Comparison of Demographic Characteristics and Social Determinants of Health Between Adults With Diagnosed HIV and All Adults in the U.S.

Introduction: Quantifying disparities in social determinants of health between people with HIV and the total population could help address health inequities, and ensure health and well-being among people with HIV in the U.S., but estimates are lacking. Methods: Several representative data sources were used to assess differences in social determinants of health between adults with diagnosed HIV (Centers for Disease Control and Prevention Medical Monitoring Project) and the total adult population (U.S. Census Bureau's decennial census, American Community Survey, Household Pulse Survey, the Current Population Survey Annual Social and Economic Supplements; the Department of Housing and Urban Development's point-in-time estimates of homelessness; and the Bureau of Justice Statistics). The differences were quantified using standardized prevalence differences and standardized prevalence ratios, adjusting for differences in age, race/ethnicity, and birth sex between people with HIV and the total U.S. population. Results: Overall, 35.6% of people with HIV were living in a household with an income at or below the federal poverty level, and 8.1% recently experienced homelessness. Additionally, 42.9% had Medicaid and 27.6% had Medicare; 39.7% were living with a disability. Over half (52.3%) lived in large central metropolitan counties and 20.6% spoke English less than very well based on survey responses. After adjustment, poverty (standardized prevalence difference=25.1%, standardized prevalence ratio=3.5), homelessness (standardized prevalence difference=8.5%, standardized prevalence ratio=43.5), coverage through Medicaid (standardized prevalence difference=29.5%, standardized prevalence ratio=3.0) or Medicare (standardized prevalence difference=7.8%), and disability (standardized prevalence difference=30.3%, standardized prevalence ratio=3.0) were higher among people with HIV than the total U.S. population. The percentage of people with HIV living in large central metropolitan counties (standardized prevalence difference=13.4%) or who were recently incarcerated (standardized prevalence ratio=5.9) was higher than the total U.S. population. Conclusions: These findings provide a baseline for assessing national-level disparities in social determinants of health between people with HIV and the total U.S. population, and it can be used as a model to assess local disparities. Addressing social determinants of health is essential for achieving health equity, requiring a multipronged approach with interventions at the provider, facility, and policy levels.

Association between passively collected walking and bicycling data and purposefully collected active commuting survey data-United States, 2019.

Commercially-available location-based services (LBS) data derived primarily from mobile devices may provide an alternative to surveys for monitoring physically-active transportation. Using Spearman correlation, we compared county-level metrics of walking and bicycling from StreetLight with metrics of physically-active commuting among U.S. workers from the American Community Survey. Our strongest pair of metrics ranked counties (n = 298) similarly for walking (rho = 0.53 [95% CI: 0.44-0.61]) and bicycling (rho = 0.61 [0.53-0.67]). Correlations were higher for denser and more urban counties. LBS data may offer public health and transportation professionals timely information on walking and bicycling behavior at finer geographic scales than some existing surveys.

Developing a granular scale environmental burden index (EBI) for diverse land cover types across the contiguous United States.

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.

Social vulnerability and county stay-at-home behavior during COVID-19 stay-at-home orders, United States, April 7-April 20, 2020.

PURPOSE: Early COVID-19 mitigation relied on people staying home except for essential trips. The ability to stay home may differ by sociodemographic factors. We analyzed how factors related to social vulnerability impact a community's ability to stay home during a stay-at-home order. METHODS: Using generalized, linear mixed models stratified by stay-at-home order (mandatory or not mandatory), we analyzed county-level stay-at-home behavior (inferred from mobile devices) during a period when a majority of United States counties had stay-at-home orders (April 7-April 20, 2020) with the Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI). RESULTS: Counties with higher percentages of single-parent households, mobile homes, and persons with lower educational attainment were associated with lower stay-at-home behavior compared with counties with lower respective percentages. Counties with higher unemployment, higher percentages of limited-English-language speakers, and more multi-unit housing were associated with increases in stay-at-home behavior compared with counties with lower respective percentages. Stronger effects were found in counties with mandatory orders. CONCLUSIONS: Sociodemographic factors impact a community's ability to stay home during COVID-19 stay-at-home orders. Communities with higher social vulnerability may have more essential workers without work-from-home options or fewer resources to stay home for extended periods, which may increase risk for COVID-19. Results are useful for tailoring messaging, COVID-19 vaccine delivery, and public health responses to future outbreaks.

