Snacks
Notes: N=9,380. Percentages are unweighted. Figures in the top panel are not conditional on availability in school.
The child food consumption questionnaire asked about the frequency of overall consumption of specific food items during the past week. Children were asked to include foods they ate at home, at school, at restaurants, or anywhere else. We examine the consumption of two unhealthy items - soda and fast food, and six healthy food items – milk, green salad, potatoes 10 , carrots, other vegetables, and fruits. The percentage of children not consuming any soda or fast food during the previous week was 16 and 29 percent, respectively, with modal responses at 1 to 3 times per week (see Appendix Table A1 ). Among the healthy foods, green salad, carrots and potatoes were consumed most infrequently with nearly half of children reporting no consumption during the past week. The modal responses for the other healthy foods were 1 to 3 times during the past week.
Detailed information on junk food availability in schools was collected from the school administrators and from children in the fifth grade. School administrators were asked whether students could purchase 17 individual food and beverage items, either from vending machines, school store, canteen, snack bar or a la carte items from the cafeteria during school hours. From these responses, we constructed an indicator variable of junk food availability in school that equals 1 if the administrator reports that students can purchase food and beverage items containing high sodium and/or sugar, including candy, chocolate, baked foods (e.g. cookies), salty snacks (e.g. potato chips), ice cream or frozen yogurt, or sweetened beverages during school hours, and zero otherwise. 11 Based on these school administrator reports, approximately 61 percent of the children had junk food availability in school. For robustness checks, we also considered two alternative measures of availability. The first is based on whether the modal child at each school reports that foods containing sugar, salty snacks, or sweetened beverages can be purchased at school. Based on this measure, about 75 percent of the children had junk foods available. And the second is based on whether the administrator reports any of the following competitive food outlets operate in the school: vending machines, school stores, canteens, snack bars, and a la carte lines. About 60 percent of the sample had at least one competitive food outlet. 12
4.1. econometric model.
The relationship between junk food availability and children’s BMI in fifth grade can be estimated cross-sectionally using the following linear regression model.
where, BMI iks , denotes fifth grade BMI for child i attending school k located in state s , JF k captures junk food availability in the child’s school, X i and S k are the vectors of individual/family (gender, age, age interacted with gender, race/ethnicity, mother’s education, household income) and school characteristics (private/public, percent minority, enrollment, urbanicity, state/region), respectively, and ε iks is the error term. The child’s baseline BMI (BBMI i ) is included to address potential heterogeneity that can bias OLS estimates such as student demand for junk foods, genetic susceptibility, and sorting. Because junk food availability is collected only in fifth grade, we do not know the length of exposure during prior school years. Therefore, BMI at school entry is the preferred baseline because it is measured prior to any exposure to the school food environment. Finally, since states differ markedly in terms of obesity prevalence in their populations as well as the policy environment geared towards combating obesity, we include state fixed effects (θ s ) to control for state-specific time-invariant unobserved heterogeneity that may be correlated with school food environments and children’s weight.
The parameter of interest in Equation (1) is β 1 . Obtaining an unbiased estimate of β 1 is challenging because the school food environment is not exogenous to the outcomes of interest. Schools that serve high-fat, energy-dense junk foods may differ on many observable and unobservable factors that are correlated with children’s weight and dietary behavior. In particular, the decision to offer junk foods in schools may be influenced by a variety of factors including budgetary pressures, demands of the student population, parental involvement, and state/district policies. These factors could independently influence children’s weight as well. For example, budgetary pressures may induce schools or districts to scale back or eliminate physical education programs, which might increase children’s weight. As a result, coefficient estimates from the ordinary least squares (OLS) estimation of Equation 1 would be biased.
We address the potential endogeneity of junk food availability using instrumental variables. Specifically, we estimate the model in Equations (2.1) and (2.2) using Two-Stage Least Squares.
Equation 2.1 represents the first-stage regression where junk food availability (JF k ) is regressed on the combined school attendance instrument (CS k ), individual (X i ) and school (S k ) characteristics, baseline BMI (BBMI i ), and state fixed effects (θ s ). Equation 2.2 represents the second stage where children’s BMI (or obesity) is regressed on the predicted availability of junk foods from the first stage (ĴF k ) in addition to the common covariates.
We also report results from the reduced form, which regresses BMI or obesity directly on the instrument ( Equation 3 ). These results have the advantage of being unbiased and providing evidence of whether a causal relationship exists in the regression of interest. 13
Our sample consists of a single cohort of 5 th graders attending schools with a variety of grade spans. Given that junk food availability is significantly higher in middle and high schools compared to elementary schools, a potentially useful instrument for junk food availability is whether the 5 th grader attends a combined school (defined as the highest grade is seventh or higher) or whether the 5 th grader is in an elementary school (defined as highest grade is 5 th or 6 th ). Our instrument considers only this dichotomy of school type: elementary versus combined. Over 70 percent of our sample attends elementary schools while the remainder attends combined schools usually with grade spans of K-8, K-12 and 5–8 (see Appendix Table A2 ).
Variation in Grade Span in Fifth Grade
Lowest Grade-Level in School | |||||||||
---|---|---|---|---|---|---|---|---|---|
Highest Grade-Level in School | Pre-K or Kindergarten | 1 | 2 | 3 | 4 | 5 | 6 | Total | |
4 | 0.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8 | Elementary 70.6% |
5 | 40.6 | 1.1 | 0.4 | 2.0 | 1.3 | 0.1 | 0.0 | 45.5 | |
6 | 19.7 | 0.1 | 0.3 | 0.4 | 1.4 | 2.4 | 0.0 | 24.3 | |
7 | 0.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2 | 0.0 | 0.3 | Combined 29.4% |
8 | 21.0 | 0.1 | 0.1 | 0.2 | 0.6 | 3.3 | 0.0 | 25.5 | |
9 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3 | |
10 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
11 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
12 | 3.0 | 0.2 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 3.3 | |
Total | 85.5 | 1.6 | 0.8 | 2.7 | 3.3 | 6.1 | 0.0 | 100.0 |
Notes: N=9380. “Combined” schools are defined as schools with highest grade equal to 7 or higher.
For combined school attendance to be a valid instrument, it must be the case that the school’s grade span has no direct effect on children’s weight except through the junk food environment. One potential concern is that there may be unobserved factors that are correlated with both the likelihood of combined school attendance as well as BMI. For example, it is well known that states differ markedly in the prevalence of childhood obesity. But, states are also likely to differ in terms of factors that contribute to school grade span such as: (1) the size of the school-age population, (2) its distribution within the state, (3) differences in the educational systems and policies, as well as (4) education budgets. Similarly, school grade span can vary across urban versus rural areas (even within states), with the latter more likely to have combined schools largely because of a smaller school-age population. The inclusion of state and urbanicity dummies in our regressions controls for unobserved differences across states and across rural/urban areas that may be correlated with combined school attendance (or grade span, more generally) and BMI.
Another potential concern with this identification strategy is that variation in grade span exposes children to older peers who may influence obesogenic behaviors. Peers, defined broadly, have been shown to influence a wide range of adolescent behaviors and outcomes. 14 However, of particular relevance to our identification strategy is the literature examining a specific type of peer effect, namely, the effect of exposure to older peers due to school grade span.
Several studies have examined peer effects on academic, social-behavioral and substance use outcomes by leveraging variation in school grade span ( Clark and Folk 2007 ; Clark and Loheac 2007 ; Eisenberg 2004 ; Bedard and Do 2005 ; Cook et al 2008 ). Most studies compare students in the same grade who attend middle versus combined schools or middle versus elementary schools . 15 These studies generally find that 6 th or 7 th graders who attend middle school fare poorly compared to those who attend elementary or combined schools. 16 However, we are not aware of any studies that compare children in the same grade level who attend elementary versus combined schools . The exception is Rickles (2005) , whose findings suggest inconsistent effects of elementary versus combined schools attendance on achievement.
