Child Nutrition - Science topic

As of my last update in January 2022, malnutrition among children under the age of two in Nigeria remains a significant concern. Nigeria has one of the highest rates of child malnutrition globally. According to UNICEF, as of 2021, about 2.5 million children in Nigeria suffer from severe acute malnutrition (SAM), with approximately 420,000 deaths annually due to malnutrition-related causes.

Malnutrition in Nigeria is often attributed to various factors, including poverty, inadequate access to nutritious food, poor healthcare infrastructure, and frequent outbreaks of diseases like malaria and diarrhoea. Efforts to address malnutrition in Nigeria typically involve a combination of interventions, including improving access to healthcare services, promoting exclusive breastfeeding, providing nutrient-rich foods, and implementing community-based nutrition programs.

For the most current and specific data on the prevalence of malnutrition among children under two in Nigeria, I recommend consulting recent reports from organizations like UNICEF, the World Health Organization (WHO), or governmental health agencies in Nigeria.

If it's BMI for 7th graders, the index must be standardized by norms. Researchers tend to use BMI z-score using the CDC norms (google search BMIz CDC), but there are other standards as well (WHO, IOFT, etc.). BMI z-score is age and gender adjusted. Note, it's not simple standardization as the z-score is based on the growth trajectories. Typically, you need to run a SAS macro to compute BMI z-scores, but a Canadian Pediatric Group has developed a shiny app to compute these scores for you. (https://cpeg-gcep.shinyapps.io/who2007_cpeg/ ; they have CDC options as well). If you are studying nutrition or feeding 'habits', I assume you have repeated observations. In this case, you would run linear mixed effect model adjusting for baseline BMI and other factors. (google search for LME or analysis of repeated observation). Depends on how data were collected, things can be quite complicated. I encourage you to consult with a biostatistician for further guidance.

Dear Hind,

Do you mean you are looking for a dietary assessment tool in which you can compare your data with recommended intake per nutrient? If so, I think you should check WHO for recommendations or other national guidelines.

The dietary assessment tools that I can think of are mostly used to give the mean nutritional values of food products.

Kind regards,

Keshia

yes , in deed , infantile obesity is very difficult to manage especially those which attributed to genetic cause

More ultra-processed foods in the diet associates with higher risks of obesity, heart disease and stroke, type-2 diabetes, cancer, frailty, depression and death. These harms can be caused by the foods' poor nutritional profile, as many are high in added sugars, salt and trans-fats. https://theconversation.com/the-rise-of-ultra-processed-foods-and-why-theyre-really-bad-for-our-health-140537

Have a look at work by Purnima Menon at IFPRI, as well as recent work by Laurie Miller in Nepal, Parul Christian from Johns Hopkins, and Katherine Kreis at BMGF.

OMS

The answer to your question is "Yes"! Please see the following RG link.

See A. Lazaridis: Dynamic Systems in Management Science. Design, Esstimation and Control, 2015, Palgrave Macmillan, UK, pp. 252-268

/*(For NFHS-3)

For Underweight (-SD), Stunting (-SD), Wasting (-SD)*/

use IAKR52FL

gen haz06=hw70

replace haz06=. if hw70>=9996

gen waz06=hw71

replace waz06=. if hw71>=9996

gen whz06=hw72

replace whz06=. if hw72>=9996

(For -2SD)

gen below2_haz = ( haz06 < -200)

replace below2_haz=. if haz06==.

gen below2_waz = ( waz06 < -200)

replace below2_waz=. if waz06==.

gen below2_whz = ( whz06 < -200)

replace below2_whz=. if whz06==.

(For -3SD)

gen below3_haz = ( haz06 < -300)

replace below3_haz=. if haz06==.

gen below3_waz = ( waz06 < -300)

replace below3_waz=. if waz06==.

gen below3_whz = ( whz06 < -300)

replace below3_whz=. if whz06==.

label variable haz06 "Length/height-for-age Z-score (stunting)"

label variable waz06 "Weight-for-age Z-score (Underweight)"

label variable whz06 "Weight-for-length/height Z-score (Wasting)"

label variable below2_haz "Stunting (-2SD)"

label variable below3_haz "Stunting (-3SD)"

label variable below2_waz "Underweight (-2SD)"

label variable below3_waz "Underweight (-3SD)"

label variable below2_whz "Wasting (-2SD)"

label variable below3_whz "Wasting (-3SD)"

save IAKR52FL, replace

Yes, it use specially because it goes up the 18 years while the graphs provided by the WHO, only show the chart until 3 years old. Do not get me wrong, it is used in babies, but sometimes, after measuring the HC of the baby, the physician may want to measure the HC of the parents and that is where having the age-extended chart is useful. And you are right, there might be differences between those groups that you mentioned as examples. Take a look to this manuscript:

The influence of feeding patterns on head circumference among Turkish

infants during the first 6 months of life, by Mustafa Metin Donma. You see, this is basically the same experiment Nellhaus proposed in 1968 but with some specific conditioning.

