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# Data analysis - PowerPoint PPT Presentation

Data analysis. Module 4. Part 1 – Key Concepts Learning Objectives. Understand the definition and purpose of data analysis Define statistical and M&E key concepts in data analysis. Data Analysis. Turning raw data into useful information

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### Data analysis

Module 4

Part 1 – Key ConceptsLearning Objectives

• Understand the definition and purpose of data analysis

• Define statistical and M&E key concepts in data analysis

• Turning raw data into useful information

• Purpose is to provide answers to questions being asked at a program site or research questions

• Even the greatest amount and best quality data mean nothing if not properly analyzed—or if not analyzed at all

• Analysis does not mean using computer software package

• Analysis is looking at the data in light of the questions you need to answer:

• How would you analyze data to determine: “Is my program meeting its objectives?”

• Question: Is my program meeting its objectives?

• Analysis: Compare program targets and actual program performance to learn how far you are from target.

• Interpretation: Why you have or have not achieved the target and what this means for your program.

• Describes the sample/target population (demographic & clinic characteristics)

• Does not define causality – tells you what, not why

• Example – average number of clients seen per month

• Statistical terms

• Ratio

• Proportion

• Percentage

• Rate

• Mean

• Median

• Comparison of two numbers expressed as:

• a to b, a per b, a:b

• Used to express such comparisons as clinicians to patients or beds to clients

• Calculation a/b

• Example – In district X, there are 600 nurses and 200 clinics. What is the ratio of nurses to clinics? 600

200

= 3 nurses per clinic, a ratio of 3:1

• In Kwakaba district, there are 160 nurses and 40 clinics

• What is the nurse-to-clinic ratio?

160

40

4:1 or 4 nurses to 1 clinic

= 4

• A ratio in which all individuals in the numerator are also in the denominator.

• Used to compare part of the whole, such as proportion of all clients who are less than 15 years old

• Example: If 20 of 100 clients on treatment are less than 15 years of age, what is the proportion of young clients in the clinic?

• 20/100 = 1/5

• Example: If a clinic has 12 female clients and 8 male clients, then the proportion of male clients is 8/20, or 2/5

• 12+8 = 20

• 8/20

• Reduce this, multiple of 4 = 2/5 of clients = male

• A way to express a proportion (proportion multiplied by 100)

• Expresses a number in relation to the whole

• Example: Males comprise 2/5 of the clients, or 40% of the clients are male (0.40 x 100)

• Allows us to express a quantity relative to another quantity. Can compare different groups, facilities, countries that may have different denominators

• Measured with respect to another measured quantity during the same time period

• Used to express the frequency of specific events in a certain time period (fertility rate, mortality rate)

• Numerator and denominator must be from same time period

• Often expressed as a ratio (per 1,000)

Source: U.S. Census Bureau, International Database.

• Calculation

• # of deaths ÷ population at risk in same time period x 1,000

• Example – 75 infants (less than one year) died out of 4,000 infants born that year

• 75/4,000 = .0187 x 1,000 = 18.7

19 infants died per 1,000 live births

In 2009, Mondello clinic had 31,155 patients on

ART. During that same time period, 1,536 ART

clients died.

1,536

31,155

49 clients died (mortality rate) per 1,000 clients on ART

=

.049 x 1,000 = 49

• Calculation

• Total number of increase ÷ time of increase

• Used to calculate monthly, quarterly, yearly increases in health service delivery. Example: increase in # of new clients, commodities distributed

• Example: Condom distribution in Jan. = 200; as of June = 1,100. What is the rate of increase?

• 1,100 - 200 = 900/6 = 150 (150 condoms per mo)

In Q1, there were 50 new FP users, and in Q2 there were 75. What was the rate of increase from Q1 to Q2?

Example: 75 - 50 = 25/3 = 8.33 new clients/mo

Measures of the location of the middle or the center of a distribution of data

• Mean

• Median

• The average of your dataset

• The value obtained by dividing the sum of a set of quantities by the number of quantities in the set

• Example: (22+18+30+19+37+33) = 159 ÷ 6 = 26.5

• The mean is sensitive to extreme values

• Average number of clients counseled per month

• January: 30

• February: 45

• March: 38

• April: 41

• May: 37

• June: 40

• (30+45+38+41+37+40) = 231÷ 6 = 38.5

• Mean or average = 38.5

• The middle of a distribution (when numbers are in order: half of the numbers are above the median and half are below the median)

• The median is not as sensitive to extreme values as the mean

• Odd number of numbers, median = the middle number

• Median of 2, 4, 7 = 4

• Even number of numbers, median = mean of the two middle numbers

• Median of 2, 4, 7, 12 = (4+7) /2 = 5.5

• Client 1 – 2

• Client 2 – 134

• Client 3 – 67

• Client 4 – 10

• Client 5 – 221

• = 67

• = 67+134 = 201/2 = 100.5

• Purpose of analysis is to provide answers to programmatic questions

• Descriptive analyses describe the sample/target population

• Descriptive analyses do not define causality – that is, they tell you what,not why

