This study was carried out in the commune of Ratoma, located to the north-west of the Kaloum peninsula, one of the five (5) communes of the city of Conakry. It covers an area of 62 km². The commune of Ratoma comprises 34 districts and 211 sectors with a population of 653934 people. It is bordered to the north by the Atlantic Ocean, to the south by the communes of Dixinn and Matam, to the west by the Atlantic Ocean and to the east by the commune of Matoto and the prefecture of Dubreka. Ratoma lies between latitude 9˚35' north and longitude 13˚39' west. It encompasses both agricultural urban areas and peri-urban areas that have been more or less taken over by the urbanisation process. The figure below shows the spatial distribution of the schools surveyed in the commune of Ratoma. Figure 1 presents the spatial distribution of schools surveyed in Ratoma commune.

The study was carried out in 10 nursery schools in the commune of Ratoma, as shown in Table 1 .

The material used for this study includes the 2012 UNICEF reference register for cases of moderate and severe malnutrition, as well as the WHO weight/height reference file. It also includes the mission order from the Ratoma DCE and the survey form. In addition, various items of equipment were mobilised to measure anthropometric parameters, including a person weight (kg), a vertical measuring rod (cm) and a tape measure.

The nutritional status of children aged 2 to 4 is assessed using the strictly brachial/head circumference ratio (PB/PC), while that of children aged 4 to 5 is determined by the weight/height ratio (P/T). Brachial circumference (BP) is measured on the left arm, midway between the acromion and the olecranon, using a non-elastic tape measure. The head circumference (PC) is also measured using a tape measure. The children were weighed barefoot using an electronic scale, and their weight and height were compared with the P/T index established by the WHO. Height was measured using a vertical measuring rod for children over 2 years of age.

This is a descriptive cross-sectional study running from Wednesday 25 January to Thursday 02 March 2023 on a population aged 2 to 5 years.

In this study, the selection of nursery schools in the municipality of Ratoma was carried out using a rigorous approach aimed at ensuring the representativeness of the results.

1) Selection criteria for schools

The selection of schools was guided by several criteria to cover a diversity of situations and ensure conclusions that can be generalized to the entire municipality.

2) Geographical criteria:

The schools were selected in a way that covers different areas of Ratoma, including urban, suburban, and disadvantaged neighborhoods, to capture disparities in living conditions and access to health services.

3) Socio-economic criteria:

The sample includes both public and private schools to assess the influence of socio-economic levels on the nutritional status of children.

4) Accessibility and collaboration criteria:

The participation of institutions was a key factor in the selection. The selected schools expressed their willingness to collaborate by facilitating access to children and providing necessary administrative information.

5) Criteria based on pre-existing data:

Some schools were chosen due to previous reports of malnutrition or nutritional vulnerability, thus allowing for the analysis of trends and comparison with schools that had no such history.

6) Inclusion criteria

Children aged 2 to 5 years were included in this study at the maternal level in our study environment.

7) Exclusion criteria

Children aged 2 to 5 in nursery schools and in cases of refusal.

8) Study variables

The information collected was:

The weight of children aged two and over was measured using a person-weight scale. For height, a horizontal scale was used for children under three, while a vertical scale was used for those over three.

In this study, the assessment of children’s nutritional status is based on the calculation of the Z-score, which corresponds to the relationship between various variables such as weight/height, height/age and weight/age. Good nutritional status is defined by a weight/height Z-score greater than −1 standard deviation (SD). Moderate acute malnutrition is characterised by a weight/height Z-score of between −2 and −3 SD, while severe acute malnutrition corresponds to a weight/height Z-score of less than −3 SD. In parallel, chronic malnutrition, or stunting, is determined by a height-for-age Z-score of less than −2 on the standard curves. The study also takes into account the number of meals the child eats each day and the child’s social situation.

The data were entered and analysed using SPSS 20, EXCEL, WORLD 2016 software. WHO Anthro software was used to determine the nutritional status of the children.

The reliability of the equipment used in this study is ensured by several factors:

Over 29 working days, we surveyed 402 children aged 2 to 5 who met our inclusion criteria in ten (10) nursery schools in the commune of Ratoma. Table 2 and Figure 2 display the distribution of the samples surveyed in each school.

