Promoting Rational Drug Use in the Community Data analysis.

Promoting Rational Drug Use in the Community

Data analysis

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Data analysisPromoting rational drug use in the community

Objectives: Session on Analysis

Describe in what ways quantitative and qualitative data can be processed

Describe how quantitative and qualitative data can best be analysed

Understand the differences between analysis of quantitative and qualitative data

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Why plan for data-processing and analysis? To make sure that all data needed to

answer research questions are indeed collected

To avoid collecting superfluous data To make sure you plan enough time and

resources for processing and analysis To make sure your research tools are

adequate and easily processed

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How to plan for data-processing and analysis? Review research questions and data-collection

tools Decide how you want to present data:

- qualitative: as texts- quantitative: as numbers

Make a list of variables for quantitative analysis Decide on key drug use measures/indicators Make dummy tables Decide on data-master sheets for analysis of

quantitative data Make a list of key themes for qualitative analysis

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Processing of quantitative data

Check if each questionnaire/interview form is complete

Sort data according to study populations (e.g. women – men; intervention community – control community)

Review all responses to categorical variables and refine the list of values for the categorical variable (you may need to add values you had not foreseen)

Assign codes to responses in questionnaires/interview forms

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Variables:

Are defined as characteristics of persons or objects which can take on different values

Categorical variables are expressed in words/categories

Numerical variables are expressed in numbers When planning for analysis of quantitative

data, make a list of all variables and their values

Assign codes to categorical variables

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Analysis of quantitative data

Summarise data on data master sheet Determine missing values Check data master sheet for

consistency/mistakes Calculate drug use measures/indicators Make relevant frequency distributions Fill in tables Do statistical tests to test hypothesis on

associations between variables

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Examples of drug use measures

Percentage of illness episodes not treated Percentage of illness episodes treated

with traditional medicines Percentage of illness episodes treated on

health worker advice Percentage of illness episodes treated in

self-care with medicines Percentage of fever episodes treated with

chloroquine Percentage of diarrhoea self-medicated

with antibiotics

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Examples of frequency distributions as way of presenting data:

Ten most commonly used medicines: calculated as relative percentage of total medications used

Main sources of medicines, calculated as the number of times medications are obtained from specific sources divided by total number of medications

Five most commonly used medicines for diarrhoea, expressed as percentage of total number of medications used to treat diarrhoea.

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Example of an illness master sheet

Numberrespondent

I llnessdescribe

TreatmentY/N

HWadviceY/N

Drugsused?Y/N

Trad Medused?Y/N

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Example of a medicine master sheet

Numberrespondent

Drugname

Genericcontent

I llnessfor whichit is used

Source

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Activity 1

Review the two data master-sheets in pairs

Are any data missing: if yes, how will you deal with it? Delete the record?

How can you check if mistakes have been made during data-entry?

Have mistakes been made? Is the data master-sheet well-designed? How could the data master-sheets be

improved?

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Activity 2

The data in the master-sheet allow for a comparison between men and women of types of drugs taken to the PRDUC course

Design a dummy table to present the data

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Processing of qualitative data

Expand notes/transcribe tapes everyday

Add comments on non-verbal communication

Order data by type/group of informants

Read notes/transcriptions, read again

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Qualitative analysis: an ongoing process Read your notes, reflect, reflect more Review your research questions: have they

been answered: what do you still need to ask? What unexpected issues/problems emerged? Do you have sufficient data for each question;

can you triangulate? Are there inconsistencies in data: do interviews confirm your observations or not?

Write down preliminary conclusions and queries

Go back to your informants: probe, ask them to explain and respond to your preliminary conclusions.

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Rapid qualitative analysis

Review your list of themes for qualitative analysis, read your notes and find out if new issues emerged

Make matrices to summarise the data by theme.

Check if you have data on all your research questions

Beware of generalising: your data are not representative.

Describe your study population using key demographic variables (age, marital status, etc.)

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Analysis of textual data

Make a list of codes Apply codes to texts Add codes as you go along Make analytical notes on the relation

between factors; how things work Make methodological notes:

observations on how the methods influenced the results; ideas on new questions to ask

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Coding of transcripts

Typ-fev

Cause-fev

Tx-fev

P.eff-Tx

Type of fever

Cause of fever

Treatment of fever

Perceived efficacy treatment

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Summarizing qualitative data

Matrix

Flow charts

Diagrams

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Example of an illness matrix

Type of fever

Signs and symptoms

Treatment

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Example of a treatment matrix

Perceived side-effect

Perceived effect

Type of treatment

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Example of a medicine source matrix

Perceived disadvantages

Perceived advantages

Source of medicines

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Choice oftherapy

Evaluation ofefficacy

Occurrenceof an illness

Determinationif hiyang

I f no

Perception ofcause

Example of a flow chart

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Drawing and verifying conclusions

Continuous process, based on: Summary of data Identifying trends - Identifying associations causations Consider confounding factors Validation in group and individual

discussions with informants

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Cite your informants to illustrate

Select case-histories which are typical

and illustrate findings

Use quotes to illustrate findings

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Strategies to confirm findings

Check for representativeness Check for observer bias -Use multi method Compare and contrast data Do additional research, include surveys t

o test hypothesis Get feedback from communities and key

informants

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Activity 3

Community sub-groups: Review the illness-recall data in the SSI

forms. If you had collected 20 of such illness-

recalls: how can you summarize these data in one or two data master-sheet(s)?

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Activity 3

Health institution sub-groups Review the simulated client visit

guidelines. If you had done 20 such visits, how

could you have summarized the data in a data-master-sheet?

Promoting Rational Drug Use in the Community Data analysis.

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