These dashboards provide insights into student behavioral health trends across Washington State, covering topics such as mental health, alcohol use, substance use, and bullying. The data come from the Healthy Youth Survey (HYS), a statewide, biannual survey of students in grades 6 through 12. Data were obtained from AskHYS.net.
Quick Overview of the Dashboards Developed using Power BI:
Dashboard 1: đ Trend Explorer: Time Series (Grades 6â12 over 2010â2023)
Purpose & Audience
This dashboard visualizes behavioral and mental health trends over time for students in grades 6, 8, 10, and 12. Itâs designed for educators, counselors, public health professionals, and policymakers who want to monitor how key outcomes, like substance access, mental health symptoms, and risk behaviors, have evolved since 2010.
What Youâll See
⢠Multi line charts showing changes over time in measures like Access to Alcohol, Binge Drinking, Current Alcohol Use, Depressive Feelings, Feeling Anxious or On Edge, etc.
⢠Grade level comparison plots, allowing cross grade insights on the same metric (e.g. 6th vs. 12th grade trends).
⢠Demographic overlays, such as gender or race breakdowns, where users can see how different subgroups compare over time.
Interactivity & How to Use It
⢠Use the âGradeâ dropdown to focus on a specific grade level.
⢠Choose âTopicâ (e.g., Alcohol, Mental Health) to narrow down the suite of available measures.
⢠Use the âMeasureâ selector to toggle between indicators (e.g. Access to Alcohol, Lifetime Alcohol Use, etc).
⢠If enabled, use demographic slicers (e.g. gender identity, race/ethnicity) to overlay subgroup trends.
⢠Hover over the lines to view exact percentages or changes at specific years.
What the Visuals Help You Uncover
⢠Trend directions over time: e.g., a steady decline in Access to Alcohol among 12th graders (from ~67% to ~49%), signaling effective policy or behavioral shifts.
⢠Grade based differences: e.g. 6th graders start from a much lower baseline access compared with 12th graders.
⢠Demographic disparities or convergence: e.g. whether males and females follow similar trajectories or diverge on certain behaviors.
Why This Matters
⢠Supports data driven monitoring of youth behavioral health over time.
⢠Helps spot where progress is being made, and where it isnât, so stakeholders can adjust prevention or intervention strategies.
⢠Enables equity-informed decision-making by highlighting demographic gaps.
Dashboard 2: Behavior & Well Being Bar Insights by Grade
Purpose & Audience
This dashboard provides a snapshot comparison across grade levels (typically grades 6, 8, 10, 12) of key mental health outcomes, substance use behaviors, emotional regulation, sleep patterns, bullying experiences, and help-seeking sources. It is tailored for educators, school counselors, public health practitioners, and policymakers who need to quickly see differences by grade group and inform targeted supports.
What You See
Grouped bar charts by grade, showing percentages of students reporting experiences such as:
⢠Feelings of hopelessness, suicidal ideation, suicide attempts, Talking to adults about sadness or hopelessness, Sources of support when feeling hopeless (parents, teachers, peers, etc.)
⢠Substance use: lifetime and recent use of alcohol, marijuana, heroin, cigarettes, other drugs
⢠Anxiety/worry frequency, sleep duration on school nights, social anxiety linked to internet use
⢠Bullying, cyberbullying, physical fights, and knowledge of how to report bullying
How to Use
⢠Grade selector allows you to focus on a single grade or compare across.
⢠Hover or click on specific bars to see exact percentage values and tooltips.
What Insights You Can Gain
⢠Comparative patterns across grades, for example, hopelessness or anxiety may rise in higher grades, substance use prevalence increases with age, sleep tends to decrease.
⢠Help-seeking trends, such as whether older students are more or less likely to talk to adults or rely on peers.
⢠Bullying and physical fight frequency and whether familiarity with reporting mechanisms changes across grade levels.
Why This Visual Approach Works
⢠Bar charts effectively compare categorical outcomes across groups (grades) and illustrate frequency distributions in a readable, intuitive format.
⢠Grouped and stacked bars allow simultaneous depiction of multiple response categories without overloading viewers.
Applying These Insights
⢠Identify developmental trends and risk inflection points (e.g. sharp increases in anxiety or substance use between grades 10 to 12).
⢠Direct age appropriate intervention efforts, such as anti-bullying programs in grades where physical fights peak or mental-health outreach where sadness reports climb.
⢠Support policy prioritization, using evidence from multiple measures to justify resources for counseling, sleep education, digital wellness, or reporting infrastructure.
Dashboard 3: Demographic Risk Profiles & Heatmaps
Purpose & Target Users
This dashboard offers a demographic-level analysis of risk patterns, highlighting how behavioral and mental health measures vary across groups defined by grades-in-school, food insecurity, migrant status, family management quality, race/ethnicity, and sexual orientation. It empowers school administrators, public health officials, and policy-makers to identify high risk populations and tailor interventions sensitively.