Furthermore, there is very limited evidence on the influence of older peers on food choices. Cullen and Zakeri (2004) compared changes in food consumption of 4 th graders who transitioned to middle school in 5 th grade and gained access to school snack bars to changes in food consumption of 5 th graders who were already in middle school. Fourth graders who transitioned to middle school consumed fewer healthy foods compared with the previous school year, but it is not clear whether this was due to the presence of older peers or the change in school food environment.
Overall, the literature suggests that the presence of older peers may adversely affect academic and social behavioral outcomes, but there is less evidence to support effects on their eating behaviors. Nevertheless, if such an effect exists, the potential bias in our IV estimates due to peer effects is likely to be upward. That is, 5 th graders might emulate older peers who are more likely to consume junk foods in school and would therefore tend to be overweight, independent of the school food environment. In that case, an insignificant finding is unlikely to be undermined.
Identification in our IV models relies on the assumption that, conditional on state and urbanicity dummies, the school’s grade span does not influence BMI except through differences in the availability of junk foods. Districts typically determine the grade span at the time of the schools’ opening based on a number of factors including transportation costs, length of bus ride, desired number of transitions, population size, site availability, preferred school size, and likelihood of parental involvement ( Paglin and Fager 1997 ) rather than children’s health outcomes. Changes in grade span over time are possible, but infrequent and similarly-motivated. For example, in our ECLS-K sample, less than 4 percent of the children who remained in the same school between kindergarten and fifth grade experienced a grade-span change from combined to elementary school or vice-versa. While unlikely, it is nevertheless possible that schools may change grade span in response to children’s physical size. Therefore, below we report results from several tests that support the validity of our instruments. These analyses are based on our preferred specification, which controls for the full set of covariates, including state and urbanicity dummies and baseline BMI.
First, we report first-stage estimates of the effect of our instrument – combined school attendance – on junk food availability in school. The first-stage estimates show that combined school attendance significantly increases the likelihood of junk food availability with an F-statistic on the instrument that exceeds 22 ( Table 2 ).
First Stage Regression Estimates of Junk Food Availability in Fifth Grade
Junk Food Availability | |
---|---|
Combined school attendance | 0.195 |
[0.041] | |
Partial R-square of excluded instruments | 0.02 |
F-statistic on excluded instruments | 22.7; p=0.000 |
Observations | 9380 |
Notes: Figures in brackets are robust standard errors clustered at the school level. Other covariates in the model include male, age (months), male*age, race/ethnicity, kindergarten BMI, mother’s education, income, private school dummy, categories for percent minority in school and school enrollment, and state and urbanicity dummies.
Second, since our instrument leverages across school variation we might be concerned that selection into different schools (or communities) might undermine the validity of our instrument. To test for differential selection into combined versus elementary schools, we regress BMI, obesity, test scores, social-behavioral outcomes, and parental involvement measured in kindergarten on combined school attendance in 5 th grade ( Table 3 ). 17 Because these outcomes are determined prior to exposure to school, these comparisons allow us to test for selection. The results suggest that, conditional on observed characteristics, combined school attendance is uncorrelated with pre-exposure BMI, obesity, test scores, social-behavioral outcomes and parental involvement.
Effect of Attending a Combined School on Kindergarten Outcomes
Log (BMI) (1) | Obese (2) | Reading Score (3) | Math Score (4) | Externalizing BP Score (5) | Internalizing BP Score (6) | Self Control Score (7) | Interpersonal Skills Score (8) | Parent Involvement (9) | |
---|---|---|---|---|---|---|---|---|---|
Combined school attendance | 0.002 | −0.004 | −0.401 | −0.333 | 0.028 | 0.019 | −0.005 | 0.001 | 0.151 |
[0.003] | [0.010] | [0.376] | [0.310] | [0.023] | [0.021] | [0.027] | [0.026] | [0.364] | |
Observations | 9380 | 9380 | 7910 | 8400 | 9050 | 9000 | 9020 | 9000 | 8250 |
Mean(std dev) of dept var | 16.4(2.2) | 0.12 | 30.1(10.0) | 23.6(9.0) | 1.6(0.6) | 1.5(0.5) | 3.2(0.6) | 3.2(0.6) | 34.4 (11.2) |
Notes: Each estimate represents a separate regression. Other covariates in the models include age, male, age*male, race/ethnicity, kindergarten BMI (not in model in Columns 1 and 2), mother’s education, income, private school dummy, categories for percent minority in school and school enrollment, and state and urbanicity dummies. Robust standard errors clustered at school level are shown in brackets. For reading, math, self control, and interpersonal skills, higher skills indicate better outcomes. For externalizing and internalizing behavior problems, higher scores indicate worse outcomes. Parent involvement is measured as the sum of the number of times/week that the parent engages in 9 activities with the child (e.g. reading books, talk about nature, do science projects, tell stories).
Third, another concern is that combined school attendance might generate peer effects on BMI, obesity, food consumption and physical activity, independent of junk food availability. We test for the presence of peer effects by regressing these outcomes on combined school attendance using only the sample of schools that do not offer junk foods ( Table 4 ). The results do not provide any support for peer effects on BMI, obesity, food consumption or physical activity. 18
Effect of Combined School Attendance on BMI, Obesity and Related Behaviors Without Junk Food Availability in Fifth Grade
Total Consumption | Days per Week of Physical Activity | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Log BMI | Obese | Soda | Fast food | Milk | Green salad | Carrots | Potatoes | Other vegetables | Fruit | ||
Combined school attendance | −0.001 | 0.001 | −0.166 | 0.156 | −0.553 | −0.341 | −0.118 | −0.081 | −0.539 | −0.27 | −0.010 |
[0.007] | [0.015] | [0.368] | [0.230] | [0.436] | [0.178] | [0.302] | [0.152] | [0.353] | [0.371] | [0.131] | |
Observations | 3620 | 3620 | 3620 | 3620 | 3620 | 3620 | 3620 | 3620 | 3620 | 3620 | 3320 |
Notes: Each estimate represents a separate regression. All models control for the full set of covariates. Robust standard errors clustered at school level are shown in brackets.
Overall, the instrument appears to be strongly predictive of junk food availability and there is no evidence that selection or peer effects threaten its validity.
We now turn to our main results, which examine the effects of junk food availability on BMI and other outcomes. We first estimate basic OLS models of BMI and obesity, then augment with state fixed effects and baseline BMI to address omitted variable bias and selection, and finally estimate the IV and reduced form specifications (Section 5.1). In Section 5.2, we examine the sensitivity of our results to alternate measures of junk food availability and various sample restrictions. We also report findings from falsification tests. And finally, in Section 5.3, we describe results from ancillary regressions that explore the potential mechanisms underlying our BMI findings. In particular, we examine in-school and total consumption of selected foods and beverages and the availability of and participation in physical activity.
Our main results focus on whether the availability of junk foods increases BMI and obesity among 5 th graders ( Table 5 ). Columns 1 and 4 in Panel A show the results of basic OLS regressions of log BMI and obesity, respectively, on junk food availability controlling for child, household, and school characteristics. 19 These regressions yield a statistically significant increase in both BMI and obesity when junk food is available, although the point estimates are small. The inclusion of state fixed effects and urbanicity dummies (Panel A, columns 2 and 5) and then baseline BMI measured in kindergarten (Panel A, columns 3 and 6) eliminates the significant coefficients. The fully-specified OLS models have very small, precisely estimated, and statistically insignificant point estimates.
Effects of Junk Food Availability on BMI and Obesity in Fifth Grade
Log BMI | Obese | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Junk food availability | 0.011 | 0.007 | 0.001 | 0.019 | 0.009 | −0.001 |
[0.005] | [0.005] | [0.003] | [0.009] | [0.010] | [0.007] | |
Junk food availability | 0.083 | 0.010 | 0.003 | 0.104 | 0.014 | 0.003 |
[0.064] | [0.029] | [0.020] | [0.114] | [0.060] | [0.046] | |
Combined school attendance | 0.009 | 0.002 | 0.001 | 0.012 | 0.003 | 0.001 |
[0.006] | [0.006] | [0.004] | [0.011] | [0.012] | [0.009] | |
Demographics | Y | Y | Y | Y | Y | Y |
State & urbanicity dummies | N | Y | Y | N | Y | Y |
Baseline BMI | N | N | Y | N | N | Y |
Notes: N=9,380. Robust standard errors clustered at school level are shown in brackets. Other covariates in the model include male, age (months), male*age, race/ethnicity, kindergarten BMI, mother’s education, income, private school dummy, categories for percent minority in school and school enrollment, and state and urbanicity dummies. First stage results are shown in Table 2 .
