| Course | MPH 510 Epidemiology in Public Health |
|---|---|
| Module | Module 3 |
| Paper type | Case-control methods paper |
| Length | About 1,049 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Master of Public Health |
| Updated | September 2026 |
Free sample paper for MPH 510 Module 3
Working Backward From Disease: The Case-Control Design and the DES Discovery
Student Name
Master of Public Health Program, Aspen University
MPH 510: Epidemiology in Public Health
Instructor Name
Month Day, Year
Working Backward From Disease: The Case-Control Design and the DES Discovery
When a disease is rare or takes years to develop, following a large cohort forward can be impractical. The case-control design works backward instead: it starts with people who have the disease, compares them with similar people who do not, and looks for differences in past exposures. This paper explains the design and its measure of association, the odds ratio, through a landmark study that uncovered a drug's harm to the children of women who took it.
The Puzzle
Between 1966 and 1969, physicians in Boston treated eight young women, aged 15 to 22, for clear-cell adenocarcinoma of the vagina, a cancer almost never seen in women so young. The cluster demanded an explanation, and the rarity of the disease made a case-control study the natural choice.
The Study
Investigators matched each of the eight cases with four controls born within five days of the case in the same hospital, on the same type of service, giving 32 controls. Mothers were interviewed about their pregnancies. Seven of the eight mothers of cases had taken diethylstilbestrol, a synthetic estrogen then prescribed to prevent miscarriage, beginning in the first trimester; none of the 32 control mothers had (Herbst et al., 1971).
Choosing Controls
Controls should be drawn from the source population of the cases, so that, had they developed the disease, they would have been cases in the study. Matching on birth date and hospital controlled for time period and hospital practices. Overmatching, matching on factors related to the exposure, can hide a real association, so investigators match only on likely confounders.
The Odds Ratio
Because a case-control study fixes the number of cases and controls, it cannot measure incidence directly. What it can compare is how often cases were exposed, expressed as odds, against how often controls were. When a disease is rare, the odds ratio closely approximates the relative risk. The table works through a composite case-control study of a different exposure to show the calculation.
| Exposed | Unexposed | Total | |
|---|---|---|---|
| Cases | a = 60 | b = 40 | 100 |
| Controls | c = 50 | d = 150 | 200 |
| Odds of exposure | Cases: 60/40 = 1.5 | Controls: 50/150 = 0.33 | |
| Odds ratio | (a x d) / (b x c) | (60 x 150) / (40 x 50) = 4.5 |
Interpreting the Composite Result
An odds ratio of 4.5 means that cases had four and a half times the odds of exposure compared with controls. With a rare disease, that suggests exposed people have about four and a half times the risk. A confidence interval would show the precision of the estimate; with 300 participants, it would be reasonably narrow.
The Zero Cell
In the Boston study, no control mother was exposed. With a zero in the control-exposed cell, the odds ratio formula divides by zero, so no finite point estimate is possible. Yet the contrast of seven of eight against none of 32 was so extreme that chance was an implausible explanation. Statistical methods for zero cells, such as exact tests, confirmed the association was highly significant.
Bias in Case-Control Studies
Recall bias occurs when cases remember past exposures differently from controls, often more thoroughly because they have searched for a cause. In the Boston study, the investigators checked mothers' reports against medical records where possible. Selection bias creeps in when the controls are unlike the source population the cases came from. Hospital controls can be biased if their conditions are linked to the exposure.
Judging Causation
The association met several of Hill's viewpoints: it was extremely strong, exposure clearly preceded disease by many years, and the finding fit biological plausibility, since estrogen affects developing reproductive tissue (Hill, 1965). Later registries confirmed the association in other populations, adding consistency.
Public Health Response
In 1971, the same year the study was published, the Food and Drug Administration advised physicians against prescribing diethylstilbestrol during pregnancy. Registries were created to follow exposed daughters, and screening programs checked them for early signs of cancer. The case shows how a small, well-designed case-control study can prompt rapid action.
Strengths and Weaknesses of the Design
Case-control studies are efficient for rare diseases and diseases with long latency, and they can examine many exposures; by contrast, the cohort that tied smoking to lung cancer needed tens of thousands of doctors followed for years (Doll & Hill, 1954). They are less suited to rare exposures, cannot directly measure incidence and are vulnerable to recall and selection bias. Nested case-control studies, which draw cases and controls from within an existing cohort, reduce some of these weaknesses.
Matched Analysis
When cases and controls are individually matched, the analysis should respect the matching. In a matched-pair analysis, only pairs that differ in exposure contribute information, and the odds ratio is the ratio of pairs in which the case was exposed and the control was not to pairs in which the reverse was true. Ignoring the matching tends to bias the odds ratio toward 1.
Case-Control Studies in Outbreaks
Case-control designs are also a mainstay of outbreak investigations when the full population exposed is unknown, such as a multistate outbreak linked to a grocery product. Investigators interview ill people and healthy controls about foods eaten and compare exposure odds. The speed of the design makes it ideal when action must be taken within days.
