| Course | HS820 Global and National Health and Health Systems |
|---|---|
| Unit | Unit 5 |
| Paper type | Burden of disease review |
| Length | About 980 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | Doctor of Health Science (DHSc) |
| Updated | October 2026 |
Free sample paper for HS820 Unit 5
One Country, Two Epidemiological Decades: A Unit 5 Burden of Disease Review Comparing Kerala and Uttar Pradesh
[Student Name]
Purdue University Global
HS820: Global and National Health and Health Systems
Unit 5 Burden of Disease Review
[Instructor Name]
[Date]
The patterns described draw on published Global Burden of Disease work; figures are rounded and labeled as approximate.
Measures
The review uses disability-adjusted life years, or DALYs, the main measure of the Global Burden of Disease study. One DALY is one year of healthy life lost. Each DALY total combines two parts: the years a population loses because people die before a reference life expectancy, and the time people spend living with illness, scaled down by a weight that reflects how severe the condition is. Rates are expressed per 100,000 people and age-standardized so that states with different age structures can be compared. Estimates come from the state-level analyses of the Global Burden of Disease study, accessed through the study's results tool (Institute for Health Metrics and Evaluation [IHME], 2024). Every estimate carries an uncertainty interval, and the review reports differences only where intervals do not overlap substantially.
Where Each State Stands
Kerala: life expectancy is the highest among large Indian states. The leading causes of DALYs are non-communicable diseases: ischemic heart disease, diabetes, stroke, chronic obstructive pulmonary disease and musculoskeletal disorders. Communicable, maternal, neonatal and nutritional conditions account for a small share of burden. Kerala's profile resembles that of many middle-income countries further along the epidemiological transition.
Uttar Pradesh: India's most populous state carries a much higher overall DALY rate. Neonatal disorders, diarrheal disease, lower respiratory infections and tuberculosis remain among the leading causes, alongside a rising burden from ischemic heart disease and stroke. Communicable, maternal, neonatal and nutritional conditions account for a far larger share than in Kerala. The state faces a double burden: old infectious and maternal problems not yet controlled, and new chronic disease growing.
Disability Versus Death
The two components tell different stories. In Kerala, years lived with disability make up a larger share of total burden, because people live longer with chronic conditions such as diabetes and musculoskeletal pain. In Uttar Pradesh, years of life lost to early death dominate, especially among infants and young children. A comparison that used deaths alone would understate Kerala's chronic disease burden and miss the disability that shapes demand for long-term care.
Kerala's challenge is keeping people well as they live longer; Uttar Pradesh's is keeping children alive while chronic disease arrives anyway.
Explaining the Gap
The review considers three explanations, each supported by evidence beyond the burden data.
First, primary care and public health capacity. Kerala has long invested in a dense network of primary health centers, high rates of institutional delivery and strong immunization coverage. Uttar Pradesh has had fewer facilities and health workers per person and lower coverage of maternal and child services.
Second, social determinants. Kerala's high female literacy and longstanding investment in education and social welfare are associated with better health across many countries and contexts. Uttar Pradesh has lower literacy and higher poverty.
Third, the timing of the epidemiological transition. Kerala reduced infant mortality and fertility earlier, so its population aged earlier and its burden shifted toward chronic disease. Uttar Pradesh is at an earlier stage.
None of these explanations can be proven from burden estimates alone. The review treats them as hypotheses consistent with the data and with comparative health systems research, which emphasizes that system performance depends on financing, delivery and stewardship together (World Health Organization [WHO], 2000).
Implications for Health Systems
The burden profiles point to different priorities. Kerala's system needs to manage chronic disease: screening, continuity of care, medicines and care for disability. Uttar Pradesh needs to finish the work of controlling infectious, maternal and neonatal conditions while building capacity for chronic disease before it overwhelms the system. A single national program applied uniformly would fit neither state well.
Financing arrangements shape whether those priorities can be met. Most health spending in both states still comes from households paying at the point of care, and public money is raised and spent largely through state budgets, so a state with a weaker tax base, like Uttar Pradesh, has less to pool for the services its burden demands. Analyzing how each state collects, pools and purchases care would show whether money follows burden or simply follows revenue (Kutzin, 2001). National insurance programs for hospital care have begun to add a national pool, but they cover inpatient stays far more than the primary care and chronic disease management that both states need.
How the Profiles Would Change a Budget
If a health planner in each state were given the same extra sum, the burden profiles suggest different first uses. In Kerala, money would go furthest in hypertension and diabetes control through primary health centers, with supplies of medicines and follow-up visits, because those conditions drive both early death and disability. In Uttar Pradesh, money would go furthest in skilled birth attendance, newborn care, safe water and sanitation, and tuberculosis case finding, because those causes kill young people and remove the most years of life per case. A budget built on the national average burden would underfund both.
