2. Using Information for Health Management; Part I
- Health Information Systems Strengthening

3. Epidemiology is the study (or the science of the study) of the patterns, causes, and effects of health and disease conditions in defined populations. It is the cornerstone of public health, and informs policy decisions and evidence-based medicine by identifying risk factors for disease and targets for preventive medicine.

4. Learning objectives
the information cycle; tools and processes for turning data into action
the relationship between data use and data quality
hierarchy of standards / essential data set
common reasons for compromised data quality, and various counter measures
different information products for communicating different meanings

5. Data, information, knowledge
observations and measurements
Information (processed data)
facts extracted from a set of data (interpreted data),
data brought together to demonstrate facts
Meaningful and useful
Knowledge
contextualized information
actionable

6. 42
"Answer to the Ultimate Question of Life, the Universe, and Everything".

7. 42 / 60 = 70% Fully Immunized Children < 1y
Where 60 is the target population

8. 42 / 60 = 70% Fully Immunized Children < 1y For month May 2010
Organization Unit: Whatever
With additional contextual insight we may act upon this information
Like we have seen with Johans examples the numerator could be wrong due to counting errors etc.
The denominatro may be wrong due to census data (not collected with health org units in mind) data divided by org. Units.
Collected for the nation or district and split into lower org units according to some algorithm –
Algorithm can be improved by involving geography etc.

9. Spatial /organizational context Immunization Coverage May 2012
Org.Unit
Immunization Coverage May 2012
Whatever 70
Notsogood 40
Verybad 15
Superduper 98

10. Immunization Coverage for Whatever
Temporal context
Month 2012
Immunization Coverage for Whatever
Jan 83
Feb 80
Mar 70
Apr 52
May 64
Jun 60
Jul 54
Aug 43
Sep 37
Oct 39

11. Information cycle; from data to action
Collection
Processing
Presentation
Action
The relationship between data and information is better seen by considering a diagram, that is called info cycle. So, information cycle is a diagrammatic way of looking at data and information. The Inf Cycle enables one to see the links between the different stages of information. What are these stages or phases? As you can see through this diagram, phases of information handling include the process of data capturing, through observation of real events (collecting), processing data (process means ensuring data quality by auditing data (3Cs), collating (aggregating) data… so that one can analyse it and turn it into information. The information is then presented, interpreted and used for decision-making.

12. Data collection
Input: Using data sources and tools to collect quality data
Common problems:
Too much to collect
Poor understanding of data collection tools
Timeliness of reporting
Low data quality
Output: relevant data
Collection
Processing
Presentation
Action

13. Processing Input: Relevant data Processes: Quality checks
Aggregation to relevant levels
Calculation of indicators
Analysis of data => information
Common problems:
Much irrelevant data
Low data quality
Limited knowledge of data needs and analysis
Output: data converted to information
Collection
Processing
Presentation
Action

14. Presentation
Input: Analysed information, to be presented to communicate meaning
Common problems:
Importance of information is lost through poor presentation
Data can be misrepresented or misinterpreted
Output: various information products
Charts
Tables
Maps
Collection
Processing
Presentation
Action

15. Action Input: Information products
Process: Interpretation of information, prioritizing action, making action plan, and execute action through budgets and plans with goals and targets
Common problems:
Incomplete information
Lack of good targets
No plan for evaluation
Output: Decision-making for health management
Collection
Processing
Presentation
Action

16. Data collection – at the source of data creation (point of care)
Service data collected by nurses and doctors in-between attending to patients
Usually several (manual) steps before it is in any database/storage
Tally sheets
Tally sheet totals at end of month
Monthly summary forms which are reported to the next level
Often too much to collect for already overworked staff
Collection
Processing
Presentation
Action

17. What data elements should be collected?
Collection
Processing
Presentation
Action
What data elements should be collected?
Cannot be obtained elsewhere (e.g. survey)
Are easy to collect (cost vs usefulness)
Do not require much additional work or time
Can be collected relatively accurately
Is part of one or more indicators
Because we are dealing with thousands of data elements, we have to select exactly the data we need, the data which will contribute for decision-making.