Figure 3 presents the distribution of respondents by gender for each school.

Of the 402 children surveyed, 51.74% were male. Table 3 shows the distribution of the number of children by age group.

Table 3 presents the breakdown of surveys by age.

Figure 4 presents the Distribution of respondents by age for each school.

The children’s ages ranged from 2 to 5 years. Children aged 5 were the most frequently surveyed (41.79%), followed by children aged 4, 3 and 2 (28.10%, 24.62% and 5.47% respectively). The table below shows the nutritional status of children aged 2 to 4.

Table 4 presents the nutritional status of children aged 2 to 4 years.

Of the 234 children aged 2 to 4, 213 (91.02%) were in a satisfactory nutritional state and 11 children were in a state of slight or moderate malnutrition. There were no cases of severe malnutrition.

Table 5 presents the nutritional status of children aged 5 years.

Of the 168 children aged 5, 146 (86.90%) were in a normal nutritional state, compared with 22 children who were moderately malnourished. There were no cases of severe malnutrition.

Table 6 presents the summary of children by nutritional status.

M A M: Moderate malnutrition; M A S: Nutritional status.

Figure 5 presents the breakdown of children by nutritional status.

Anthropometric measurements (weight, height, age, brachial circumference and head circumference) of 402 children revealed that 359 children (89.30%) were in a normal nutritional state and 43 children (10.69%) in a state of moderate acute malnutrition.

Table 7 presents the distribution of moderate malnourished between the sexes.

Table 7. Distribution of moderate malnourished between the sexes.

Figure 6 presents the distribution of malnourished children by gender.

Our study revealed a predominance of females: 24 cases, or 55.81%, compared with 19 cases of males, or 44.18%. Sex has no influence on the occurrence of malnutrition; it is simply used to indicate the population of children of one sex suffering from malnutrition compared with those of the opposite sex.

Table 8 and Figure 7 present the distribution of malnourished in the 10 schools surveyed.

M A M: Moderate malnutrition.

It is true that the sample/enrolment ratio is not the same in the schools visited; however, there were 0 cases of malnutrition in Kabassan and Hadja Kany schools, and higher percentages in José Marty (18.60%); Santa Maria (16.27%); Wariah (16.27%); Hadja Habibata (13.95%); Elhadj Naby (11.62%) and Safia (11.62%).

The Kabassan school, which had a high percentage of children surveyed (64.70%) after Oumou Diaby (78.57%), did not record any cases of malnutrition, whereas schools such as José Marty, Wariah and Safia, which had a low percentage of children surveyed, recorded cases of malnutrition.

Table 9 presents the distribution of malnourished children by school and age group.

Children aged 5 account for more than half of malnourished children. There were 51.16% of cases among 5-year-olds, followed by 23.25% of 4-year-olds, 16.27% of 3-year-olds and 9.30% of 2-year-olds. Children aged 2 to 4 represented a cumulative percentage of 48.82%.

A predominance of females was also reported overall, with 24 cases (55.81%) compared with 19 cases (44.18%) in males.

Une prédominance féminine a également été rapportée dans l’ensemble avec 24 cas soit 55,81% contre 19 cas de prédominance masculine soit 44.18%.

M A M: Moderate malnutrition.

The study in question, which focused on assessing the nutritional status of children aged between 2 and 5, involved a sample of 402 children. Specific results were obtained, but these differ from the conclusions of the study conducted by Compaoré in 2006, where a rate of 63.6% was observed [12] . This discrepancy may be explained by several factors, including the type of study and the context in which it was conducted. Methodologies and contexts can influence the results obtained: differences in the population studied (sample size, socio-economic characteristics of participants, etc.), the measuring instruments used, and the criteria for assessing nutritional status can all contribute to these variations. In addition, the geographical and temporal framework of the studies, as well as public health conditions at the time of each study, may also play a determining role in the differences observed. The results of this study, which show a lower malnutrition rate among children aged 2 to 5 years compared to the study by Compaoré (2006), can be explained by several factors. In fact, the methodological differences, particularly the measuring tools used and the criteria for assessing nutritional status, may partly explain this discrepancy [13] . Moreover, improvements in socio-economic and health conditions, as well as better access to healthcare and nutrition, may have contributed to better outcomes in our study. These factors, combined with geographical and temporal variations, can significantly influence the study results, highlighting the importance of considering the specific context of each study when interpreting the findings [13] .