However, the coefficients from these OLS models may be biased if junk food availability is related to unobserved determinants of children’s BMI. For example, districts with a large population of students at risk for obesity may adopt more stringent nutritional policies that reduce the availability of junk foods in school. In such situations, OLS regressions may show no significant relationship or even a negative relationship between junk food availability and BMI. OLS estimates might also suffer from attenuation bias due to the presence of measurement error in the junk food availability measures.
To address these issues, we estimate instrumental variables (IV) and reduced form regressions using grade span as the instrument: whether the 5 th grader attends a combined school with older peers. 20 The IV point estimates are relatively larger than the OLS estimates, but less precisely estimated rendering them statistically insignificant ( Table 5 , Panel B). 21 , 22 IV estimates from models that do not control for state and urbanicity dummies and baseline BMI (columns 1 and 4) are much larger than those in our preferred specification (Columns 3 and 6), although they are not statistically significantly different from each other. Even if the IV point estimates in our preferred specification (columns 3 and 6) were significant, they would represent only small increases in BMI and obesity of less than one-third of one percent. Hausman tests that check for the endogeneity of junk food availability by comparing estimates from the fully-specified OLS regression with the IV cannot reject the null hypothesis that both estimates are consistent. Therefore, we also report the reduced form estimates of BMI and obesity regressed directly on our instrument ( Table 5 , Panel C). The coefficients on the instrument are close to zero and very precisely estimated, which further confirm the null findings. Given concerns about unobserved heterogeneity in the OLS specifications and the larger standard errors in the IV specifications, the reduced form estimates are preferred.
We conducted a number of sensitivity analyses to test the robustness of our findings. In this section, we report results from a few key analyses and then turn to falsification tests. 23 These analyses control for the full set of covariates, including state and urbanicity dummies and baseline BMI.
For the sensitivity analyses, we first re-estimate our BMI and obesity regressions with the two alternate measures of junk food availability ( Table 6 ). Both the child-reported measure of junk food availability and the school-administrator reported measure of competitive food outlet show no effect of junk food availability on BMI or obesity. Next, we re-estimate the models with the exclusion of three particular groups that might confound our instrument ( Table 7 ). First, because combined schools are much more likely to be private, our instruments may simply capture variation across public versus private schools students, even though the regressions control for private school attendance. We re-estimate the models on a sample that excludes children who attend private schools ( Table 7 , Panel A) and find no effects on BMI and obesity. 24 Second, even though Section 4.2.2 suggests there are no peer effects on BMI and related behaviors, we test the sensitivity of our results to exclusion of the oldest peers (e.g., grade 9 or higher), but still find no evidence of an effect on BMI and obesity ( Table 7 , Panel B). Finally, children who switch schools for unobservable reasons potentially related to junk food availability may bias our estimates, but estimates from models that exclude children who changed schools between kindergarten and fifth grade confirm no effects ( Table 7 , Panel C). The point estimates from the OLS, IV and reduced form regressions for these sensitivity checks are essentially zero, though less precisely estimated in the IV models. 25
Effects of Alternate Measures of Junk Food Availability on BMI and Obesity in Fifth Grade
Log BMI | Obese | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
OLS Estimates | 0.006 | −0.002 | 0.004 | 0.007 | −0.007 | 0.003 |
[0.005] | [0.005] | [0.004] | [0.010] | [0.011] | [0.008] | |
IV Estimates | 0.054 | 0.008 | 0.003 | 0.069 | 0.012 | 0.002 |
[0.037] | [0.025] | [0.017] | [0.070] | [0.051] | [0.039] | |
(school admin) | ||||||
OLS Estimates | 0.014 | 0.008 | 0.005 | 0.025 | 0.015 | 0.009 |
[0.005] | [0.005] | [0.003] | [0.009] | [0.009] | [0.007] | |
IV Estimates | 0.042 | 0.009 | 0.003 | 0.053 | 0.012 | 0.003 |
[0.027] | [0.025] | [0.018] | [0.053] | []0.052 | [0.040] | |
Demographics | Y | Y | Y | Y | Y | Y |
State & urbanicity dummies | N | Y | Y | N | Y | Y |
Baseline BMI | N | N | Y | N | N | Y |
Notes: N=9,380
Effects of Junk Food Availability on BMI and Obesity in Fifth Grade with Alternate Sample Restrictions
Log BMI | Obese | |
---|---|---|
Junk food availability | 0.003 | 0.003 |
[0.004] | [0.008] | |
Junk food availability | 0.005 | 0.011 |
[0.024] | [0.052] | |
Combined school attendance | 0.001 | 0.002 |
[0.005] | [0.010] | |
Junk food availability | 0.001 | −0.002 |
[0.003] | [0.007] | |
Junk food availability | 0.006 | −0.003 |
[0.023] | [0.053] | |
Combined school attendance | 0.001 | 0.000 |
[0.004] | [0.009] | |
| ||
Junk food availability | 0.001 | −0.004 |
[0.004] | [0.008] | |
Junk food availability | −0.007 | 0.019 |
[0.029] | [0.070] | |
Combined school attendance | −0.001 | 0.003 |
[0.005] | [0.012] |
Notes: All models include the full set of covariates. Robust standard errors clustered at school level are shown in brackets. Hausman tests for consistency of OLS estimates could not be rejected in any case. The tests are not reported in the table.
As falsification tests, we examined whether junk food availability in the fifth grade influenced children’s height in the fifth grade and their pre-exposure BMI. Height should clearly be unrelated. And indeed, the coefficients are essentially zero and insignificant ( Table 8 ). Because BMI and obesity in kindergarten is measured prior to exposure to junk foods in school, any effects would suggest unobserved heterogeneity. The OLS, IV and reduced form point estimates are close to zero (though the IV estimates are less precise) and the reduced form specifications also show no relationship ( Table 9 , Panel A). Results for BMI and obesity measured in first and third grade likewise confirm insignificant effects of junk food availability during fifth grade ( Table 9 , Panels B and C). However, because our data do not contain information on junk food availability prior to 5 th grade, these results are also consistent with the absence of junk foods in earlier grades.
Effect of Junk Food Availability in School on Height in Fifth Grade
Log (5 Grade Height) | |||
---|---|---|---|
(1) | (2) | (3) | |
Junk food availability | 0.000 | 0.001 | 0.000 |
[0.001] | [0.001] | [0.001] | |
Junk food availability | 0.020 | 0.007 | 0.006 |
[0.016] | [0.008] | [0.008] | |
Combined school attendance | 0.002 | 0.001 | 0.001 |
[0.001] | [0.002] | [0.001] | |
Demographics | Y | Y | Y |
State & urbanicity dummies | N | Y | Y |
Baseline BMI | N | N | Y |
Note: N=9,380. Robust standard errors clustered at school level are shown in brackets.
Effects of Junk Food Availability on BMI and Obesity in Kindergarten, First, and Third Grade
Log BMI | Obese | |
---|---|---|
Junk food availability | 0.005 | 0.007 |
[0.003] | [0.008] | |
Junk food availability | 0.005 | −0.019 |
[0.021] | [0.051] | |
Combined school attendance | 0.002 | −0.004 |
[0.003] | [0.010] | |
Junk food availability | 0.002 | −0.003 |
[0.004] | [0.008] | |
Junk food availability | −0.015 | −0.000 |
[0.026] | [0.056] | |
Combined school attendance | −0.004 | −0.000 |
[0.003] | [0.011] | |
Junk food availability | 0.002 | 0.008 |
[0.005] | [0.009] | |
Junk food availability | −0.002 | 0.014 |
[0.029] | [0.063] | |
Combined school attendance | −0.001 | 0.003 |
[0.003] | [0.012] |
Notes: Each estimate represents a separate regression. All models include the full set of covariates. Robust standard errors clustered at school level are shown in brackets.
