About

Julian Chun-Chung Chow is a professor and the Hutto-Patterson Charitable Foundation Professor at Berkeley Social Welfare. He is a leading scholar in community practice with a research focus on understanding why racial, ethnic, and immigrant minorities struggle to access available social services. His work aims to identify ways to transform social services for underserved populations and improve service delivery for immigrant communities. His recent projects include examining social services for China's migrant populations and exploring the development of social work education in China. Chow holds a PhD in Social Welfare and an MSSA in Social Work from Case Western Reserve University, as well as a BA in Sociology and Social Work from Tunghai University. He has contributed extensively to the field through research, teaching, and publications, and has been recognized with numerous awards and honors, including fellowship in the American Academy of Social Work and Social Welfare and the Society for Social Work and Research. His work emphasizes the application of data science and innovative approaches to social welfare policy and practice, with a particular interest in community-based participatory research and social innovation in China.

Research topics

• Psychology
• Business
• Sociology
• Medicine
• Economic growth

Selected publications

• Modeling data-driven clusters of social determinants of health (SDoH) and their associations with US suicide from 2009-2019

  European Psychiatry · 2025-04-01

  articleOpen access

  Introduction Social determinants of health (SDOH) have been linked to disparities in suicide rates across various demographics, including racial/ethnic groups, sex, age, and geography in the U.S. However, most studies have focused on individual or selected SDOH, rather than examining comprehensive, multi-dimensional SDOH factors. A more nuanced understanding of how clusters of SDOH contribute to suicide disparities across counties is needed to inform targeted prevention strategies. Objectives To identify multi-dimensional SDOH county clusters and estimate their geographic and temporal associations with county-level suicide rates. Methods This study used national SDOH data from 3,109 U.S. counties over three time periods (2009, 2014, and 2019), matching them with county-level suicide rates from the National Vital Statistics System aggregated into three-year periods (2008-2010, 2013-2015, and 2018-2020). A total of 284 county-level SDOH variables, spanning six domains (social context, economic context, education, physical infrastructure, healthcare context, and natural environment), were analyzed using unsupervised machine learning algorithms to identify SDOH clusters. Associations between SDOH clusters and county-level suicide rates were estimated using negative binomial and LASSO regression. Results Three distinct SDOH clusters were identified (Figure 1): • Cluster 1 (“REMOTE”) included rural counties with elderly, marginalized populations and substandard housing. • Cluster 2 (“COPE”) represented counties with complex family dynamics, overburdened health systems, poverty, and extreme heat challenges. • Cluster 3 (“DIVERSE”) encompassed densely populated areas with immigrants, racial/ethnic minorities, environmental challenges, and economic inequality. Geographically, REMOTE was more common in North and Central U.S., COPE in the South and Central U.S., and DIVERSE along the coasts. Suicide rates were highest in REMOTE counties, especially among men. COPE counties had elevated suicide rates among Whites, while DIVERSE counties saw higher rates among women and Black/Hispanic populations. Most counties (70%) remained within the same cluster over time, with stable suicide rate associations. Conclusions This study identified three multi-dimensional SDOH clusters that were associated with varying suicide rates across U.S. counties. These clusters offer insight into the social and environmental conditions contributing to suicide risk. Future prevention strategies should focus on addressing the distinct challenges within each cluster, such as housing inadequacies, healthcare access, and economic inequality, to reduce overall suicide rates and related disparities. Disclosure of Interest None Declared

  PublisherOA PDFDOI

• Machine learning to investigate policy-relevant social determinants of health and suicide rates in the United States