Differences in rapid increases in county-level COVID-19 incidence by implementation of statewide closures and mask mandates - United States, June 1-September 30, 2020.

BACKGROUND AND OBJECTIVE: Community mitigation strategies could help reduce COVID-19 incidence, but there are few studies that explore associations nationally and by urbanicity. In a national county-level analysis, we examined the probability of being identified as a county with rapidly increasing COVID-19 incidence (rapid riser identification) during the summer of 2020 by implementation of mitigation policies prior to the summer, overall and by urbanicity. METHODS: We analyzed county-level data on rapid riser identification during June 1-September 30, 2020 and statewide closures and statewide mask mandates starting March 19 (obtained from state government websites). Poisson regression models with robust standard error estimation were used to examine differences in the probability of rapid riser identification by implementation of mitigation policies (P-value< .05); associations were adjusted for county population size. RESULTS: Counties in states that closed for 0-59 days were more likely to become a rapid riser county than those that closed for >59 days, particularly in nonmetropolitan areas. The probability of becoming a rapid riser county was 43% lower among counties that had statewide mask mandates at reopening (adjusted prevalence ratio = 0.57; 95% confidence intervals = 0.51-0.63); when stratified by urbanicity, associations were more pronounced in nonmetropolitan areas. CONCLUSIONS: These results underscore the potential value of community mitigation strategies in limiting the COVID-19 spread, especially in nonmetropolitan areas.

Opening of Large Institutions of Higher Education and County-Level COVID-19 Incidence - United States, July 6-September 17, 2020.

During early August 2020, county-level incidence of coronavirus disease 2019 (COVID-19) generally decreased across the United States, compared with incidence earlier in the summer (1); however, among young adults aged 18-22 years, incidence increased (2). Increases in incidence among adults aged ≥60 years, who might be more susceptible to severe COVID-19-related illness, have followed increases in younger adults (aged 20-39 years) by an average of 8.7 days (3). Institutions of higher education (colleges and universities) have been identified as settings where incidence among young adults increased during August (4,5). Understanding the extent to which these settings have affected county-level COVID-19 incidence can inform ongoing college and university operations and future planning. To evaluate the effect of large colleges or universities and school instructional format* (remote or in-person) on COVID-19 incidence, start dates and instructional formats for the fall 2020 semester were identified for all not-for-profit large U.S. colleges and universities (≥20,000 total enrolled students). Among counties with large colleges and universities (university counties) included in the analysis, remote-instruction university counties (22) experienced a 17.9% decline in mean COVID-19 incidence during the 21 days before through 21 days after the start of classes (from 17.9 to 14.7 cases per 100,000), and in-person instruction university counties (79) experienced a 56.2% increase in COVID-19 incidence, from 15.3 to 23.9 cases per 100,000. Counties without large colleges and universities (nonuniversity counties) (3,009) experienced a 5.9% decline in COVID-19 incidence, from 15.3 to 14.4 cases per 100,000. Similar findings were observed for percentage of positive test results and hotspot status (i.e., increasing among in-person-instruction university counties). In-person instruction at colleges and universities was associated with increased county-level COVID-19 incidence and percentage test positivity. Implementation of increased mitigation efforts at colleges and universities could minimize on-campus COVID-19 transmission.

Association Between Social Vulnerability and a County's Risk for Becoming a COVID-19 Hotspot - United States, June 1-July 25, 2020.