Case-Control Studies and Public Health Surveillance
Health departments use case-control studies to investigate clusters reported by residents, such as childhood cancers near an industrial site. These studies must be designed carefully, because clusters often arise by chance and exposure data may be poor. Clear communication about what the study can and cannot show is part of the work.
Sample Size
The number of cases available usually fixes the size of a case-control study, so power is increased by adding controls. Beyond about four controls per case, additional controls add little precision, which is one reason the Boston investigators chose a four-to-one ratio.
Conclusion
The diethylstilbestrol discovery shows the power of the case-control design: eight cases and 32 matched controls revealed a drug's harm across a generation. Understanding how controls are chosen, how the odds ratio is calculated and interpreted, and where bias can creep in allows public health professionals to use this efficient design wisely and to judge its findings critically.
References
Doll, R., & Hill, A. B. (1954). The mortality of doctors in relation to their smoking habits: A preliminary report. BMJ, 1(4877), 1451-1455. https://doi.org/10.1136/bmj.1.4877.1451
Herbst, A. L., Ulfelder, H., & Poskanzer, D. C. (1971). Adenocarcinoma of the vagina: Association of maternal stilbestrol therapy with tumor appearance in young women. New England Journal of Medicine, 284(16), 878-881. https://doi.org/10.1056/NEJM197104222841604
Hill, A. B. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. https://doi.org/10.1177/003591576505800503
Reading the MPH 510 Module 3 assignment instructions
Aspen describes MPH 510 as the study of how epidemiology finds causes of disease, and because the third module's instructions are shown only to enrolled students, this sample takes on the case-control design. Such assignments often ask you to explain how cases and controls are chosen, work out an odds ratio from a table of cases and controls, and discuss bias. Confirm whether your instructor provides data. Lay out the table with labeled cells, then show the formula. Explain when the odds ratio approximates relative risk. Name the biases most likely in your example and how the investigators addressed them. A real historical study gives your paper concrete detail and checkable facts. Say how controls were drawn from the same population as cases.
How the MPH 510 Module 3 example is put together
The example covers roughly 1,000 words under fifteen headings and includes a four-column two-by-two table with calculations. It sets up the Boston puzzle, describes the study and control selection, then works through the odds ratio and interprets it. Sections on the zero cell, bias, causation, the public health response and strengths and weaknesses follow, together with matched analysis, outbreak uses, surveillance uses and sample size. The margin comment by the study section explains why the exact matching criteria matter. The conclusion shows how a small, well-designed study changed practice for a generation of pregnancies. Numbers from the real study and the composite example are kept in separate sections. The public health response section names the agency action and the registries that followed.
Where the marks sit in the MPH 510 Module 3 rubric
Case-control papers are usually assessed on correct description of case and control selection, accurate odds ratio calculation, sound interpretation, attention to bias and a clear link to public health action. The sources, the original 1971 report, Hill's causation lecture and the British doctors study for contrast, appear in APA format. The two-by-two table labels every cell and shows the formula. The zero-cell discussion shows statistical understanding beyond the formula. Bias is explained with specifics from the study. Graders give extra credit when a paper explains why the design fit the problem, here a rare disease with long latency. Keeping the real study's figures separate from the teaching example also shows care.
MPH 510 Module 3 help: mistakes that cost marks
Students often reverse the table, putting exposure in rows and disease in columns, and then calculate the odds ratio incorrectly. Others treat the odds ratio as a relative risk without noting the rare-disease condition. Label rows and columns clearly and check each cell against the text. When describing bias, say which direction it would push the estimate. Our tutors can check a two-by-two table and calculation with you before you submit, so small errors do not cost marks. Close by stating what action the finding justified. If your data contain a zero cell, say so and explain why no point estimate is possible rather than forcing a number. Mention exact tests as the usual alternative.
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 Aspen University 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.
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- MPH 501 Module 8: The Future of Public Health Delivery
- MPH 530 Module 1: Foundations of Environmental Health
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MPH 510 Module 3 questions, answered
What does MPH 510 Module 3 usually ask for?
Aspen's MPH 510 covers the methods epidemiologists use to find causes of disease, so explaining the case-control design and calculating an odds ratio is a typical assignment. Confirm with your classroom prompt.
How is an odds ratio calculated?
Multiply the exposed cases by the unexposed controls and divide by the product of the unexposed cases and exposed controls, (a x d) / (b x c).
When does the odds ratio approximate the relative risk?
When the disease is rare in the population studied.
Where can I find a free MPH 510 Module 3 sample paper?
Read the DES case-control paper below the introduction; it includes a labeled two-by-two table that walks through the odds ratio.
What makes the case-control design suited to rare diseases in MPH 510 Module 3?
Because it begins from existing cases, investigators do not need to follow huge populations for years to accumulate enough cases.