SHAPE OF CARE: PATTERNS OF FAMILY CAREGIVING ACTIVITIES AMONG OLDER ADULTS FROM MIDLIFE TO LATER AGES IN CHINA AND THE U.S.

This dissertation consists of three papers that investigate the long-term family caregiving patterns among Chinese and American older adults. Family caregiving has long been an essential fabric of long-term care services. Due to the prolonged life expectancy and the declined family size, older adults today are more likely to care for multiple family members for longer years than the previous cohorts. However, studies on caregiving predominately focus on singular care experiences over a short period time. As older adults transition into and out of multiple care roles, the overall caregiving patterns are overlooked. Leveraging two rich longitudinal datasets (the China Health and Retirement Study and the Health and Retirement Study), this dissertation aims to fill this current research gap by developing long-term family caregiving typologies. The first paper develops a care typology for Chinese older adults, and thoroughly assesses how gender, hukou status, living arrangement, and significant life transitions are associated with the long-term caregiving patterns. In the second paper, using linear mixed-effects models, I continue exploring the positive and negative health consequences of each caregiving pattern among Chinese older adults. The third paper focuses on developing a long-term family caregiving pattern for American older adults. In addition to prolonged life expectancies and the decline in family size, the U.S. has experienced complex transitions in family structures over the past few decades, leading to more diverse family networks and international relations in later life. After establishing the long-term care typology, the third paper pays closer attention to the variations of family caregiving patterns across the War Babies cohort, Early Baby Boomer, and the Middle/Late Baby Boomer cohort. Moreover, I explore how gender, race, and socioeconomic status are linked with these patterns. In the context of global aging, this dissertation highlights the heterogeneity in the family caregiving experiences and identifies the most vulnerable demographic groups who shoulder the heaviest care burden over time. In the end, the findings from the dissertation provide guidance for the investment and design of long-term care services in rapidly aging contexts

Comparison of Growth and the Cytokines Induced by Pathogenic Yersinia enterocolitica Bio-Serotypes 3/O: 3 and 2/O: 9

Pathogenic Yersinia enterocolitica is widely distributed in China where the primary bio-serotypes are 3/O: 3 and 2/O: 9. Recently, the distribution of 2/O: 9 strains are being gradually replaced by 3/O: 3 strains where presently 3/O: 3 strains are the major pathogenic Y. enterocolitica in China. To identify the growth conditions and cytokines induced by Y. enterocolitica and providing some clues for this shift, we performed competitive growth in vitro and in vivo for these two bio-serotype strains; and we also compared the cytokines induced by them in infected BALB/C mice. We found 2/O: 9 strains grew more in vitro, while 3/O: 3 strains grew more in vivo regardless of using single cultures or mixed cultures. The cytokines induced by the two strains were similar: interleukin-6 (IL-6), IL-9, IL-13, granulocyte colony-stimulating factor (G-CSF), chemokines (KC), monocyte chemotactic protein 1 (MCP-1), macrophage inflammation protein-1α (MIP-1α), tumor necrosis factor-α (TNF-α), and RANTES were statistically up-regulated upon activation of normal T cells compared to the control. The cytokine values were higher in mixed infections than in single infections except for IL-6, G-CSF, and KC. The data illustrated the different growth of pathogenic Y. enterocolitica bio-serotype 3/O: 3 and 2/O: 9 in vitro and in vivo, and the cytokine changes induced by the two strains in infected BALB/C mice. The growth comparisons of two strains maybe reflect the higher pathogenic ability or resistance to host immune response for Y. enterocolitica bio-serotype 3/O: 3 and maybe it as one of the reason for bacteria shift

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.https://doi.org/10.3390/ijerph1717635