| Course | HCI 5073 Public Health Informatics |
|---|---|
| Module | Module 3 |
| Paper type | Registry data analysis |
| Length | 1,250 words, about 5 pages plus title and reference pages |
| Format | APA 7 student paper |
| School | American College of Education |
| Program | Master of Public Health |
| Updated | September 2026 |
Free sample paper for HCI 5073 Module 3
Younger and Later: Using State Cancer Registry Data to Examine Colorectal Cancer in Adults Under 50 and to Plan Screening Outreach
Student Name
American College of Education
HCI5073: Public Health Informatics
Module 3 Assignment
Instructor Name
October 19, 2026
Why Registry Data
Most public health data systems sample, estimate or wait for people to seek care. A population-based cancer registry is different: it aims to record every new cancer diagnosed among residents of a defined area. White et al. (2017), describing the growth of cancer surveillance in the United States, explain that because registry data provide a census of cases, they can be used to monitor incidence at the local, state and national levels, to study patterns of treatment and to evaluate whether public health efforts are preventing cancers and improving survival. That makes registries one of the few sources that can answer questions about small areas and specific groups with confidence.
This paper examines how a composite Midwestern county health department uses its state central cancer registry to study colorectal cancer among adults younger than 50 and the stage at which cancers are found. It describes how the registry data are produced and checked, what they show, how they inform screening outreach and where their limits lie.
How the Registry Is Built
Every hospital, pathology laboratory, radiation center and many physician offices in the state are required by law to report cancer diagnoses to the central registry. Trained cancer registrars, known as tumor registrars in many hospitals, abstract each case from medical records into a standard set of data items: patient demographics and residence at diagnosis, the date of diagnosis, the tumor's primary site and histology, stage at diagnosis, the first course of treatment and, through later linkage with death records, vital status. The state registry combines reports from multiple facilities for the same patient, removes duplicates and follows national standards so that its data can be pooled with other states. It receives funding through the CDC's National Program of Cancer Registries, one of the two federal programs that support registry work alongside the National Cancer Institute's Surveillance, Epidemiology, and End Results program (White et al., 2017).
The process is thorough but slow. Because cases arrive from several sources and must be consolidated and checked, data for a given diagnosis year are usually released about two years later. The county's most recent complete year is therefore 2024.
Data Quality Standards
Registry data are unusual in public health because their quality is formally measured and certified each year. The state registry submits its data to the North American Association of Central Cancer Registries, which reviews estimated completeness of case ascertainment, the share of cases known only from a death certificate, the proportion of records missing key items such as age, sex, race and county, duplicate reports and the timeliness of submission. The state has met the association's highest certification level in each of the last ten years, which requires estimated completeness of at least 95% and very few death-certificate-only cases.
For the county's purposes, two items need closer inspection. Stage at diagnosis was recorded as unknown for about 6% of colorectal cases, and those cases were more common among patients diagnosed at small rural hospitals. Race and ethnicity were missing or listed as unknown for about 3% of cases. Both gaps matter because the county's planning depends on stage and on differences between groups.
What the County's Data Show
From 2015 through 2024, the registry recorded 1,184 colorectal cancers among county residents. Two patterns stand out. First, the share diagnosed before age 50 rose from 9% in the first half of the decade to 14% in the second, and the number of cases among adults aged 45 to 49 nearly doubled. Second, the share diagnosed at regional or distant stage, meaning the cancer had already spread beyond the colon or rectum, was higher among younger patients than among those over 50, 68% compared with 55%, likely because younger adults were not being screened and their symptoms were often attributed to other causes.
The county's patterns mirror national trends. Siegel et al. (2023), using population-based registry data, reported that the proportion of colorectal cancers diagnosed in people younger than 55 rose from 11% in 1995 to 20% in 2019, and that 60% of all new cases were advanced in 2019, compared with 52% in the mid-2000s, reversing earlier gains from screening. National findings tell the county it is not an outlier; local data tell it where and among whom the shift is concentrated.
Using the Data to Plan Screening
The registry data informed three decisions. First, they supported the county's campaign to publicize the updated national screening age. The US Preventive Services Task Force (2021) recommended screening for adults aged 45 to 49 as well as those aged 50 to 75, and the county's rising count of cases in the 45 to 49 group gave local weight to that message. Second, mapping late-stage cases by census tract of residence showed three contiguous tracts where the proportion of distant-stage diagnoses was nearly twice the county average; those tracts now receive mailed stool-based test kits through a partnership with a federally qualified health center. Third, the data provided a baseline against which the county will measure progress, using stage distribution rather than incidence, because effective screening can temporarily raise incidence by finding cancers earlier before lowering it by removing precancerous polyps.
Limits of Registry Data
Registry data have limits that any analysis must acknowledge. The two-year delay means that the county is always planning with data that predate its latest interventions. Small numbers force suppression of counts in tract-level public reports to protect confidentiality, and they make rates for small groups unstable from year to year. The registry records residence at diagnosis, not where a person lived during the decades when a cancer developed, so it cannot identify environmental or occupational causes without additional data. Most importantly for this purpose, the registry records cancers, not screening. It can show that younger adults are diagnosed at later stages, but it cannot show whether they had been offered screening, declined it or lacked insurance. Answering those questions requires other data sources.
