HLTH 5433 Module 2 Data-Informed Decision Analysis Example

From Attendance Records to a Pilot Decision: Using District Data to Choose Where and How to Address Health-Related Chronic Absenteeism

Student Name

American College of Education

HLTH5433: Leadership in Contemporary Health Education

Module 2 Assignment

Instructor Name

October 12, 2026

The Decision to Be Made

In Module 1, the health and wellness coordinator of a composite district of roughly nine thousand students in a small city adopted adaptive leadership to address chronic absenteeism, which affects 24% of the district's students. Before school teams can take on the work, the coordinator must answer two practical questions. Where should the district begin, given that it cannot launch in all 14 schools at once? And which health causes of absence should the first pilot address? This paper describes how the coordinator used district data to make those decisions, the criteria applied, the gaps in the evidence and how the decision will be tested.

A Process for Using Data

Mandinach (2012) described data-driven decision making in education as a process that turns raw data into information and then into knowledge that can guide action, with the results of each decision feeding back into the next round. Collecting and organizing data produces facts; analyzing and summarizing them produces information; synthesizing and prioritizing produces usable knowledge. The framework also emphasizes that educators need data literacy, the ability to ask good questions of data and to understand their limits, and that data use is most effective when it is part of the school's culture rather than an occasional report. The coordinator used this sequence to organize her analysis.

The Data Available

Four sources were available. The student information system recorded daily attendance for every student, including whether absences were marked excused or unexcused and, since the previous fall, a reason code chosen by the attendance clerk from a short list. School nurses kept visit logs noting complaints, medications given and whether students were sent home. Nurses also maintained lists of students with diagnosed asthma and other chronic conditions on file. Finally, a dental screening conducted by the county health department the previous spring recorded untreated decay among second and third graders at six elementary schools. Each source was imperfect: reason codes depended on what families reported, nurse logs varied in detail between schools and the dental screening covered only two grades.

From Data to Information

The coordinator first summarized chronic absence rates by school and grade. Rates ranged from 11% to 38%, and three elementary schools serving the neighborhoods near the older apartment complexes had rates above 30%. Among chronically absent students at those three schools, illness was the most common reason code, accounting for about half of recorded absences, compared with about a third district-wide. Students on nurses' asthma lists at those schools were chronically absent at nearly twice the rate of classmates without asthma, and the nurse logs showed frequent visits for breathing problems in the winter months. The dental screening found untreated decay in 41% of screened children at the same three schools, compared with 22% at the other screened schools, and nurses at two of the three schools recorded regular visits for tooth pain.

These patterns are consistent with published research. Hsu et al. (2016), using a national survey of children with asthma, found that children who missed school because of asthma were more likely to have poorly controlled asthma and to have visited an emergency department or urgent care center, and that mold in the home and cost barriers to care were associated with asthma-related absence. Jackson et al. (2011) found that children with poor oral health were more likely to miss school because of dental pain and that absences caused by pain were associated with poorer school performance.

Criteria for the Decision

To move from information to a decision, the coordinator set criteria with the assistant superintendent and two principals. The pilot school should have a high chronic absence rate, a large share of absence plausibly linked to health, a principal and nurse willing to lead and available community partners. The first health causes to address should be common among absent students, supported by evidence that intervention can reduce absence and within reach of existing partners.

Applying the criteria, one of the three high-absence elementary schools stood out. It had a 34% chronic absence rate, the highest concentration of students with asthma, the highest untreated decay rate, a principal who had asked for help and a nurse with strong ties to families. It is also within walking distance of the federally qualified health center, which had expressed interest in school-based services. For the health causes, asthma and dental pain met all three criteria. Anxiety-related absence, although important, was concentrated in middle schools, and the district's counseling capacity for a pilot was not yet in place, so it was scheduled for a second phase.

What the Data Could Not Show

Data literacy includes knowing what data cannot tell. The reason codes were unreliable; families may report illness to avoid scrutiny when the true reason is transportation, caregiving duties or a child's fear of school. The nurse logs covered only students who came to school and visited the nurse, not those who stayed home. The dental screening was a year old and covered only two grades. None of the data captured housing instability directly, although the district's list of students identified under federal homeless education rules suggested it was substantial at the pilot school. And the data showed associations, not causes: a child with asthma who is chronically absent may also face other barriers that asthma care alone will not remove. The coordinator therefore treated the decision as a well-informed starting point rather than a certainty and planned to gather family perspectives before finalizing the pilot's design.

Sharing Data Responsibly

Using student-level data raises privacy obligations. Attendance and health records are education records protected by federal law, and nurse logs contain sensitive health information. The coordinator worked only with de-identified summaries when presenting to principals and partners and limited access to student-level lists to school staff with a legitimate educational interest. Sharing data with the health center will require a formal agreement or written parent consent, a question examined in Module 4. Presenting the data carefully also matters for relationships: showing school-level comparisons in a way that implies blame could undermine the trust that adaptive leadership depends on, so the coordinator presented the pilot school's numbers as a reason for support, not criticism.

Checking the Decision

The feedback loop in Mandinach's framework (Mandinach, 2012) means the decision must be tested against results. The coordinator set indicators to be reviewed monthly by the school team: the chronic absence rate for the whole school and for students with asthma, the number of students with asthma action plans on file, nurse visits for breathing problems and tooth pain, dental sealant and treatment referrals completed and the share of absences coded as illness. If absence among students with asthma does not fall after one semester, the team will examine why before expanding. The coordinator will also compare the pilot school with the two other high-absence elementary schools, which will begin in the second year, to see whether changes are due to the pilot or to districtwide trends.

Conclusion

District data, organized and interpreted with care, allowed the coordinator to choose a pilot school with high need, strong leadership and nearby partners, and to begin with asthma and dental pain, the health causes most clearly linked to absence among its students. The data also revealed their own limits, pointing to the need for family input and privacy safeguards. Treating the decision as the first step in a cycle of data use, rather than a final answer, keeps the district learning. Module 3 will turn to the relationships the pilot requires.

References

Hsu, J., Qin, X., Beavers, S. F., & Mirabelli, M. C. (2016). Asthma-related school absenteeism, morbidity, and modifiable factors. American Journal of Preventive Medicine, 51(1), 23-32. https://doi.org/10.1016/j.amepre.2015.12.012

Jackson, S. L., Vann, W. F., Jr., Kotch, J. B., Pahel, B. T., & Lee, J. Y. (2011). Impact of poor oral health on children's school attendance and performance. American Journal of Public Health, 101(10), 1900-1906. https://doi.org/10.2105/AJPH.2010.200915

Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71-85. https://doi.org/10.1080/00461520.2012.667064