| Course | MPH 504 Cultural Competency and Global Public Health |
|---|---|
| Week | 3 |
| Paper type | Disease burden analysis |
| Length | About 1,170 words, 5 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | Master of Public Health (MPH) |
| Updated | October 2026 |
Free sample paper for MPH 504 Week 3
Disease Burden Analysis: Tuberculosis in the Philippines Compared With Vietnam and the United States, and How Far the Numbers Can Be Trusted
Student Name
Chamberlain University
MPH 504: Cultural Competency and Global Public Health
Instructor Name
Month Day, Year
Disease Burden Analysis: Tuberculosis in the Philippines Compared With Vietnam and the United States, and How Far the Numbers Can Be Trusted
The Condition and the Settings
This analysis examines the burden of tuberculosis in the Philippines, the country of birth of many patients in the Alder County TB program where I work. It compares the Philippines with two reference settings chosen for different reasons. Vietnam is a regional neighbor with a similar income level and a national TB program of similar age, so differences between the two are more likely to reflect program and health system choices than wealth alone. The United States is the setting in which I practice and the place where people born in the Philippines who carry latent infection may later develop disease. The analysis covers adults and children together, uses the most recent year for which World Health Organization estimates are available, 2024, and treats pulmonary and extrapulmonary disease together.
Measures and Why More Than One Is Needed
Four measures are used. Estimated incidence counts new cases arising each year per 100,000 population, including cases never diagnosed. Estimated mortality is TB deaths per 100,000, which captures severity and the failure of care. Notification is the number of cases actually reported by the national program, which shows what the health system sees. Case detection is notification divided by estimated incidence, expressed as a percentage. Incidence alone would hide the fact that a large share of cases is never found; mortality alone would understate a disease that most people survive with treatment but that costs months of work and income.
The Figures
Table 1
Tuberculosis Burden Estimates, 2024
| Measure, 2024 estimates | Philippines | Vietnam | United States |
|---|---|---|---|
| Estimated incidence per 100,000 (uncertainty interval) | 625 (344-1,020) | 182 | 3.2 (2.6-3.9) |
| Estimated new cases | 724,000 | 184,000 | 11,000 |
| Estimated deaths per 100,000, all TB | 32 | 12 | 0.19 |
| Case detection, % of estimated incidence | 75 | 58 | Not estimated for 2024 |
Note. Note. World Health Organization (2025) estimates; deaths include TB among people with HIV. U.S. case detection for 2023 was 86%.
The Philippines' estimated incidence, 625 per 100,000, is about three and a half times Vietnam's and about 200 times that of the United States. Its estimated mortality, 32 per 100,000, is more than two and a half times Vietnam's. Roughly 37,000 people died of TB in the Philippines in 2024, against about 650 in the United States (World Health Organization [WHO], 2025). By any of these measures, the Philippines carries one of the heaviest TB burdens in the world.
Comparison Against the Reference Settings
The comparison with Vietnam is the most informative. The two countries differ in incidence by more than threefold, yet the Philippines reports a higher case detection figure, 75% against 58%. Taken at face value, that would mean the Philippines finds a larger share of its cases. But case detection is a ratio of two numbers, one of them an estimate with a very wide interval, so it cannot bear much weight on its own. The comparison with the United States matters for my practice for a different reason: in 2023, 76% of U.S. TB cases occurred among people born outside the country, and the rate among non-U.S.-born people, 15.0 per 100,000, was nearly 19 times the rate among U.S.-born people, 0.8 (Williams et al., 2024). Much of that difference reflects infection acquired in high-burden countries and reactivated years later.
Data Quality as Part of the Argument
The uncertainty interval for the Philippines' incidence runs from 344 to 1,020 per 100,000. That interval is not a footnote; it means the true incidence could be about half or nearly double the central estimate. The interval is wide because incidence is not counted directly. It is modeled, in the Philippines' case largely from the 2016 national TB prevalence survey, which screened a representative sample of adults with symptom questions and chest radiography (Lansang et al., 2021). That survey found 1,159 bacteriologically confirmed cases per 100,000 adults, a prevalence 3.1 times higher than notifications would suggest, and no evidence of decline since the 2007 survey.