What the Visuals Include
⢠Heatmaps display percentage-based risk levels for key indicators (e.g., Access to Alcohol, Binge Drinking, Perceived Risk) across demographic groups. Cells are colored (e.g., red = high risk >20%, orange = medium risk 10â20%, green = low risk <10%), making deviations immediately visible. ⢠Radar charts (âspider plotsâ) present a multidimensional risk profile for each subgroup, visualizing related measures (e.g., access, lifetime use, binge, perceived risk) together to reveal patterns of strengths and vulnerabilities. How to Use It
⢠Use the Year, Grade, and Topic filters to update both heatmaps and radar charts for the selected cohort.
⢠Explore the heatmap to quickly spot demographic groups with elevated risks on specific measures.
⢠Reference the radar charts to understand each groupâs overall profile, does one group show high access but low perceived risk, while another shows the reverse?
Why Itâs Effective: Visualization Best Practices
⢠The heatmapâs color-coded grid provides an at-a-glance view of where risk is clustering, simplifying prioritization of interventions (high risk = red)
⢠Radar charts excel at visualizing multivariate data per group, enabling quick pattern recognition, especially helpful when comparing multiple related behavioral risk factors.
⢠Consistent use of filters ensures coordinated updates, reinforcing user-driven interactive analysis and drill-down capability.
Recommendations for Policy & Practice
⢠Prioritize resources to groups identified as high risk in heatmaps (e.g., food-insecure, poor family management groups).
⢠Design demographic-tailored interventions based on radar profile nuances, some groups may need risk perception strengthening, others may need access reduction or parental engagement.
⢠Use the dashboard in stakeholder meetings, combining quantitative visual evidence with narrative explanation of group differences to inform resource allocation, program development, or targeted outreach.
Dashboard 4: Snapshot Overview: Behavioral Health & Contextual Indicators
Purpose & Target Audience
This dashboard provides a concise, up-to-date snapshot of selected behavioral health outcomes and related social determinants, such as mental health status, substance use, smoking and proxy poverty measures, across demographic groups. Itâs ideal for decision-makers, policy advisors, community health teams, and educators seeking a high-level yet actionable view of current conditions to inform timely responses.
What the Visuals Include
⢠Tables displaying the most recent prevalence and severity of selected measures (e.g., percentage of students reporting depression, anxiety, substance use, food insecurity).
⢠Demographic breakouts, showing how each variable differs by subgroup (e.g., by gender, race/ethnicity, academic risk, socioeconomic indicators).
⢠Comparison highlights summarizing group differences (e.g., âQuestioningâ youth vs. âHeterosexualâ by perceived risk of daily alcohol use).
⢠Grade progression bars to show variation across schools or grades within the same snapshot year.
How to Use It
⢠Use Year, Grade, and Topic filters to refresh all visuals and cards for the selected cohort.
⢠Hover over cards or bars for exact values and confidence intervals.
⢠Read narrative callouts (e.g. â72.6% of 12th-graders perceive daily alcohol use as riskyâ) for at-a-glance interpretation of key metrics.
⢠Compare demographic groups side-by-side using structured visuals and summary callouts for rapid insight into disparities.
Recommendations for Policy & Practice
⢠Use snapshot visuals for strategic briefings or board reports, offering contextualized data to guide planning and resource allocation.
⢠Integrate snapshot insights with deeper dashboards (e.g., trend analysis or risk-profiles) to trace where high-risk groups have been trending over time, and evaluate impact.
⢠Use demographic inequalities surfaced in the snapshot as a foundation for targeted intervention design, such as targeted mental health supports for high-risk identities or socioeconomic groups.
⢠Complement quantitative snapshots with qualitative or community-collected data to understand the âwhyâ behind numeric disparities, avoiding over-reliance on percentages and enhancing contextual understanding.
â ď¸ Limitations & Data Considerations
⢠Unavailable data for certain yearâgradeâmeasure combinations: Some visuals (such as tables or charts) may appear blank or empty when data isnât available for a specific grade in a particular year or for certain outcome measures. This isnât a rendering error, it reflects true absence of data for that slice. Users should interpret blank visuals as indicating no available data, not zero prevalence.
⢠Missing data impacts interpretation: Gaps in data can reduce the statistical power and introduce bias, particularly if the missingness is systematic rather than random (e.g., certain groups or questions not surveyed in some years).
⢠Threshold-based visualization pitfalls: Heatmaps using color thresholds (e.g. >20% = high risk) simplify interpretation but mask nuance. Slight variations around boundaries may not reflect meaningful differences, so treat color zones as guides, not exact divisions.
⢠Small subgroup sample instability: For minor demographic categories (e.g., Pacific Islander students, students identifying as âQuestioningâ), the sample sizes may be too small to yield stable percentage estimates. Wide confidence intervals or gaps may result.
⢠Self-reported data limitations: Survey responses may contain reporting bias, recall bias, and differential participation across groupsâfactors that could influence how certain behaviors are reported.
⢠Correlation â Causation: Trends observed (e.g. concurrent declines in alcohol access and binge drinking) do not prove causal links. These are descriptive patterns over time that prompt further investigation.
⢠Context is key: demographic differences can reflect structural inequities, e.g. food insecurity co-occurs with other risk driversâso interpretation should consider broader socioeconomic context.