### Part 2: Basic analyses

• Identify approaches for setting targets

• Understand common analyses that calculate program coverage and retention

• Calculate program coverage and retention

• Indicator

• Target

• Program coverage

• Service availability

• Service utilization

• Program retention

• Program element that needs tracking

• Measures an aspect of a program’s performance

• Measures changes over a period of time

• # of new family planning users

• # of clients currently on ART

• Expressed as a number or percentage

• A specified level of performance for a measure (indicator), at a predetermined point in time (i.e., achieve ‘x’ by ‘y’ date)

• Overall target

• Annual targets

• Targets help program staff with:

• Planning

• Staffing and service delivery

• Commodities

• Monitoring progress

• Break long-term goals into manageable pieces

• Check progress on indicators

• The range of values for a given indicator can be from 0% to 100%.

• Example: The theoretical range for the Polio indicator is between 0% of children immunized (bad) and 100% immunized (ideal)

• Is it appropriate to set the Polio indicator target at 100% for a given program? Why/why not?

• Example: In Somalia, the national CPR from 2007 to 2009 was15%. The following year, a national target was set for 70%.

• Is it appropriate to set the CPR target for Somalia at 70%? Why/why not?

• There are three approaches to set a target :

• Established long-term goals by contacting that national program

• Past performance (of your program, increasing by no more than 10%)

• Local high performer (a stellar program nearby)

• Consider the number of clients your program can realistically expect to serve during a given period of time

• Determine the increase your program needs to gain to reach your overall target

• Divide that number by the number of years in which you would like to achieve the target

• Ensure you have an agreed-upon and realistic definition of target population

• Set a realistic target to achieve in the long term and short term

Importance of Defining the Target Population: Case Example

• Target was 372 children to be immunized

• Actual was 488 children immunized

• To calculate the % target achieved, use (Actual/Target) * 100

• 488/372 = 1.31*100 = 131%

• How could the clinic have surpassed its target by so much?

• You don’t really know to what extent you’re fully immunizing the children in your setting

• If your program purchases commodities (e.g., vaccines) based on the target set, supply could run out

• If you set your target too low, you may not have enough vaccines, leading to disease outbreaks

• Program Coverage

• Extent to which a program reaches its intended target population, institution, or geographic area

• Compare current performance to prior year/quarter

• Compare performance between sites

• Program Retention

• Extent to which the range of services is being delivered as initially intended so that client drop-outs are minimal

• To understand program progress

• To determine if the target is reached

• To determine if one target is reached more effectively than another

• Are there underserved area/regions, subpopulations?

• Extent to which a program reaches its intended target population, institution, or geographic area

• Utilization:

• Is the target population utilizing services, accessing commodities, being reached with services?

• Availability:

• Are the services available where there is a need?

Percentage of the target population utilizing services

# of individuals in target population

using a service

------------------------------------------- X 100

# of individuals in target population

• No. of persons educated as of 6/12/09 = 300

• Goal for 12/31/09 = 900

300

900

• You have reached 33% of your target group with education messages

• = 0.33 x 100 = 33%

• Compare percentage achieved toward target for different time periods, different sites, etc.

• Rate of increase

• As of January, 70 people educated; by June, 300 people

• 300 – 70 = 230 increase in people educated

• 230/6 = 38.3 new people educated per month over the 6 months

Utilization of PMTCT Programs

All pregnant women (2,000)

Utilization =

Service users

Target population

PMTCT

Target

(1,000)

Sought prenatal care (600)

Utilization =

600/1,000 = 0.6

0.6 x 100 = 60%

Counseled & Tested for HIV (500)

• Extent to which a program reaches its intended target population, institution, or geographic area

• Utilization:

• Is the target population utilizing services, accessing commodities, being reached with services?

• Availability:

• Are the services available where there is a need?

• Number of service outlets available per target population

• # of clinics with PMTCT per # of pregnant women

• Expressed as a ratio

• There are 8 clinics offering PMTCT & 100,000 pregnant women in region X.

• Ratio of clinics to pregnant women 8:100,000

• Reduce to (1:12,500) pregnant women

• The standard recommendation is 1 clinic with PMTCT services per 10,000 pregnant women

• Clinic availability is not reaching the target

• Service availability is 1:12,500

• Service availability target is 1:10,000

• PMTCT service utilization is 25% off the target

• What can we conclude?

• Service availability and utilization are too low; the program is not meeting the needs of pregnant women.

• Measures if the range of services are being delivered as initially intended

• Determines program retention, i.e., is the project keeping clients through entire package of services?

