Data Story

Introduction: Why Student Health Matters

School-Based Health Centers (SBHCs) are a vital part of Connecticut's public health infrastructure. In areas where pediatric and adolescent health care, as well as family resources, are limited, SBHCs provide essential medical, dental, and mental health services. However, not all SBHCs offer the same combination of services, and access varies significantly across school districts. This data story explores these differences from two perspectives. First, we examine which service combinations are most common across Connecticut schools. Second, we investigate whether there is a correlation between student income levels and the availability of SBHC services, with a particular focus on whether low-income families are receiving the support they need.

To understand these differences, the first part of our analysis focused on the guiding question: “How do SBHCs vary by types of services: medical, dental, and/or mental health?” Using the full statewide dataset provided for this project by the Connecticut Association of School-based Health Centers, we categorized each SBHC based on its available services, combining Dental and Mobile Dental into a single category for consistency. We then organized the data into two groups:those that offer only one service and those that provide multiple services, to compare service frequency across different SBHCs. This structure allowed us to identify both the prevalence of single service centers and the most common service combinations.

Next, we looked to answer our second guiding question: "How do student and school resources affect SBHC access?" Since SBHCs fill gaps that private practice fails to provide due to cost, insurance, and/or proximity, among other reasons, we wanted to see whether low-income families were getting the full spectrum of care they needed. We found, as will be shown later, that low-income schools tend to offer all three services at a higher rate than their middle- and high-income counterparts, indicating that SBHCs are filling the gaps they intended to.

Service Distribution

To answer the question "How do SBHCs vary by types of services: medical, dental, and/or mental health?” we analyzed the full dataset of School-Based Health Centers (SBHCs) in Connecticut and organized the sites into two major categories: For clarity, we combined Dental and Mobile Dental into a single category, since both represent dental care delivered through the SBHC—even if delivered in different formats. Focusing first on SBHCs that provide a single service only, our analysis found that Dental-only sites were the most common, with 55 schools (16.42% of all SBHCs). Mental Health only centers were the next most common, totaling 37 schools (11.04% of all SBHCs). Medical only SBHCs were rare, appearing in just 6 schools (1.79% of all SBHCs). Importantly, these counts exclude any SBHCs that offer more than one service. For example, a center offering both Medical and Dental services would not be counted in the “Medical only” or “Dental only” categories. The table below illustrates these findings by showing the number of SBHCs that offer only one service and what percentage of all SBHCs each category represents.

In addition to examining single service SBHCs, we also analyzed schools that offer multiple types of services within the same health center. This revealed a very different pattern: most SBHCs in Connecticut provide two or more services, with Mental Health appearing in nearly all common combinations. The two largest groups were SBHCs offering Mental Health + Medical services and those offering the full combination of Mental Health + Medical + Dental, each totaling 84 schools (25.30% of all SBHCs). Another substantial group included SBHCs that provide Mental Health + Dental services, accounting for 64 schools (19.28%). By contrast, very few schools only 5 sites (1.51%)—offered Medical + Dental without Mental Health. These findings indicate that while single service SBHCs exist, the majority rely on a multi-service model, with Mental Health services serving as a consistent core component. The table below summarizes the counts and percentages for each service combination.

Income and Services

To meaningfully compare schools by income level, we grouped them into three categories (low, middle, and high) based on the percentage of students receiving free or reduced-price lunch, a common proxy for family income. The histogram below displays the distribution of schools in smaller buckets, which helps us understand the spread of the data. We used the 33rd percentile, median, and 67th percentile to define our three income groups, allowing us to visualize how schools with different income compositions are distributed across Connecticut.

Source: SBHC Data, CT School Health

To examine how income relates to SBHC service availability, we developed a simple scoring system. Each school received a score from 0 to 3 based on the number of service types it offered (medical, dental, and mental health). For example, a school offering all three services scored a 3, while a school offering none scored a 0. Combined with our income groupings based on free- and reduced-price lunch eligibility, this allowed us to analyze whether lower-income schools have different levels of SBHC access than their higher-income counterparts.

Our findings, shown below, were promising. Schools serving predominantly low-income students had the highest proportion of comprehensive, three-service SBHCs, suggesting that these centers are successfully targeting the communities that need them most. This is particularly important because students from lower-income families often face greater barriers to accessing healthcare outside of school, making SBHCs a critical safety net for their well-being.

