Health Policy

Housing, Neighborhoods, and Children’s Health

Housing, Neighborhoods, and Children’s Health
Future of Children, Volume 25 Number 1 Spring 2015

Ingrid Gould Ellen and Sherry Glied
09/17/2015

In theory, improving low-income families’ housing and neighborhoods could also improve their children’s health, through any number of mechanisms. For example, less exposure to environmental toxins could prevent diseases such as asthma; a safer, less violent neighborhood could improve health by reducing the chances of injury and death, and by easing the burden of stress; and a more walkable neighborhood with better playgrounds could encourage children to exercise, making them less likely to become obese.

Yet although neighborhood improvement policies generally achieve their immediate goals— investments in playgrounds create playgrounds, for example—Ingrid Gould Ellen and Sherry Glied find that many of these policies don’t show a strong effect on poor children’s health. One problem is that neighborhood improvements may price low-income families out of the very neighborhoods that have been improved, as new amenities draw more affluent families, causing rents and home prices to rise. Policy makers, say Ellen and Glied, should carefully consider how neighborhood improvements may affect affordability, a calculus that is likely to favor policies with clear and substantial benefits for low-income children, such as those that reduce neighborhood violence.

Housing subsidies can help families either cope with rising costs or move to more affluent neighborhoods. Unfortunately, demonstration programs that help families move to better neighborhoods have had only limited effects on children’s health, possibly because such transi- tions can be stressful. And because subsidies go to relatively few low-income families, the presence of subsidies may itself drive up housing costs, placing an extra burden on the majority of families that don’t receive them. Ellen and Glied suggest that policy makers consider whether granting smaller subsidies to more families would be a more effective way to use these funds.

 

Performance Standards for Restaurants: A New Approach to Addressing the Obesity Epidemic.

Performance Standards for Restaurants: A New Approach to Addressing the Obesity Epidemic.
Cohen D, Bhatia R, Story MT, Sugarman SD, Economos CD, Whitsel LP, Williams JD, Elbel B, Harris J, Kappagoda M, Champagne CM, Shields K, Lesser LI, Fox T, Becker N. Performance Standards for Restaurants: A New Approach to Addressing the Obesity Epidemic. Santa Monica, CA: RAND Corporation; 2013.

Cohen D, Bhatia R, Story MT, Sugarman SD, Economos CD, Whitsel LP, Williams JD, Elbel B, Harris J, Kappagoda M, Champagne CM, Shields K, Lesser LI, Fox T, Becker N.
09/10/2015

The Introduction of a Supermarket via Tax-Credits in a Low-Income Area: The Influence of Purchasing and Consumption.

The Introduction of a Supermarket via Tax-Credits in a Low-Income Area: The Influence of Purchasing and Consumption.
Elbel B, Mijanovich T, Kiszko K, Abrams C, Dixon LB. The Introduction of a Supermarket via Tax-Credits in a Low-Income Area: The Influence of Purchasing and Consumption. American Journal of Health Promotion. In press.

Elbel B, Mijanovich T, Kiszko K, Abrams C, Dixon LB.
09/10/2015

Calorie Labeling and Consumer Estimation of Calories Purchased.

Calorie Labeling and Consumer Estimation of Calories Purchased.
Taksler GB, Elbel B. Calorie Labeling and Consumer Estimation of Calories Purchased. International Journal of Behavioral Nutrition and Physical Activity. 2014; 11: 91.

Taksler GB, Elbel B.
09/10/2015

BACKGROUND:

Studies rarely find fewer calories purchased following calorie labeling implementation. However, few studies consider whether estimates of the number of calories purchased improved following calorie labeling legislation.

FINDINGS:

Researchers surveyed customers and collected purchase receipts at fast food restaurants in the United States cities of Philadelphia (which implemented calorie labeling policies) and Baltimore (a matched comparison city) in December 2009 (pre-implementation) and June 2010 (post-implementation). A difference-in-difference design was used to examine the difference between estimated and actual calories purchased, and the odds of underestimating calories.Participants in both cities, both pre- and post-calorie labeling, tended to underestimate calories purchased, by an average 216-409 calories. Adjusted difference-in-differences in estimated-actual calories were significant for individuals who ordered small meals and those with some college education (accuracy in Philadelphia improved by 78 and 231 calories, respectively, relative to Baltimore, p = 0.03-0.04). However, categorical accuracy was similar; the adjusted odds ratio [AOR] for underestimation by >100 calories was 0.90 (p = 0.48) in difference-in-difference models. Accuracy was most improved for subjects with a BA or higher education (AOR = 0.25, p < 0.001) and for individuals ordering small meals (AOR = 0.54, p = 0.001). Accuracy worsened for females (AOR = 1.38, p < 0.001) and for individuals ordering large meals (AOR = 1.27, p = 0.028).

CONCLUSIONS:

We concluded that the odds of underestimating calories varied by subgroup, suggesting that at some level, consumers may incorporate labeling information.

Corner store purchases in a low-income urban community in NYC.

Corner store purchases in a low-income urban community in NYC.
Kiszko K, Cantor J, Abrams C, Ruddock C, Moltzen K, Devia C, McFarline B, Singh H, Elbel B. Corner store purchases in a low-income urban community in NYC. Journal of Community Health. In press.