Linkage With Other Data
Linking registry records with other data can fill some of these gaps. Linkage with hospital discharge and Medicaid claims data could show whether patients had a colonoscopy or stool test in the years before diagnosis. Linkage with death records already supports survival analysis. Survey data on screening, available only at larger geographic levels, can be compared with registry stage patterns to judge whether low screening explains late diagnosis. Each linkage raises privacy and governance questions. The state registry's data release policy requires a formal application, review by an institutional review board for identifiable data and agreements limiting how results are reported, and those steps can take months. The county has begun an application for a Medicaid linkage covering adults aged 45 to 64, which would let it test whether late-stage cases cluster among adults who were enrolled but never screened.
Conclusion
The state cancer registry gives the county a complete, certified count of colorectal cancers and shows two troubling shifts: more cases in adults under 50 and a larger share found after the cancer has spread. Those data guided outreach to the tracts most affected and set a baseline for evaluation. Their limits, including delay, small numbers, missing stage for some rural cases and the absence of screening history, point to linkage as the next informatics step, which a later module will examine alongside the tools used to present these data to the public.
References
Siegel, R. L., Wagle, N. S., Cercek, A., Smith, R. A., & Jemal, A. (2023). Colorectal cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(3), 233-254. https://doi.org/10.3322/caac.21772
US Preventive Services Task Force. (2021). Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA, 325(19), 1965-1977. https://doi.org/10.1001/jama.2021.6238
White, M. C., Babcock, F., Hayes, N. S., Mariotto, A. B., Wong, F. L., Kohler, B. A., & Weir, H. K. (2017). The history and use of cancer registry data by public health cancer control programs in the United States. Cancer, 123(S24), 4969-4976. https://doi.org/10.1002/cncr.30905
The HCI 5073 Module 3 assignment instructions
The third HCI 5073 module frequently moves to registry data. Prompts usually ask you to choose a cancer or chronic disease registry, explain how its data are collected and quality-checked, analyze what the data reveal about a population and discuss how they support planning, research or evaluation. Some sections expect you to work with published registry tables or an online query tool; others accept secondary data from state reports. Include the limits of registry data, since graders look for them and reward an honest account. If your project follows one community across the course, keep the same population. See Canvas for whether a table or figure summarizing the data is required, and note whether the instructor wants age-adjusted rates or simple counts, since that choice changes how the comparison is written.
How this HCI 5073 Module 3 example is built
The example turns to colorectal cancer in a composite Midwestern county. It explains what registries are for, how the state registry is built from hospital and laboratory reports, and how certification measures its quality, while pointing out the missing stage and race values that affect local planning. County figures on age and stage are compared with Siegel's national statistics. Three planning uses follow: supporting screening from 45, mapping late-stage tracts for mailed kits and setting a stage-based baseline. Sections on limits and on linkage with Medicaid and discharge data lead to a short conclusion that sets up the dashboard module to come.
HCI 5073 Module 3 rubric: what full marks look like
Rubrics for this module typically reward accurate description of how registry data are produced before any interpretation. Show that you understand quality measures, such as completeness and missing items, and how they affect your conclusions. Present the data clearly, in a table where it helps, compare local and national patterns and explain what the numbers mean for action. Graders often look for a candid discussion of limits, including reporting lag, small numbers and what the registry does not record. Privacy and data governance should appear where linkage is proposed, and citations follow APA 7, with registry reports cited by agency and year.
HCI 5073 Module 3 help: mistakes that cost points
Registry papers go wrong when students treat the numbers as self-explanatory. If your prompt asks for cancer, diabetes or immunization registry data and you are not sure how to describe the collection process or quality measures, we can help. Tell us the registry and the population you are studying and attach the grading criteria. A Module 3 paper built for you explains where the data come from, reads them carefully against national figures and turns them into a planning decision with its limits stated plainly. It can also suggest which linkages would answer the questions the registry leaves open, since that step often earns credit in later modules.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official American College of Education document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More HCI 5073 and Master of Public Health sample papers
- HCI 5073 Module 1: Surveillance System Assessment
- HCI 5073 Module 2: Data Quality Analysis
- HCI 5073 Module 4: Dashboard Evaluation
- HCI 5073 Module 5: Improvement Proposal
- HLTH 5023 Module 4: Data Privacy and Surveillance
- HLTH 5083 Module 3: Capstone Options Analysis
- HLTH 5003 Module 5: Program Evaluation Plan
- HLTH 5083 Module 1: Capstone Problem Definition
HCI 5073 Module 3 questions, answered
What does HCI5073 Module 3 usually ask for?
In HCI5073, the third module commonly asks you to examine data from a cancer or chronic disease registry: how the data are collected and checked, what they show about a population and how they can guide public health action.
How is a cancer registry different from other surveillance data?
A population-based cancer registry aims to record every new cancer diagnosed among residents of an area, abstracted by trained registrars using national data standards, so it works as a census rather than a sample.
Why is cancer registry data usually two years old?
Cases are reported by many facilities, consolidated for each patient, checked for duplicates and missing items and certified before release, which typically takes about two years after the diagnosis year.
Where can I find a free HCI 5073 Module 3 sample paper?
This page carries a full Module 3 registry paper that uses state cancer registry data to study colorectal cancer in adults under 50, stage at diagnosis and screening outreach, with the registry's limits and linkage options.
Can I use a chronic disease registry instead?
Yes, if your prompt allows it. Diabetes, asthma, stroke or immunization registries can be analyzed the same way: how data are produced, their quality, what they show and what they cannot show.