Quality of child anthropometric data from SISVAN, Brazil, 2008-2017

Silva, Natanael de Jesus; Silva, Juliana Freitas de Mello e; Carrilho, Thaís Rangel Bousquet; Pinto, Elizabete de Jesus; Andrade, Rafaella da Costa Santin de; Silva, Sara Araújo; Pedroso, Jéssica; Spaniol, Ana Maria; Bortolini, Gisele Ane; Fagundes, Andhressa; Nilson, Eduardo Augusto Fernandes; Fiaccone, Rosemeire Leovigildo; Kac, Gilberto; Barreto, Maurício Lima; Ribeiro-Silva, Rita de Cássia

doi:10.11606/s1518-8787.2023057004655

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Original Article • Rev. Saúde Pública 57 • 2023 • https://doi.org/10.11606/s1518-8787.2023057004655 copy

Quality of child anthropometric data from SISVAN, Brazil, 2008-2017

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

OBJECTIVE

To evaluate the quality of anthropometric data of children recorded in the Food and Nutrition Surveillance System (SISVAN) from 2008 to 2017.

METHOD

Descriptive study on the quality of anthropometric data of children under five years of age admitted in primary care services of the Unified Health System, from the individual databases of SISVAN. Data quality was annually assessed using the indicators: coverage, completeness, sex ratio, age distribution, weight and height digit preference, implausible z-score values, standard deviation, and normality of z-scores.

RESULTS

In total, 73,745,023 records and 29,852,480 children were identified. Coverage increased from 17.7% in 2008 to 45.4% in 2017. Completeness of birth date, weight, and height corresponded to almost 100% in all years. The sex ratio was balanced and approximately similar to the expected ratio, ranging from 0.8 to 1. The age distribution revealed higher percentages of registrations from the ages of two to four years until mid-2015. A preference for terminal digits “zero” and “five” was identified among weight and height records. The percentages of implausible z-scores exceeded 1% for all anthropometric indices, with values decreasing from 2014 onwards. A high dispersion of z-scores, including standard deviations between 1.2 and 1.6, was identified mainly in the indices including height and in the records of children under two years of age and residents in the North, Northeast, and Midwest regions. The distribution of z-scores was symmetric for all indices and platykurtic for height/age and weight/age.

CONCLUSIONS

The quality of SISVAN anthropometric data for children under five years of age has improved substantially between 2008 and 2017. Some indicators require attention, particularly for height measurements, whose quality was lower especially among groups more vulnerable to nutritional problems.

Data Reliability; Food and Nutrition Surveillance; Health Information Systems; Anthropometry; Child