The consistent lack of significant findings for BMI and obesity raises questions regarding how the energy balance equation is affected by junk food availability. While we cannot measure children’s energy intake and expenditure explicitly with these data, we can examine whether junk food availability influences general food consumption patterns and physical activity. Unlike BMI and obesity, the consumption and physical activity measures are based on parents’ and children’s reports . As a result, they are subject to measurement error and consequently produce noisier estimates particularly for the IV models. Nevertheless, they represent our best opportunity for understanding important mechanisms underlying our null finding. Therefore, for the in-school junk food purchases, total consumption, and physical activity analyses, we focus mainly on the reduced form results (though we provide OLS results for comparison). 26
One potential explanation for our null findings for BMI and obesity may be that availability does not impact overall food consumption. This may happen for several different reasons. First, young children may not purchase significant amounts of junk food in school either due to limited access to such foods or fewer discretionary resources to purchase them. Second, children may not change their total consumption of junk food because junk food purchased in school simply substitutes for junk food brought from home. Or third, children may not change their overall consumption during the day, but simply substitute between junk food consumed in-school and out-of-school.
Unfortunately, we cannot completely separate out these possible explanations because the ECLS-K does not provide us with full information about the daily dietary intake of each child. However, we do have information about in-school purchases of foods with sugar, salty snacks, and sweetened beverages for those children with in-school availability. We also have total (in-school plus out-of-school) consumption of soda, fast food, and a variety of healthy foods for all children in the sample. While not definitive, we can use this information to gain some insight into underlying eating behaviors and lend support for our BMI and obesity findings.
Not surprisingly, our analysis of in-school consumption of junk foods does confirm that children purchase junk food when it is available. 27 The OLS estimates show a significant relationship for purchases of all types of junk food when junk foods are available in schools ( Table 10 , Panel A). And the reduced form estimates show that children in combined schools are between 5 and 9 percentage points more likely to purchase junk foods compared to those in elementary schools Table 10 , Panel B).
Effect of Junk Food Availability on In-School Junk Food Purchases in Fifth Grade
Purchased junk food in school | |||
---|---|---|---|
Bought any sweets | Bought any salty snacks | Bought any soda | |
Explanatory Variable | (1) | (2) | (3) |
Junk food availability | 0.175 | 0.113 | 0.078 |
[0.012] | [0.012] | [0.011] | |
Combined school attendance | 0.051 | 0.066 | 0.092 |
[0.017] | [0.017] | [0.018] |
Notes: N=9380. Each estimate represents a separate regression. Dependent variables in columns (1)–(3) are dichotomous and capture whether any purchase of that item was made in school during the last week. All regressions include the full set of covariates. Robust standard errors clustered at school level are shown in brackets.
To provide a sense of the caloric contribution of these purchases, we multiplied the increase in the probability of purchase from attending a combined school by the median number of times that food was purchased among children who purchased at least once, by the number of the calories per unit. 28 Summing across the three junk food groups yields 50 calories per week (7 calories per day) from in-school junk food purchases. The caloric contribution of in-school purchases is much higher (435 calories per week or 62 calories per day for the median child) among children who purchase these foods (as opposed to merely having them available). But even the 62 calories per day represents less than a quarter (23 percent) of the daily discretionary calorie allowance (267 calories) for a moderately active fifth grader. 29
It is possible that children substitute in-school purchases for snacks brought from home or eaten at home either due to satiation or parental monitoring. With our simple dietary recall measures, we cannot explicitly test the nature of potential substitution. We can, however, examine the total intake of soda and fast food consumed in and out of school. Soda is of particular interest because it is the only item for which children were asked about both their in-school and total consumption separately. Fast food, on the other hand, does not correspond exactly to the in-school snack food consumption categories. We find that junk food availability is not associated with significant increases in children’s total consumption of soda or fast foods ( Table 11 , Columns 1 and 2). 30 The OLS regressions show negative, though generally insignificant, estimates. 31 More importantly, the reduced form estimates confirm that there is no relationship between combined school attendance and total consumption of soda and fast food. The fact that children who consume soda and other junk food in schools show no evidence of an increase in total consumption provides support for the substitution hypothesis. This finding is also consistent with the literature, which indicates that only 27 percent of soda and sweetened drinks consumed in elementary schools are bought at school compared to 67 percent brought from home ( Briefel et al 2009 ).
Effect of Junk Food Availability on Total Consumption of Selected Unhealthy and Healthy Foods in Fifth Grade
Dependent Variable | ||||||||
---|---|---|---|---|---|---|---|---|
Unhealthy | Healthy Foods | |||||||
Soda | Fast Foods | Milk | Green salad | Carrots | Potatoes | Other vegetables | Fruits | |
Explanatory Variable | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
Junk food availability | −0.075 | −0.054 | −0.263 | 0.074 | −0.21 | −0.133 | −0.280 | −0.317 |
[0.189] | [0.120] | [0.220] | [0.105] | [0.133] | [0.084] | [0.156] | [0.202] | |
Combined school attendance | −0.193 | −0.109 | −0.256 | 0.15 | −0.05 | −0.072 | −0.008 | −0.086 |
[0.268] | [0.147] | [0.305] | [0.126] | [0.157] | [0.105] | [0.203] | [0.239] | |
Median of dept var | 2 | 2 | 7 | 2 | 2 | 2 | 2 | 5 |
Mean(std dev) of dept var | 6.14(7.58) | 2.9(4.7) | 10.72(9.40) | 2.28(4.20) | 2.97(5.53) | 1.91(3.49) | 5.19(6.36) | 7.82(8.16) |
Notes: N=9380. Each estimate represents a separate regression. Dependent variable captures the number of times the food or beverage item was consumed during the last 7 days. All models include the full set of covariates. Robust standard errors clustered at school level are shown in brackets.
While BMI is a widely-used outcome measure, it does not capture nutritional changes. Just because children are not gaining weight does not mean that their diets are not adversely affected by junk food availability. If children are consuming junk food in lieu of healthy foods, there may still be concerns about their nutrition. Columns 3 through 8 of Table 11 examine whether children with in-school availability of junk foods consume less milk, green salad, carrots, potatoes, other vegetables, and fruit. The OLS results show no significant associations with junk food availability. Moreover, reduced form regressions also show that combined school attendance does not significantly impact total consumption of the healthy foods. 32
The absence of any effects of junk food availability on BMI despite the in-school purchases of junk food also raises questions regarding potential compensatory changes in the availability of and participation in physical activity. For example, revenues from junk food sales may be used to fund playgrounds or pay for physical education instructors. Or it may be that combined schools simply offer more opportunities for physical activity due to their scale and organization relative to elementary schools. Another possibility is that parents or children may increase children’s physical activity to balance junk food intake. If physical activity is greater, then we may find no change in BMI or obesity despite an increase in caloric intake.
OLS and reduced form estimates for school- and parent-reported physical activity measures are reported in Table 12 . OLS estimates show no relationship between junk food availability and minutes per week of physical education at school, minutes per week of recess at school, and parent-reported participation in physical activity (measured as the number of days per week that the child engaged in exercise that causes rapid heart beat for 20 continuous minutes or more). The reduced form regressions show no significant effects of combined school attendance on minutes per week of physical education instruction. Children attending combined school have fewer minutes of recess ( Table 12 , Column 2), but slightly higher days of parent-reported physical activity ( Table 12 , Column 3) though neither finding is statistically significant at.conventional levels. Overall, the regressions do not provide consistent evidence that increased energy expenditure explains the null finding for BMI and obesity.
Effects of Junk Food Availability on Physical Education, Recess and Physical Activity in Fifth Grade
Minutes/Week Physical Education Instruction in School (1) | Minutes/Week Recess in School (2) | Parent-Reported Days/Week of Physical Activity (3) | |
---|---|---|---|
Junk food availability | 0.751 | −0.562 | 0.002 |
[1.761] | [3.220] | [0.050] | |
Combined school attendance | 3.012 | −10.004 | 0.120 |
[2.753] | [5.193] | [0.072] | |
Observations | 9010 | 8940 | 8650 |
Mean(std dev) of dept var | 77.6 (31.3) | 87.5 (57.3) | 3.7 (1.9) |
Notes: Each estimate represents a separate regression. All models include the full set of covariates as well as the baseline (kindergarten) measure of the dependent variable. Robust standard errors clustered at school level are shown in brackets.
