Assignment: Mortality risk in patients admitted to hospital with seasonal influenza ACTL3141/ACTL5104, T1 2025

Assignment: Mortality risk in patients admitted to hospital with
seasonal influenza
ACTL3141/ACTL5104, T1 2025
Due time: Friday 11th April 2025 11.55 am (sharp)
1 Skills developed
This assignment allows you to apply survival analysis techniques learned in the course to a real-world actuarial
problem: assessing mortality risk in hospitalised patients with influenza and COVID-19.
You will develop the ability to:
• Conduct descriptive statistical analysis on real-world health data.
• Apply Kaplan-Meier estimators and Cox proportional hazards models to assess mortality risk.
• Compare disease mortality rates using survival modelling techniques.
• Critically evaluate the ethical implications of health insurance pricing based on emerging treatments.
This assignment aligns with the UNSW Business School Program Goals, particularly:
• “5. Responsible Business Practice” – Evaluating the ethical considerations in actuarial decision-making.
• “3. Business Communication” – Communicating technical results in a clear and professional manner.
2 Background
The COVID-19 pandemic, caused by SARS-CoV-2, created a significant global health challenge. As of March
9, 2025, the World Health Organisation (WHO) reported 777,519,152 confirmed cases and 7,090,776 deaths
(WHO Coronavirus (COVID-19) Dashboard).
Seasonal influenza, another acute respiratory infection, has been studied extensively. While both diseases
share similarities in transmission and symptoms, COVID-19 has shown a higher mortality risk, especially
among individuals with pre-existing conditions such as obesity and dementia (WHO Fact Sheet on Influenza).
Understanding the mortality risk of hospitalised patients with these illnesses is essential for insurance compa-
nies, policymakers, and healthcare providers. From an actuarial perspective, assessing differences in survival
probabilities, risk factors, and long-term health consequences informs underwriting and pricing strategies in
private health insurance.
Some policymakers, media commentators, and public figures have argued that COVID-19 is “just another
flu,” with former Brazilian President Jair Bolsonaro famously referring to it as “a little flu” (Euronews, 2020)
suggesting that the response to the pandemic was excessive. This claim has influenced public perception
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and, in some cases, insurance underwriting strategies. This assignment will explore whether this claim is
justified by empirical evidence, comparing mortality rates and risk factors between COVID-19 and seasonal
influenza.
3 Task
You work as an actuary for an international insurance company with business operations in Brazil. Your role
is to assess the mortality and health risks within the company’s private health insurance portfolio.
Your boss has provided two datasets from the Brazilian Ministry of Health Database (SIVEP-Gripe),
which contain demographic details, pre-existing comorbidities, vaccination status, and outcomes of hospi-
talised patients with seasonal influenza and COVID-19.
To support decision-making, your boss has asked you to analyse these datasets and prepare a short report
summarising your findings. Specifically, your report should address the following:
1. Descriptive analysis of hospitalised patient profiles: Summarise key characteristics of patients
admitted with influenza and COVID-19, including age distribution, comorbidities, and vaccination
rates. Identify and compare trends between influenza and COVID-19 patients in terms of risk factors.
2. Survival analysis of influenza patients: Apply Kaplan-Meier survival curves and Cox proportional
hazards models to analyse mortality risk among influenza patients. Examine how age, race, vaccination
status, and comorbidities (particularly obesity) influence survival outcomes. Report and interpret
hazard ratios for key risk factors, discussing their potential relevance to pricing and underwriting
strategies.
3. Evaluating the claim that “COVID-19 is just another flu”: Some media and public figures
have suggested that COVID-19 is no more dangerous than seasonal influenza, influencing public per-
ception and underwriting strategies. Using the available data, assess whether this claim is supported
by statistical comparisons of mortality rates, survival probabilities, and key risk factors
between COVID-19 and influenza patients.
4. Ethical implications of obesity drug use in insurance pricing: New drugs such as Ozempic
are gaining popularity for obesity treatment. While some insurers, such asMunich Re, highlight their
potential to improve health outcomes (Munich Re, 2025), others express concerns over unexpected
increases in claims (ABC News, 2024). Your company is considering whether the use of such med-
ications should be factored in pricing and underwriting for health insurance. Discuss the ethical
and actuarial implications of using obesity medication uptake as a rating factor, considering:
• Should insurers incentivise obesity drug use through lower premiums?
• What ethical concerns arise from differentiating premiums based on medication use?
See more details on the tasks below.
