MPH 570 Module 4 Public Health Data Sources Example

Finding the Numbers: Choosing and Interpreting Public Health Data Sources

Student Name

Master of Public Health Program, Aspen University

MPH 570: Evidence-Based Practice in Public Health

Instructor Name

Month Day, Year

Finding the Numbers: Choosing and Interpreting Public Health Data Sources

Evidence-based practice needs both research on what works and data on local conditions. Public health professionals must know which data sources exist, what they measure well and where they fall short. This paper reviews major sources of public health data, evidence on the reliability of self-reported measures and newer small-area estimates, and shows how a student would assemble data on youth vaping for a composite county.

Surveillance Surveys

The Behavioral Risk Factor Surveillance System surveys adults by telephone in every state about health behaviors, chronic conditions and preventive care. The Youth Risk Behavior Survey collects data from high school students on behaviors including tobacco and e-cigarette use. The National Youth Tobacco Survey focuses specifically on tobacco among middle and high school students.

How Reliable Are Self-Reports

Surveys rely on what people report. A systematic review of studies assessing the reliability and validity of the adult behavioral risk survey found that many measures, such as smoking, diabetes and health insurance, showed moderate to high reliability and validity, while others, such as physical activity and some dietary measures, were less consistent (Pierannunzi et al., 2013). Analysts should interpret each measure in light of such evidence.

Vital Statistics and Health Care Data

Birth and death certificates provide complete counts of births, deaths and causes of death. Hospital discharge and emergency department data show serious illness and injury, such as poisonings or lung injuries. Notifiable disease reports capture specific infections. These sources complement surveys but miss behaviors that do not lead to care.

Small-Area Estimates

Surveys rarely have enough respondents to estimate rates for small areas. The PLACES project uses small-area estimation methods with national survey data to produce model-based estimates of dozens of health measures down to the level of individual census tracts and ZIP codes, supporting local planning where direct data are lacking (Greenlund et al., 2022). Model-based estimates are useful but should not be treated as direct measurements.

Comparing Sources

The table summarizes key sources.

National Context for the Issue

For youth vaping, national survey data provide essential context: current use in 2022 reached 14.1% among high school students and 3.3% among middle schoolers, and flavored products dominated (Cooper et al., 2022). National figures show the scale of the issue even where local data are sparse.

Assembling Local Data

For the composite county, the student gathered the county's results from the state youth survey, which put past-month vaping among the county's high schoolers at 19%; school discipline records on vaping incidents; poison center calls involving nicotine liquid among young children; retailer compliance check results showing 22% of stores sold to underage decoys; and focus groups with students. Together these sources describe the problem's size, setting and drivers.

Judging Data Quality

For each source, analysts should ask: who is included and excluded, how the measure is defined, how data are collected, what the response rate is, how recent the data are and whether estimates are precise enough. Confidence intervals and sample sizes should be reported with every estimate. Where two sources disagree, the student will examine differences in population, timing and question wording before deciding which to emphasize.

Ethics and Privacy

Data use must protect privacy. Small counts may identify individuals, so agencies suppress or combine them. School and health records require agreements and approvals. Students using data for internships should follow agency rules and institutional review requirements. Cells with fewer than ten students will be suppressed in any shared tables.

Timeliness

Survey data often lag by one to two years, and some surveys are conducted every other year. For fast-changing issues, more timely sources, such as school incident data, poison center calls or syndromic surveillance, can signal changes before survey results arrive.

Qualitative Data

Numbers show how common a problem is; qualitative data explain why. Focus groups and interviews with students revealed where they obtained devices and why they vaped, information no survey provided. Combining quantitative and qualitative data gives a fuller picture.

Presenting Data for the Capstone

The student will present data in a table listing each source, its year, the measure and the estimate with its confidence interval, followed by a short narrative explaining what the combined data show about youth vaping in the county. Clear presentation strengthens the problem statement that follows.

Data Access and Partnerships

Some data require agreements. The student obtained school incident data through the health department's existing partnership with the district, and compliance check records through the tobacco program. Starting these requests early prevented delays during the internship.

Limitations of Local Data

The county youth survey had a response rate of 64%, and students absent on survey day, who may be more likely to vape, were missed. Compliance checks covered only licensed stores, not online or social sources. Noting these limits prevents overconfident conclusions.

Linking Data to the Problem Statement

Each data point in the local profile will appear in the problem statement or its supporting text: prevalence from the survey, retail access from compliance checks and experiences from focus groups. This link ensures that the statement rests on documented evidence rather than impressions.

Data Visualization

A simple bar chart comparing county, state and national youth vaping rates, and a map of compliance violations by area, will help stakeholders see the problem quickly. Visuals must show sources and years.

Conclusion

Public health data sources each have strengths and limits: surveys measure behaviors but rely on self-report, vital statistics are complete but narrow, health care data capture serious events and small-area estimates offer local detail from models. Combining sources, judging their quality and placing local data in national context, as the student did for youth vaping, produces a sound foundation for defining a health issue.

References

Cooper, M., Park-Lee, E., Ren, C., Cornelius, M., Jamal, A., & Cullen, K. A. (2022). Notes from the field: E-cigarette use among middle and high school students: United States, 2022. MMWR. Morbidity and Mortality Weekly Report, 71(40), 1283-1285. https://doi.org/10.15585/mmwr.mm7140a3

Greenlund, K. J., Lu, H., Wang, Y., Matthews, K. A., LeClercq, J. M., Lee, B., & Carlson, S. A. (2022). PLACES: Local data for better health. Preventing Chronic Disease, 19, Article E31. https://doi.org/10.5888/pcd19.210459

Pierannunzi, C., Hu, S. S., & Balluz, L. (2013). A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004-2011. BMC Medical Research Methodology, 13, Article 49. https://doi.org/10.1186/1471-2288-13-49