Junk food availability is a prominent issue for middle and high schools in the U.S. However, there is also widespread legislation and regulation targeting junk foods even in elementary school ( Trust for American’s Health 2009 ). Young children’s access to junk foods in school is an important concern due to the strong correlation between childhood overweight and obesity in adolescence and adulthood ( Institute of Medicine 2005 ). In this paper, we examined whether junk food availability increased BMI and obesity among a national sample of 5th graders. Those 5th graders who attend a combined school are much more likely to have junk food availability relative to those in elementary school. While estimates from naïve models that only control for a limited set of covariates suggest a positive association between junk foods in school and BMI and obesity, fully-specified OLS models that control for BMI at school entry and state fixed-effects demonstrate no statistically or economically significant relationships among these young children. Likewise, the IV and reduced form models, which are not subject to the potential bias undermining OLS models, confirm the null findings for BMI and obesity. These results are not sensitive to various robustness checks including alternate measures of junk food availability and sample restrictions.
Finally, we provide further support for the null findings by examining in-school and overall food consumption patterns as well as physical activity. The null effects on BMI and obesity cannot be explained entirely by limited access or limited discretionary resources among young children because 5 th graders do purchase junk food when it is available in schools. However, our results suggest that the caloric contribution of in-school purchases is likely to be small. Moreover, we find no evidence of significant changes in the overall frequency of consumption of soda and fast food, which is consistent with children substituting in-school purchases of junk food for that taken from or eaten at home. Alternative explanations such as compensatory changes children’s consumption of healthy foods and in their opportunities for and participation in physical activity do not appear to play a significant role in explaining our null findings for BMI and obesity.
Our findings may have implications in the current economic environment. Half of the states are projecting budget shortfalls that threaten staffing, compensation, extracurricular activities, and policy initiatives such as mandated limits on class size. 33 Many schools subsidize their funding with revenue from the sale of junk foods. In total, elementary schools earn approximately $442 million annually from junk food sales ( Institute of Medicine 2007 ). In light of our findings, certain policy measures, such as outright bans on junk food sales (at least among elementary school children), might appear premature given that they remove a key source of discretionary funds.
While our results are robust, we caution that we could not consider the full range of consequences of junk food availability. Not only are the dietary intake measures in the ECLS-K limited, but we are also not able to examine whether related health outcomes such as diet quality or dental caries are influenced by junk food availability. Also, we are unable to examine the generalizability of our findings to older children who may have greater junk food access and intake both in and outside school. And finally, we could not consider whether exclusive contracts between schools and beverage/snack companies influence students’ food choices in the longer run through product or brand recognition. Additional research is necessary to fully understand the potential consequences before costly legislation is implemented. Such research might also consider the consequences of junk food regulations on school finances and the extent to which these financial consequences could be mitigated by the sale of more nutritious alternatives or through alternative financing mechanisms.
Means by Attendance in Elementary Versus Combined School and by Private/Public
Public School Sample | Private School Sample | |||
---|---|---|---|---|
5 Grade Covariates | Elementary | Combined | Elementary | Combined |
Male | 0.51 | 0.50 | 0.46 | 0.50 |
Child’s race/ethnicity: White | 0.55 | 0.61 | 0.82 | 0.75 |
Black | 0.12 | 0.13 | 0.03 | 0.05 |
Hispanic | 0.20 | 0.15 | 0.08 | 0.12 |
Asian | 0.08 | 0.04 | 0.04 | 0.05 |
Other | 0.05 | 0.06 | 0.03 | 0.04 |
Mother’s Education: Less than high school | 0.13 | 0.11 | 0.01 | 0.01 |
High school diploma | 0.34 | 0.37 | 0.20 | 0.19 |
Some college | 0.28 | 0.33 | 0.30 | 0.31 |
Bachelor’s degree or more | 0.25 | 0.19 | 0.50 | 0.49 |
Household Income < $15,000 | 0.13 | 0.14 | 0.01 | 0.02 |
≤ $15,000 Income < $25,000 | 0.14 | 0.13 | 0.03 | 0.03 |
≥ $25,000 Income < $35,000 | 0.14 | 0.15 | 0.05 | 0.07 |
≥ $35,000 Income < $50,000 | 0.17 | 0.20 | 0.12 | 0.12 |
≥ $50,000 Income < $75,000 | 0.17 | 0.18 | 0.26 | 0.23 |
≥ $75,000 | 0.26 | 0.20 | 0.53 | 0.52 |
School enrollment: 0 – 149 students | 0.02 | 0.03 | 0.12 | 0.09 |
150 – 299 | 0.11 | 0.17 | 0.53 | 0.43 |
300 – 499 | 0.37 | 0.25 | 0.35 | 0.30 |
500 – 749 | 0.32 | 0.35 | 0.00 | 0.18 |
750 & above | 0.18 | 0.20 | 0.00 | 0.00 |
Minorities in school <10% | 0.26 | 0.40 | 0.52 | 0.49 |
10% to less than 25% | 0.18 | 0.16 | 0.25 | 0.20 |
25% to less than 50% | 0.20 | 0.12 | 0.17 | 0.13 |
50% to less than 75% | 0.12 | 0.04 | 0.03 | 0.04 |
75% or more | 0.26 | 0.28 | 0.03 | 0.13 |
Urbanicity: Central city | 0.34 | 0.24 | 0.43 | 0.54 |
Suburb | 0.40 | 0.27 | 0.30 | 0.30 |
Town or rural | 0.27 | 0.49 | 0.27 | 0.15 |
Region: Northeast | 0.18 | 0.19 | 0.10 | 0.22 |
Midwest | 0.22 | 0.46 | 0.43 | 0.32 |
South | 0.35 | 0.25 | 0.29 | 0.25 |
West | 0.26 | 0.09 | 0.18 | 0.21 |
Observations | 6343 | 1196 | 275 | 1564 |
Notes: N=9,380.
Effect of Grade-Span on Academic and Social-Behavioral Outcomes Among Schools Without Junk Food Availability in Fifth Grade
Externalizing BP Score | Internalizing BP Score | Self-Control/ Interpersonal Skills Score | Reading Score | Math Score | |
---|---|---|---|---|---|
Combined school attendance | 0.154** | 0.059 | −0.115* | 0.146 | 0.767 |
[0.044] | [0.037] | [0.050] | [1.102] | [1.187] | |
Observations | 2780 | 2730 | 2670 | 2910 | 3170 |
This research was funded by grants from the Robert Wood Johnson Foundation’s Healthy Eating Research Program, NIH R01 HD057193, the Bing Center for Health Economics at RAND, and the RAND Labor and Population Program. All opinions are those of the authors and do not represent opinions of the funding agencies.
1 Other studies have examined the effects of price reductions, increases in availability, and promotion of low-fat foods in secondary schools on sales and purchases of these foods ( French et al 2004 , 2001 , 1997a , 1997b , Jeffery et al 1994 ) as well as their consumption ( Perry et al 2004 ) within experimental settings and found positive effects.
2 Kubik et al (2003) find that a la carte availability in school is negatively associated with overall intake of fruits and vegetables and positively associated with total and saturated fat intake among 7 th graders attending 16 Minneapolis-St Paul schools. Using the same data, Kubik et al (2005) show that using competitive foods as rewards and incentives is positively associated with BMI.
3 Also, using the ECLS-K, Fernandes (2008) found small positive associations between soda availability in schools and both in-school and overall soda consumption of fifth graders.
4 Their results for the other school policies, pouring rights contracts, and food and beverage advertisements are smaller and less precise.
5 For example, California’s first nutrition policy (SB 677) implemented beverage standards for elementary and middle schools, not high schools.
6 All sample sizes have been rounded to the nearest 10 per the ECLS-K’s restricted-use data agreement.
7 Obesity is defined as BMI greater than the 95 th percentile for age and gender on the Center for Disease Control growth charts.