Methods

To visualize how School-Based Health Centers combine different services, we used a datasheet containing all of the data about SBHCs currently available from the Connecticut Association of School Based Health Centers. We first created a clean worksheet that listed every School-Based Health Center along with indicators showing which services each school offers. To do this, we used XLOOKUP to pull the 1/0 service values from my initial reference table into a new, flat dataset where each school appeared only once. This step was essential because the original data was not arranged in a format suitable for service combination analysis. By using XLOOKUP, we generated consistent columns for Mental Health, Medical, Dental, and Mobile Dental services, which then allowed us to calculate the total number of services offered at each SBHC and create a readable service combination label for further grouping. We then created a pivot table that summarized how many schools offer each type of care. The table used school names as rows and service types Mental Health, Medical, Dental, and Mobile Dental as columns, with COUNTA values indicating whether a service was present. This layout made it easy to identify which combinations existed and how often each service appeared across the dataset. To further analyze patterns, we generated a calculated column using a TEXTJOIN formula that automatically listed all services offered by each school. This transformed raw binary indicators into readable labels such as Mental Health + Medical, allowing the dataset to be grouped by unique service combinations. After creating a second pivot table to count how frequently each combination occurred. We then split the data into two groups, one group focused on schools that only offer ONE service in their SBHCs and a group of schools that have SBHCs offering multiple services. These two groups were used to create the two tables shown in the Service Distribution section of our data story.We chose to use a table with integrated bar charts because this visualization type allows viewers to see exact numerical values and percentages while also benefiting from a clear visual comparison. Since the goal of this part of the analysis is to understand how often each service combination occurs, the table format gives precise counts, while the bars provide an immediate sense of scale across categories. The table structure also helps organize combinations such as “Mental Health Only” or “Mental Health + Dental” in a readable way, while the bar chart components highlight which patterns are most and least common. This format supports the data story by making the distribution of SBHC service information digestible for the reader. Please see the attached sheet below to see the workings.

Methodology and Worksheet

For the first step in our methodology in answering our second question, how does income affect SBHC access, we had to figure out a comprehensive way to score the schools based on the services offered. We needed to do this so we could quantify the services in a way that would be easy to visualize later. We did this by using the “find and replace function” in Google Docs to replace the designations for medical, dental, or mental health (signified in the sheet as M, D, or MH) with “1”. This way, we could use the SUM function to add up all the columns with services offered to score the school. The lowest a school could score was 0, and the highest was 3. Next on a new sheet we used the XLOOKUP function to match the schools and service score onto a new sheet. Then we used the XLOOKUP function again to pull our raw free, reduced, and non-subsidized lunch data to begin building our school income-level percentages. Since families' income data is kept private, free, reduced, and non-subsidized lunch is a standard proxy to model student income data. With the raw counts and totals in our sheet, we divided the count of students in each category: free, reduced, and non-subsidized lunch, by the total students, so we could get a breakdown of the student income in each school. We then visualized a raw distribution of the schools to get an idea of the distribution. Next, we found the median (58.3%) of the low-income student percentage data, as well as the 33rd (69.7% )percentile and the 67th percentile (51.4%) ur middle low and high income groups. Then, using an IF function with our established definitions, we grouped our schools into the appropriate groupings. Finally we created a pivot table using the School, Income, and Service score to begin building the framing for our visulzation to find the our income vs SBHC correlation. This table was transported into datawrapper were the visulzation was prepared.

Please see link below to sheet to see work and formulas.

Methodology and Worksheet

Works Cited

Connecticut Association of School Based Health Centers. https://ctschoolhealth.org/

Connecticut Department of Education. "Enrollment Dashboard." EdSight: CT.Gov, https://public-edsight.ct.gov/students/enrollment-dashboard

"CT SBHC Updated 2025-11-11." Google Sheets, https://docs.google.com/spreadsheets/d/1wsycNjSOLnj9gZ7wepTs7jyuzS7PvquCZEUsugSmMJs/

"Ct-Schools-Free-Reduced-Lunch-2024-25." Google Sheets, https://docs.google.com/spreadsheets/d/1LA-QUrkm-i3G_2Z3ZvThI-7K_Vl0DYVyu4dXAm2ZDZ4/

Advice and consultation from: Nellie Conklin, Jack Dougherty, and Alison MacDougall