Assessing Data Quality Assignment Answer

Assignment questions 

Part 1: Data cleaning, documentation and data dictionary update 65% 

Explore the data and decide on the approach to clean GP and ED datasets. For this assignment, you should  concentrate on within-dataset cleaning. You are not required to cross-check inconsistencies between the two  datasets, so no need to merge them together for this assignment.  

For Part 1, you are required to:

1. Present your work in cleaning GP data in the following forms (25 marks):

• A written document explaining the process of GP data exploration, data cleaning, decisions  made, and results of your analyses (10 marks); 
• A flowchart to graphically present procedures taken for cleaning GP data (5 marks); and SAS code showing analysis for GP data exploration, data cleaning and annotations (10 marks).

2. Present your work in cleaning ED data in the following forms (20 marks):

• A written document explaining the process of ED data exploration, data cleaning, decisions  made, and results of your analyses (8 mark);  
• A flowchart to graphically present procedures taken for cleaning ED data (5 marks); and  SAS code showing analysis for ED data exploration, data cleaning and annotations (7 marks). 

3. Create new variables in the GP data (see definitions in Table 1) (10 marks):

• Create variable smoke_status_GP to indicate a person’s smoking status. Describe/justify your  decision in the written document (6 marks); 
• Create variable risky_alcohol_GP to classify health risk alcohol consumption (1 mark); Calculate BMI score (variable BMI_GP) (1 mark); 
• Create variable obese_GP to indicate whether a person is obese (1 mark); and Create variable highBP_GP to indicate whether a person has high blood pressure (1 mark). 

Table 1: New health risk factors variables, values and definitions (in people aged 18 years and over) 

New variable Values, definition, and label of values

smoke_status_GP 0=Never smoked  1=Current smoker  2=Ex-smoker

risky_alcohol_GP 0=No (≤2 drinks per day)  1=Yes (>2 drinks per day)

BMI_GP Weight/Height2 (weight divided by the square of the height; weight is  measured in kg, height is measured in meters)

obese_GP 0=No (BMI<30)  1=Yes (BMI ≥30)

highBP_GP 0=Normal blood pressure (systolic<135mmHg & diastolic<85mmHg) 1=High blood pressure (systolic≥135mmHg or diastolic≥85mmHg)

4. Update the data dictionaries. If you decide not to update a data dictionary, you should provide  reasons for not updating the data dictionary (10 marks). 

• Present an updated data dictionary for GP dataset, based on results of data cleaning step A and C above (5 marks). 
• Present an updated data dictionary for ED dataset, based on results of data cleaning step B above (5 marks).

Part 2: Research Question 20% 

The manager of the Medical Plus GP practice is planning for health care coordination within the practice and  wants to know characteristics of their clients such as socio-demographic characteristics, lifestyle factors,  health status and others. You will be helping the practice manager to analyse the GP dataset and report on  patient characteristics. Both practice manager and you have agreed that the analysis is based on a cleaned  GP dataset (i.e. the dataset you cleaned in Part 1) and the report (in Word or PDF) is reproducible. 

For Part 2, you are required to analyse the dataset that you cleaned in Part 1 and: 

1. In the report, present results of your analysis in tabular format (7 marks).  Your table(s) should be presented in an academic format similar to what would be found in the  results section of a published journal article. You can present more than one table. 
2. In the report, provide written interpretation of the results (8 marks).  

Your written interpretation of results should be presented in academic writing styles. 

1. Include SAS code that generates the results that you report above (5 marks).  Your SAS code should be well annotated and functioning. 

Part 3: Data linkage 15% 

No data analysis is required to answer Part 3 questions. 

Medical Plus GP Manager wishes to link their GP dataset to the Registry of Births, Deaths and Marriages  (RBDM) deaths and PBS data to examine medication compliance among their patients. As the data  custodian, the Medical Plus GP has access to patient identification information (names, addresses, dates of  birth and Medicare number). RBDM data custodian has access to the identification information (names,  addresses, and dates of birth). PBS data custodian has access to the Medicare number only. A research  institute will be contracted to analyse linked data. 

For this assignment, we can assume that patients have given consent for data linkage and ethics approval  has been granted. The linkage will be carried out by the Centre for Health Record Linkage (CHeReL). 