Uncertainty and Data Limits
State-level estimates rest on data of uneven quality. Vital registration is more complete in Kerala than in Uttar Pradesh, so estimates for Uttar Pradesh rely more on modeling and carry wider uncertainty. Differences in disability estimates are especially sensitive to assumptions about prevalence and severity. The review therefore emphasizes large, consistent differences rather than small ones.
Why Compare States Rather Than Countries
Comparing states within one country holds constant national policy, currency and many national programs, which makes the differences in burden harder to attribute to national factors. It shows that national averages for large countries can hide variation as large as that between countries.
References
Institute for Health Metrics and Evaluation. (2024). GBD results tool. University of Washington.
Kutzin, J. (2001). A descriptive framework for country-level analysis of health care financing arrangements. Health Policy, 56(3), 171-204. https://doi.org/10.1016/S0168-8510(00)00149-4
World Health Organization. (2000). The world health report 2000: Health systems: Improving performance. World Health Organization.
The Purdue Global HS820 Unit 5 instructions
The fifth unit generally calls for a review of burden of disease data for two populations, with an explanation of differences. Expect a structured paper of several pages. Define your measures first, especially DALYs and their two components, and explain age standardization. Use the Global Burden of Disease results tool or a published state or country analysis, and report uncertainty intervals. Compare leading causes and the split between early death and disability. Offer explanations as hypotheses supported by other evidence, such as primary care capacity or literacy, rather than as proven causes. Link burden to health system priorities and financing. Note data quality limits, since estimates for places with weak vital registration rely more on modeling.
How this HS820 Unit 5 example is built
The sample opens with a measures section defining DALYs, age standardization and uncertainty, citing the IHME results tool. It then profiles each state's leading causes and the share of communicable, maternal, neonatal and nutritional conditions. A section on disability versus death shows why counting deaths alone would hide Kerala's chronic burden. The highlighted sentence contrasts keeping people well as they age with keeping children alive. Three explanations follow, primary care capacity, social determinants and the timing of the transition, framed as hypotheses with a WHO citation. Later sections cover implications, financing through Kutzin's functions, how an equal extra budget would be spent, uncertainty and why comparing states within one country is useful. Each section uses the same order for the two states, so a reader can line them up quickly.
Where the points sit in the HS820 Unit 5 rubric
Burden of disease reviews in this course are usually marked on correct use of measures, accuracy of data, quality of explanation, links to health systems, sources and writing. Explanation and health system links tend to weigh heaviest. Markers confirm that DALYs are defined correctly and that both components are used. Data must come from a named source with year and uncertainty. Explanations earn credit when they are supported by evidence beyond the burden figures and labeled as hypotheses. Reviews that list causes without interpreting them, or treat modeled estimates as exact, lose marks. Discussion of data quality is often rewarded. The IHME tool needs its own reference entry, like every other source.
HS820 Unit 5 help from the desk
Learners frequently confuse DALYs with QALYs, or use deaths alone. Others list leading causes for each population with no explanation of the gap. Some state causes as proven when the burden data cannot show them. Uncertainty intervals are often left out. If your populations are different, perhaps two countries in one region or two U.S. states, send the prompt, and we can pull the estimates and draft a review that defines its measures, compares components and explains the differences carefully. A further weakness is reporting rates that are not age-standardized, which makes an older state look sicker than it is. Some reviews also skip the link to financing, leaving the health system half of the course untouched.
Get an HS820 Unit 5 example written to your instructions
Say which population your Unit 5 review covers and whether the prompt specifies GBD or another source, then add the rubric. The review will define its metrics exactly, report burden for a named year with uncertainty, and explain it through structure while hedging every association. Expect delivery in 24-48h. The first custom sample carries no charge. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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HS820 Unit 5 questions, answered
What is the difference between a DALY and a QALY?
A DALY counts healthy life lost, summing years lost to early death and years lived with disability, and is used to measure population burden. A QALY counts healthy life gained or lived, and is used mainly in cost-effectiveness analysis of treatments. The two are built differently and should not be substituted for each other. A burden review uses DALYs.
Where can I get GBD estimates?
IHME's GBD Results tool and its GBD Compare visualization provide estimates by country, and for some countries by state or region, cause, age and sex. Record which GBD round you use, since each release revises past figures. For India, the state-level initiative's publications add context and methods. Cite the round and the date you accessed it.
Can the review compare countries instead of states?
Yes, and many prompts ask for that. The structure holds: define the metric, present burden for a named year with uncertainty, explain differences through structure, and discuss data limits. Comparing regions within one country has an advantage worth noting, since national policies are held roughly constant, which makes structural explanations easier to argue.
Where can I find a free HS820 Unit 5 sample paper?
The full Unit 5 review is here, comparing Kerala and Uttar Pradesh with GBD estimates, defined measures and three explanations.
Why separate years lost to early death from years lived with disability?
Because they point to different priorities; Kerala's burden leans toward disability from chronic illness, Uttar Pradesh's toward early death among children.