  Nature Mental Health · 2025-05-12 · 1 citations

  article
  PublisherDOI

• OLDER ADULTS’ BEHAVIOR IN SEEKING SERVICE INFORMATION DURING THE EARLY COVID-19 PANDEMIC

  Innovation in Aging · 2024-12-01

  articleOpen accessSenior author

  Abstract This study examines the expanding digital divide affecting older adults in the context of the COVID-19 pandemic. It focuses on exploring the factors that influence how older adults access information during COVID-19, emphasizing the challenges faced by those from disadvantaged backgrounds due to limited technology experience. Utilizing survey data conducted in May 2020 from the San Francisco Human Services Agency, which includes responses from 3,255 older adults in households receiving public benefits, this research employs logistic regression analysis to examine how demographic factors—such as race/ethnicity, education level, and primary language—impact the ways in which older adults access information during the pandemic. The analysis encompasses a range of information sources, including both digital and traditional media, to uncover disparities in access. The results unveil stark disparities: Black older adults exhibited significantly lower odds of accessing information via online news and email compared to their White counterparts, indicating a pronounced digital exclusion. Conversely, Chinese older adults demonstrated a higher propensity for social media engagement, suggesting cultural and community-based differences in information dissemination preferences. Education emerged as a critical determinant, with older adults possessing less formal education showing diminished access to digital information sources. These nuanced findings highlight the critical need for targeted interventions aimed at bridging the digital divide. By tailoring educational and training initiatives to the unique needs of diverse older adult populations, the study underscores the importance of ensuring equitable access to essential information, thereby empowering all older adults to navigate public health crises more effectively.

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• Early Impact of the COVID-19 Pandemic on Public Benefits Recipients

  Journal of Policy Practice and Research · 2024-11-15

  articleOpen access

  Abstract The COVID-19 pandemic has severely impacted employment, housing, and food security for low-income public benefits recipients. The present study seeks to understand public recipients’ self-reported critical and ongoing needs at the outset of the COVID-19 pandemic. This study uses logistic regression to analyze survey data gathered on 10,089 public benefits recipients in the early stage of the pandemic to better understand their self-reported critical and ongoing needs. We also explored variations in need among different racial/ethnic groups and public benefits receipt status. Our research found that respondents from most racial/ethnic minority groups indicated a significant need for food, housing, and back-rent, with variation among different racial/ethnic groups in expressing specific needs for finding employment and help with applying for public benefits. Our findings also identify SNAP/CalFresh recipients as a particularly vulnerable group, and they were more likely to need help with food insecurity, finding employment, applying for public benefits, and paying backrent. While numerous federal, state, and local programs and initiatives were created to address widespread need, this study identifies potential gaps in these efforts and increases understanding of how to target aid for low-income populations in times of crisis.

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• Community support, social isolation and older adults’ life satisfaction: evidence from a national survey in China

  Aging & Mental Health · 2023-11-03 · 19 citations

  articleOpen access

  OBJECTIVES: Despite the recognized importance of community social service and community built facility for enhancing older adults' life satisfaction, the mechanisms underlying their relationship have not been thoroughly examined. This study aims to complement the existing knowledge by investigating the mediating role of social disconnectedness and loneliness in the association between community support and life satisfaction among older adults. METHODS: Using data from the 2018 China Longitudinal Aging Social Survey, the study analyzes responses from 9,874 Chinese older adults (mean age = 71.30 years, SD = 7.30). We conducted descriptive statistics and Pearson's correlation to explore the variables. This study also used Mplus 8.0 to conduct a path analysis model that evaluated both the direct and indirect effects of community social service and built facility on life satisfaction. Social disconnectedness and loneliness were included as mediating variables in this model. RESULTS: The present study results show that both community social service and community built facility are positively associated with life satisfaction among older adults, and community social service is more imporatant for enhancing the life satisfaction. In addition, these associations are mediated by social disconnectedness and loneliness. CONCLUSION: Our research suggests that strengthening community social service programs and improving the built environment can reduce social disconnectedness and loneliness among older adults, ultimately enhancing their life satisfaction. Specifically, policymakers can invest in targeted interventions to enhance social connectedness and reduce loneliness, with the goal of improving the overall well-being of older adults.