Hope it helps.

Hi  

I'm agree to the above replies....

after you enter the data to SPSS  software program  you should use paired T test for compare consumptions of children nutritions  with RDA 's  

The researchers, led by Dr Andreas Meyer-Lindenberg of the Central Institute of Mental Health in Mannheim, Germany, were trying to find out more about how the brains of different people handle stress. They discovered that city dwellers' brains, compared with people who live in the countryside, seem not to handle it so well.

To be specific, while Meyer-Lindenberg and his accomplices were stressing out their subjects, they were looking at two brain regions: the amygdalas and the perigenual anterior cingulate cortex (pACC). The amygdalas are known to be involved in assessing threats and generating fear, while the pACC in turn helps to regulate the amygdalas. In stressed citydwellers, the amygdalas appeared more active on the scanner; in people who lived in small towns, less so; in people who lived in the countryside, least of all.

And something even more intriguing was happening in the pACC. Here the important relationship was not with where the the subjects lived at the time, but where they grew up. Again, those with rural childhoods showed the least active pACCs, those with urban ones the most. In the urban group moreover, there seemed not to be the same smooth connection between the behaviour of the two brain regions that was observed in the others. An erratic link between the pACC and the amygdalas is often seen in those with schizophrenia too. And schizophrenic people are much more likely to live in cities.

When the results were published in Nature, in 2011, media all over the world hailed the study as proof that cities send us mad. Of course it proved no such thing – but it did suggest it. Even allowing for all the usual caveats about the limitations of fMRI imaging, the small size of the study group and the huge holes that still remained in our understanding, the results offered a tempting glimpse at the kind of urban warping of our minds that some people, at least, have linked to city life since the days of Sodom and Gomorrah.

The year before the Meyer-Lindenberg study was published, the existence of that link had been established still more firmly by a group of Dutch researchers led by Dr Jaap Peen. In their meta-analysis (essentially a pooling together of many other pieces of research) they found that living in a city roughly doubles the risk of schizophrenia – around the same level of danger that is added by smoking a lot of cannabis as a teenager.

At the same time urban living was found to raise the risk of anxiety disorders and mood disorders by 21% and 39% respectively. Interestingly, however, a person's risk of addiction disorders seemed not to be affected by where they live. At one time it was considered that those at risk of mental illness were just more likely to move to cities, but other research has now more or less ruled that out.

So why is it that the larger the settlement you live in, the more likely you are to become mentally ill? Another German researcher and clinician, Dr Mazda Adli, is a keen advocate of one theory, which implicates that most paradoxical urban mixture: loneliness in crowds. "Obviously our brains are not perfectly shaped for living in urban environments," Adli says. "In my view, if social density and social isolation come at the same time and hit high-risk individuals … then city-stress related mental illness can be the consequence."

Meanwhile, a group of researchers at Hammersmith hospital, in London, are among many who believe that dopamine could hold the answer. Dopamine is a neurotransmitter with many functions, one of which is to infuse your brain when something important – good or bad – is happening. It might be that you are tasting an ice cream and your body wants you to eat the lot while you can, or it might be that a volcano is erupting and your body wants you to find your car keys nice and promptly. Dopamine levels are often very high in parts of schizophrenic peoples' brains. Cities, the theory goes, might be part of the reason why a person's dopamine production starts to go wrong in the first place. Repeated stress is thought to lead to this problem in some people, so if high social density combined with social isolation could be shown to do so, and thus to alter the dopamine system, we might have the first rough sketches of a map from city living all the way to schizophrenia, and perhaps other things.

Many other possible impacts of city living on brain function are also being investigated. Aircraft noise might inhibit children's learning, according to a recent study from Queen Mary University in London. (Although traffic noise, perversely, might help it.) Researchers in the US and elsewhere have also found that exposure to nature seems to offer a variety of beneficial effects to city dwellers, from improving mood and memory, to alleviating ADHD in children. Much of this research considers the question of "cognitive load", the wearying of a person's brain by too much stimulation, which is thought to weaken some functions such as self-control, and perhaps even contribute to higher rates of violence.

When we live in cities there is a much richer environment. There is also better healthcare, better education, a better standard of living. All these are protective factors. Indeed for those people at lower risk, which may well be most of us, city life might even be indirectly beneficial for our mental health. For instance, being cuddled, played with and generally well cared for by your parents is powerfully associated with fewer social and emotional problems in later life.

