Runaway and Throwaway Youth: Time for Policy Changes and Public Responsibility

The traditional American dream of owning a home, obtaining a college education, and working at a good, paying job is only that, a dream, for scores of homeless youth in America today. There is a growing street population of young people who have been thrown out of their homes by their caretakers or their families, and who face life-threatening situations each day. For these youth, the furthest thing in their lives is reaching the so-called “American Dream;” and their most immediate need is survival, simply living out the day in front of them. They have few options that lead to a decent and safe living environment. Their age, lack of work experience, and absence of a high school diploma make it most difficult to find a job. As a result, they turn to other means for survival; runaways and throwaways are most vulnerable to falling prey to the sex trade, selling drugs, or being lured into human trafficking, and some steal or panhandle. Street youth end up spending their nights in bus stations or finding a room in an abandoned building or an empty stairwell to sleep. Attempting to identify a specific number of homeless youth is difficult at best, but what is even more perplexing is our continued inability to effectively protect our children. We are left with a basic question framed by the fundamental tenets of justice: what is a community’s responsibility to its youth who, for whatever reason, end up living on the streets or in unsafe, abusive environments? The purpose of this paper is to briefly outline the characteristics of homeless youth, in particular differentiating between throwaways and runaways; explore the current federal response to homeless youth; and finally, address the nagging question that swirls around all children: can we aggressively aspire to be a community where every child is healthy and safe, and able to realize his or her fullest potential?

That Wall is Around My Heart: Family Centered Practice

The author uses a clinical case study, in which he works with a teenager and his adoptive parents to illustrate how placement and adoption decisions can provide physical safety while at the same time exacerbating and extending overlooked and destructive effects of child abuse. The case study highlights the continuing impact of childhood trauma on the interpersonal patterns of behavior within the family, whether biological, kinship, foster or adoptive. The tendency for patterns of aggression and reactivity to be repeated by the victim and his or her caregivers in a foster or adoptive home, and then to extend into the next generation, is an integral aspect of the cycle of child abuse and underscores a critical challenge for skilled and patient staff in family-based service programs.

A space-time analysis of reported shigellosis and salmonellosis cases in Texas from 2000 to 2004

Background. The purpose of this study was to describe the risk factors and demographics of persons with salmonellosis and shigellosis and to investigate both seasonal and spatial variations in the occurrence of these infections in Texas from 2000 to 2004, utilizing time series analyses and the geographic information system digital mapping methods. ^ Methods. Spatial Analysis: MapInfo software was used to map the distribution of age-adjusted rates of reported shigellosis and salmonellosis in Texas from 2000–2004 by zip codes. Census data on above or below poverty level, household income, highest level of educational attainment, race, ethnicity, and urban/rural community status was obtained from the 2000 Decennial Census for each zip code. The zip codes with the upper 10% and lower 10% were compared using t-tests and logistic regression to determine whether there were any potential risk factors. ^ Temporal analysis. Seasonal patterns in the prevalence of infections in Texas from 2000 to 2003 were determined by performing time-series analysis on the numbers of cases of salmonellosis and shigellosis. A linear regression was also performed to assess for trends in the incidence of each disease, along with auto-correlation and multi-component cosinor analysis. ^ Results. Spatial analysis: Analysis by general linear model showed a significant association between infection rates and age, with young children aged less than 5 and those aged 5–9 years having increased risk of infection for both disease conditions. The data demonstrated that those populations with high percentages of people who attained a higher than high school education were less likely to be represented in zip codes with high rates of shigellosis. However, for salmonellosis, logistic regression models indicated that when compared to populations with high percentages of non-high school graduates, having a high school diploma or equivalent increased the odds of having a high rate of infection. ^ Temporal analysis. For shigellosis, multi-component cosinor analyses were used to determine the approximated cosine curve which represented a statistically significant representation of the time series data for all age groups by sex. The shigellosis results show 2 peaks, with a major peak occurring in June and a secondary peak appearing around October. Salmonellosis results showed a single peak and trough in all age groups with the peak occurring in August and the trough occurring in February. ^ Conclusion. The results from this study can be used by public health agencies to determine the timing of public health awareness programs and interventions in order to prevent salmonellosis and shigellosis from occurring. Because young children depend on adults for their meals, it is important to increase the awareness of day-care workers and new parents about modes of transmission and hygienic methods of food preparation and storage. ^

The relationship between food preparation time, time spent grocery shopping, and BMI

