CHIC601 Introduction to Applied Epidemiology assignment

Project assignment covid-19 patients

CHIC601 Introduction to Applied Epidemiology

Jonathan Read

CHICAS, Lancaster Medical School, Lancaster University

jonatha.read@lancaster.ac.uk

• 1 Assignment instructions
  
  
  
  • 1 Use of generative AI
  
  
  • 2 Submission of reports
  
  
  
  


• 2 Further details of the study on which the data is based
  
  
  
  • 1 Participants
  
  
  • 2 Data collection
  
  
  • 3 Outcomes
  
  
  
  


• 3 Patient data
  
  
  
  • 1 Data fields
  
  
  
  


• 4 Additional data sources

1 Assignment instructions

You will be provided with data on covid-19 patients admitted to UK hospitals during 2020 based on data collected by the ISARIC study (described below).

The aim of this assignment is for you to demonstrate your ability to define a sensible and coherent research hypothesis, conduct an appropriate statistical test, and interpret your findings from an epidemiological context and with reference to the wider literature. You should develop a hypothesis with clinical or public health relevance. Analysis of the data with R should give consideration to the study design and collection of data, data quality and completeness. Your analytical approach should be principled, but be pragmatic the potential for confounding should be considered. You should also present the strengths and limitations of your anlaysis. The interpretation of your findings should be from epidemiological and public health perspectives, you are not expected to expand on the clinical implications of your findings.

The assignment is your chance to demonstrate your understanding of epidemiological bias, being able to apply an appropriate statistical analysis to data to address the research question, your understanding of the limitations of that data, analysis and the study design which collected the data, and your ability to correctly interpret the results in a wider clinical or public health context.

You should write up and submit your work as a short scientific report or paper, which must include the following sections:

• Abstract


• Introduction (including stated research aim)


• Methods


• Results


• Discussion (including limitations)


• Conclusion


• References


• Statement on use of generative AI

Other than these sections, there is no set style for the report and you may include further subsections. For example, the format of a BMJ research article would be suitable; an example is here (note, you wont need to include an Ethics or Data Availability statement).

You are expected to read around the subject, and to compare your findings with relevant peer-reviewed publications in the scientific and medical literature. Scientific writing demands that all statements made in a report, unless they are about the research conducted by the authors, are supported by a citation, preferably citing an article in a reputable journal. The leading journals in this area are: BMJ, Clinical Infectious Diseases, Journal of Infectious Diseases, Lancet Infectious Diseases, Nature, New England Journal of Medicine, PNAS, The Lancet, Science. Avoid, where possible, citing non-peer reviewed manuscripts (such as on medRxiv or non-journal websites), an exception would be national public health agency and government reports (e.g., from UKHSA).

There is no minimum word count (brevity and precision are virtues in scientific writing), but there is a maximum word count of 7,000, excluding figures, tables, references and any appendix. If you wish to include tables or figures in your report, there is a maximum limit of 4 figures and 4 tables in the main text you can include more in an Appendix if absolutely necessary.

1.1 Use of generative AI

In line with the Universitys guidance around use of Generative Artificial Intelligence tools in teaching, make sure you have read the Universitys statement on using AI for assessments. You must also include a statement on your use or not of generative AI at the end of your submitted report, describing explicitly for what purposes you used AI during your assignment. Using gen AI to explore or understand a topic, or to see examples of R code would be acceptable use, but must be acknowledged. Sharing of the data assigned to you, or using gen AI to conduct analysis or generate text used in your report would be considered a serious breach of academic integrity. If you did not use generative AI at all, please state so.

1.2 Submission of reports

Reports should be submitted via Moodle as a Word document or PDF, with accompanying R script file, by the deadline provided.

You should also upload a bug-free and fully commented copy of the R code you used to conduct the analysis and to produce your results. If Moodle has difficulty accepting your R file(s), dont panic, just send me them in an email.

2 Further details of the study on which the data is based

The ISARIC study is an ongoing prospective cohort study in 208 acute care hospitals in England, Scotland, and Wales.