You may find these papers helpful:

I hope the above papers will facilitate your project.

Hello Adam,

Have you moved forward with your study?  I am interested to hear more if you have.

Hello,

Few low cost changes to help improve hemoglobin level among rural women:

1. Cooking meals in an iron container

2. Adding jaggery/gud to diet (processed in iron vessels)

3. If giving iron supplementation to child-bearing age women, advise them to:

  (a) Take some lime juice with iron tablet (Vitamin C helps iron absorption)

  (b) Avoid tea/coffee intake close to iron tablet

  (c) Avoid taking any dairy products close to iron tablets (Calcium interferes with absorption of iron)

Hope these help!

Thanks

Questionnaires are not submitted to evaluations of reliability or validity as are scales. As I understand the data (information) you are trying to obtain, a scale would not be necessary as you simple want responses to three particular questions (a questionnaire).

Your questionnaire as presented, would contain three questions (aside from respondent's demographics, age, gender, grade in school?, etc.).

The children's' age, where and when you collect the data (how much time you have to gather the data) and reading comprehension should determine your method of data collection (child's self-report or interview) , whether you use open-ended or forced responses to your questions, and how you phrase and design your questionnaire. 

If what I've described is the extent of the information you are trying to gather, there is no reliability or validity to be tested so much as a need to construct your questions and responses in a way that the children are able to understand and answer your questions accurately.

Hi Ben, There are a few questions asking about appetite embedded in a few pediatric assessment tools. I am not aware of one by itself.

Dear Ms Thida,

You will find overviews of the latest studies on WASH & Nutrition using this link: https://sanitationupdates.wordpress.com/?s=nutrition

Dear colleague:

If you are interested, here in  Cuba Mercedes Esquivel headed a group who some years ago developed the Cuban references for the 0-19 years old t population. I feel that a Cuban Reference Table is better suited for a third world population like yours than a USA developed one.

You can reach me if you are interested in receiving the publication or be in touch with Mercedes, declares

Dr Kindelan, kindelan@infomed.sld.cu

There is a very nice screening tool which is very simple to screen for child malnutrition. I agree that while children can be difficult to deal with and maternal habits certainly affect their children, maternal BMI is a poor predictor of child malnutrition. The screening tool is below.

Dear Sandeep !!!

Greetings from Udaipur !!! Determination of Nutrtional Status is a vast Area to discuss. Nutritional Status in terms of what ??? Under the umbrella of Nutrition there are so many disorders. One population may have high frequency of night blindness and the other may have high frequency of marusmus !!! Another one may have high iodine deficiency and the fourth may have high iron deficiency anemia. How you can compare them in terms of nutritional status. BMI, Height and Weight for age shows different situation of nutritional status. The best way is to tell the actual situation rather than to compare in terms of Nutritional Status. Infact, in a particular area also like BMI , it will show different result in a single population with the application of different indices. New indices are also available like IOTF, WHO 2007. You can have a look into my publication in North American journal of Medical Sciences for that.  The other better way is to use the Composite Index of Malnutrition where you can add thinness, stunting as well as underweight at the same time. Best of luck and regards.   

In the context of the classic article by Campbell & Fiske (1959, Psychological Bulletin, 81-105), the answer is no. Construct validity involves both convergent (or discriminant) validity--a measure's lack of relationship with unrelated measures--and convergent (or concurrent) validity--a measure's relationship with related measures.

You might want to specifically consider Iodine deficiency if you collect data on nutritional habits of people living in mountainous regions. From the Himalayas to the Balkans people suffer from Iodine deficiency disorders especially where the natural pattern includes alternating flood plains and mountains. Iodine deficiency is associated with high prevalence of endemic cretinism, goiter, short stature, and deafness resulting in loss of intellectual capacity.

Even in areas with moderate iodine deficiency (iodine ingestion 20 to 49 μg/day), clinically euthyroid children and adults often have definite abnormalities of psychomotor and intellectual development.

Delange F, Bürgi H, Chen ZP et al. Thyroid 2002;12:915–24

Berbel P, Obregón MJ, Bernal J et al. Trends Endocrinol

Metab 2007;18:338–43

Modern Nutrition 11th ed. Chapter 95

Hope that helps

IG

Hi Carol,

Have you tried posting the question on the Collective Impact Forum? Here's the link: http://www.collectiveimpactforum.org/

Marty

This may be true; however, normal weight children who do not eat fruits and vegetables are probably an exception. The larger public health issue is obesity and its long-term effects from childhood through adulthood.

Lesley Haynes no longer practises as a Registered Dietitian and is now retired.

