Child Nutritional Physiological Phenomena

Based on the UNICEF framework,⁵ its adaption in the Lancet Maternal and Child Nutrition Series,⁴ and previous practices,^{9 (link),10 (link),16 (link)} we selected 20 factors for our primary analysis and 6 additional factors on paternal characteristics and maternal autonomy for supplementary analyses. We classified these 26 factors associated with child anthropometric failures either directly or via intermediary causes. A total of 9 direct factors were identified, including child nutrition (dietary diversity score, breastfeeding initiation, vitamin A supplements, and use of iodized salt), disease occurrence (infectious disease in past 2 weeks), health behaviors (oral rehydration therapy for diarrhea, care seeking for suspected pneumonia, full vaccination), and living conditions (indoor pollution). The association between each of these direct factors and child anthropometric failures has been documented previously.^{17 (link),21 (link),22 (link)} The remaining 17 indirect factors included household socioeconomic status (household wealth, maternal and paternal education), parents’ nutritional status (maternal and paternal height and BMI), maternal autonomy (for health care, movement, and money), environmental conditions (water source, sanitation facility, and stool disposal), maternal reproductive care (antenatal care, skilled birth attendant at delivery, family planning needs), and maternal marriage age. Prior studies have indicated that household wealth, maternal characteristics, and household environment are strongly associated with child anthropometric failures.^{8 (link),23 (link),24} Although only a few studies have investigated the role of paternal nutritional status, we included it in the supplementary analysis owing to potential biological and psychosocial channels between fathers and their offspring.^{6 (link),25 (link)} We also included maternal reproductive care variables that represent the care mothers received during pregnancy,^{26 (link)} the risk the child faced during birth,^{27 (link)} and the families’ desired birth spacing and their capacity to reach it.^{28 (link)} A detailed list and definitions of these factors are presented in Table 1.^{9 (link),29 (link),30 ,31 ,32 ,33 (link),34 (link),35 ,36}

The Child Health and Nutrition Research Initiative (CHNRI) started as an initiative of the Global Forum for Health Research in Geneva, Switzerland [4 (link)]. One of its main aims was to develop a tool that could assist decision–making and priority setting in health research investments to improve child health and nutrition. Their method also sought to achieve an acceptable balance between fundamental research, translational research and implementation research in order to maximize the potential of health research in reducing both disease burden and the inequities among the world's children [5 (link)].
The CHNRI method was developed between 2005 and 2007 through 12 consecutive meetings of a trans–disciplinary panel of 15 experts, supported with funding from the World Bank. The experts worked together to address a number of key challenges related to the multi–dimensional problem of setting priorities in health research investments [5 (link)–7 (link)]. The method aimed to carefully define the context for health research priority setting. The components of the context were: (i) the health issue on which the research is focused; (ii) the affected population that would benefit from the investments in health research; (iii) the timeframe within which the impact of supported research was expected (eg, short, medium or long term); (iv) the style of investment (eg, risk aversive or risk–seeking); and (v) the expected returns from investment (eg, burden reduction, patents, or various forms of public recognition) [6 (link)–8 (link)].
The method also introduced a systematic approach to listing many competing research questions. It identified four fundamental instruments of health research – “the four D’s” – research to achieve (i) description (through epidemiological research), (ii) discovery (through basic, ie, fundamental research), (iii) development (through translational research) and (iv) delivery (through health policy and systems research, which includes operations and implementation research). Moreover, it addressed the difference in depth and breadth of suggested research questions by categorizing them in broad research avenues, more focused research options (which correspond to a 5–year research program), and very specific research ideas/questions (which correspond to a typical research article). Finally, the method introduced a transparent set of criteria that could discriminate between many competing research options. CHNRI’s “standard” set of criteria followed a simple conceptual framework that demonstrated how the process of health research generates new knowledge. The five suggested criteria were (i) answerability, (ii) effectiveness, (iii) deliverability, (iv) the potential for a substantial reduction of disease burden and (v) the impact on equity [6 (link)–8 (link)].
The typical CHNRI process involves a small management team that reaches out to a large number of researchers (but also policy–makers and program managers, depending on focus of the exercise) who contribute hundreds of research ideas [9 (link),10 (link)]. Once a list of a manageable number of research ideas/questions (usually up to 200) is consolidated by removing overlapping ideas and integrating related ideas, a number of researchers (from 20 to up to several hundreds, depending on the context) are invited to score all proposed research questions against each priority–setting criterion [7 (link),10 (link)]. Their input measures “collective optimism” on a scale 0–100. In the final step, external stakeholders are invited to set different thresholds and weights for each of the priority–setting criteria, giving some criteria greater importance over the others, so that the overall score also includes the value system of a wider community [2 (link)]. The final output of the CHNRI process is a list that ranks up to 200 research ideas/questions by their scores against several transparent priority–setting criteria [7 (link)]. This serves to reveal strengths and weaknesses of all submitted research questions to the research community, judged by a subset of this community using several key criteria for prioritization [8 (link)].