8 Sweets include candy, ice cream, cookies, brownies or other sweets; salty snack foods include potato chips, corn chips, Cheetos, pretzels, popcorn, crackers or other salty snacks, and sweetened beverages include soda pop, sports drinks or fruit drinks that are not 100 percent juice.
9 To validate the ECLS-K estimates, we examined the Third School Nutrition and Dietary Assessment Study (SNDA-III), which collected 24-hour dietary recall from 2,300 children attending a nationally representative sample of public schools in 2005. Similar to the ECLS-K, eighty percent of elementary school children reported no competitive food purchases. Among children who made a purchase, the median daily caloric intake from these foods was 185 calories. The SNDA estimate is higher than our ECLS-K estimates (62 calories reported in Section 5) because it includes healthy foods purchased from competitive food venues: for example, milk was by far the most popular item purchased from competitive food venues and yogurt also ranked highly.
10 The “potatoes” category excluded French fries, fried potatoes, and potato chips.
11 The questionnaire separately asked about availability of high- and low-fat options for baked foods, salty snacks, and ice cream/frozen yogurt/sherbert. We include both the low- and high-fat options in our measure, however, in sensitivity analyses, we used only the high-fat versions to construct our school-administrator based measure of junk food availability and found results to be similar.
12 We rely mainly on the first measure of junk food availability because it is the most specific with respect to the quality of foods and because school-level policies regarding junk food availability are frequently set by school principals and staff ( Gordon et al 2007a ). We prefer this measure over the simple dichotomy of having any (unregulated) competitive food outlets because the outlet-based measure does not differentiate the type of foods sold (e.g. milk vs. soda). We also prefer it over the child-report because children who do not consume junk foods are less likely to accurately report availability and because children reported only the availability of any sweets, salty snacks, or sweetened beverages, but did not differentiate specific items (e.g. low-fat vs. high-fat).
13 The value of reduced form regressions has been highlighted by Angrist and Krueger (2001) and, more recently, Chernozhukov and Hansen (2008) formally show that the test for instrument irrelevance in the reduced form regression can be viewed as a weak-instrument-robust test of the hypothesis that the coefficient on the endogenous variable in the structural equation is zero.
14 This literature examines peer effects on a wide range of outcomes including substance use ( Lundborg 2006 ; Eisenberg 2004 ; Case and Katz 1991 ; Gaviria and Raphael 2001 ), crime ( Case and Katz 1991 ; Glaeser, Sacerdote, and Scheinkman 1996 ; Regnerus 2002 ), teenage pregnancy ( Crane 1991 ; Evans, Oates and Schwab 1992 ), discipline ( Cook et al 2008 ), academic achievement ( Hanushek et al 2003 ; Cook et al 2008 ), adolescent food choices ( Perry, Kelder, Komro 1993 ; Cullen et al 2001 ; French et al 2004 ) and weight ( Trogdon, Nonnemaker and Pais 2008 ).
15 However, Clark and Loheac (2007) estimate how substance use behavior of students within the same school who are one year older influences adolescent substance use and find a positive relationship.
16 One exception is Eisenberg (2004) who finds that 7 th and 8 th graders who attend schools with older peers are no more likely to use substances relative to those who attend schools with younger peers.
17 We also examined unadjusted differences in children’s individual, family and school characteristics during the 5 th grade (see Appendix Table A3 ). There were slight differences for some of the covariates. However, there was no overall pattern in the socioeconomic factors that would threaten the validity of the IV approach: that is, some differences imply better BMI outcomes for one group and others worse. For example, in our sample, elementary school students are more likely to be Hispanic and Asian while combined school students are more likely to be white. There are no differences in the share that are Black. Similarly, there is no consistent pattern in maternal education. Elementary school students are more likely to have poorly and highly educated mothers (less than high school, more than Bachelors).
18 To check whether these null findings are merely due to lack of power instead of absence of peer effects, we estimated the same models using social-behavioral outcomes and test scores as dependent variables because the literature finds evidence of peer effects on these outcomes. We were able to identify statistically significant peer effects on social-behavioral outcomes (but not test scores), which suggests that lack of power is an unlikely explanation for the finding of null peer effects on BMI and related outcomes.
19 In all models, we estimate robust standard errors clustered at the school level.
20 In alternate analyses, we used continuous measures of the highest and lowest grades in the school as instruments. In these over-identified models, both instruments had a strong positive association with junk food availability (i.e. increases in the highest and lowest grades available at the school were strongly predictive of junk food availability). This approach yielded qualitatively similar results as the exactly-identified models (available upon request).
21 The IV regressions were also estimated without baseline BMI. The point estimates, first-stage F-statistics, and Hausman tests yield similar results (available upon request).
22 A concern with our IV specification estimated via two-stage least-squares is that our first stage models do not account for the dichotomous nature of the treatment variable ( Maddala 1983 ). Estimates from binary treatment effect IV models confirm that the effects of junk food availability on BMI are neither substantive nor significant (available upon request).
23 We also conducted additional sensitivity analyses not reported here. First, given that we do not know the exposure to junk food in previous grades and given concerns that genetic susceptibility may not have a constant proportional effect on BMI at every point in the life cycle, we controlled for 1 st or 3 rd grade BMI instead of BMI in Kindergarten and obtained similar results. Second, inclusion of controls for school meal participation did not change our findings. Third, we used BMI z-scores as the dependent variable to accurately control for age and gender influences on BMI and obtained qualitatively similar results. Fourth, we estimated quantile regressions to test whether the effects of junk food availability varied across the BMI distribution, but found no evidence for heterogeneous effects. Finally, we also re-estimated our BMI and obesity models separately for each gender. The results for junk food availability mirrored those for the full sample. The OLS, IV, and RF models show no significant effects of junk food availability for either boys or girls. Still we may be concerned about differential peer effects, for example, if girls are influenced by older peers’ concerns about body image, which would bias our IV estimates downward. Restricting the sample to those boys and girls attending schools without junk food availability, the coefficients from the reduced form were nearly identical to those based on the full sample of boys and girls, which suggests that peer effects are not an issue even when regressions are gender-specific.
24 Estimates based only on the sample of private schools yield small and statistically insignificant effects of competitive food availability on BMI in both OLS and IV specifications, although the F-statistics for the instrument in the first stage were smaller (Results available upon request).
25 Hausman tests cannot reject the consistency of fully-specified OLS estimates in any of our sensitivity checks.
26 Although not shown, the IV (Wald) estimates are easily calculated by dividing the reduced form estimates in Table 10 – Table 12 by 0.2 (first stage estimate from Table 2 ). The IV coefficients are never significant in part due to the larger standard errors in the regressions of reported eating behaviors and physical activity.
27 We dichotomize the in-school purchase variables and estimate linear probability models since much of the variation in junk food purchases at school occurs on the extensive margin.
28 The median number of times an item is purchased in school among children who purchase at least once is 1.5 times (1–2 times per week). We assume that salty snacks add 140 calories (typical calories from a bag of potato chips), sweets add 200 calories (typically calories from a candy bar), and soda adds 150 calories. Given the limitations of the consumption data in the ECLS-K, we caution the reader to treat these caloric intake calculations as approximations.
29 Discretionary calories are the difference between an individual’s total energy requirement and the energy necessary to meet nutrient requirements. According to Dietary Guidelines for Americans, the discretionary allowance for a 2000 calorie diet is 267 calories. See: http://www.health.gov/dietaryguidelines/dga2005/document/html/chapter2.htm#table3 accessed August 22, 2008.
30 The total consumption variables are not dichotomized because there is sufficient variation on the intensive margin.
31 Negative binomial models with a binary treatment variable to account for the count-data distribution of the total consumption variable and the binary nature of junk food availability produced qualitatively similar results. (Results available upon request).
32 Given the limitations of the ECLS-K’s consumption variables, we again examined the SNDA-III data and found no evidence that combined school attendance increases total caloric intake.
33 “Schools expect budget cuts as economy sours: State problems, decline in property values eat away at district funds”. Available at: http://www.msnbc.msn.com/id/23116409/ (Accessed February 10, 2009).