The first step is to LISTEN. In my research of infant feeding advice and influences on mothers' decisions in Australia last century, what I noticed was that the public health sector TOLD mothers what to do, anxious to impart the information they considered important, rather than exploring how her lived situation was relevant. Often this advice exhorted them to breastfeed, but insisting on rigid feeding schedules undermined this process.  It was interesting to see the contrast in how commercial entities approached mothers, listening to their fears in order design advertising to build on mothers' fears and aspirations - to sell foods to feed babies by  bottle. The advertisements would have given the the impression that someone was listening, that somebody cared. Both mothers and the grandmothers, who were a source of advice, were exposed to advertising

As breastfeeding is an embodied experience, mothers typically require support in the early days, at least. This is even more so in cultures such as the UK and the US, where artificial feeding has a very long history and is deeply embedded culturally.  Breastfeeding simply isn't traditionally how babies are fed in some regions.  A 2007 Cochrane Review by Britten et al found that the optimal support is a combination of professional and lay support, that is, the right support from the right person at the right time. Predictably, they found that personal support was better than telephone support. Some helpful forms of support for the breastfeeding mother can come from people who know very little about breastfeeding, but provide an encouraging word, a helpful contact, or a chair so that she can breastfeed if her baby becomes hungry while shopping.

Numerous forms of support for mothers to breastfeed are described by our chapter authors from round the world in the book I co-edited with Melissa Vickers.  Besides the better-known support groups and peer counsellors, a lovely example is the Baby Cafes in the UK and now elsewhere.  [Thorley V, Vickers MC. The 10th Step & Beyond: Mother Support for Breastfeeding (Amarillo TX: Hale Publishing, 2012).]

The reference that Eliz suggested will be useful for sure. If you go to the WHO website you can search WHO anthro and this is software that you can use to calculate z scores used to determine the different forms of malnutrition.

The fact is that poor nutrition affects child growth and that chronic malnutrition can lead to retarded linear growth leading to infants and young children being of shorter stature than standard expectations. This is stunting! However the stunting does not only affect linear growth but also development in general including brain and cognitive develoment with long term effects on educational outcomes. The link between educational outcomes and malnutrition in developing countires makes poverty difficult to combat with further impact on economic development. Besides being caused by chronic malnutrition stunting can also be caused by what is termed hidden hunger which refers to micronutrient deficiencies resulting from low nutrient  density diets. This is why there is so much emphasis on micronutrients currently in nutrition globally.

Overweight on the other hand is usually as a result of dietary energy intake in excess of requirments leading to a Body mass index higher than 25Kg/m² for children older than 9 years and adults. This situation can further lead to obesity. For infants and young children up to 9 years Weigh-for-height z scores above +2 SD. This puts the child at risk of developing noncommunicable diseases like diabetes and others and puts the child at risk of the same later in adulthood also with impact on health.

I hope this quick summary will help you.

In severe iron deficency anemia we must exclude a pseudothrombocytosys caused by a very low MCV. In these cases if an impedentiomethric analyzer is used, the PLT count can be falsely increased by smaller RBCs.

You can observe the PLT/RBC histogram and use an optical platelet counter.

Hoping as well! Good luck!

Intense strength training and plyometrics are ususlly posponded at the end of growth age. However researchers found that there is no particular aversion against the use of strength training in children provided that they have medical clearance, trained by a qualified professional and gradual overload. Several benifits of strength training were also documented s/a improved motor skill and body composition, increased bone health etc. So it seems that strength training may be safe and benificial in children

Julie,

Please go the Nurses Health Study (NHS) I/II/III website and download their sample questionnaires. It includes an extensive FFQ that's been validated. For the children, try looking up the Growing Up Today Study (GUTS)

I suggest to ask Prof. kersting from the German institute of child nutrition in Dortmund.

You can correspond in English, they will come back to you.

This research group has worked a lot with schools and day care and kindergarten. They pretty good in nutritional and dietary methods.

Good luck

correlation does not merit causation. However, I believe that the study shows "thinner" children typically have thinner children/grandchildren.

Thanks.

Salivary amylase is not normally present in large quantity until about 6 months and pancreatic amylase is not produced in adequate amounts until molar teeth are fully developed. Sevenhuysen et al (1984)found that alpha-Amylase activity increases rapidly from low values at birth to approximately two-thirds of adult values by 3 months.

Dezan et al (2002) studied Flow rate, amylase activity, and protein and sialic acid concentrations of saliva from children aged 18, 30 and 42 months attending a baby clinic. This is available at Archives of oral biology Volume 47, Issue 6, June 2002, Pages 423–427

Davis et al (2009) studied Developmental differences in infant salivary alpha-amylase and cortisol responses to stress. Psychoneuroendocrinology, Volume 34, Issue 6, July 2009, Pages 795–804