18. Comparability of collected data
Collection
Processing
Presentation
Action
Comparability of collected data
Stable standardised definitions
To ensure spatial comparability between different facilities, districts, provinces and nations
To ensure comparability over time
Note:
Revising poor indicators /data sets /data elements may not be advisable due to cost and loss of backward comparability

19. Balancing varying information needs
Collection
Processing
Presentation
Action
Balancing varying information needs

20. Essential data sets (EDS)
Collection
Processing
Presentation
Action
Essential data sets (EDS)
As data flows from lower levels to higher levels, less data is required. Data collection normally begins at the community/facility level. It  should then be sent to the district office. From there it may go to a regional office and then to a provincial level before being sent to a national level. The national level will have international reporting requirements. Each level has different requirements. The facility might want to know how many swabs are used on a daily basis. This information would be irrelevant to the national level. However, information such as the infant mortality rate is of international concern and thus important for all levels of the health system.
Hierarchy of standards

21. EDS Example: vaccination data
Collection
Processing
Presentation
Action
EDS Example: vaccination data
Input (community)
Staff attendence, vaccines, to whom, when, where
Process (district)
Vaccination of children
Output (province)
Coverage of child immunization
Outcome (national)
Immunization rates going up
Impact (international)
Healthier children, less disease. Synergies

22. Where do we get data from?
Collection
Processing
Presentation
Action
Where do we get data from?
Routine data collection
Routine health unit and community data
Activity data about patients seen and programmes run, routine services and epidemiological surveillance
Semi-permanent data about the population served, the facility itself and staff that run it
Civil registration (vital events being integrated with health e.g. India)
Non-routine data collection
Surveys
Population census (headcounts proportion/facility catchment’s area)
Quantitative or qualitative rapid assessment methods
Special registers (birth registers, TB registers, mental health registers)

23. Data Sources in the HMN data warehouse concept
Collection
Processing
Presentation
Action
Data Sources in the HMN data warehouse concept

24. Client record cards Road to Health card
Collection
Processing
Presentation
Action
Client record cards
Road to Health card
Record details of the client’s interaction with the health service, e.g.:
Health facility record system (traditional)
Associated with misfiling and loss vs
Client-held record system (Road to Health Card, Child Health Booklet, Women’s Health Book, TB patient treatment card);
Associated with efficiency of the individual concern, suitable for mobile community 24

25. Collection
Processing
Presentation
Action
Tally sheets
Easy way of counting identical events that do not have to be followed-up
(e.g. headcounts, children weighed)

26. Registers
Collection
Processing
Presentation
Action
Record data that need follow-up over long periods such as ANC, immunisation, Family Planning, Tuberculosis (TB)

27. Collection
Processing
Presentation
Action

28. Reports weekly, monthly, quarterly Collection Processing Presentation
Action
Reports
weekly,
monthly,
quarterly

29. Processing; assuring data quality and calculate indicators
Turning data into information
How to assess data quality?
What are indicators, and why do we need them?
Collection
Processing
Presentation
Action
Review the situation of overloaded health workers who collect a range of stats without knowing why (% nursing time spent on stats collection)
Comment on how health workers are often expected to collect stats and send them up without expectation of analysis, feedback & use of information
Reinforce how the aim of a DHIS, to improve coverage & quality of local health services, is facilitated by only collecting data that can be analysed & used at the local level

30. Why checking data is vital?
Collection
Processing
Presentation
Action
Why checking data is vital?
Use of inaccurate data leads to
Wrong priorities (focus on the wrong data)
Wrong decisions (not applying the right actions)
Garbage in = garbage out
Producing data is expensive
Waste of resources to collect poor data

31. Routine data should be.. Reliable: Correct, Complete, Consistent
Collection
Processing
Presentation
Action
Routine data should be..
Reliable: Correct, Complete, Consistent
Timely: fixed deadlines for reporting
Actionable: no action = throw data away
Comparable:
same numerator and denominator definitions used by all data processers BUT
striving for comparability can compromise local relevance

32. Complete data? Spatial: submission by all (most) reporting facilities
Collection
Processing
Presentation
Action
Complete data?
Spatial: submission by all (most) reporting facilities
Temporal: can you do analysis over time?
Does provided services cover the full population? Many indicators depend on population figures as denominators
Many developing countries have low service utilization – many people do not use offical health serives or live out of reach

33. Correct data? Are we collecting the data we need?
Collection
Processing
Presentation
Action
Correct data?
Are we collecting the data we need?
The data values seems sensible/plausible?
The same definition applied uniformly?
Are there any preferential end digits used?
JAN
FEB
MARCH
APRIL
MAY
JUNE
JULY
250
230
245
225
240

34. Collection
Processing
Presentation
Action
Consistent data?
Data in the similar range as this time last year or similar to other organization units
No large gaps or missing data
No multiplicity of data (same data from multiple sources –which one to trust?)

