![Kids playing basketball on a sunny day](/sites/g/files/omnuum10826/files/styles/hwp_21_9__1920x825/public/datasmart/files/steven-abraham-fkwtcoij4e0-unsplash.jpg?itok=DWlNZ8Cr) 

 



 

#  The Importance of Time-bound Data for Public Health 

 





February 18, 2025

 

 

 [ Kristianny Ruelas-Vargas ](#kristiannyruelasvargas) [ Audrey Yun ](#audreyyun) 

Across the US, city government officials are creating climate-conscious policies to address the environmental health complications that result from poor air quality and extreme heat. Data is a driving force behind these policies, with sources such as fire department calls, hospitalization records, air quality monitoring systems, and weather data informing decision-making. Much of this data is also being spatially mapped and analyzed with geographic information systems (GIS) applications, which can then be used by multiple agencies to create vulnerability index maps. These maps are essential for identifying at-risk communities and guiding policymakers and local officials in implementing environmental and public health programs, policies, or projects.

This article explores how cities can use not just location-based but also time-bound data to address environmental health issues, specifically around extreme heat and poor air quality. This piece focuses on two types of time-bound data, real-time and longitudinal, with examples from across the country that highlight why breaking data down temporally is crucial for developing and refining data-driven policies.

**Real-time data**: provides immediate insights into current conditions of a location. Examples include the[ US Air Quality Index AirNow.gov](https://www.airnow.gov/) and the[ OSHA-NIOSH Heat Safety Tool app](https://www.cdc.gov/niosh/topics/heatstress/heatapp.html).

**Longitudinal data**: refers to data that has been collected over an extended period of time and provides insights into long-term trends, impacts, and changes over time . Examples include the[ National Longitudinal Study of Adolescent to Adult Health](https://addhealth.cpc.unc.edu/) and[ NASA’s GISS Surface Temperature Analysis](https://data.giss.nasa.gov/gistemp/).

Of course, most initiatives and policies rely on a combination of these types of data. Real-time data helps cities respond to immediate needs of the community and longitudinal data ensures that long-term environmental and public health trends are being analyzed and addressed. The following examples will highlight how these two types of time-specific data lend themselves to different projects or goals; intermediate mid-term data is usually paired with or complementary to the short- or long-term types, and therefore isn’t the main focus of this article.

## Real-Time Data: Denver’s Clean Air Promise

The city of Denver is a leader in the clean air space thanks to the[ innovative and award-winning Love My Air program](https://www.denvergov.org/Government/Agencies-Departments-Offices/Agencies-Departments-Offices-Directory/Public-Health-Environment/Environmental-Quality/Air-Quality/Love-My-Air) from the city’s Department of Public Health and Environment. The heart of the program is real-time air quality monitoring at Denver public schools with[ low-cost sensors](/strategies-enhancing-air-quality-and-public-health-through-low-cost-sensors) that provide minute-by-minute data. The city has even[ developed a bilingual data viz app](https://denver.lovemyair.com/) that shows a map of Denver with 10 different pollutants by sensor locations and sensor type.

The immediately accessible, real-time air quality data is critically important for a few different reasons. First, air quality can fluctuate throughout the day due to factors like traffic, industrial activity, weather patterns, and wildfires. So, a once-daily assessment of air quality could miss significant changes throughout a 24-hour period. Having access to this data in real time enables safer decision-making and appropriate behavior modifications.

Especially for schools and parents/caregivers, data helps to determine whether it's safe for children to play outside or if recess and physical activities should be moved indoors to avoid high pollution levels. The Love My Air app even states if it’s a good day to be active outside, or if “unusually sensitive people” should reduce “prolonged or heavy exertion.” These warnings allow residents with respiratory conditions like asthma, or elderly people and young children, to make informed decisions and enact precautionary measures at appropriate times.

   ![Denver air quality app that shows 'good' air quality for Jefferson County, with green dots on Denver map representing AQ sensors](/sites/g/files/omnuum10826/files/styles/hwp_1_1__720x720_scale/public/datasmart/files/denver_aq.png?itok=VIq7DGs9) 

 

This ability to respond dynamically based on current air quality conditions ultimately leads to better health outcomes, especially for those most sensitive to pollution. Michael Ogletree, the architect of Love My Air, spoke about the importance of transparently sharing up-to-date air quality data and co-developing solutions with the community on[ episode 58 of the Data-Smart City Pod](/local-approach-improving-urban-air-quality). He also[ encouraged other cities to use the replication toolkit](https://www.denvergov.org/files/assets/public/v/1/public-health-and-environment/documents/eq/love-my-air/lma-replication-summary_network.pdf) that is freely available from the city of Denver.

## Longitudinal Data: Adolescent Development and the Environment

On the other end of time-bound data is longitudinal data, which tracks information over long periods of time and draws insights from trends and patterns that persist over years. It can also be used to measure the impacts of interventions that require lengthy periods of time for effect. There are some well known examples of longitudinal studies in the medical field, like the [Harvard Study of Adult Development](https://www.adultdevelopmentstudy.org/) which has been tracking participants for 80+ years and recently began a “second generation” study to follow children of the original study participants. However, there are fewer pieces of longitudinal research on environmental health impacts, especially related to climate change over the past 30 years.