Ashlesha Datar, RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407, USA, gro.dnar@ratad , Phone: 1-310-393-0411 x7367, Fax: 1-310-260-8161.
Nancy Nicosia, RAND Corporation, 20 Park Plaza, 7th Floor, Suite 720, Boston, MA 02116, USA, gro.dnar@aisocin , Phone: 1-617-338-2059 x4227.
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Monkeypox virus particles (shown in this coloured electron micrograph) can spread through close contact with people and animals. Credit: NIAID/Science Photo Library
When the World Health Organization (WHO) declared a public-health emergency over mpox earlier this month , it was because a concerning form of the virus that causes the disease had spread to multiple African countries where it had never been seen before. Since then, two people travelling to Africa — one from Sweden and one from Thailand — have become infected with that type of virus, called clade 1b, and brought it back to their countries.
Monkeypox virus: dangerous strain gains ability to spread through sex, new data suggest
Although researchers have known about the current outbreak since late last year, the need for answers about it is now more pressing than ever. The Democratic Republic of the Congo (DRC) has spent decades grappling with monkeypox clade I virus — a lineage to which Ib belongs. But in the past, clade I infections usually arose when a person came into contact with wild animals, and outbreaks would fizzle.
Clade Ib seems different, and is spreading largely through contact between humans, including through sex . Around 18,000 suspected cases of mpox, many of them among children, and at least 600 deaths potentially attributable to the disease have been reported this year in the DRC alone.
How does this emergency compare with one declared in 2022, when mpox cases spread around the globe? How is this virus behaving compared with the version that triggered that outbreak, a type called clade II? And will Africa be able to rein this one in? Nature talks with researchers about information they are rushing to gather.
It’s hard to determine, says Jason Kindrachuk, a virologist at the University of Manitoba in Winnipeg, Canada. He says that the DRC is experiencing two outbreaks simultaneously. The clade I virus, which has been endemic in forested regions of the DRC for decades, circulates in rural regions where people get it from animals. That clade was renamed Ia after the discovery of clade Ib. Studies in animals suggest that clade I is deadlier than clade II 1 — but Kindrachuk says that it’s hard to speculate on what that means for humans at this point.
Even when not fatal, mpox can trigger fevers, aches and painful fluid-filled skin lesions.
Growing mpox outbreak prompts WHO to declare global health emergency
Although many reports state that 10% of clade I infections in humans are fatal, infectious-disease researcher Laurens Liesenborghs at the Institute of Tropical Medicine in Antwerp, Belgium, doubts that this figure is accurate. Even the WHO’s latest estimate of a 3.5% fatality rate for people with mpox in the DRC might be high.
There are many reasons that fatality estimates might be unreliable, Liesenborghs says. For one, surveillance data captures only the most severe cases; many people who are less ill might not seek care at hospitals or through physicians, so their infections go unreported.
Another factor that can confound fatality rates is a secondary health condition. For example, people living with HIV — who can represent a large proportion of the population in many African countries — die from mpox at twice the rate of the general population 2 , especially if their HIV is untreated. And the relatively high death rate among children under age 5 could be partly because of malnutrition, which is common among kids in rural parts of the DRC, Liesenborghs says.
The clade 1b virus has garnered particular attention because epidemiological data suggest that it transmits more readily between people than previous strains did, including through sexual activity, whereas clade Ia mostly comes from animals. An analysis posted ahead of peer review on the preprint server medRxiv 3 shows that clade Ib’s genome contains genetic mutations that seem to have been induced by the human immune system, suggesting that it has been in humans for some time. Clade Ia genomes have fewer of these mutations.
But Liesenborghs says that the mutations and clades might not be the most important factor in understanding how monkeypox virus spreads. Although distinguishing Ia from Ib is useful in tracking the disease, he says, the severity and transmissibility of the disease could be affected more by the region where the virus is circulating and the people there. Clade Ia, for instance, seems to be more common in sparsely populated rural regions where it is less likely to spread far. Clade Ib is cropping up in densely populated areas and spreading more readily.
Jean Nachega, an infectious-disease physician at the University of Pittsburgh in Pennsylvania, says that scientists don’t understand many aspects of mpox transmission — they haven’t even determined which animal serves as a reservoir for the virus in the wild, although rodents are able to carry it. “We have to be very humble,” Nachega says.
Just as was the case during the COVID-19 pandemic, health experts are looking to vaccines to help curb this mpox outbreak. Although there are no vaccines designed specifically against the monkeypox virus, there are two vaccines proven to ward off a related poxvirus — the one that causes smallpox. Jynneos, made by biotechnology company Bavarian Nordic in Hellerup, Denmark, contains a type of poxvirus that can’t replicate but can trigger an immune response. LC16m8, made by pharmaceutical company KM Biologics in Kumamoto, Japan, contains a live — but weakened — version of a different poxvirus strain.
Hopes dashed for drug aimed at monkeypox virus spreading in Africa
Still, it’s unclear how effective these smallpox vaccines are against mpox generally. Dimie Ogoina, an infectious-disease specialist at Niger Delta University in Wilberforce Island, Nigeria, points out that vaccines have been tested only against clade II virus in European and US populations, because these shots were distributed by wealthy nations during the 2022 global outbreak . And those recipients were primarily young, healthy men who have sex with men, a population that was particularly susceptible during that outbreak. One study in the United States found that one dose of Jynneos was 80% effective at preventing the disease in at-risk people, whereas two doses were 82% effective 4 ; the WHO recommends getting both jabs.
People in Africa infected with either the clade Ia or 1b virus — especially children and those with compromised immune systems — might respond differently. However, one study in the DRC found that the Jynneos vaccine generally raised antibodies against mpox in about 1,000 health-care workers who received it 5 .
But researchers are trying to fill in some data gaps. A team in the DRC is about to launch a clinical trial of Jynneos in people who have come into close contact with the monkeypox virus — but have not shown symptoms — to see whether it can prevent future infection, or improve outcomes if an infection arises.
Mpox vaccines have been largely unavailable in Africa, but several wealthy countries have pledged to donate doses to the DRC and other affected African nations. The United States has offered 50,000 Jynneos doses from its national stockpile, and the European Union has ordered 175,000, with individual member countries pledging extra doses. Bavarian Nordic has also added another 40,000. Japan has offered 3.5 million doses of LC16m8 — for which only one jab is recommended instead of two.
Monkeypox in Africa: the science the world ignored
None of them have arrived yet, though, says Espoir Bwenge Malembaka, an epidemiologist at the Catholic University of Bukavu in the DRC. Low- and middle-income nations cannot receive vaccines until the WHO has deemed the jabs safe and effective. And the WHO has not given its thumbs up yet. It is evaluating data from vaccine manufacturers, delaying donors’ ability to send the vaccines.
Even when the vaccines arrive, Bwenge Malembaka says, “it’s really a drop in the bucket”. The African Centres for Disease Control and Prevention in Addis Ababa, Ethiopia, estimates that 10 million doses are needed to rein in the outbreak.
Bwenge Malembaka says that the uncertainty over vaccine arrival has made it difficult for the government to form a distribution plan. “I don’t know how one can go about this kind of challenge,” he says. Bwenge Malembaka suspects that children are likely to receive doses first, because they are highly vulnerable to clade I, but officials haven’t decided which regions to target. It’s also unclear how the government would prioritize other vulnerable populations such as sex workers, who have been affected by clade Ib. Their profession is criminalized in the DRC, so they might not be able to come forward for treatment.
Researchers lament that public-health organizations didn’t provide vaccines and other resources as soon as the clade I outbreak was identified, especially given lessons learnt from the 2022 global mpox outbreak. “The opportunity was there a couple months ago to cut this transmission chain, but resources weren’t available,” Liesenborghs says. “Now, it will be more challenging to tackle this outbreak, and the population at risk is much broader.”
doi: https://doi.org/10.1038/d41586-024-02793-9
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Tsukuba, Ibaraki (JP)
National Institute for Materials Science (NIMS)
IFReC, Osaka University in Japan offers Advanced Postdoc Positions for Immunology, Cell Biology, Bioinformatics and Bioimaging.