• Important in clinical programs where drug adherence is an issue (TB, HIV/AIDS, immunization) and there are multiple steps (PMTCT)

Utilization

Completion

All pregnant women (2,000 women)

PMTCT

Target

(1,000)

Sought prenatal care (600)

350 received HIV- result or no result

Tested for HIV (500)

PMTCT Program Retention

1,000

Sought prenatal care

500

Tested for HIV

PMTCT Program Retention

PMTCT

Target

(1,000)

Sought prenatal care (600)

Tested for HIV (500)

PMTCT Program Retention

PMTCT

Target

(1,000)

Sought prenatal care (600)

350 received HIV- result or no result

Tested for HIV (500)

PMTCT Program Retention

PMTCT

Target

(1,000)

Sought prenatal care (600)

350 received HIV- result or no result

Tested for HIV (500)

• Target Setting – A specified level of performance for a measure (indicator) at a predetermined point in time. Both overall and annual targets are set

• Coverage – extent to which a program reaches its intended target population, institution, or geographic area

• Retention– the extent to which the range of services are being delivered as initially intended, with clients retained throughout the full package of services

### Part 3: Data Presentation and Interpretation

• Understand different ways to best summarize data

• Choose the right table/graph for the right data

• Interpret data to consider the programmatic relevance

• Tables

• Simplest way to summarize data

• Data are presented as absolute numbers or percentages

• Charts and graphs

• Visual representation of data

• Data are presented as absolute numbers or percentages

• Ensure graphic has a title

• Label the components of your graphic

• Indicate source of data with date

• Provide number of observations (n=xx) as a reference point

Set of categories with numerical counts

number of values within an interval

total number of values in the table

x 100

Percentage of births by decade between 1900 and 1929

Source: U.S. Census data; 1900–1929.

• Charts and graphs are used to portray:

• Trends, relationships, and comparisons

• The most informative are simple and self-explanatory

• Charts and graphs

• Bar chart: comparisons, categories of data

• Line graph: display trends over time

• Pie chart: show percentages or proportional share

Bar chartComparing categories

Q1 Jan–Mar

Q2 Apr–June

Q3 July–Sept

Q4 Oct–Dec

Data Source: Program records, AIDS Relief, January 2009 – December 2009.rce: Quarterly Country Summary: Nigeria, 2008

Percentage of new enrollees tested for HIV at each site, by quarter

Target

Data Source: Program records, AIDS Relief, January 2009 – December 2009.. uarterly Country Summary: Nigeria, 2008

Stacked bar chart quarter Represent components of whole & compare wholes

Number of Months Female and Male Patients Have Been

Enrolled in HIV Care, by Age Group

Number of months patients have been enrolled in HIV care

Data source: AIDSRelief program records January 2009 - 20011

Line graph quarter

Displays trends over time

Number of Clinicians Working in Each Clinic During Years 1–4*

*Includes doctors and nurses

Line graph quarter

Number of Clinicians Working in Each Clinic During Years 1-4*

Y4 1998

Y1 1995

Y2 1996

Y3 1997

Zambia Service Provision Assessment, 2007.

*Includes doctors and nurses

Pie chart quarter Contribution to the total = 100%

N=150

### Interpreting data quarter

Interpreting data quarter

• Adding meaning to information by making connections and comparisons and exploring causes and consequences

• Adding meaning to information by making connections and comparisons and exploring causes and consequences

• Does the indicator meet the target?

• How far from the target is it?

• How does it compare (to other time periods, other facilities)?

• Are there any extreme highs and lows in the data?

• Supplement with expert opinion

• Others with knowledge of the program or target population

• Use routine service data to clarify questions

• Calculate nurse-to-client ratio, review commodities data against client load, etc.

• Use other data sources

• Situation analyses

• Demographic and health surveys

• Performance improvement data

• Data gap conduct further research

• Methodology depends on questions being asked and resources available

Key messages quarter

• Use the right graph for the right data

• Tables – can display a large amount of data

• Graphs/charts – visual, easier to detect patterns

• Label the components of your graphic

• Interpretingdata adds meaning by making connections and comparisons to program

• Service data are good at tracking progress & identifying concerns – do not show causality

### Activity 5: Calculating coverage and retention quarter

Learning Objectives quarter

• Use basic statistics to measure coverage and retention

• Develop graphs that display performance measures (utilization, trends)

• Interpret performance measures for programmatic decision making

Small group activity quarter

• Form groups of 4–6

• Each group reviews 2 worksheets from Excel file and answers the questions (1 hr 45 min)

• Each group presents 2 findings from each worksheet, focusing on the programmatic relevance of the findings (10 min per group)

• Audience provides feedback on analysis and interpretation (notes errors, additional interpretation) (10 min per group)

THANK YOU! quarter

MEASURE Evaluation is a MEASURE project funded by the U.S. Agency for International Development and implemented by the Carolina Population Center at the University of North Carolina at Chapel Hill in partnership with Futures Group International, ICF Macro, John Snow, Inc., Management Sciences for Health, and Tulane University. Views expressed in this presentation do not necessarily reflect the views of USAID or the U.S. Government. MEASURE Evaluation is the USAID Global Health Bureau's primary vehicle for supporting improvements in monitoring and evaluation in population, health and nutrition worldwide.

Visit us online at http://www.cpc.unc.edu/measure.