A 2023 study from Utrecht University examined the [longitudinal associations of neighbourhood environmental exposures with mental health problems during adolescence](https://www.sciencedirect.com/science/article/pii/S0160412023004154) using data from the longitudinal TRAILS (Tracking Adolescents’ Individual Lives Survey) data set. The researchers chose this area specifically because there are “few longitudinal studies mainly focused on single exposure-based analyses and rarely assessed the mental health associations with environmental changes.” The environmental exposures included traditionally positive ones, like neighborhood green spaces, and negative ones, like PM2.5 air pollution (fine particulate matter) and [noise pollution](/map-monday-noisy-city).

Researchers included socio-economic and demographic factors to see what role those played in both exposure levels and mental health; for example, number of parents in the home, sex at birth, and if the adolescent’s family moved house. They also used two well-established dimensions of mental health problems, [“between-person” or externalizing](https://www.sciencedirect.com/topics/psychology/externalizing-problems) and [“within-person” or internalizing](https://www.sciencedirect.com/topics/psychology/internalizing-problems). Between-person covers behaviors that are directed outwards or externalized, such as bullying, aggression, and delinquency. Within-person behaviors are internal or self directed, such as depression or anxiety.

Based on their analysis, the researchers suggest that both air and noise pollution “[may threaten adolescent mental health](https://www.sciencedirect.com/science/article/pii/S0160412023004154).” In particular, they found that adolescent externalizing problems were associated with higher PM2.5 concentrations when they exceeded 15 µg/m3 and suggested that this could be a target area for adolescent mental health interventions.

The researchers highlighted exactly why longitudinal data is so crucial for this type of investigation, stating that in the past “[most studies only used environmental measurements at a single point in time](https://www.sciencedirect.com/science/article/pii/S0160412023004154) and implicitly assumed that adolescents’ residential environments could only be changed by residential relocation…time-varying environmental measurements are therefore needed to capture adolescents’ actual environmental exposures over time.”

## The Potential of Intermediate Data

One important piece of the time-bound data discussion is the role of intermediate data. While real-time and longitudinal can offer immediate feedback for reaction and long-term analysis respectively, intermediate data is sometimes a missing piece of the puzzle. For example, when closely tracking results over a period of weeks or months, city teams can more quickly identify problems or anomalies, and pivot as needed. [Midpoint evaluations can save time and money](https://web.archive.org/web/20250216045037/https://bloombergcities.jhu.edu/news/5-ways-certified-cities-are-leading-data) and help redirect funding or attention. However, remarks from city representatives during a recent[ Community Data Health Initiative](/about-community-data-health-initiative) event highlighted the need to incorporate more intermediate and midpoint checks in the process to confirm efficacy and make mid-course adjustments.

A key aspect of intermediate data is the integration of qualitative, community-based feedback. In the realm of environmental and public health work, real-time and longitudinal data are usually quantitative, e.g. data based on a minute-by-minute detection of pollutants in the air or population-level incidents of asthma or heart disease over decades. Yet intermediate data that focuses on weeks or months of information can reveal things like[ community sentiment around public health campaigns](/news/article/sentiment-analysis-local-public-health-tool) or urban greening needs.

Utilizing multiple types of time-bound data from diverse sources is important for public health and environmental goals. Naturally, some projects will rely more heavily on one type or the other. However, short-, mid-, and long-term data should be used to inform corresponding objectives and common goals, especially for such a complex and evolving challenge like climate change and public health. Local leaders should integrate all three types of data into their decision-making processes, with a focus on bringing in community voices at each step.



 

 

 

##  About the Author 

   ![Headshot of Kristianny Ruelas-Vargas ](/sites/g/files/omnuum10826/files/styles/hwp_1_1__100x100_scale/public/datasmart/files/ruelas_headshot_itgh.jpg?itok=-DBMRz0w) 

 

### Kristianny Ruelas-Vargas 

Kristianny Ruelas-Vargas is a graduate student of Wellesley College and a participant in the Health, Technology, & Society 2024 Summer Education Program at the Institute for Technology and Global Health. Her academic research interest lies in exploring how technology and policy can be utilized to address health disparities faced by marginalized communities through an intersectional lens. Previously, she worked as a research assistant in a language and cognitive development laboratory at Wellesley and analyzed the role of trust during COVID-19 at the University of Southern California. Kristianny resides in Mesa, Arizona and holds a bachelor’s degree in Data Science and Women’s & Gender Studies.



 

##  About the Author 

### Audrey Yun

   ![Headshot of Audrey Yun](/sites/g/files/omnuum10826/files/styles/hwp_1_1__100x100_scale/public/datasmart/files/audrey_yun.png?itok=jF9hZrc1) 

 

Audrey Yun is an undergraduate student at Northeastern University, currently participating in the Health, Technology, & Society Summer Education Program at the Institute for Technology and Global Health. Her academic interests began with machine learning and neuroendocrinology, eventually expanding to explore how policy can address health inequities. A previous internship in Washington, D.C. inspired Audrey to focus on the intersection of public health and policy, emphasizing the importance of listening to and amplifying voices. Originally from Yorba Linda, California, Audrey is on track to graduate in 2025 with a bachelor’s degree in Biochemistry and a minor in Behavioral Neuroscience.



 

 



 

 See also:- [ Civic Data ](/topics/civic-data)
- [ Environment ](/topics/environment)
- [ Public Health ](/topics/public-health)
 
 

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