Poverty, crowded housing, and other community attributes associated with social vulnerability increase a community's risk for adverse health outcomes during and following a public health event (1). CDC uses standard criteria to identify U.S. counties with rapidly increasing coronavirus disease 2019 (COVID-19) incidence (hotspot counties) to support health departments in coordinating public health responses (2). County-level data on COVID-19 cases during June 1-July 25, 2020 and from the 2018 CDC social vulnerability index (SVI) were analyzed to examine associations between social vulnerability and hotspot detection and to describe incidence after hotspot detection. Areas with greater social vulnerabilities, particularly those related to higher representation of racial and ethnic minority residents (risk ratio [RR] = 5.3; 95% confidence interval [CI] = 4.4-6.4), density of housing units per structure (RR = 3.1; 95% CI = 2.7-3.6), and crowded housing units (i.e., more persons than rooms) (RR = 2.0; 95% CI = 1.8-2.3), were more likely to become hotspots, especially in less urban areas. Among hotspot counties, those with greater social vulnerability had higher COVID-19 incidence during the 14 days after detection (212-234 cases per 100,000 persons for highest SVI quartile versus 35-131 cases per 100,000 persons for other quartiles). Focused public health action at the federal, state, and local levels is needed not only to prevent communities with greater social vulnerability from becoming hotspots but also to decrease persistently high incidence among hotspot counties that are socially vulnerable.

Reduction in Cardiovascular Risk Among Latino Participants in a Community-Based Intervention Linked With Clinical Care.

INTRODUCTION: Community Heart Health Actions for Latinos at Risk is a community health worker-led cardiovascular disease risk reduction program targeting low-income urban Latinos. The impact of community programs linked with clinical care has not been well characterized. METHODS: Community Heart Health Actions for Latinos at Risk provided 12 weeks of lifestyle education. Changes in risk factors were assessed before and after completion. Univariate, bivariate, and multivariate analyses were used to determine factors associated with changes in risk factors. RESULTS: From 2009 to 2013, a total of 1,099 participants were recruited and 768 had risk factors measured at baseline and 12 weeks. All analyses were performed in 2016. In participants with abnormal baseline risk factors, significant (all p<0.001) median reductions in systolic blood pressure (-11 mmHg, n=244); low-density lipoprotein cholesterol (-14 mg/dL, n=201); glucose (-8 mg/dL, n=454); triglycerides (-57 mg/dL, n=242); and Framingham risk score (-2.3%, n=301) were observed. Program completion (eight of 12 classes) was associated with the reduction in low-density lipoprotein cholesterol (p=0.03) and systolic blood pressure (p=0.01). After adjustment, low-density lipoprotein cholesterol reduction was greatest in participants newly prescribed lipid-lowering drugs (-30%, 95% CI= -48, -15). CONCLUSIONS: A community health worker-led intervention lowered cardiovascular disease risk among vulnerable Latinos. Integration with primary care services was an essential program component.

Reducing relative food reinforcement in infants by an enriched music experience.

OBJECTIVE: The reinforcing value of food may be established early in life. Research shows that infant weight status is related to the relative reinforcing value of food versus non-food alternatives (food reinforcing ratio, FRR). The purpose of this pilot study was to assess the effects of a 6-week music enhancement program (Music Together®, n = 14) versus an active play date control group (n = 13) on the FRR in 9- to 16-month-old infants who were high in relative food reinforcement. METHODS: Participating parents and infants attended six weekly 45-min group classes. Parents in the music group and the play date group were encouraged to listen to the Music Together program CD or play with the play date group's toy with their infants at home, respectively. RESULTS: Intent-to-treat analysis showed a decrease in FRR for infants in the music group (mean ± SD: -0.13 ± 0.13) in comparison to a slight increase in the control group (0.04 ± 0.11) (F[1, 24] = 11.86, P = 0.002). CONCLUSIONS: These findings provide evidence that relative reinforcing value of food can be reduced by promoting alternative reinforcers at an early age.

Experimental analysis of the effect of taxes and subsides on calories purchased in an on-line supermarket.

Taxes and subsidies are a public health approach to improving nutrient quality of food purchases. While taxes or subsidies influence purchasing, it is unclear whether they influence total energy or overall diet quality of foods purchased. Using a within subjects design, selected low nutrient dense foods (e.g. sweetened beverages, candy, salty snacks) were taxed, and fruits and vegetables and bottled water were subsidized by 12.5% or 25% in comparison to a usual price condition for 199 female shoppers in an experimental store. Results showed taxes reduced calories purchased of taxed foods (coefficient = -6.61, CI = -11.94 to -1.28) and subsidies increased calories purchased of subsidized foods (coefficient = 13.74, CI = 8.51 to 18.97). However, no overall effect was observed on total calories purchased. Both taxes and subsidies were associated with a reduction in calories purchased for grains (taxes: coefficient = -6.58, CI = -11.91 to -1.24, subsidies: coefficient = -12.86, CI = -18.08 to -7.63) and subsidies were associated with a reduction in calories purchased for miscellaneous foods (coefficient = -7.40, CI = -12.62 to -2.17) (mostly fats, oils and sugars). Subsidies improved the nutrient quality of foods purchased (coefficient = 0.14, CI = 0.07 to 0.21). These results suggest that taxes and subsidies can influence energy purchased for products taxed or subsidized, but not total energy purchased. However, the improvement in nutrient quality with subsidies indicates that pricing can shift nutritional quality of foods purchased. Research is needed to evaluate if differential pricing strategies based on nutrient quality are associated with reduction in calories and improvement in nutrient quality of foods purchased.