Recent data have shown that the percentage of time spent preparing food has decreased during the past few years, and little information is know about how much time people spend grocery shopping. Food that is pre-prepared is often higher in calories and fat compared to foods prepared at home from scratch. It has been suggested that, because of the higher energy and total fat levels, increased consumption of pre-prepared foods compared to home-cooked meals can lead to weight gain, which in turn can lead to obesity. Nevertheless, to date no study has examined this relationship. The purpose of this study is to determine (i) the association between adult body mass index (BMI) and the time spent preparing meals, and (ii) the association between adult BMI and time spent shopping for food. Data on food habits and body size were collected with a self-report survey of ethnically diverse adults between the ages of 17 and 70 at a large university. The survey was used to recruit people to participate in nutrition or appetite studies. Among other data, the survey collected demographic data (gender, race/ethnicity), minutes per week spent in preparing meals and minutes per week spent grocery shopping. Height and weight were self-reported and used to calculate BMI. The study population consisted of 689 subjects, of which 276 were male and 413 were female. The mean age was 23.5 years, with a median age of 21 years. The fraction of subjects with BMI less than 24.9 was 65%, between 25 and 29.9 was 26%, and 30 or greater was 9%. Analysis of variation was used to examine associations between food preparation time and BMI. ^ The results of the study showed that there were no significant statistical association between adult healthy weight, overweight and obesity with either food preparation time and grocery shopping time. Of those in the sample who reported preparing food, the mean food preparation time per week for the healthy weight, overweight, and obese groups were 12.8 minutes, 12.3 minutes, and 11.6 minutes respectively. Similarly, the mean weekly grocery shopping for healthy, overweight, and obese groups were 60.3 minutes per week (8.6min./day), 61.4 minutes (8.8min./day), and 57.3 minutes (8.2min./day), respectively. Since this study was conducted through a University campus, it is assumed that most of the sample was students, and a percentage might have been utilizing meal plans on campus, and thus, would have reported little meal preparation or grocery shopping time. Further research should examine the relationships between meal preparation time and time spent shopping for food in a sample that is more representative of the general public. In addition, most people spent very little time preparing food, and thus, health promotion programs for this population need to focus on strategies for preparing quick meals or eating in restaurants/cafeterias. ^

Predictors of HIV-1 slow progression among vertically infected children in Botswana

HIV-1 infected children display a highly variable rate of progression to AIDS. Data about reasons underlying the variable progression to AIDS among vertically-infected children is sparse, and the few studies that have examined this important question have almost exclusively been done in the developed world. This is despite the fact that Sub-Saharan Africa is home to over 90% of all HIV infected children around the world.^ The main objective of this study was to examine predictors of HIV-1 slow progression among vertically infected children in Botswana, using a case control design. Cases (slow progressors) and controls (rapid progressors) were drawn from medical records of HIV-1 infected children being followed up for routine care and treatment at the BBCCCOE between February 2003 and February 2011. Univariate and Multivariate Logistic Regression Analyses were performed to identify independent predictors of slow disease progression and control for confounding respectively. ^ The study population comprised of 152 cases and 201 controls with ages ranging from 6 months to 16 years at baseline. Low baseline HIV-1 RNA viral load was the strongest independent predictor of slow progression (adjusted OR = 5.52, 95% CI = 2.75-11.07; P <0.001). Other independent predictors of slow disease progression identified were: lack of history of PMTCT with single dose Nevirapine plus Zidovudine (adjusted OR = 4.45, 95% CI = 1.45-13.69; P = 0.009) and maternal vital status (alive) (adjusted OR = 2.46, 95% CI = 1.51-4.01; P < 0.00 ).^ The results of this study may help clinicians and policy-makers in resource-limited settings to identify, at baseline, which children are at highest risk of rapid progression to AIDS and thus prioritize them for immediate intervention with HAART and other measures that would mitigate disease progression. At the same time HAART may be delayed among children who are at lower risk of disease progression. This would enable the highly affected, yet impoverished, Sub-Saharan African countries to use their scarce resources more efficiently which may in turn ensure that their National Antiretroviral Therapy Programs become more sustainable. Delaying HAART among the low-risk children would also lower the occurrence of adverse drug reactions associated with antiretroviral drugs exposure.^ Keywords. Slow Progressors, Rapid Progressors, HIV-1, Predictors, Children, Vertical Transmission, Sub-Saharan Africa^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^