Quality indicator	Breakdown of indicator	Data used
Completeness
Coverage of the target population, SUS users (%)	Number of children with nutritional status recorded in SISVAN divided by the number of children under 5 years old using the SUS, multiplied by 100. The population using the SUS was obtained by subtracting the total population (estimated by the IBGE) by the population covered by private health insurance (obtained from the ANS).	All children with nutritional status records in SISVAN. A single record per child each year (last follow-up date) was considered for the calculation of coverage.
Coverage of the total population, residents (%)	Number of children with nutritional status records in SISVAN divided by the total number of children under five years old (estimated by IBGE), multiplied by 100.	A single record per child each year (last follow-up date) was considered for the calculation of coverage.
Completeness of birth date (%)	Number of records with complete day, month, and year of birth divided by the total number of child records in SISVAN, multiplied by 100.	All nutritional status follow-up records in SISVAN.
Completeness of anthropometric measurements (%)	Number of records with completed weight and height measurements divided by the total number of child records in SISVAN, multiplied by 100.	All nutritional status follow-up records in SISVAN.
Sex ratio
Sex ratio	Number of boys divided by the number of girls. The expected ratio is that represented by the national population.	All nutritional status follow-up records in SISVAN.
Age distribution
Histograms of age (in years, months and month of birth)	Histograms were used to assess the pattern of age distribution in months, years, and month of birth. An approximately uniform distribution is expected. The age of the child was calculated from the date of birth and date of nutritional status monitoring.	All records with complete birth month and year.
Index of dissimilarity for age (in months)	The Myers “unblended” index was analyzed, according to the formula below, to identify the percentage of records deviating from a uniform distribution of age in months (0-59) $\frac{\sum_{i = 1}^{60} \| X_{i s} - X_{i e} \|}{2}$ in which Xis is the observed percentage and Xie is the expected percentage. The index ranges from 0 to 90, being 0 the optimum value.	All records with complete birth month and year.
Preference for height and weight digits
Histograms of terminal digits and whole numbers for height (cm) and weight (kg)	Histograms were used to assess the pattern of distribution of terminal digits and whole numbers of height and weight. An approximately uniform distribution is expected.	All records with weight and/or height information.
Index of dissimilarity for terminal digits of height (cm) and weight (kg)	The Myers “unblended” index was analyzed, according to the formula below, to identify the percentage of records deviating from a uniform distribution for terminal digits of height and weight $\frac{\sum_{i = 1}^{10} \| X_{i s} - X_{i e} \|}{2}$ in which X_is is the observed percentage and X_ie is the expected percentage. The index ranges from 0 to 90, being 0 the optimum value.	All records with weight and/or height information.
Implausible z-score values
Implausible z-scores for each anthropometric index (%)	Number of records with implausible z-score values divided by the total number of child records in SISVAN, multiplied by 100. Using the WHO macro “STATA igrowup package” flagging system, implausible z-score values were detected according to plausibility criteria (WHO, 1995): H/A (-6, +6), W/H (-5, +5), W/A (-6, +5) and BMI/A (-5, +5). Percentages above 1% is indicative of poor data quality.	All records with date of birth, weight and/or height information.
Dispersion of z-score values
Standard deviation of plausible z-score values for each anthropometric index	Standard deviation was calculated using the following formula $\sqrt{\frac{\sum_{i = 1}^{n} {(Y_{i} - \bar{Y})}^{2}}{n - 1}}$ in which n is the total number of observations, Yi is each value in the database, and Yˉ is the mean of observations	All records with biologically plausible z-score values for the anthropometric index of interest.
Normality of z-score values
Distribution of plausible z-score values for each anthropometric index	Kernel density plots were used to examine the distribution pattern of the z-score values for each index.	All records with biologically plausible z-score values for the anthropometric index of interest.
Skewness of plausible z-score values for each anthropometric index	Skewness was calculated by the Fisher-Pearson coefficient: $\frac{\sum_{i = 1}^{n} {(Y_{i} - \bar{Y})}^{3} / n}{s^{3}}$ in which Yˉ = mean, s = standard deviation (calculated with ‘n’ in the denominator instead of n-1) and n = sample size. It is generally accepted that a coefficient <-0.5 or >0.5 indicates skewness	All records with biologically plausible z-score values for the anthropometric index of interest.
Kurtosis of plausible z-score values for each anthropometric index	Kurtosis was calculated by the Fisher-Pearson coefficient: $\frac{\sum_{i = 1}^{n} {(Y_{i} - \bar{Y})}^{4} / n}{s^{4}}$ in which Yˉ = mean, s = standard deviation (calculated with ‘n’ in the denominator instead of n-1) and n = sample size. In general, it is accepted that a coefficient <2 or >4 indicates kurtosis.	All records with biologically plausible z-score values for the anthropometric index of interest.