FTO polymorphisms moderate the association of food reinforcement with energy intake.

Food reinforcement (RRVfood) is related to increased energy intake, cross-sectionally related to obesity, and prospectively related to weight gain. The fat mass and obesity-associated (FTO) gene is related to elevated body mass index and increased energy intake. The primary purpose of the current study was to determine whether any of 68 FTO single nucleotide polymorphisms (SNPs) or a FTO risk score moderate the association between food reinforcement and energy or macronutrient intake. Energy and macronutrient intake was measured using a laboratory ad libitum snack food consumption task in 237 adults of varying BMI. Controlling for BMI, the relative reinforcing value of reading (RRVreading) and proportion of African ancestry, RRVfood predicted 14.2% of the variance in energy intake, as well as predicted carbohydrate, fat, protein and sugar intake. In individual analyses, six FTO SNPs (rs12921970, rs9936768, rs12446047, rs7199716, rs8049933 and rs11076022, spanning approximately 251kbp) moderated the relationship between RRVfood and energy intake to predict an additional 4.9-7.4% of variance in energy intake. We created an FTO risk score based on 5 FTO SNPs (rs9939609, rs8050136, rs3751812, rs1421085, and rs1121980) that are related to BMI in multiple studies. The FTO risk score did not increase variance accounted for beyond individual FTO SNPs. rs12921970 and rs12446047 served as moderators of the relationship between RRVfood and carbohydrate, fat, protein, and sugar intake. This study shows for the first time that the relationship between RRVfood and energy intake is moderated by FTO SNPs. Research is needed to understand how these processes interact to predict energy and macronutrient intake.

Women who are motivated to eat and discount the future are more obese.

OBJECTIVE: Food reinforcement and delay discounting (DD) independently predict body mass index (BMI), but there is no research studying whether these variables interact to improve prediction of BMI. METHODS: BMI, the relative reinforcing value of high (PMAXHED ) and low (PMAXLED ) energy dense food, and DD for $10 and $100 future rewards (DD10 , DD100 ) were measured in 199 adult females. RESULTS: PMAXHED (P = 0.017), DD10 (P = 0.003), and DD100 (P = 0.003) were independent predictors of BMI. The interaction of PMAXLED × DD10 (P = 0.033) and DD100 (P = 0.039), and PMAXHED × DD10 (P = 0.038) and DD100 (P = 0.045) increased the variance accounted for predicting BMI beyond the base model controlling for age, education, minority status, disinhibition, and dietary restraint. Based on the regression model, BMI differed by about 2 BMI units for low versus high food reinforcement, by about 3 BMI units for low versus high DD, and by about 4 BMI units for those high in PMAXHED , but low in DD versus high in PMAXHED and high in DD. CONCLUSION: Reducing DD may help prevent obesity and improve treatment of obesity in those who are high in food reinforcement.

Food reinforcement, dietary disinhibition and weight gain in nonobese adults.

OBJECTIVE: Food reinforcement is cross-sectionally related to BMI and energy intake in adults, and prospectively predicts weight gain in children, but there has not been any research studying food reinforcement as a predictor of adult weight gain. DESIGN AND METHODS: This study examined whether the relative reinforcing value of food versus sedentary activities, as measured on a progressive ratio schedule, predicts 12-month weight gain in a sample of 115 nonobese (BMI < 30) adults. Dietary disinhibition and dietary restraint were also examined as potential moderators of this relationship. RESULTS: In a hierarchical regression controlling for baseline age and weight, dietary hunger, income, sex, and minority status, food reinforcement and predicted weight gain (P = 0.01) significantly increased the variance from 6.3% to 11.7% (P = 0.01). Dietary disinhibition moderated this relationship (P = 0.02) and increased the variance an additional 4.7% (P = 0.02), such that individuals with high food reinforcement had greater weight gain if they were also high in disinhibition. CONCLUSIONS: These results suggest that food reinforcement is a significant contributor to weight change over time, and food reinforcement may have the biggest effect on those who are most responsive to food cues.

