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To assess the relationship between food insecurity, sleep quality, and days with mental and physical health issues among college students.
An online survey was administered. Food insecurity was assessed using the ten-item Adult Food Security Survey Module. Sleep was measured using the nineteen-item Pittsburgh Sleep Quality Index (PSQI). Mental health and physical health were measured using three items from the Healthy Days Core Module. Multivariate logistic regression was conducted to assess the relationship between food insecurity, sleep quality, and days with poor mental and physical health.
Twenty-two higher education institutions.
College students (n 17 686) enrolled at one of twenty-two participating universities.
Compared with food-secure students, those classified as food insecure (43·4 %) had higher PSQI scores indicating poorer sleep quality (P < 0·0001) and reported more days with poor mental (P < 0·0001) and physical (P < 0·0001) health as well as days when mental and physical health prevented them from completing daily activities (P < 0·0001). Food-insecure students had higher adjusted odds of having poor sleep quality (adjusted OR (AOR): 1·13; 95 % CI 1·12, 1·14), days with poor physical health (AOR: 1·01; 95 % CI 1·01, 1·02), days with poor mental health (AOR: 1·03; 95 % CI 1·02, 1·03) and days when poor mental or physical health prevented them from completing daily activities (AOR: 1·03; 95 % CI 1·02, 1·04).
College students report high food insecurity which is associated with poor mental and physical health, and sleep quality. Multi-level policy changes and campus wellness programmes are needed to prevent food insecurity and improve student health-related outcomes.
To estimate the prevalence of high, marginal, low and very low food security among a sample of college students and identify characteristics associated with the four different food security status levels and note differences in associations from when food security status is classified as food-secure v. food-insecure.
Cross-sectional online survey.
A large public university in North Carolina.
4829 college students who completed an online survey in October and November 2016.
Among study participants, 56·2 % experienced high, 21·6 % experienced marginal, 18·8 % experienced low and 3·4 % experienced very low food security. Characteristics significantly associated with food security status when using the four-level variable but not two-level variable were age, international student status and weight status. Characteristics that significantly differed between the marginal and high food security groups included age, race/ethnicity, year in school, international student status, employment status, financial aid receipt, perceived health rating, cooking frequency and participation in an on-campus meal plan. Characteristics with differences in significant associations between the low and very low food security groups were gender, international student status, having a car, weight status and participation in an on-campus meal plan. Even where similarities in the direction of association were seen, there were often differences in magnitude.
We found differences in characteristics associated with food security status when using the four-level v. two-level food security status variable. Future studies should look separately at the four levels, or at least consider separating the marginal and high food-secure groups.
This chapter introduces in nontechnical terms the principal techniques used in this book: Theil's T statistic, cluster analysis, and discriminant function analysis.
How does one measure whether one society is more equal than another? Or whether an economy is more equal than it had been in the past? Equality is a broad concept with many layers of meaning: There is equality and inequality in legal rights, in social and political standing, and in many matters of culture. And even within the economic dimension, one may focus on inequalities of wealth, of family income, of individual earnings, and of wage rates. Each of these has its own importance, and each is measured from different sources of data and in slightly different ways.
Most of the theoretical literature on economic inequality is concerned with the determinants of pay: with wage rates and employment prospects, which together determine earnings in particular industries and occupations. Pay rates and job openings are a characteristic of the employer and the workplace. Yet most of the empirical work on inequality derives from surveys whose focus is on employees and their households, which are aimed mainly at assessing the distribution of family or household income. This is an important, even vital, issue, obviously: The distribution of household income is a key social fact. Yet data sources based on household surveys of income only indirectly provide information about the distribution of wage rates.
This book presents a global analysis of the distribution of pay, deploying systematic new measurements on a large scale. Contributions cover the US wage structure back to 1920 and up to 1998, pay inequality and unemployment in Europe since 1970, and the evolution of inequality alongside industrial growth, liberalization, financial crisis, state violence and industrial policy in more than fifty developing countries. The essays evaluate the major debates over rising inequality, and support the emerging view that there exists a powerful macro-dynamics of pay inequality in both rich and poor countries - a view whose origins go back to Keynes and Kuznets. Several papers present detailed descriptions of a new global pay inequality data set based on Theil's T statistic; theoretical and methodological chapters permit students and specialists full access to the measurements and to the non-parametric statistical techniques underlying these studies.