Two of its findings explain why routine data undercount. Only 32% of people with confirmed TB reported the symptoms that the screening questions asked about, so most would never have been referred for testing on symptoms alone. And among people with symptoms, only 19% had consulted a health worker (Lansang et al., 2021). A lower notification rate in one region of the Philippines therefore cannot be read as less disease; it may mean less screening, fewer clinics or more people paying for care at private pharmacies that do not report.
Estimates also change between reports. The 2024 WHO report gave the Philippines an incidence of 643 per 100,000 for 2023; the 2025 data set revised the same year to 632. A student comparing years across reports could mistake a revision for a trend. For the United States, by contrast, the national surveillance rate of 2.9 per 100,000 for 2023 is a count of reported cases (Williams et al., 2024), while the WHO estimate for that year, 3.1, adds an allowance for undiagnosed cases. Neither is wrong; they measure different things.
Determinants Behind the Pattern
The pattern is not explained by how people in the Philippines choose to live. TB spreads in crowded housing and poorly ventilated workplaces, progresses more often in people who are undernourished or have diabetes, and is found late when care is costly or far away (Lönnroth et al., 2009). In the Philippines, the cost of care is a direct barrier. The first national TB patient cost survey found that 42.4% of TB-affected households faced catastrophic costs, defined as costs above 20% of annual household income, even though diagnosis and drugs are provided free by the national program, because transport, food and lost income make up most of the cost (Florentino et al., 2022). Men were 2.5 times as likely as women to have pulmonary TB in the prevalence survey (Lansang et al., 2021), which points to occupational exposure, smoking and men's lower use of health services rather than to biology alone.
What the Comparison Implies
Three implications follow. First, in the Philippines, the biggest returns probably lie in finding people who would never be referred on symptoms, through screening of household contacts and high-risk communities with chest radiography, since the prevalence survey showed that symptom-based case finding misses most cases. Second, reducing the costs that fall on households, especially lost income and transport, matters as much as free drugs, because cost delays care and pushes families into debt. Third, for my own program in the United States, the burden in the Philippines means that adults born there carry a high likelihood of latent infection, which supports offering testing and short-course preventive treatment to that group rather than waiting for disease to appear.
References
Florentino, J. L., Arao, R. M. L., Garfin, A. M. C., Gaviola, D. M. G., Tan, C. R., Yadav, R. P., Hiatt, T., Morishita, F., Siroka, A., Yamanaka, T., & Nishikiori, N. (2022). Expansion of social protection is necessary towards zero catastrophic costs due to TB: The first national TB patient cost survey in the Philippines. PLOS ONE, 17(2), e0264689. https://doi.org/10.1371/journal.pone.0264689
Lansang, M. A. D., Alejandria, M. M., Law, I., Juban, N. R., Amarillo, M. L. E., Sison, O. T., Cruz, J. R. B., Ang, C. F., Buensalido, J. A. L., Cañal, J. P. A., Castillo-Carandang, N. T., Cordero, C. P., Gaviola, D. M. G., Ladia, M. A. J., Mantaring, J. B. V., Mendoza, M. T., Salamat, M. S. S., Lam, H. Y., Tadolini, M., . . . Philippine NTPS 2016 Group. (2021). High TB burden and low notification rates in the Philippines: The 2016 national TB prevalence survey. PLOS ONE, 16(6), e0252240. https://doi.org/10.1371/journal.pone.0252240
Lönnroth, K., Jaramillo, E., Williams, B. G., Dye, C., & Raviglione, M. (2009). Drivers of tuberculosis epidemics: The role of risk factors and social determinants. Social Science & Medicine, 68(12), 2240-2246. https://doi.org/10.1016/j.socscimed.2009.03.041
Williams, P. M., Pratt, R. H., Walker, W. L., Price, S. F., Stewart, R. J., & Feng, P. I. (2024). Tuberculosis: United States, 2023. MMWR Morbidity and Mortality Weekly Report, 73(12), 265-270. https://doi.org/10.15585/mmwr.mm7312a4
World Health Organization. (2025). Global tuberculosis report 2025. https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports
MPH 504 Week 3 instructions, in plain terms
In week three you analyze the burden of one health condition in one country or region, using recognized global data sources. Choose the measures you will use and explain why, since incidence, prevalence, mortality and disability-adjusted life years each answer different questions. Compare the setting with at least one reference setting so the numbers have meaning. The analysis should describe the main determinants behind the pattern, including social and economic conditions, not only biology. It should also address the quality of the data, because global estimates are often modeled and revised. Finish by stating what the burden implies for public health action. Use tables where they help and cite the data source for every figure you report.