Food reinforcement partially mediates the effect of socioeconomic status on body mass index.

OBJECTIVE: Low socioeconomic status (low SES), as defined by income or educational attainment, has been associated with obesity in industrialized nations. Low SES persons have limited resources and may experience food insecurity that increases food reinforcement. Food reinforcement has been positively related to energy intake and weight status, and increased food reinforcement may explain the higher prevalence of obesity among low SES individuals who have restricted access to low-energy-dense foods and nonfood reinforcers. DESIGN AND METHODS: Annual household income, highest education level completed and food reinforcement in 166 adults of varying body mass index (BMI, kg m(-2) ) was measured. RESULTS: Multivariate linear regression analyses controlling for age, sex, minority status, session hunger, and the reinforcing value of nonfood alternatives showed that household income was related to food reinforcement (P = 0.048) and BMI (P = 0.019), and that food reinforcement was related to BMI (P = 0.0017). Path analyses revealed a significant indirect effect of household income on BMI through food reinforcement, suggesting that the relationship between lower household income and greater BMI was mediated in part by increased food reinforcement. A similar pattern of results was observed when education level was used as the proxy for SES. CONCLUSIONS: These findings support the hypothesis that deprivation and restricted food choice associated with low SES enhance food reinforcement, increasing the risk for obesity.

Two functional serotonin polymorphisms moderate the effect of food reinforcement on BMI.

Food reinforcement, or the motivation to eat, has been associated with increased energy intake, greater body weight, and prospective weight gain. Much of the previous research on the reinforcing value of food has focused on the role of dopamine, but it may be worthwhile to examine genetic polymorphisms in the serotonin and opioid systems as these neurotransmitters have been shown to be related to reinforcement processes and to influence energy intake. We examined the relationship among 44 candidate genetic polymorphisms in the dopamine, serotonin, and opioid systems, as well as food reinforcement and body mass index (BMI) in a sample of 245 individuals. Polymorphisms in the monoamine oxidase A (MAOA-LPR) and serotonin receptor 2A genes (rs6314) moderated the effect of food reinforcement on BMI, accounting for an additional 5-10% variance and revealed a potential role of the single nucleotide polymorphism, rs6314, in the serotonin 2A receptor as a differential susceptibility factor for obesity. Differential susceptibility describes a factor that can confer either risk or protection depending on a second variable, such that rs6314 is predictive of both high and low BMI based on the level of food reinforcement, while the diathesis stress or dual-gain model only influences one end of the outcome measure. The interaction with MAOA-LPR better fits the diathesis stress model, with the 3.5R/4R allele conferring protection for individuals low in food reinforcement. These results provide new insight into genes theoretically involved in obesity, and support the hypothesis that genetics moderate the association between food reinforcement and BMI.

Food characteristics, long-term habituation and energy intake. Laboratory and field studies.

Greater food variety is related to increased energy intake, and one approach to reduce food intake is to reduce food variety. The effects of varying the variety of foods at the dinner meal to reduce energy intake was assessed in laboratory and field experiments. Experiment 1 randomly assigned 31 overweight children to one of three conditions that provided one laboratory meal per day over a week. Conditions were the SAME macaroni and cheese, SIMILAR types of macaroni and cheese, or a VARIETY of high-energy-dense foods. On days 1 and 5 all children consumed the same macaroni and cheese meal. Results showed significant differences in energy consumed between SAME and SIMILAR versus VARIETY from day 1 to 5, with SAME and SIMILAR decreasing and VARIETY increasing energy intake. Trials to habituation, a potential mechanism for the variety effect, showed the same pattern of between group differences as energy intake. Experiment 2 randomly assigned 30 overweight children to conditions that provided the SAME, SIMILAR or VARIETY of high-energy-dense entrees along with a variety of low-energy-dense dinner entrees to eat in their homes for 4 weeks. Results showed significant between group differences in energy intake across weeks, with significant decreases over weeks for the SAME and SIMILAR versus VARIETY groups. The pattern of results across the experiments shows the same pattern of reduction in energy intake if children eat the same or similar characteristics of foods (types of macaroni and cheese), which may provide ideas about how to develop dietary variety prescriptions that can reduce intake and be tested in clinical trials.

