| Plausibility study with internal control
|
Morris et al
1414 . Shusterman D, Murphy MA. Nasal hyperreactivity in allergic and non-allergic rhinitis: a potential risk factor for non-specific building-related illness.
Indoor Air.
2007;17(4):328-33. DOI:10.1111/j.1600-0668.2007.00482.x
https://doi.org/10.1111/j.1600-0668.2007...
PBA |
1,347 children under 7 included in the PBA and 483 excluded due to administrative errors (measurements taken); 472 children receiving and 158 excluded under 3 years old (reported measurements) |
Cross-sectional study and retrospective cohort study |
4 municipalities in the Northeast 2002 |
Z-sores for weight/age, difference in weight gain 6 months after staring to receive PBA |
Children included had lower z-score for weight/age than those excluded. Each additional month in the PBA (total of 6 months) was associated with 31 g less weight gain after adjusting for socioeconomic characteristics. |
Possible bias from the receipt of another benefit (School Grant) by the beneficiaries of the PBA. Lack of a measure of weight and height prior to the start of the program. Sample of only four municipalities in the Northeast. |
|
Plausibility study with external control
|
| Paes-Sousa et al17 PBF |
22.375 children under 5 years old from areas with low socioeconomic levels (included and not included in the PBF) |
4 cross-sectional studies |
419 municipalities in Brazil (4 Chamadas Nutricionais) 2005/2006 |
Z score for weight/age and height/age |
Children included in the PBF showed 26% greater chance of having appropriate height/age and weight/age. Greater effect among children older than 35 months, after adjusting for socioeconomic characteristics. |
Cross-sectional study has inherent limitations. Unable to determine the time of exposure to the program or the possible biases related to participation in programs other than the PBF. Some variables not assessed could explain residual confounding such as family income, food consumption and nutritional status before entry into the program. |
| Piperata et al20 PBF |
469 individuals in 2002 429 individuals in 2009 Sub-sample of 204 individuals (longitudinal) |
2 cross-sectional and one longitudinal study |
7 rural communities in 2 municipalities in Pará 2002 and 2009 |
Z score for weight/age and height/age in individuals aged under 18 |
Significant positive effect of PBF on the difference in height/age between the two studies for both sexes and for males, after adjusting for socioeconomic characteristics. |
The doubt remained as to whether the effect came from the cash transfer itself or from another aspect of the PBF, such as the conditionalities. Small sample size. |
Oliveira et al
1515 . Sundell J. On the history of indoor air quality and health.
Indoor Air.
2004. 14 Suppl7:51-8. DOI:10.1111/j.1600-0668.2004.00273.x
https://doi.org/10.1111/j.1600-0668.2004...
PBF |
443 children aged from 6 to 84 months (262 included and 184 not included), with
per capita
income < R$ 120.00 |
Cross-sectional study |
One municipality in the Southeast 2007 |
Malnutrition (z-score for weight/age and height/age < -2) |
There were no statistically significant differences between the prevalence of malnutrition among the groups for any anthropometric index, after adjusting for socioeconomic characteristics. |
Inclusion of siblings of children selected to compose the study. |
| Oliveira et al16 PBF |
443 children aged from 6 to 84 months registered to receive the PBF (184 not included and 262 included) |
Cross-sectional study |
One municipality in the Southeast 2007 |
Z-score for weight/age and height/age and BMI/age |
There were no statistically significant differences between the nutritional status of children included in the PBF and the length of time receiving the benefit, without adjusting for other factors. |
The prevalence rates cannot be extrapolated for all Brazilians. Cross-sectional analysis means it cannot be guaranteed that the results represent the effect of the program or whether they already existed before the PBF started. |
| Saldiva et al25 PBF |
411 families and 164 children under 5 (included and not included in the PBF) |
Cross-sectional study |
One municipality in the Northeast 2005 |
Z-score for weight/height, weight/age and height/age |
There were no statistically significant differences between the nutritional status of children and being included in the PBF, without adjusting for other factors. |
No limitations indicated. |
| Paula et a18l PBF |
115 children aged from 6 ato10 years old |
Cross-sectional study |
1 municipal school of a municipality in the Southeast 2009 |
Stunted (height/age index) and BMI/age |
3.0% of stunted in children not in the de PBF and 0% in children included in the de PBF (p = 0.28). Increased risk of overweight or overweight of 27.6% in those not included and 16.2% in those included in the PBF (p = 0.16). |
Not possible to evaluate association between doing physical exercise and nutritional status. Minimum sample size not calculated. Small sample size. Study carried out in one municipal school in Belo Horizonte. |
|
Studies of accuracy
|
Lima et al
99 . Mendell M, Heath G. Do indoor pollutants and thermal conditions in schools influence student performance? A critical review of the literature.
Indoor Air.
2005;15(1):27-52. DOI:10.1111/j.1600-0668.2004.00320.x
https://doi.org/10.1111/j.1600-0668.2004...
PBF |
747 adults included in the PBF |
Population based cross-sectional study |
One municipality in the South 2006/2007 |
Excess weight (BMI > 25 kg/m2) risk of CVD (waist circumference). |
Prevalence of 29.0% overweight and 27.1% obesity. Higher chance of being overweight in men, being over 40 and being single. 46.2% of adults at increased risk of CVD. |
No limitations indicated. |
| Silva28 PBF |
79,795 children aged 5 to 10 years old, receiving the PBF (DATASUS/ SISVAN records). |
3 cross-sectional studies |
State of Sergipe 2008 to 2010 |
Prevalence of overweight and obesity by sex, year of study and each health region. |
Prevalence of overweight in girls went from 12.2% in 2008 to 13.2% in 2010 and obesity from 11.0% to 11.9%. In males, prevalence of overweight went from 12.4% to 13.2% and obesity form 11.0% to 15.1%. Higher prevalence in regions with lower HDI. |
Use of secondary data meant not controlling for possible data input and recording errors, as well as possible underreporting. |
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