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consistent medical care among people living with HiV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. We developed a machine learning model to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data. The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flagging the top 10% highest risk patients as needing interventions, performing better than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baseline rate of 11.1% [SD: 0.02]. Machine learning methods can improve the prediction ability in HIV care clinics to proactively identify patients at risk for not returning to medical care. Consistent medical care is essential for the health of people living with HIV. HIV-positive individuals who receive regular medical care are more likely to receive antiretroviral therapy, less likely to develop Acquired Immune Deficiency Syndrome (AIDS), and have improved survival rates compared to HIV-positive individuals who do not receive regular medical care 1-3. In the field of HIV medicine, patients who receive regular medical care are considered 'retained in care. ' Retention in care is not only important for the individual health of people living with HIV, but also for public health. HIV-positive individuals who are retained in care and taking antiretroviral therapy are able to suppress the HIV viral level in their serum to undetectable levels, effectively eliminating the risk of transmitting HIV to others. Accordingly, retention in care is a critical pillar of public health agency plans to eliminate HIV transmission in the United States 4-6. Despite the clear benefits of retention in care for individual and public health, less than half of individuals living with HIV in the U.S. are retained in care. Lack of access to healthcare is one reason that patients may not be retained in care 7. However, for patients who lack health insurance, state and federal programs such as the Ryan White HIV/AIDS Program provide funding to pay for HIV care visits and antiretroviral medications. Despite these programs, many patients living with HIV still do not regularly attend medical appointments. Additional barriers to retention in care remain, including mental illness, substance use, insecure housing, poverty, neighborhood violence, and stigma 8-16. Interventions that are effective for improving retention in care include intensive case management, peer navigation, and multi-faceted outreach progra...

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Food security (FS) is a function of food availability, accessibility, stability and utilization. Food value chains (FVCs) are part of the food system and are characterized by five main components: natural resources, food production, processing, markets and consumption. Many methods are available to assess single FVC components, but few cover a series of FVC components. This paper introduces an integrated research framework which combines both qualitative and quantitative methodologies across a generic FVC. Furthermore, this approach provides mechanisms to identify the contribution to FS of each component in the FVC. The methodology uses an FVC as an analytical framework within which to assess FS in a systematic approach. Starting with a working scenario, each tool was evaluated according to its potential to assess FS indicators in different components, and then classified according to its temporal and spatial scales. The advantages, challenges and limitations of this conceptual approach are evaluated and discussed.

Abstract. Consumption of ecosystem goods and services is the foundation of coupled human-natural systems. This paper reported the change in fuelwood consumption in remote northwestern Chinese villages and the ecological consequences that have occurred as a result of the Sloping Land Conversion Program (SLCP), one of China's biggest ecological restoration programs. We conducted this study using structured questionnaires that collected data on fuelwood consumption in 1999 and 2008. For these villages, fuelwood was the most important fuel source (84% of total fuel consumption in 1999). The SLCP restricted cutting of forests, so fuelwood consumption decreased to 39% of the 1999 total by 2008. In response to this decrease and increased planting of trees, the forest area increased. However, a spatial imbalance in fuelwood consumption persisted; the remaining demand for fuelwood meant that forests near villages were under high pressure, with harvesting often exceeding the natural productivity. To meet the demand for fuel and replace the fuelwood, coal consumption increased by 562%. The switch from fuelwood to coal increased CO 2 emissions by 339% from 1999 to 2008. These results have clear consequences for the region's ecology and suggest the need to take measures to account for the consequences of the SLCP.