Functional characterization of reductive dehalogenases by using blue native polyacrylamide gel electrophoresis.

Dehalococcoides mccartyi strains are obligate organohalide-respiring bacteria harboring multiple distinct reductive dehalogenase (RDase) genes within their genomes. A major challenge is to identify substrates for the enzymes encoded by these RDase genes. We demonstrate an approach that involves blue native polyacrylamide gel electrophoresis (BN-PAGE) followed by enzyme activity assays with gel slices and subsequent identification of proteins in gel slices using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). RDase expression was investigated in cultures of Dehalococcoides mccartyi strain BAV1 and in the KB-1 consortium growing on chlorinated ethenes and 1,2-dichloroethane. In cultures of strain BAV1, BvcA was the only RDase detected, revealing that this enzyme catalyzes the dechlorination not only of vinyl chloride, but also of all dichloroethene isomers and 1,2-dichloroethane. In cultures of consortium KB-1, five distinct Dehalococcoides RDases and one Geobacter RDase were expressed under the conditions tested. Three of the five RDases included orthologs to the previously identified chlorinated ethene-dechlorinating enzymes VcrA, BvcA, and TceA. This study revealed substrate promiscuity for these three enzymes and provides a path forward to further explore the largely unknown RDase protein family.

Usual energy intake mediates the relationship between food reinforcement and BMI.

The relative reinforcing value of food (RRV(food)) is positively associated with energy consumed and overweight status. One hypothesis relating these variables is that food reinforcement is related to BMI through usual energy intake. Using a sample of two hundred fifty-two adults of varying weight and BMI levels, results showed that usual energy intake mediated the relationship between RRV(food) and BMI (estimated indirect effect = 0.0027, bootstrapped 95% confidence intervals (CIs) 0.0002-0.0068, effect ratio = 0.34), controlling for age, sex, minority status, education, and reinforcing value of reading (RRV(reading)). Laboratory and usual energy intake were correlated (r = 0.24, P < 0.001), indicating that laboratory energy intake could provide an index of eating behavior in the natural environment. The mediational relationship observed suggests that increasing or decreasing food reinforcement could influence body weight by altering food consumption. Research is needed to develop methods of modifying RRV(food) to determine experimentally whether manipulating food reinforcement would result in changes in body weight.

Food reinforcement and obesity. Psychological moderators.

The relative reinforcing value of food (RRV(food)) provides an index of the motivation to eat. Research has shown that RRV(food) is higher in obese individuals than their non-obese peers, is associated with greater energy intake, predicts weight gain and interacts with impulsivity to predict energy intake. This study was designed to determine whether dietary restraint, dietary disinhibition or hunger moderate the effect of RRV(food) on the weight status and energy intake in 273 adults of various body mass index (BMI) levels. Hierarchical regression was used to assess the independent effects of RRV(food) on BMI and energy intake, controlling for age, sex, income, education, minority status, and RRV(reading). Results showed that greater RRV(food), but not RRV(reading), was associated with greater BMI and energy intake. Dietary disinhibition and dietary restraint moderated the relationship between RRV(food) and BMI, with dietary disinhibition being a stronger moderator of this relationship (r(2)=0.20) than dietary restraint (r(2)=0.095). In addition, dietary disinhibition moderated the effect of RRV(food) on energy intake. These results replicate the importance of RRV(food) as a predictor of obesity, and show that psychological factors moderate the effect of food reinforcement on body weight and energy intake. This trial was registered at clinicaltrials.gov as NCT00962117.

Starting out - Family's concerns led me to clarify my thinking on end of life practice.

During a recent community placement, I accompanied one of the nursing sisters to see an older, female patient with metastatic pancreatic cancer.