35. Timely data? Late reports weaken the potential for comparison,
Collection
Processing
Presentation
Action
Timely data?
Late reports weaken the potential for comparison,
action can be too late,
but still useful for documenting trends
Sierra Leone

36. What are the causes of poor data quality?
Collection
Processing
Presentation
Action
What are the causes of poor data quality?
Too many forms to fill out that are not useful to health workers
Data collection tools are poorly designed and hard to understand
Too many steps of manual aggregation and transfer of figures
Limited feedback on data quality to those who collect it
Data is not used

37. Accuracy enhancing principles
Collection
Processing
Presentation
Action
Accuracy enhancing principles
Capacity building through training
(90% of HISP activities)
User-friendly collection/collation tools
Feedback on data errors (but not only!)
Availability of processed data
Local Use of information
Essential Data Set
Clear definitions; standards
Information sharing

38. Collection
Processing
Presentation
Action

39. What can be done to improve data quality?
Collection
Processing
Presentation
Action
What can be done to improve data quality?
Systemic changes
Essential dataset
Feedback routines
Information for action
Promote information use at all levels
Data validation mechanisms
Min/Max rules in software
Data validation rules, check for consistency in logic of data
Completeness and timeliness reports

40. Indicators - measure service COVERAGE and QUALITY
Collection
Processing
Presentation
Action
Indicators
- measure service COVERAGE and QUALITY
Calculated by combining two or more pieces of data, so that
They can measure trends over time
They can provide a yardstick whereby facilities / teams can compare themselves to others (spatial, organizational)
monitor progress towards defined targets
Good for measuring change
To do this, indicators need to have a numerator and denominator

41. Indicator types
Collection
Processing
Presentation
Action
Reinforce that indicators are measures of the frequency of events
Unpack how indicators can be viewed as calculation types – count, proportion & ratio
Explore the 3 types of calculations that can be carried out on indicators
Explore how indicators can be used to measure events in terms of – count (raw numbers), proportions (aggregated) & ratios

42. numerator indicator = = % denominator X 100
Collection
Processing
Presentation
Action
numerator
indicator =
X 100 = %
denominator
Example: How is maternal mortality rate defined?
“the number of maternal deaths per 1,000 women of reproductive age in the population (generally defined as 15–44 years of age).”
What about maternal mortality ratio?
“the number of maternal deaths per 100,000 live births in same time period.”
Numerator: Number of deaths assigned to pregnancy-related causes during a given time interval
Denominator: Number of live births during the same time interval
Multiplier: 100,000
Millenium Development Goals have a set of proposed indicators [weblink]
The maternal mortality rate, the number of maternal deaths per 1,000 women of reproductive age in the population (generally defined as years of age) .
The maternal mortality ratio, the number of maternal deaths per 100,000 live births in same time period.

43. a count of the event being measured
Collection
Processing
Presentation
Action
Atop the line – numerators (activities / interventions / events / observations / people)
a count of the event being measured
How many occurrences are there:
morbidity (health problem, disease)
mortality (death)
resources (manpower, funds, materials)
Generally raw data (numbers)
Unpack the meaning & use of numerators (illustrate with appropriate examples)

44. Under the line – denominators (population at risk)
Collection
Processing
Presentation
Action
Under the line – denominators (population at risk)
size of target population at risk of the event
What group do they belong to:
general population (total, catchment, target)
gender population (male / female)
age group population (<1, >18, 15-44)
cases / events – per (live births, TB case)
Unpack the meaning & use of denominators (illustrate with appropriate examples)
Explore population groups used most frequently – age group cohorts

45. An ideal indicator RAVES !!!
Collection
Processing
Presentation
Action 
An ideal indicator RAVES !!! 

46. Collection
Processing
Presentation
Action
Indicators should be
RELIABLE gives the same result if used by different people
APPROPRIATE fits with context, capacity, culture and the required decisions
VALID truly measures what you want to measure
EASY feasible to collect the data
SENSITIVE immediately reflects changes in events being measured
Unpack formulation of indicators – how to write them
Explore how indicators are used in the management & planning of health services
Clarify the use of terms: input, process, output & outcome, as they relate to aspects of service management

47. Essential indicators: determines the essential data set at each level
Collection
Processing
Presentation
Action
Essential indicators: determines the essential data set at each level
As data flows from lower levels to higher levels, less data is required. Data collection normally begins at the community/facility level. It  should then be sent to the district office. From there it may go to a regional office and then to a provincial level before being sent to a national level. The national level will have international reporting requirements. Each level has different requirements. The facility might want to know how many swabs are used on a daily basis. This information would be irrelevant to the national level. However, information such as the infant mortality rate is of international concern and thus important for all levels of the health system. Become familiar with the information flows within and outside your district.
Hierarchy of standards

48. Indicator Operationalization
Collection
Processing
Presentation
Action
Defining the sources of the data – both numerator & denominator (how is it to be collected?)
Determining the frequency of collection and processing of the indicator (How often should it be collected, reported, analyzed?)
Determining appropriate levels of aggregation
(To where should it be reported and analyzed?)
Setting levels of thresholds and target
What will be the nature of the action (decision) once the indicator reaches the threshold?