How this MPH 504 Week 3 example is built
This sample compares the Philippines with Vietnam, a regional neighbor at a similar income level, and with the United States, where the writer's patients now live. It defines four measures before showing any figures, then sets out 2024 estimates in a single table: incidence of 625 per 100,000 in the Philippines against 182 and 3.2, with mortality and case detection beside them. The comparison notes that a higher case detection figure in the Philippines may reflect a larger denominator rather than better finding. A data-quality section treats the uncertainty interval and year-to-year revisions as part of the argument, and the conclusion points to contact screening with chest radiography because most confirmed cases in the prevalence survey had no symptoms that would prompt a test.
Where the points sit in the MPH 504 Week 3 rubric
The rubric for this analysis rewards accurate data, sound comparison and an honest account of what the numbers can show. Analyses that report one statistic and stop lose points, as do those that list several measures without explaining what each adds. A frequent weakness is comparing two countries without saying why those settings were chosen. Another is treating modeled estimates as exact counts, which the week's material warns against. Students also lose marks when determinants are reduced to lifestyle choices. Strong work defines its measures, compares meaningfully, explains the determinants in social terms, discusses uncertainty and revisions, cites every figure and ends with implications that follow from specific findings rather than from the size of the problem.
MPH 504 Week 3 help from the desk
Global data can be confusing, especially when sources give different numbers for the same year. If you tell us the condition and the country you have chosen, we can help you find the right data source, select measures that answer your question and pick a reference setting that makes the comparison useful. We can also help you read uncertainty intervals and explain revisions without losing the reader. If your instructor named a source such as the Global Burden of Disease, we will work from it. Your choice of condition and setting, and every conclusion in the paper, remain yours, and we can build a custom sample around them in time for your deadline.
Get a MPH-504 Week 3 example written to your instructions
Send the Week 3 instructions with the settings and data sources your section requires, and a custom example is written to that specification and returned inside 24 to 48 hours. The first one is free. Which two settings you set against each other stays your decision.
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MPH 504 Week 3 questions, answered
Which burden measures should I use?
Whatever your section names, and at least one that captures something other than death where the choice is open. Conditions that disable for decades look trivial in mortality data and enormous in measures accounting for years lived with disability. Naming why you chose each measure is usually worth marks in itself.
How do I handle unreliable data?
As part of the analysis rather than as an apology. Say what the surveillance system in that setting can and cannot capture, and then say whether you think the figure understates the truth and by roughly how much. A paper that reasons about data quality reads as informed; one that reports a caveat and then uses the number anyway does not.
Is it acceptable to compare a low income and a high income setting?
Yes, and it is often what the assignment wants, provided the comparison is made carefully. State what differs beyond the condition itself, since income, age structure, surveillance and health system capacity all shape what gets counted. Comparisons that treat two settings as though only the disease differed are the ones marked down.
Where can I find a free MPH 504 Week 3 sample paper?
You can read a full MPH 504 Week 3 tuberculosis burden analysis, comparing the Philippines with Vietnam and the United States, on this page.
Why does the MPH 504 Week 3 sample treat data quality as a finding?
Because the Philippines' incidence interval runs from 344 to 1,020 per 100,000 and estimates are revised between reports, which limits what the comparison can claim.