Indicators	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
Total of registrations and followed-up children
Total number of children	2,172,824	2,752,361	3,339,585	3,263,701	3,189,390	4,216,064	4,343,156	4,943,025	5,134,115	5,097,377
Total of unique records	3,612,015	5,134,933	5,987,400	6,446,453	6,252,667	7,894,213	7,822,944	9,756,986	10,357,127	10,480,285
Total of duplicate records	1,969,446	2,908,094	2,950,561	2,888,367	2,595,380	2,287,606	2,197,925	2,988,357	3,172,167	3,209,888
Completeness
Coverage of the target population, SUS users (%)	17.7	22.9	28.8	28.6	28.6	38.5	39.9	44.7	45.9	45,4
Coverage of the total population, resident (%)	14.3	18.3	22.5	22.2	21.8	28.9	29.8	33.7	34.9	34,6
Completeness of birth date (%)	99.9	99.9	99.9	99.9	99.9	99.9	99.9	100	100	100
Completeness of anthropometric measurements (%)	100	99.9	100	100	100	100	100	100	98.8	100
Sex ratio
Sex ratio	1	1	1	1	0.8	0.8	0.9	1	1	1
Age distribution
Dissimilarity Index for age in months (%)	20.4	14.1	12.6	11.9	11.2	10.3	9.7	4.8	1.5	3.8
Preference for height and weight digits
Index of dissimilarity for terminal digits of height (%)	88.8	88	88.2	87.9	87.7	89.7	89.9	89.4	88.3	88.8
Index of dissimilarity for terminal digits of weight (%)	50.2	47	47.6	46.1	44.3	45.3	44.2	42.8	40.1	40.4
Implausible z-score values
Implausible z-scores of H/A (%)	3	3.4	3.8	3.4	3	3.3	3.5	3	2.9	1.9
Implausible z-scores of W/H (%)	2.4	2.4	2.7	2.5	2.5	3.3	3.2	3	2.4	2.2
Implausible z-scores of W/A(%)	2.1	1.6	1.9	1.8	1.7	2.5	2.4	2.1	1.2	0.8
Implausible z-scores of BMI/A (%)	3.9	4.1	4.6	4.2	3.9	4.8	4.8	4.2	3.1	2.9
Dispersion of z-score values
Standard Deviation of H/A z-scores	1.5	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6
Standard deviation of W/H z-scores	1.3	1.4	1.5	1.5	1.5	1.5	1.5	1.5	1.5	1.5
Standard deviation of W/A z-scores	1.2	1.3	1.3	1.3	1.3	1.3	1.3	1.3	1.3	1.3
Standard deviation of BMI/A z-scores	1.4	1.5	1.5	1.5	1.5	1.5	1.5	1.5	1.5	1.5
Normality of z-score values
Skewness of H/A z-scores	0.1	0.2	0.2	0.2	0.1	0.2	0.2	0.1	0.1	0.1
Skewness of W/H z-scores	0	-0.1	-0.1	-0.1	-0.1	-0.1	-0.1	-0.1	0	0
Skewness of W/A z-scores	0	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1
Skewness of BMI/A z-scores	-0.1	-0.1	-0.1	-0.1	-0.1	-0.1	-0.1	-0.1	-0.1	-0.1
Kurtosis of H/A z-scores	4.7	4.5	4.5	4.5	4.5	4.5	4.5	4.4	4.5	4.5
Kurtosis of W/H z-scores	3.9	3.8	3.8	3.8	3.8	3.7	3.7	3.7	3.7	3.7
Kurtosis of W/A z-scores	4.3	4.1	4.1	4.1	4.1	4.1	4.1	4.1	4.1	4.1
Kurtosis of BMI/A z-scores	3.9	3.8	3.7	3.7	3.7	3.7	3.7	3.7	3.7	3.7

Regions and states	Target population (SUS user)		Total population (resident)

	2008	2017	2008	2017
North	15.5	47.8	14.3	43.2
Acre	11.7	49.9	11.3	47.9
Amapá	8.4	31	7.7	28.5
Amazonas	15.7	51.1	14	45.5
Pará	15.3	50	14.1	45
Rondônia	15	29.7	14.1	26.9
Roraima	16.6	35.2	15.8	33.5
Tocantins	22.5	58.8	21.5	54.3
Northeast	23.2	56.7	21.3	49.1
Alagoas	23.6	56.9	21.8	49.4
Bahia	23.2	54.7	21.4	48.6
Ceará	27.9	58.9	25.1	48.3
Maranhão	21.8	58.9	21.1	54.7
Paraíba	32.9	69.7	30.4	60.9
Pernambuco	16.5	49.4	14.5	41.5
Piauí	24.8	69.4	23.2	59.8
Rio Grande do Norte	23.5	47.6	20.6	39.3
Sergipe	17.4	55.3	15.7	46.3
Midwest	10.5	30.7	9.2	24.2
Distrito Federal	0.9	13.3	0.7	9.7
Goiás	12.6	27.9	11.3	22.3
Mato Grosso	11	38.6	10	31.1
Mato Grosso do Sul	14.1	41.8	12.3	33
Southeast	14.2	38.5	9.5	25.1
Espírito Santo	24.1	31.5	18.7	23.4
Minas Gerais	19.5	71.6	15.5	51.7
Rio de Janeiro	9.7	26.8	6.6	17.8
São Paulo	11.6	27.1	7	16.5
South	17.8	42.3	14.2	31.4
Paraná	19.9	48	15.9	34.6
Rio Grande do Sul	16.3	35.6	13.1	26.7
Santa Catarina	16.4	43.3	13.1	33.2
Brasil	17.7	45.4	14.3	34.6

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[1] Correspondence: Natanael de Jesus Silva Center for Data and Knowledge Integration for Health Gonçalo Moniz Institute Oswaldo Cruz Foundation Edf. Tecnocentro, Sl. 315 Rua Mundo, 121, Trobogy 41745-715 Salvador, BA, Brazil E-mail: silva_natanael@hotmail.com