1. What data linkage strategies will CHeReL use to link the GP dataset to RBDM deaths and PBS data? Justify your decision (5 marks).
2. B. Draw a diagram depicting variable exchange information between data custodians (GP, PBS and RBDM data), CHeReL and analyst to depict information interchange for data linkage and analysis purpose. Justify this data exchange process (10 marks).

GP data dictionary 

Variable	Description	Variable type	Format name	Allowable entries
ID	Unique person ID	Number
GP_last	Date of most recent GP visit	Date	DDMMYY10.	Dates in the range   01/01/2014 – 31/12/2014
age	Age of patient at the most recent GP  visit in 2014	Number
sex	Gender of the patient	Character		1=male   2=female
cob	In what country were you born?	Number	cobf.	1= Born in Australia   2= Born overseas
healthcare_card	Do you have a healthcare card1?	Number	ynf.	1= Yes   0= No
ever_smoked	Have you ever been a regular  smoker?	Number	ynf.	1= Yes   0= No
smoke_now	Are you a regular smoker now?	Number	ynf.	1= Yes   0= No
age_start	How old were you when you started  smoking regularly?	Number		Invalid if <10 or >105
age_stop	How old were you when you stopped  smoking? Or when did you stop  smoking?	Number		Invalid if <10 or >105
drinks_day	About how many alcoholic drinks do  you drink per day?	Number		Invalid if >20
height	How tall are you without shoes?  (meters)	Number		Invalid if <0.55m or >2.40m
weight	About how much do you weigh?  (kilograms)	Number		Invalid if <5.0kg or >270kg
adverse_reaction	Have you had any adverse reaction  to any medication?	Number	ynf.	1= Yes   0= No
syst_bp	Systolic blood pressure (mmHg)	Number
diast_bp	Diastolic blood pressure (mmHg)	Number
reason	Reason for the most recent GP visit	Character		HEADACHE   NAUSEA   TINNITUS   VOMITING   ITCHING   ABDOMINAL PAIN  DIZZINESS   SKIN RASH   PALPITATIONS   HALLUCINATIONS

1 Australian residents may be eligible to have a Health Care Card if they receive financial support from the government. Benefits include a lower fee  for prescription medicines under the Pharmaceutical Benefits Scheme, higher refunds for medical expenses through the Medicare Safety Net, and  some other social concession.

ED data dictionary

Variable	Description	Variable type	Format name	Allowable entries
ID	Unique person ID	Number
ed_admission	Date of ED presentation	Date	DDMMYY10.	Dates in the range   01/01/2014 – 31/12/2014
ed_separation	Date of ED separation	Date	DDMMYY10.	Dates in the range   01/01/2014 – 31/12/2014
age_ed	Age of patient at ED   presentation	Number
sex_ed	Gender of the patient	Number	sexf.	1=male   2=female
cob_ed	In what country were you born?	Number	cobf.	1= Born in Australia   2= Born overseas
interpreter	An interpreter is needed?	Number	ynf.	1= Yes   0= No
health_insurance	Do you have private health  insurance?	Number	ynf.	1= Yes   0= No
triage_category	Urgency of presentation	Number	triagef.	1 = Resuscitation   2 = Emergency   3 = Urgent   4 = Semi urgent   5 = Non urgent
dx1	Principal presenting diagnosis  (ICD-10-AM codes)	Character		International Statistical   Classification of Diseases and  Related Health Problems, Tenth  Revision, Australian   Modification 8th edition
dx2-dx5	Up to 4 additional diagnoses  (ICD-10-AM codes)	Character		International Statistical   Classification of Diseases and  Related Health Problems, Tenth  Revision, Australian   Modification 8th edition
separation_mode	Status of the person at   separation from emergency  department	Number	sepmodef.	1 = Admitted to hospital   2 = Departed ED   3 = Died in ED   4 = Dead on arrival

Marking rubric for quality of Assignment 2A 

The grading rubric is outlined below. 

Criteria	HD  85 – 100	D  75 – 84	Credit  65 – 74	Pass  50 – 64	Fail  Less than 50
Written   documentation of  reproducible   notes – data   dictionary,   cleaning notes,   flowcharts   Question answers	Full logic shown  with elaborate and  very coherent   descriptions which  goes beyond   replication  All correct   answers, full   working shown,  with in-depth   reflection and   suggestions	Full logic shown  with coherent   description  All correct   answers, full   working shown,  with some   critical reflection	Most logic shown  with mainly   adequate detail   Mostly correct   answers, with   some   misinterpretation,  working shown	Some   documentation  presented but   logic not clear  Incomplete   answers,   misinterpreted  results in most  parts.	Not   presented  No answer

SAS code Fully functioning and reproducible code with extensive and consistent  annotation and commenting throughout Fully functioning and reproducible code with  moderate but  consistent  annotation Functioning code with some  annotation Code with some  errors, lacking  annotation. Code with  errors in  most parts,  or no code  provided.