4 Additional information and mark allocation
4.1 Data
For this assignment, you have access to a sample of hospitalised COVID-19 and influenza patients from
the Brazilian Ministry of Health Database (SIVEP-Gripe)1. You will work with two pre-processed
datasets: “dataBrasilInfluenza.csv” and “dataBrasilCovid.csv”.
1Data accessed on March 12th, 2025. (Source)
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The “dataBrasilInfluenza.csv” dataset contains information on 24,440 individuals who were admitted
to hospital due to seasonal influenza between 2015 and 2022. It includes details on pre-existing
comorbidities, obesity, and vaccination status, among other variables. The dataset structure is as
follows:
Variable Description
year: Year of hospitalisation.
dateBirth: Date of birth of the patient.
dateHosp: Date in which the patient was hospitalised.
dateEndObs: Date of the end of observation. This can be either the date of death or the date
the patient was discharged from hospital.
death: TRUE if the patient died from influenza or FALSE if the patient recovered from
influenza and was discharged.
race: race of the patient in Portuguese. It can take values “Amarela”,
“Branca”, “Indígena”, “Parda”, or “Preta”, corresponding to “Asian”,
“White”, “Indigenous”, “Mixed-race”, or “Black”.
cardio: TRUE if the patient had a cardiovascular disease or FALSE otherwise.
pneumopathy: TRUE if the patient had a pneumopathy or FALSE otherwise.
immuno: TRUE if the patient had any immunodeficiencies or FALSE otherwise.
renal: TRUE if the patient had any renal diseases or FALSE otherwise.
obesity: TRUE if the patient had obesity or FALSE otherwise.
covidVaccine: TRUE if the patient had received at least one dose of COVID-19 vaccine or FALSE
otherwise. This variable has some missing values.
influenzaVaccine: TRUE if the patient had received at least one dose of influenza vaccine or FALSE
otherwise. This variable has some missing values.
The “dataBrasilCovid.csv” dataset contains information on 52,062 individuals who were admitted to
hospital due to COVID-19 in 2022.
This dataset has the same variables as “dataBrasilInfluenza.csv”, with one key difference: The death
variable is TRUE if the patient died from COVID-19, and FALSE if the patient recovered and was
discharged.
4.2 Analysis, Modelling, and Discussion [85 Marks]
The mark allocation for the assignment can be found in the attached rubric. Refer to the details below
for a breakdown of each task.
There is extensive research on the risk of death from COVID-19 and seasonal influenza, so you may
wish to incorporate additional sources beyond your own analysis. While extra research is encouraged and can
help strengthen your arguments, it is not required to achieve full marks. The assignment is designed
so that full marks can be attained based on your analysis alone.
4.2.1 Descriptive analysis of hospitalised influenza and COVID-19 patients [15 Marks]
In this section, you should compute summary statistics for hospitalised patients, such as age distribu-
tion, pre-existing comorbidities, and vaccination status. You may also include other relevant metrics
of your choice.
Your summary statistics should be accompanied by a discussion of key insights derived from the data,
highlighting any significant trends or differences between influenza and COVID-19 patients.
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4.2.2 Survival analysis of influenza patients [30 Marks]
For this section, focus on the risk of death among hospitalised influenza patients using the
“dataBrasilInfluenza.csv” dataset.
Your survival analysis should include:
• A discussion of the modelling techniques used and justification for selecting them.
• The application of appropriate survival analysis methods (e.g., Kaplan-Meier estimators, Cox
proportional hazards models).
• A detailed interpretation of your results and insights, explaining how they contribute to understand-
ing mortality risk.
All technical details, model outputs, and additional statistical tests should be included in the
technical appendix, while key results and interpretations should be presented in the main report.
4.2.3 Is COVID-19 just another flu? [20 Marks]
In this section, use the “dataBrasilCovid.csv” dataset to compare mortality risk between hospitalised
COVID-19 and influenza patients.
• Select and apply appropriate statistical methods to compare the mortality risk between the two
diseases.
• Provide a discussion of key findings, highlighting any significant differences in survival probabilities,
mortality rates, or risk factors.
• Your analysis should be accompanied by insights drawn from both your data analysis and, if
applicable, relevant literature.
4.2.4 Ethical implications of using obesity medication uptake as a rating factor [20 Marks]
In this section, discuss whether insurers should use the uptake of new obesity medications as a rating
or underwriting criterion in private health insurance policies.
