered when targeting

stunting in this locality. Contrary to the study reported by Herrador et al.[16]

in Ethiopia, sex

of child, family size and maternal education status were not statistically associated with

stunting. Children of 12 to 23 month olds were most affected by stunting compared to those

of other age group. This could be a result of poor dietary diversity and complementary

feeding practices, as significant associations between height-for-age Z scores with

complementary feeding practices[18,19]

and with dietary diversity[20,21]

were shown in young

children. It was noted that children born with low weight were more likely to be stunted. This

was in line with previous study reporting that low birth weight is a determinant of protein

energy undernutrition in 0 - 5 years children.[22]

WAZ is a composite index that reflects both chronic and acute undernutrition although it is

not distinguishing between the two. In this study, the prevalence of underweight was similar

to the prevalence reported at national level (15%) by EDS-MICS[8]

and would be classified as

poor in WHO classification for severity of undernutrition. Almost all factors associated with

wasting or stunting were associated with underweight except child sex, and education level of

the mother, which were uniquely associated with underweight. Male children were more

likely to be underweight than female children; similar results were reported in Vietnam and

Ethiopia.[23,24]

Behavioural patterns including preferential treatment of females and

favouritism towards daughters[25]

were reported to be the main reason of sex differences

associated with child undernutrition in sub-Saharan Africa. We found that children of

educated women were less likely to be underweight than children of uneducated women.


109


Education is one of the most important resources that enable women to provide suitable care

for their children. Education of women is believed to exert an impact on health and

nutritional status of children since it provides the mother with the necessary skills for

childcare, increase awareness of nutritional needs and preference of modern health facilities

as well as change of traditional beliefs about diseases cause, and use of contraceptives for

birth spacing.[26]

CONCLUSION

The present study showed that undernutrition is prevalent among pre-school age children in

Evodoula Health District. It was interesting to note that the prevalence of wasting in this area

were higher than the prevalence reported at national level and even for the regions of

Adamawa and East which are among the 4 least privileged regions in the country. Multiple

factors were significantly associated to child undernutrition including child sex, child age,

birth weight, family size, mother education, mother age and whether the father had a job. It is

advisable to take this into account when designing the policy to tackle child undernutrition.

List of abbreviations

MUAC (mid-upper arm circumference), BMI (body mass index), OR (odd ratio), CI

(confidence interval), WAZ (weight-for-age Z-score), WHZ (weight-for-height Z-score),

HAZ (height-for-age Z-score), WHO (World Health Organization).

DECLARATIONS

Ethics approval and consent to participate

The protocol was approved by the ethics committee of the Institute of Medical Research and

Medicinal Plants Studies (IMPM). Informed written consent was obtained from each

caregiver of children participating in the study.

ACKNOWLEDGEMENTS

We are grateful to the children and caregivers who generously gave their time and

commitment to fulfil the need for taking part in this study. We acknowledge the contributions

from Henriette Dimodi, Thomas Ndanga and Philomene Emale who participated in the field

activities.


110


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