Kiszko K, Cantor J, Abrams C, Ruddock C, Moltzen K, Devia C, McFarline B, Singh H, Elbel B.
09/10/2015

We assessed purchases made, motivations for shopping, and frequency of shopping at four New York City corner stores (bodegas). Surveys and purchase inventories (n = 779) were collected from consumers at four bodegas in Bronx, NY. We use Chi square tests to compare types of consumers, items purchased and characteristics of purchases based on how frequently the consumer shops at the specific store and the time of day the purchase was made. Most consumers shopped at the bodega because it was close to their home (52 %). The majority (68 %) reported shopping at the bodega at least once per day. The five most commonly purchased items were sugary beverages, (29.27 %), sugary snacks (22.34 %), coffee, (13.99 %), sandwiches, (13.09 %) and non-baked potato chips (12.2 %). Nearly 60 % of bodega customers reported their purchase to be healthy. Most of the participants shopped at the bodega frequently, valued its convenient location, and purchased unhealthy items. Work is needed to discover ways to encourage healthier choices at these stores.

Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance.

Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance.
13. Lee DC, Long JA, Wall SP, Braithwaite RS, Elbel B. Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance. American Journal of Public Health. In press.

Lee DC, Long JA, Wall SP, Braithwaite RS, Elbel B.
09/10/2015

Development and Evaluation of the US Healthy Food Diversity Index.

Development and Evaluation of the US Healthy Food Diversity Index.
Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N. Development and Evaluation of the US Healthy Food Diversity Index. British Journal of Nutrition. 2014; 112(9): 1562-1574.

Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N.
09/10/2015

Varied diets are diverse with respect to diet quality, and existing dietary variety indices do not capture this heterogeneity. We developed and evaluated the multidimensional US Healthy Food Diversity (HFD) index, which measures dietary variety, dietary quality and proportionality according to the 2010 Dietary Guidelines for Americans (DGA). In the present study, two 24 h dietary recalls from the 2003-6 National Health and Nutrition Examination Survey (NHANES) were used to estimate the intake of twenty-six food groups and health weights for each food group were informed by the 2010 DGA. The US HFD index can range between 0 (poor) and 1 - 1/n, where n is the number of foods; the score is maximised by consuming a variety of foods in proportions recommended by the 2010 DGA. Energy-adjusted Pearson's correlations were computed between the US HFD index and each food group and the probability of adequacy for fifteen nutrients. Linear regression was run to test whether the index differentiated between subpopulations with differences in dietary quality commonly reported in the literature. The observed mean index score was 0·36, indicating that participants did not consume a variety of healthful foods. The index positively correlated with nutrient-dense foods including whole grains, fruits, orange vegetables and low-fat dairy (r 0·12 to 0·64) and negatively correlated with added sugars and lean meats (r - 0·14 to - 0·23). The index also positively correlated with the mean probability of nutrient adequacy (r 0·41; P< 0·0001) and identified non-smokers, women and older adults as subpopulations with better dietary qualities. The US HFD index may be used to inform national dietary guidance and investigate whether healthful dietary variety promotes weight control.

Dietary Variety is Inversely Associated with Body Adiposity among US Adults Using a Novel Food Diversity Index.

Dietary Variety is Inversely Associated with Body Adiposity among US Adults Using a Novel Food Diversity Index.
Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N. Dietary Variety is Inversely Associated with Body Adiposity among US Adults Using a Novel Food Diversity Index. Journal of Nutrition. 2015; 145(3): 555-563.

Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N.
09/10/2015

BACKGROUND:

Consuming a variety (vs. monotony) of energy-poor, nutrient-dense foods may help individuals adhere to dietary patterns favorably associated with weight control.

OBJECTIVE:

The objective of this study was to examine whether greater healthful food variety quantified using the US Healthy Food Diversity (HFD) index favorably influenced body adiposity.

METHODS:

Men and nonpregnant, nonlactating women aged ≥20 y with two 24-h recalls from the cross-sectional NHANES 2003-2006 (n = 7470) were included in this study. Dietary recalls were merged with the MyPyramid Equivalent database to generate the US HFD index, which ranges from 0 to ∼1, with higher scores indicative of diets with a higher number and proportion of healthful foods. Multiple indicators of adiposity including BMI, waist-to-height ratio, android-to-gynoid fat ratio, fat mass index (FMI), and percentage body fat were assessed across US HFD index quintiles. ORs and 95% CIs were computed with use of multivariable logistic regression (SAS v. 9.3).

RESULTS:

The US HFD index was inversely associated with most adiposity indicators in both sexes. After multivariable adjustment, the odds of obesity, android-to-gynoid ratio >1, and high FMI were 31-55% lower (P-trend < 0.01) among women in quintile 5 vs. quintile 1 of the US HFD index. Among men, the odds of obesity, waist-to-height ratio ≥0.5, and android-to-gynoid ratio >1 were 40-48% lower (P-trend ≤ 0.01) in quintile 5 vs. quintile 1 of the US HFD index.

CONCLUSIONS:

Higher US HFD index values were inversely associated with indicators of body adiposity in both sexes, indicating that greater healthful food variety may protect against excess adiposity. This study explicitly recognizes the potential benefits of dietary variety in obesity management and provides the foundation to support its ongoing evaluation.

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