Your discussion should:
1. Present the pros and cons of using obesity drug uptake as a rating factor.
2. Formulate a recommendation based on your analysis.
To help complete this task, refer to weeks 4 and 5 of the course, which focus on ethical perspectives
in actuarial work.
For the purpose of this discussion, assume that there are no regulatory restrictions on rating factors
for private health insurance in the insurer’s operating environment.
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4.3 Presentation Format and Communcation [15 Marks]
Communication of quantitative results in a concise and easy-to-read manner is a skill that is vital in practice.
As such, marks will be given for the presentation of your results. In order to maximise your marks for
presentation you may wish to consider issues such as: table size/readability, figure axis/formatting, ease
of reading, grammar/spelling, and report structure. You may also wish to consider the use of executive
summaries and appendixes, where appropriate. Provide sufficient details in the main body of the reader so
that they can judge what you are doing, using appendices for non-essential but useful results as necessary.
Note that sufficient detail must be provided (in either the report body and/or appendices) so that the
reviewer can follow all the steps and derivations required in your work.
Note that a maximum page limit of 6 pages (including tables and graphs but excluding references)
is applicable to the main body of the report.2 You should also consider the rubric for the presentation
component. There is no limit to the size of the appendix. Furthermore your answer should satisfy the
following formatting requirements: (i) font: Times, 12 pt or equivalent size and (ii) margins: all four of at
least 2cm.
4.4 Software
You may choose which software packages to use (e.g. R, Excel or other), however, most functions you will
be required to use for this task are available in R. Note also that most of the code enabling you to perform
the calculation and analysis are in the R tutorials.
4.5 Assignment submission procedure
4.5.1 Turnitin submission
Your assignment report must be uploaded as a unique document. As long as the due date is still future,
you can resubmit your work; the previous version of your assignment will be replaced by the new version.
Assignments must be submitted via the Turnitin submission box that is available on the course Moodle
website. Turnitin reports on any similarities between their own cohort’s assignments, and also with regard
to other sources (such as the internet or all assignments submitted all around the world via Turnitin). More
information is available at: [click]. Please read this page, as we will assume that you are familiar with its
content.
Please also attach any programming code and/or sample spreadsheet output used in your analysis
as a separate file in the dedicated “code only submission” and “code or excel file submission” Moodle
assignment boxes on the course webpage. These will be referred to by the marker only if needed, and in
particular the main assignment (with appendix) should be self contained.
4.5.2 Late submission
Please note that it is School policy that late submission of assignments will incur in a penalty.
When an assessment item had to be submitted by a pre-specified submission date and time and was submitted
late, the School of Risk and Actuarial Studies will apply the following policy. Late submission will incur
a penalty of 5% per day or part thereof (including weekends) from the due date and time. An assessment
will not be accepted after 5 days (120 hours) of the original deadline unless special consideration has been
approved. An assignment is considered late if the requested format, such as hard copy or electronic copy,
has not been submitted on time or where the ‘wrong’ assignment has been submitted. Students who are
2Please kindly note that this is a maximum - you should feel free to use less pages if it is sufficient!
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late must still submit their assignment via Turnitin. The Turnitin date and time of submission of the main
report determines the submission time for the purposes of calculating the penalty.
You need to check your document once it is submitted (check it on-screen). We will not mark assignments
that cannot be read on screen.
Students are reminded of the risk that technical issues may delay or even prevent their submission (such
as internet connection and/or computer breakdowns). Students should then consider either submitting
their assignment from the university computer rooms or allow enough time (at least 24 hours is
recommended) between their submission and the due time. The Turnitin module will not let you
submit a late report. No paper copy will be either accepted or graded.
4.5.3 Plagiarism awareness
Students are reminded that the work they submit must be their own. While we have no problem with
students working together on the assignment problems, the material students submit for assessment must
be their own.
Students should make sure they understand what plagiarism is—cases of plagiarism have a very high prob-
ability of being discovered. For issues of collective work, having different persons marking the assignment
does not decrease this probability.
4.5.4 Generative AI Policy
You are allowed to use Generative AI to assist with editing, planning, idea generation, or coding.
However, your use of AI must comply with UNSW’s Guidelines under the category “Assistance with
Attribution” (UNSW AI Policy).
In addition, you must include an Appendix in your report titled “Generative AI Usage”, where you:
• Explain how AI was used in completing your assignment.
• Outline any prompts you used when generating content.
If you did not use Generative AI, state explicitly in the Appendix that no AI assistance was used.
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