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• Unpacking the Association between Material Deprivation and Children’s Life Satisfaction in 14 Countries: The Mediating Roles of Bullying Victimization by Peers and Siblings and the Moderating Role of Indulgent Culture

  Applied Research in Quality of Life · 2023-08-04 · 7 citations

  articleOpen access
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• Patterns of Social Determinants of Health and Child Mental Health, Cognition, and Physical Health

  JAMA Pediatrics · 2023-10-16 · 83 citations

  articleOpen access

  Importance: Social determinants of health (SDOH) influence child health. However, most previous studies have used individual, small-set, or cherry-picked SDOH variables without examining unbiased computed SDOH patterns from high-dimensional SDOH factors to investigate associations with child mental health, cognition, and physical health. Objective: To identify SDOH patterns and estimate their associations with children's mental, cognitive, and physical developmental outcomes. Design, Setting, and Participants: This population-based cohort study included children aged 9 to 10 years at baseline and their caregivers enrolled in the Adolescent Brain Cognitive Development (ABCD) Study between 2016 and 2021. The ABCD Study includes 21 sites across 17 states. Exposures: Eighty-four neighborhood-level, geocoded variables spanning 7 domains of SDOH, including bias, education, physical and health infrastructure, natural environment, socioeconomic status, social context, and crime and drugs, were studied. Hierarchical agglomerative clustering was used to identify SDOH patterns. Main Outcomes and Measures: Associations of SDOH and child mental health (internalizing and externalizing behaviors) and suicidal behaviors, cognitive function (performance, reading skills), and physical health (body mass index, exercise, sleep disorder) were estimated using mixed-effects linear and logistic regression models. Results: Among 10 504 children (baseline median [SD] age, 9.9 [0.6] years; 5510 boys [52.5%] and 4994 girls [47.5%]; 229 Asian [2.2%], 1468 Black [14.0%], 2128 Hispanic [20.3%], 5565 White [53.0%], and 1108 multiracial [10.5%]), 4 SDOH patterns were identified: pattern 1, affluence (4078 children [38.8%]); pattern 2, high-stigma environment (2661 children [25.3%]); pattern 3, high socioeconomic deprivation (2653 children [25.3%]); and pattern 4, high crime and drug sales, low education, and high population density (1112 children [10.6%]). The SDOH patterns were distinctly associated with child health outcomes. Children exposed to socioeconomic deprivation (SDOH pattern 3) showed the worst health profiles, manifesting more internalizing (β = 0.75; 95% CI, 0.14-1.37) and externalizing (β = 1.43; 95% CI, 0.83-2.02) mental health problems, lower cognitive performance, and adverse physical health. Conclusions: This study shows that an unbiased quantitative analysis of multidimensional SDOH can permit the determination of how SDOH patterns are associated with child developmental outcomes. Children exposed to socioeconomic deprivation showed the worst outcomes relative to other SDOH categories. These findings suggest the need to determine whether improvement in socioeconomic conditions can enhance child developmental outcomes.

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• COVID-19 Policies, Pandemic Disruptions, and Changes in Child Mental Health and Sleep in the United States