Suita Campus, Osaka University in Osaka, Japan
Immunology Frontier Research Center, Osaka University
Seeking tenure-track assoc. professor for interdisciplinary research in nanoprobe life sciences or related fields at WPI Nano Life Science Institute.
Kanazawa, Ishikawa, Japan (JP)
Nano Life Science Institute, Kanazawa University
Center for Virology and Vaccine Research at Beth Israel Deaconess Medical Center is seeking Assistant or Associate Professor.
Boston, Massachusetts (US)
Beth Israel Deaconess Medical Center (BIDMC)
One tenure-track faculty position is open to establish an active research program in all disciplines of molecular and cellular biology.
Taipei (TW)
The Institute of Molecular Biology, Academia Sinica, Taiwan
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Running head: Childhood Obesity 1. Childhood Obesity: Turning a Risk Factor into a Solution by Rebecca Jordan. Author's Note This paper was prepared for the 2014 APA TOPSS Competition for High School Psychology Students. Childhood Obesity 2 Abstract Obesity is a chronic health condition that is increasing at alarming rates in the United ...
Introduction. Childhood and adolescent obesity have reached epidemic levels in the United States, affecting the lives of millions of people. In the past 3 decades, the prevalence of childhood obesity has more than doubled in children and tripled in adolescents. 1 The latest data from the National Health and Nutrition Examination Survey show that the prevalence of obesity among US children and ...
This updated synthesis of obesity prevention interventions for children aged 6-18 years, found a small beneficial impact on child BMI for school-based obesity prevention interventions. A more comprehensive assessment of interventions is required to identify mechanisms of effective interventions to inform future obesity prevention public health policy, which may be particularly salient in for ...
Obesity in School Children Essay. Decent Essays. 972 Words. 4 Pages. Open Document. The Center for Disease Control and Prevention (CDC) has reported sixty percent of U.S children age 6 to 11 -approximately one in six children are victims of obesity-related illnesses that threaten to shorten their lives. That is more than nine million children.
Society in general and primary care providers in particular are often highly biased toward people with obesity. 24 A study with obese children aged 9 to 11 years found implicit bias to be around 5%. 25 Rather than ignoring or denying bias, primary care providers must instead develop the self-awareness to recognize and manage it, realize that it ...
Introduction. Obesity in children and adolescents is a global health issue with increasing prevalence in low-income and middle-income countries (LMICs) as well as a high prevalence in many high-income countries. 1 Obesity during childhood is likely to continue into adulthood and is associated with cardiometabolic and psychosocial comorbidity as well as premature mortality.2, 3, 4 The provision ...
Introduction. Childhood overweight and obesity is a serious public health problem worldwide in the 21 st century. The prevalence of overweight and obesity has increased in almost all countries worldwide especially in economically developed countries [1-3].The traditional perception that a heavy child is a healthy child has changed based on evidence that overweight and obesity in childhood is ...
Furthermore, Johnson et al Citation 147 provided research-based evidence by conducting a systematic literature review on the risk factors of overweight and obesity among school children and found that screening of 24 studies, living in a poor region, and limited access to food stores were significant risk factors for overweight and obesity ...
Taking Action on Childhood Obesity Childhood obesity is one of the most serious global public health challenges of the 21st century, affecting every country in the world. In just 40 years the number of school-age children and adolescents with obesity has risen more than 10-fold, from 11 million to 124 million (2016 estimates).1 In addition, an
Obesity in childhood can have significant long-term effects on a child's physical health, increasing the risk of developing chronic conditions such as type 2 diabetes, heart disease, and hypertension. Furthermore, obesity can have a profound impact on a child's psychological well-being, leading to low self-esteem, depression, and social isolation.
Worldwide, the prevalence of obesity in children and adolescents remains high or is increasing. This Primer by Lister and colleagues discusses the epidemiology, mechanisms, diagnosis, prevention ...
134 Childhood Obesity Essay Topics & Examples. Updated: Mar 2nd, 2024. 15 min. If you're writing an academic paper or speech on kids' nutrition or weight loss, you will benefit greatly from our childhood obesity essay examples. Besides, our experts have prepared a list of original topics for your work.
Long Essay on Childhood Obesity 500 Words in English. Long Essay on Childhood Obesity is usually given to classes 7, 8, 9, and 10. Obesity is a severe threat to children's health today. Childhood obesity is increasing year of year. A survey by The American Heart Association shows that obesity in children has more than tripled from 1971 to 2011.
February 12, 2024 by Prasanna. Obesity Essay: Obesity is a condition that occurs when a person puts on excess body fat. It is a sudden and unusual increase in body fat. It can lead to heart-related diseases, blood pressure, hypertension, cholesterol, and various other health issues. The main cause of obesity is over-eating.
Introduction. Childhood obesity is emerging as a major public health issue of the twenty-first century with an alarming rise in its prevalence in several developing countries.[] As per the WHO statistics, the prevalence of obesity among children in the age group of 5-19 years has increased from 4% in 1975 to 18% in 2016, which is much higher than a three-time increase.[]
Household income levels are also a strong predictor of obesity levels - obesity is more common among poorer households. For example, national data for England shows that the prevalence of 10- and 11-year-old children living with severe obesity was over four times as high for children living in the most deprived areas (9.4%) compared with those living in the least deprived areas (2.1%).
The effect of obesity in children is vital. Self-esteem and confidence of the yout are usually affected. Overweight children have experienced being bullied by other kids. Consequently, depression ...
BMI is often used to define obesity in teens. It has 2 categories: BMIs at the 95th percentile or more for age and gender, or BMIs of more than 30, whichever is smaller. BMI findings in this category mean the child should have a full health checkup. BMIs between the 85th and 95th percentile, or BMIs equal to 30, whichever is smaller.
The studies were published Aug. 28 in the journal Obesity. Previous research has shown that interventions that provide comprehensive, family-centered nutrition and behavioral education, and at least 26 contact hours with families over 3 to 12 months, are effective at treating childhood obesity.
Obesity in childhood is the most challenging public health issue in the twenty-first century. It has emerged as a pandemic health problem worldwide. The children who are obese tend to stay obese in adulthood and prone to increased risk for diabetes and cardiac problems at a younger age. Childhood obesity is associated with increased morbidity and premature death.[1] Prevention of obesity in ...
(1) Background: Overweight children usually have lower levels of physical activity (PA) than their normal-weight peers. Low PA predisposes to an increase in body fat mass. The aim of this study was to evaluate the effectiveness of school-based PA intervention on the anthropometric indicators and body composition of overweight and obese children during a two-year observation period, from the ...
Childhood obesity has become a pervasive global health concern with far-reaching implications beyond cosmetic considerations. Consult a doctor. Ask a doctor online. ... School-Based Interventions: Recognizing the influential role of schools in shaping children's habits, implementing interventions within the educational system is crucial ...
More concerning is that the gap in obesity rates between these states increases as kids gets older, from 2.1 percentage points for children 10-17, to 2.7 percentage points for high schoolers, to 4 ...
They also propose invalidating state laws intended to stem gun violence, a leading cause of death for children in the U.S. Project 2025 would even eliminate Head Start, a critical program for ...
Young children's access to junk foods in school is an important concern due to the strong correlation between childhood overweight and obesity in adolescence and adulthood (Institute of Medicine 2005). In this paper, we examined whether junk food availability increased BMI and obesity among a national sample of 5th graders.
Around 18,000 suspected cases of mpox, many of them among children, and at least 600 deaths potentially attributable to the disease have been reported this year in the DRC alone.
Introduction. Obesity in children and adolescents is a global health issue with increasing prevalence in low-income and middle-income countries (LMICs) as well as a high prevalence in many high-income countries.1 Obesity during childhood is likely to continue into adulthood and is associated with cardiometabolic and psychosocial comorbidity as ...
Video, 'Why I dey help children go school for Nigeria', Duration 2,41 29th August 2024. 3:11. Video, 'How I travel round di entire Nigeria with small money ...
The crime took place on the night of 9 August, when the woman, who was a junior doctor at the city's RG Kar Medical College, had gone to a seminar room to rest after a gruelling 36-hour shift.