The scoring rubric for quality of Assignment 2A is outlined below. 

Assessment of written document* and SAS code**	Max score	Your mark
Part 1. Documentation of data cleaning decision
1.A – GP dataset exploration and cleaning of (25 marks)
A written document* explaining results of exploration, decisions made, and  results of GP data cleaning	10
Flow chart for GP data cleaning	5
SAS code showing work for GP data exploration, decisions made and  producing results of cleaning	10
1.B – ED dataset exploration and cleaning (20 marks)
A written document* explaining results of exploration, decisions made, and  results of ED data cleaning	8
Flow chart for ED data cleaning	5
SAS code showing work for ED data exploration, decisions made and  producing results of cleaning	7
1.C – SAS code to create 4 new variables (10 marks)
Smoke_status_GP (including documented justification)	6
Risky_alcohol_GP	1
BMI_GP	1
Obese_GP	1
HighBP_GP	1
1.D – Update data dictionaries (10 marks)
Updated data dictionary for GP dataset	5
Updated data dictionary for ED dataset	5
Part 2. Analyse cleaned GP data and report (20 marks)
Tabular presentation of results	7
Written interpretation of results*	8
SAS code to generate results	5
Part 3. Data linkage (15 marks)
Data linkage strategies	5
Diagram depicting variable exchange information	10
TOTAL MARK	100

*: Assessment of written document includes the use of academic and linguistic conventions, coherent and succinct  discussion with supporting information presented.  

**: Assessment of SAS code include i) correct use of common SAS commands introduced in the course, ii) effective  creation of variables that takes into account variable type and values, and iii) clear annotation that enables  reproducibility.

HDAT9400 Management and Curation of Health Data 

Management and Curation of Australian Health Data 

Assignment 2A -Group work 

Please grade (Excellent to No contribution) and provide a score (0 to 20) for each other member of  your group for their contribution to group process. If a peer made absolutely no contribution to the  group, please score zero. 

	Contribution grading						Score  (0 to 20)	Comment
	t n e l l e c x E	d o o g   y r e V	y r o t c a f s i t a S	r i a F	r o o P	n o i t    u o b i N r t n o c
1. Insert Peer’s name
2. Insert Peer’s name
3. Insert Peer’s name
4. Insert Peer’s name

Group check list (not for submission, just for your information) 

For group tasks and assignments, it is in everybody’s interest to ensure an effective contribution  from all group members, to make sure that the finished assignment is of high quality. For this  assignment, peer assessment is used to determine the relative contributions of everyone to the  group process and this will be used to moderate the marks for the assignment. 

The check list below is recommended to guide peer assessment and to monitor and improve  effective group process. Please refer to the UNSW Guide https://student.unsw.edu.au/groupwork for the value of work group and principles for effective group work. 

Check list	Myself	Peer	Comments
Effectively clarifying task or objective at each stage?
Checking on progress?
Clarifying and recording what the group decides?
Clarifying who is going to do what?
Clarifying when each task is to be done by?
Establishing procedures for handling meetings?
Keeping to agreed procedures?
Listening to each other?
Dominating / Allowing some members to dominate?
Withdrawing / Allowing some members to withdraw?
Compromising individuals wants for the sake of the team?
Recognising the feelings of other members?
Contributing equally to team progress?
Following agreed procedures for writing and file naming?

Peer assessment to moderate individual final mark 

Individual contribution score (0 to 20) will be calculated as an average of scores given by peers. If  individual contribution score is greater than zero, the final mark of an individual is the sum of  scores for assignment quality and individual contribution. If individual contribution score is zero (i.e.  no contribution was made), the final mark for that particular individual is zero. Table below  illustrates how final mark will be moderated based on quality of group work and individual  contribution.

Group member	Quality of group work (marks out of 80)	Individual   contribution score (marks out of 20)	Final mark  (marks out of 100)
Member 1	75	18	93
Member 2	75	15	90
Member 3	75	5	80
Member 4	75	0 (zero)	0 (zero)