2.1 Participants

Inclusion criteria were people of all ages who were admitted to one of 208 acute care hospitals in England, Scotland, and Wales with proven or high likelihood of infection with a pathogen of public health interest, defined as SARS-CoV-2 for this event by Public Health England. Reverse transcriptase polymerase chain reaction was the only mode of testing available during the period of study. The decision to test was at the discretion of the clinician attending the patient, and not defined by protocol. The enrolment criterion high likelihood of infection reflects that a preparedness protocol cannot assume that a diagnostic test will be available for an emergent pathogen. Site training emphasises that only patients who tested positive for covid-19 were eligible for enrolment.

National guidance was provided by Public Health England and other UK public health agencies that advised who to test based on clinical case definitions for possible covid-19. We also included patients who had been admitted for a separate condition but had tested positive for covid-19 during their hospital stay. Patients were only enrolled during their index admission.

Patients had clinical information from their routine health records uploaded into the case report form. Consent was not required for collection of depersonalised routine healthcare data for research in England and Wales. A waiver for consent was given by the Public Benefit and Privacy Panel in Scotland.

2.2 Data collection

Baseline demographic data was collected on a paper case report form, which was developed by ISARIC and WHO for use in outbreak investigations. Data were uploaded from admission, and usually before hospital episodes were complete, to a REDCap database (Research Electronic Data Capture, Vanderbilt University, US, hosted by University of Oxford, UK).

2.3 Outcomes

The main recorded outcome was within-hosptial mortality, palliative discharge, or discharge.

3 Patient data

Each student has bespoke data, sampled from the patient data, and also sampling of co-morbidity variables. Your personal data file will be in a file with naming format name_covid.csv only use the data assigned to you. All the data files are different.

The data is based on the findings of the COCIN study, a patient cohort established in January 2020 using a preprepared sleeping protocol to be used in the event of an emergence of a new infectious disease. The study aimed to characterise the clinical aspect of people infected with SARS-CoV-2 virus and diagnosed with covid-19 disease. The study population are people admitted to hospital with a diagnosis of covid-19. The study began in February 2020, shortly after covid-19 was recognised following the emergence of the new virus SARS-CoV-2. The data was primarily recorded and entered by extremely busy nursing staff. Not all data variable entry fields required a mandatory entry think about what NA might actually represent if you see a large number of missing data for a Yes/No variable.

While the provided data reflect the real patient data and the properties of the data collected by the study, it is not actual patient data. It can, however, be treated as a randomly-selected subset of all study participants, with admission on or after 14th February 2020 and before 1st June 2020. Thus, the data are a sample of patients admitted to hospital during the first wave of the covid-19 pandemic in England. Note, the data does not include patients admitted to hospitals in Northern ireland, Scotland, or Wales.

3.1 Data fields

Data fields provided are listed below. Administrative and demographic information:

• admission_date date of patients first admission


• age Age group at enrolment into study (years)


• sex Sex at birth

Co-morbidity information (note, your data may not have all of these fields):

• cardiac Chronic cardiac disease, including congenital heart disease (not hypertension)


• hypertension Hypertension (physician diagnosed)


• pulmonary Chronic pulmonary disease (not asthma)


• asthma Asthma (physician diagnosed)


• renal Chronic kidney disease


• liver_mod_severe Moderate or severe liver disease


• liver_mild Mild liver disease


• neurological Chronic neurological disorder


• malignant_neoplasm Malignant neoplasm, e. cancer


• obesity Obesity (as defined by clinical staff at enrolment)


• diabetes Diabetes Type Outcome data:

• outcome clinical outcome, categorical see below


• outcome_date the date of the outcome The categories for the outcome variable are:


• Death patient died in hospital of covid-19


• Palliative discharged patient discharged to home or palliative care facility, and expected to die


• Discharged alive patient recovered from covid-19 sufficiently to be discharged


• Transferred patient transferred to another hospital


• Remains in hospital patient still admitted and undergoing care for covid-19 on

outcome_date

• Unknown no record made regarding outcome

4 Additional data sources

For context, admission data for England (including by age group) during the time period of this data is provided in the file Covid-Publication-13-08-2020.xlsx, published by the NHS on 8th August 2020.

Community infection data and hospital admission data for the time period can be downloaded from the UKHSA Covid-19 data archive.

Population census estimates for 2021 can be accessed from the Office for National Statistics.