The results obtained in this study, where boys represent 51.74% of the children studied, show a notable difference compared to those observed by Courpotin and Delpeuch, who found a proportion of boys of 65.4% and 65%, respectively [14] [15] . This variation can be explained by several methodological and contextual factors, such as differences in sample size, the age range of the children studied, and the geographical and socio-economic context of the research.

First, the sample size in this study may play a significant role. A smaller sample, like the one used here, could make the results more sensitive to fluctuations due to factors such as the natural variability in the sex distribution in smaller groups of children. In comparison, Courpotin and Delpeuch worked with larger samples, which could explain the higher proportion of boys observed in their studies [14] [15] .

Next, the age range of the children studied could also influence the sex distribution. Doumbia and Golden, in their research, may have included groups of children of different ages, which could explain differences in sex distribution based on the demographic structure of each age group [16] [17] . This study specifically examined children aged 2 to 5 years, and this segmentation may have an impact on the observed proportion of boys and girls.

Moreover, geographical and socio-economic differences between the studies must be taken into account. Courpotin and Delpeuch conducted their research in different contexts, where social and economic factors may have influenced the sex distribution based on local conditions. For example, cultural differences or public health practices may have an influence on the demographics of the samples studied. In this sense, the geographical context of this current study, in terms of location and access to healthcare, could also play a role in the observed difference.

In this study, the majority of children were 5 years old, representing 41.79% of the sample, with an average age of 3.97 ± 0.87 years. This result differs significantly from that of the Hobbs study, where the mean age was 1 year [18] . This discrepancy may be explained by differences in the age groups studied.

In this study, the majority of children (94.1%) had at least three meals a day, while only 5.9% had fewer than three meals a day. This result is relatively similar to that of Kanté’s 2008 study, where 79.1% of children had more than three meals a day, while 16% did not have enough [19] . This similarity could suggest general trends in children’s eating habits in similar contexts. However, it is important to note that although the results are close, differences remain, particularly in the proportion of children having fewer than three meals a day. This variation can be attributed to a number of factors, such as differences in socio-economic background, family practices or even the geographical region of the studies, which could influence eating habits and access to food [20] .

This study found that the prevalence of acute malnutrition in children was 10.70%, with all cases classified as moderate acute malnutrition and none as severe acute malnutrition. This result is notably lower than that observed by Lokonon, who found a 27% prevalence of acute malnutrition, including 10.7% severe cases [21] . The significant difference between the two studies could be attributed to several contextual and methodological factors. These include differences in the populations studied, the criteria used for evaluating malnutrition, and the periods during which the studies were conducted. The contextual factors, such as local healthcare systems, nutritional programs, and public health initiatives, may also contribute to differences in prevalence. Additionally, variations in study design, such as sample size, age range, and data collection methods, could lead to different results.

Regarding age, this study revealed that children aged 4 to 5 years were twice as likely to suffer from acute malnutrition, although the difference was not statistically significant (p = 0.419). This contrasts with the findings of Mariko, who identified children aged 12 to 23 months as the most affected by acute malnutrition, with a statistically significant difference (p = 0.025) [22] . These age-related discrepancies may be influenced by the specific demographic characteristics of the target population in each study. In the current study, the higher prevalence among children aged 4 to 5 years could be attributed to factors such as diet diversification, socio-economic status, and potential changes in caregiving patterns as children grow older. In contrast, Mariko’s study might have focused on a population with different feeding practices or health conditions, making younger children more vulnerable to malnutrition.

As for gender, the study found that 51.74% of male children were affected by acute malnutrition, compared to their female counterparts. This result is lower than the 58.3% prevalence observed in the Mukalay study, but the difference was statistically insignificant (p = 0.186) [23] . Although the current study suggests a slightly higher prevalence of acute malnutrition in boys, the difference is not large enough to be considered statistically significant, pointing to the possibility that other factors, such as socio-economic or environmental conditions, may be influencing the results. Moreover, gender differences in malnutrition rates can be subtle and context-dependent, varying according to the specific settings and populations studied.