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This image is related to my research because it focuses on how air monitoring can be conducted in cities.

The authors review literature on the datafication of health, which they identify as the way through which health has been quantified on a number of different scales and registers. They focus primarily on the datafication of health in clinical health care and self-care practices, rather than medical research and public health infrastructures. From this literature, they identify three key themes: datafied power (the ways through which data permeates and exerts power over forms of life), living with data (focused on datafication as an intimate form of surveillance, and a technology of the self), and data-human mediations (which emphasizes the nonhuman elements mediating datafication dynamics and experiences—such as algorithms, data infrastructure and data itself).

In examining literature on datafied power, the authors acknowledge a lack of scholarship on understanding data and datafication in terms agency, rather than simply power and domination. For instance, data is sometimes mobilized in “creative and even pioneering ways (Rapp 2016)” (265).

They describe literature on “living with data” as increasingly focus examining the social, narrative, and affective dimensions of data practices and experiences (e.g. work on the “Quantified Self,” a group seeking self-knowledge through numbers – a form of relationality that might be described as datasociality). Some scholars have argued that data can render “‘feelings and problems more tangible and comparable” (Sharon & Zandbergen 2016, p. 11)” (267). Some have also acknowledged as well a “curious resonance between the vision of empowered, resisting individuals that many ethnographers of self-tracking celebrate, and the rhetoric of consumer empowerment found in discourses of digital health (Schull 2017, Sharon 2017)” (267).

The literature on data-human mediations emphasizes the agency, liveliness and/or performativity of nonhuman elements—essentially, how they structure and shape the possibilities for action. For instance: “as social expectations of normality and health become embedded in tracking devices’ target numbers, presentation of scores, and gamified incentives (Depper & Howe 2017, Whitson 2013), a “numerical ontology” comes to suffuse everyday practices and “the ways in which people relate to their own bodies” (Oxlund 2012, p. 53; see also Jethani 2015, p. 40)” (269). Perspectives and action can be enabled or disabled by wide variety of factors: the design and performativity of data technology software (user interface, operational and analytical algorithms), hardware (devices, sensors), data itself (as illustrated in different ways), and data infrastructures (labs, data centers, serve and cloud storage, and networks that organize how data is stored and circulated). An analytically constructive focus in this literature has emerged by applying the concept of “assemblage” as a way of tracing how data moves: “where it flows, where it finds impasses, how algorithms act on it along the way” (270).

Lastly, the authors identify scholarship on “data activism” as an emerging focus on exploring how data technology capacities might be employed to promote social justice, collective action, and political participation, as well as to challenged dominant norms and ideologies: “Individual self-tracking data, for instance, can have social and political potential when it is pooled to identify health inequalities, collective environmental exposure, or disparities in quality of life (Gabrys 2014).” (271)

Creators of the Student Health Index recommend using the tool in combination with qualitative data collection and stakeholder/community engagement (e.g. working with school leaders, local community leaders, and healthcare providers).

A full guide to using the dashboard is available here.

Data sources utilized by the index are not always the most current due to data collection limitations (e.g. covid-19 has caused disruptions in the collection of CDE data).

The Index is limited in that it does not offer data for schools that were not large enough to warrant the construction of a School-based Health Center. Thus, schools that did not meet specific enrollment targets were excluded from the dashboard. This includes rural schools (designed as such by the USDA) with an enrollment under 500 students, urban schools (without a high school) with less than 500 students, and urban schools (with a high school) with less than 1000 students. California had more than 10,000 active public schools in 2020-21. The final dashboard for the Student Health Index includes 4,821 schools.

The lack of available data on health indicators at a school-level restricted the Student Health Index to using proxies for the health outcomes. Some health indicators are included, but they are not school-specific, instead linked to specific schools geographically through the census tract. However, community-level data does not always accurately reflect the characteristics of a school’s population. As a result, school-level indicators in the Index were weighted more heavily than community-level indicators.

Additionally, race was not included as a measure in the Student Health Index because of California’s Proposition 20, which prohibits the allocation of public resources based on race and ethnicity. However, the dataset does contain measures of non-white students at each school. 

The Index has also been limited as a quantitative measure of need, which may overlook the influence of other factors that might be better illuminated through qualitative evidence (e.g. stakeholder engagement, focus groups, interviews, etc.).

No information thus far

The Student Health Index can produce visualizations that represent data on conditions, school characteristics and risk factors that affect education outcomes and could be improved through access to school-based health care. These visualizations can be used to demonstrate need for expanding school-based health care access in California.

In addition to maps, the index can also be used to generate graphs and visual displays of data (e.g. ratio of highest need schools to all schools, by county).

The visualizations can be used to demonstrate the correlations between final need scores and race, the impact of specific indicators in health, and the concentration of need to certain regions of California (hot spot analysis).

The Student Health Index enables users to identify where SBHCs will have the most impact for students. The index uses 12 indicators, each of which can be scored from 1 to 4 for any given school. These scores are generated using percentiles and represent relative values. The 12 indicator scores are combined into a Need Score, which is calculated using percentiles along a scale of 1 to 4. Schools with a score of 4 (in the 4th quartile) have the highest Need scores relative to other schools in California.

The index is composed of 12 diverse indicators (percentages, rates, and index values) that have been transformed using percentiles in order to enable comparisons on a common scale. These indicators are divided into 3 categories: health indicators, school-level indicators, and socioeconomic indicators.

Health Indicators

1. Diabetes
2. Asthma ED admissions
3. Teen birth
4. Health Professional Shortage Areas (HPSA)

Socioeconomic Indicators

1. Poverty among individuals under 18
2. Uninsured among under 19
3. Healthy Places Index

School-Level Indicators

1. Percent FRPL (students eligible for free or reduced-price meals)
2. Percent English Learners
3. Percent Chronically Absent
4. Percent experiencing homelessness
5. Suspension rate

Other Data

1. Mental health hospitalization rate
2. Percent in foster care

Indicator selection was guided by CDC estimations on the primary contributing factors that shape health (social determinants of health, medical care, and health behaviors). The indicators included in the index are all either directly associated with the absence of health services that could be provided at a school level, act as proxies for health behaviors, or represent social determinants of health that could be addressed through access to school-based health services.

Indicator selection was influenced by recommendations from the Research Initiative of the Campaign for Educational Equity at Columbia Teachers College, which found that seven health disparities affecting school-aged youth could be addressed through school health programs. These disparities include: (1) vision, (2) asthma, (3) teen pregnancy, (4) aggression and violence (including bullying), (5) physical activity, (6) hunger, and (7) inattention and hyperactivity.

More detailed description of the rationale shaping indicator selection is available here.

Census tract, school-level

The Student Health Index draws from data that is publicly available and up to date on a statewide level. Sources include the University of California San Francisco Health Atlas, the American Community Survey, the U.S. Census Bureau, the California Department of Education’s Downloadable Data Files site, and the CDC.

Detailed list of sources:

PLACES Project, CDC (available through the UCSF Health Atlas)

CalEnviroScreen (available through the UCSF Health Atlas)

Opportunity Atlas (available through the UCSF Health Atlas)

Health Resources and Services Administration (available through the UCSF Health Atlas)

American Community Survey (available through the UCSF Health Atlas)

California Department of Education’s Downloadable Data Files site

Kidsdata.org