Publications

Food insecurity continues to be a major challenge in the United States, affecting 49 million individuals. Quantitative studies show that food insecurity has serious negative health impacts among individuals suffering from chronic illnesses, including people living with HIV/AIDS (PLHIV). Formulating effective interventions and policies to combat these health effects requires an in-depth understanding of the lived experience and structural drivers of food insecurity. Few studies, however, have elucidated these phenomena among people living with chronic illnesses in resource-rich settings, including in the United States. Here we sought to explore the experiences and structural determinants of food insecurity among a group of low-income PLHIV in the San Francisco Bay Area. Thirty-four semi-structured in-depth interviews were conducted with low-income PLHIV receiving food assistance from a local non-profit in San Francisco and Alameda County, California, between April and June 2014. Interview transcripts were coded and analysed according to content analysis methods following an inductive-deductive approach. The lived experience of food insecurity among participants included periods of insufficient quantity of food and resultant hunger, as well as long-term struggles with quality of food that led to concerns about the poor health effects of a cheap diet. Participants also reported procuring food using personally and socially unacceptable strategies, including long-term dependence on friends, family, and charity; stealing food; exchanging sex for food; and selling controlled substances. Food insecurity often arose from the need to pay high rents exacerbated by gentrification while receiving limited disability income--​a situation resulting in large part from the convergence of long-standing urban policies amenable to gentrification and an outdated disability policy that constrains financial viability. The experiences of food insecurity described by participants in this study can be understood as a form of structural violence, motivating the need for structural interventions at the policy level that extend beyond food-specific solutions.

A new T helper cell signature in asthma patients highlights the potential impact of a personalized approach to asthma care (Choy et al., this issue).

INTRODUCTION

Diarrhea remains one of the most common and most deadly conditions affecting children worldwide. Accurately assessing dehydration status is critical to determining treatment course, yet no clinical diagnostic models for dehydration have been empirically derived and validated for use in resource-limited settings.

METHODS

In the Dehydration: Assessing Kids Accurately (DHAKA) prospective cohort study, a random sample of children under 5 with acute diarrhea was enrolled between February and June 2014 in Bangladesh. Local nurses assessed children for clinical signs of dehydration on arrival, and then serial weights were obtained as subjects were rehydrated. For each child, the percent weight change with rehydration was used to classify subjects with severe dehydration (>9% weight change), some dehydration (3-9%), or no dehydration (<3%). Clinical variables were then entered into logistic regression and recursive partitioning models to develop the DHAKA Dehydration Score and DHAKA Dehydration Tree, respectively. Models were assessed for their accuracy using the area under their receiver operating characteristic curve (AUC) and for their reliability through repeat clinical exams. Bootstrapping was used to internally validate the models.

RESULTS

A total of 850 children were enrolled, with 771 included in the final analysis. Of the 771 children included in the analysis, 11% were classified with severe dehydration, 45% with some dehydration, and 44% with no dehydration. Both the DHAKA Dehydration Score and DHAKA Dehydration Tree had significant AUCs of 0.79 (95% CI = 0.74, 0.84) and 0.76 (95% CI = 0.71, 0.80), respectively, for the diagnosis of severe dehydration. Additionally, the DHAKA Dehydration Score and DHAKA Dehydration Tree had significant positive likelihood ratios of 2.0 (95% CI = 1.8, 2.3) and 2.5 (95% CI = 2.1, 2.8), respectively, and significant negative likelihood ratios of 0.23 (95% CI = 0.13, 0.40) and 0.28 (95% CI = 0.18, 0.44), respectively, for the diagnosis of severe dehydration. Both models demonstrated 90% agreement between independent raters and good reproducibility using bootstrapping.

CONCLUSION

This study is the first to empirically derive and internally validate accurate and reliable clinical diagnostic models for dehydration in a resource-limited setting. After external validation, frontline providers may use these new tools to better manage acute diarrhea in children.