The study was carried out based on the recommendations of the Joanna Briggs for scoping reviews (https://jbi.global), and the study protocol was registered with the Open Science Framework (https://osf.io/q2acf/). A systematic search of the literature was performed on MEDLINE via PubMed, Embase, and Web of Science databases. The following research questions were addressed:

Which anthropometric and body composition measures are used for evaluating the nutritional status of children and adolescents with CF in clinical practice and in research?

Are standardized procedures employed to obtain these anthropometric measurements? and

Which reference populations are used to classify the nutritional status of people with CF?

The search criteria were based on the PCC strategy (population, concept, and context). The population comprised children and adolescents with CF of both genders. Concept was defined as anthropometric data, including the use of simple anthropometric measurements:⁹
body weight, body height, waist circumference, and skinfolds; the use of anthropometric indices: weight-for-age, height-for-age, BMI, and BMI/A; and the use of body composition-related measurements or indices obtained by BIA, DXA, or an equation: lean mass, body fat mass, and percentage of fat. Context was defined as CF.
Eligibility criteria were as follows: articles that included children and adolescents with CF, age 6–18 years, even if individuals of other ages were also assessed; involved evaluation of nutritional status or evolution of childhood growth, or the statistical relationship of anthropometric and body composition measurements with clinical outcomes, such as pulmonary function; and used at least one of the anthropo-metric measurements or indices cited in the main concept of the present study.
Exclusion criteria were as follows: studies that included hospitalized subjects, individuals on a lung, pancreas, or liver transplantation waiting list, those who had undergone any type of transplant surgery, pregnant women, and individuals with associated conditions such as celiac disease, Crohn’s disease, or cancer. Experimental studies assessing the pharmacodynamics and pharmacokinetics of drugs or studies with self-referenced body composition or anthropometric measurements were also excluded.
The search strategy was defined by two reviewers (FMDE and DPB) who conducted independent searches. The search included observational studies or clinical trials pursuant to the study objective. To make the scoping review possible, the studies included were restricted to those published between January 2014 and December 2021 and to those articles in English and Portuguese.
Specific descriptors for each database were used: MeSH terms (Medical Subject Head) in Pubmed and thesaurus Emtre® in Embase®. Initially, keywords associated with the PCC acronym were searched in Pubmed and then in the other databases; adaptations were made when no corresponding matches were found in the MeSH terms. The terms were linked by Boolean operators AND (restriction) and OR (addition) and constituted search phrases (Figure 1) used on the databases. The use of EndNote online (Clarivate Analytics, Boston, MA, USA) helped manage and organize the studies retrieved, removing duplicates.
An initial selection of the studies was made based on titles and abstracts, independently by two authors (FMDE and DPB). When abstracts did not include age group or a description of anthropometric or body composition measurements, the methods section of the article was consulted. Differences were resolved by consensus.
As part of the selection process, two independent reviewers (FMDE and DPB) applied the Downs and Black checklist.^{10 (link)}
The original checklist contains 27 questions and was devised and validated to assess the methodological quality of observational studies and clinical trials on the domains of reporting, external validity, internal validity (bias), confounding/selection bias, and power. Because the present study included articles with different designs, only questions 1, 2, 3, 5, 6, 7, 10, 11, 12, 16, and 20 were applied. Differences were resolved by consensus. The maximum possible score for each article was 12 points. Articles scoring 9 or more points were considered eligible. For question 20 [“Were the main outcome measures used accurate (valid and reliable)?”],^{10 (link)}
only the methodological aspects pertaining to the use of anthropometric and body composition data to focus on the abovementioned concept were evaluated.
Data were extracted to characterize the studies and methodological aspects that allowed the use of standardized anthropometric data collection procedures,⁹
namely,

Anthropometric and body composition measures and indices used;

Source of each measurement – either by direct measurement or extracted from medical record;

Details on instruments, such as manufacturer, type, and scale;

Calibration of instruments;

Details on measuring procedures, such as descriptions of measuring techniques, clothing worn, and presence of accessories;

Training given;

Use of anthropometric reference manual;

Reference curves; and

Criteria for grading nutritional status.

The data extracted were recorded by the two independent evaluators (FMDE and DPB) using a chart. Differences were resolved by consensus.
The data obtained were analyzed using the statistical software package Stata version 13 (Stata Corp LP, TX, USA) and expressed qualitatively (descriptive text) or quantitatively (in tables) in the form of absolute and relative frequencies.

The independent variables included the sociodemographic characteristics of the mothers and child, clinical characteristics, maternal and cord lactate levels at birth, and nutritional status of the child.
At the time of the sodium bicarbonate trail, maternal lactate and amblical cord lactate were taken for each participant. This cord lactate level was retrieved from the data and was as one of the independent variables for this study.