  JAMA Network Open · 2023-03-13 · 20 citations

  articleOpen access

  Importance: The adverse effects of COVID-19 containment policies disrupting child mental health and sleep have been debated. However, few current estimates correct biases of these potential effects. Objectives: To determine whether financial and school disruptions related to COVID-19 containment policies and unemployment rates were separately associated with perceived stress, sadness, positive affect, COVID-19-related worry, and sleep. Design, Setting, and Participants: This cohort study was based on the Adolescent Brain Cognitive Development Study COVID-19 Rapid Response Release and used data collected 5 times between May and December 2020. Indexes of state-level COVID-19 policies (restrictive, supportive) and county-level unemployment rates were used to plausibly address confounding biases through 2-stage limited information maximum likelihood instrumental variables analyses. Data from 6030 US children aged 10 to 13 years were included. Data analysis was conducted from May 2021 to January 2023. Exposures: Policy-induced financial disruptions (lost wages or work due to COVID-19 economic impact); policy-induced school disruptions (switches to online or partial in-person schooling). Main Outcomes and Measures: Perceived stress scale, National Institutes of Health (NIH)-Toolbox sadness, NIH-Toolbox positive affect, COVID-19-related worry, and sleep (latency, inertia, duration). Results: In this study, 6030 children were included in the mental health sample (weighted median [IQR] age, 13 [12-13] years; 2947 [48.9%] females, 273 [4.5%] Asian children, 461 [7.6%] Black children, 1167 [19.4%] Hispanic children, 3783 [62.7%] White children, 347 [5.7%] children of other or multiracial ethnicity). After imputing missing data, experiencing financial disruption was associated with a 205.2% [95% CI, 52.9%-509.0%] increase in stress, a 112.1% [95% CI, 22.2%-268.1%] increase in sadness, 32.9% [95% CI, 3.5%-53.4%] decrease in positive affect, and a 73.9 [95% CI, 13.2-134.7] percentage-point increase in moderate-to-extreme COVID-19-related worry. There was no association between school disruption and mental health. Neither school disruption nor financial disruption were associated with sleep. Conclusions and Relevance: To our knowledge, this study presents the first bias-corrected estimates linking COVID-19 policy-related financial disruptions with child mental health outcomes. School disruptions did not affect indices of children's mental health. These findings suggest public policy should consider the economic impact on families due to pandemic containment measures, in part to protect child mental health until vaccines and antiviral drugs become available.

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• Issue Information

  International Journal of Social Welfare · 2023-06-01

  paratextOpen access

  The puzzles of daily life: The temporal orders of families

  PublisherOA PDFDOI

• Poverty Alleviation Takes Shape in Guizhou, China

  2023-11-28

  book-chapterOpen accessSenior author

  In 2020, China eliminated absolute poverty, one of the UN 2030 Agenda for Sustainable Development Goals (SDGs). Guizhou, a historically low GDP province, has been recognized as a successful case under the Chinese government's poverty alleviation initiative in the big data era. Guizhou demonstrates how the Chinese government piloted an innovative, industry-led, eco-friendly agenda for targeted poverty alleviation. In this chapter, we start by outlining a pilot poverty alleviation model aligning with the Chinese government's 14th Five-Year Plan (2021–2025). Then we illustrate the blueprint for Guizhou as the first pilot area (“Guizhou Poverty Alleviation Cloud”) to break the information and logistic barriers between the economic, housing, education, and health departments, nationally and internationally. We use a community-driven, grassroots case study of Anlong village in Guizhou to describe how the innovation of social media, technology, eco-friendly products (i.e., Shihu, 石斛), and locally owned small business has built up a sustainable poverty alleviation system. We finish the discussion with the hope of shedding light on future innovation in poverty alleviation initiatives that integrate the implementation of multiple UN SDGs. In particular, we propose integrating the “Social +” infrastructure into the existing poverty alleviation model.

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Frequent coauthors

• Claudia J. Coulton

  Case Western Reserve University

  12 shared

• Yunyu Xiao

  Presbyterian Hospital

  12 shared

• J. John Mann

  New York State Psychiatric Institute

  9 shared

• Marcia K. Petchers

  7 shared

• Alexander C. Tsai

  Center for Global Health

  6 shared

• Michael J. Austin

  6 shared

• Yuwen Lyu

  Guangzhou Medical University

  6 shared

• Lonnie R. Snowden

  University of California, Berkeley

  5 shared

Education

• Ph.D., Jack, Joseph and Morton Mandel School of Applied Social Sciences

  Case Western Reserve University

Awards & honors

• Fellow, American Academy of Social Work and Social Welfare (…
• Honorary Member for The Phi Tau Phi Scholastic Honor Society…
• Model Alumni, Tunghai University Alumni Association, Taiwan,…
• Outstanding American by Choice Recognition, U.S. Citizenship…
• Distinguished Alumni Lectureship Award, Tunghai University,…