binary indicator of an Emergency Admission in the following year and so is the primary outcome or dependent variable

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

The aim of the final assignment is to write a report of your analyses in the form of a BMJ paper with an introduction, methods, results and discussion (see http://bmj.bmjjournals.com for details of format; n.b you can leave out the box which contains ‘What is already known’ and ‘What does this study add’.) Carry out appropriate analyses on the emergency admissions dataset (n ~112,000 ) to create a prediction model for emergency admissions and complete a report. Be selective for tables and figures as generally there should not be more than 6 in a paper (e.g. 4 tables and 2 figures or 5 tables and 1 figure, etc.). You can put additional tables, if necessary, in appendices. In the introduction, I do not expect a full literature review of CPRs for predicting Emergency Admissions. Maximum word length is 3,000. The final assignment accounts for 60% of the module marks. The deadline for the final report is 29th November. Description of SPSS File – EADATA.sav The SPSS file contains data on approximately 112,000 individuals from around Scotland. The data variables are split into three main groupings:

1) Demographic data such as age, smoking status, gender, SIMD, etc.

2) Type of prescriptions used in the previous three years e.g. BNF_1.3 is chapter 1.3 of the British National Formulary which covers ulcerhealing drugs. These are coded as Binary (1 = Yes, 0 = No). Each BNF category also has a CBNF… which stands for the Count or number of these drugs in the previous 3 years.

3) Emergency Admissions data. POST_EM_ADM is binary indicator of an Emergency Admission in the following year and so is the primary outcome or dependent variable. PRE_EM_ADM is also binary and is an indicator of a previous emergency admission. LOS stands for length of stay and is split into all admissions and emergency admissions as well as pre- and post. 

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