MINISTRY OF HEALTH
HEALTH MANAGEMENT INFORMATION SYSTEM
DATA MANAGEMENT, DEMAND AND USE
TRAINER’S MANUAL
APRIL 2012
Ministry of Health -Resource Centre
April 2012
1
FORWARD
National health programs and other partner institutions are working towards achieving
goals as stipulated in the Health Sector Strategic and Investment Plan - Monitoring and
Evaluation (HSSIP M&E) plan 2010/2011 – 2014/15.
In that regard, the Ministry of
Health recognizes the importance of using the National Health Management
Information System (HMIS) for the successful planning, delivery and implementation of
the minimum health services as indicated in the Health Sector Strategic & Investment
Plan (HSSP). Both the HSSIP 2010/11- 2014/2015 and the National health policy have
emphasized the use of information for decision making and monitoring and evaluating
progress.
To achieve this, the HMIS should not only be seen as a mechanism for collecting
information and passing it to successively higher levels but Information should be used
at the level at which it is collected for evidence-based decisions. In a bid to promote
utilisation of the data generated from HMIS, Ministry of Health Resource Centre in close
collaboration with MOH technical departments, donors and implementing partners has
developed harmonized standard data demand and use tools for
training in data
management , analysis and interpretation and use at national, district and health facility
levels.
In designing the data demand and use manual, the Ministry is emphasising that, any
data to be recorded at a service level must have a use (i.e ability to enforce action),
efforts should be made to make better use of existing data at all levels through practical
analysis and improved presentation of data and utilization. This is to be achieved by
everyone remembering that HMIS is intended to provide information for action-it is like
a bell that is intended to sound a warning to all data users of the result of the data. The
HMIS provides strategic information for decision makers who may then combine this
information with other strategic information from other agreed upon sources to make
evidence-based decisions.
The MOH warmly recommends its partners to use the HMIS data demand and use
manuals as an important tool that will help promote the ongoing developments and
Ministry of Health -Resource Centre
April 2012
2
strengthening of the health management information system in Uganda.These tools
should be utilized appropriately at the various levels of Health Care Service delivery to
instil a culture of collection and utilization of quality data for evidence-based decision
making.
It is my appeal that the various health institutions and departments across the country
use the standard and harmonized processes in this manual during the implementation
to improve the Health Care Service delivery at all level in Uganda
Please feel free to send feedback to improve this manual. The official email to which
concerns can be addressed is hmisdatabank@yahoo.com.
FOR GOD AND MY COUNTRY
Dr. Aceng Jane Ruth
Director General Health Services
Ministry of Health -Resource Centre
April 2012
3
ACKNOWLEDGEMENTS:
The Data demand and use manual –have been developed by the Ministry of Health – Resource
Centre in collaboration with the Ministry of Health technical programmes, districts, and various
HMIS Implementing Partners. The MOH-Resource Centre is grateful to all stakeholders including
local governments, organizations and individuals who have given assistance and supported the
planning, development and finalization of the DDU Manual. The following individuals in
particular contributed at different stages in the development of the Manual:
Technical Team members who participated in the development of the Manual:
Name
Title/Organization
Name
Title/Organization
Dr. Eddie Mukooyo
Assistant Commissioner
Resource Center
Aguti Evelyn Francis
Training officer/Coordinator –
PREFA
Mr Ally Kibwika
Assistant Commissioner- Human
Resource Development
Lyazi Micheal Ivan
M&E officer PREFA
Ms. Caroline Kyozira
Principal Biostatistician
Mary Namuyomba
Kayunga District HMIS –FP
Natseri Nasan
Data Manager/WHO
John Kissa
Statistician /RC
Allan Nsubuga
Statistician /RC
Yawe Moses
Nakaseke District Biostatistician
Joel Kisubi
Program management specialist
USAID
M&E Manager - STOP Malaria
Dennis Businge
M&E Director /STAR EC
Kayanja Edward
Luweero District HMIS-FP
Areke James Stephen
Soroti District Biostatistician
Etoma Chalres
Masindi District Biostatistician
Kusiima Wilson
Buliisa District HMIS FP
Iteset Ruth Atiro
Kaberamaido District HMIS FP
Asiimwe Patricia
Hoima District HMIS FP
Bright Asiimwe
Wandera
Kayongo Emmanuel
Flavia Kazibwe
Andrew Anguko
Dr. Ebony Quinto
Dr. Suzie Nasar
Project
M&E officer STOP Malaria
Project
Data officer STOP Malaria
Project
M&E specialist – Malaria
consortium
M&E specialist- National
Malaria Control Program
Malaria Advisor PMI/CDC
Ssetuba Abasolom
Strategic information officer
STAR – South West
Angida Teddy
Senior Statistician – Mulago
National Referral Hospital
Kasozi Sam
HIS Analyst – CDC
Opolot Francis
Kumi District HMIS –FP
Anna Collator Awor
Statistician – CDC
Thomas Emeetai
Data Manager/STRIDES
Dr Moses Walakira
Director SI/STAR SW
Nakamya Phellister
M&E Specialist – UCCM
secretariat
Training Officer, PREFA
Evelyn Aguti Frances
Ministry of Health -Resource Centre
April 2012
4
LIST OF ACRONYMS:
DHMT: District Heath Management Team
DHO : District Health Officer
DHT: District Heath Team
HCT: HIV Counselling and Testing
HMIS: Health Management Information System
HSD: Health Sub-District
HSSIP: Health Sector Strategic and Investment Plan
HSSP: Health Sector Strategic Plan
HUMC: Health Unit management Committee
IPD: In-Patient Department
MDG: Millennium Development Goals
MOH: Ministry Of Health
OPD: Out-Patient Department
UNMHCP: Uganda National Minimum Health Care Package
VHT: Village Health Team
WHO: World Health Organization
Ministry of Health -Resource Centre
April 2012
5
Table of Contents
FORWARD.................................................................................................................................................... 2
ACKNOWLEDGEMENTS: ............................................................................................................................ 4
Table of Contents .......................................................................................................................................... 6
INTRODUCTION AND ORGANIZATION OF THE GUIDE ........................................................................ 9
GENERAL INTRODUCTION ......................................................................................................................... 9
PURPOSE OF THE MANUAL ..................................................................................................................... 10
HOW TO USE THIS MANUAL ................................................................................................................... 10
CONTENTS OF THE MANUAL .................................................................................................................. 11
OVERVIEW OF REQUIRED TRAININGS................................................................................................... 13
TRAINING MODAL /STRUCTURE: ............................................................................................................ 13
PERSONEL and REQUIRED TRAININGS: ................................................................................................... 13
Table of list of participants art various levels ......................................................................................... 15
OUTCOMES OF THE TRAINING ................................................................................................................ 15
INTRODUCTION TO THE WORKSHOP / TRAINNING .......................................................................... 17
SETTING THE STAGE: ............................................................................................................................... 17
MODULE 1: USING DATA TO INFORM POLICIES & PROGRAMS ....................................................... 19
MODULE 2: GENERATING/CREATING DEMAND FOR DATA. ............................................................ 20
Objectives ............................................................................................................................................... 20
DETERMINANTS OF DATA DEMAND: HEALTH FACILITY STAKEHOLDERS’ FORA .................................... 20
MODULE 3: HMIS DATA FLOW, DATA FORMATS AND ANALYSIS METHODS ............................... 23
3.1 DATA FLOW STRUCTURE WITH THE MOH HMIS: ............................................................................. 23
3.2 DATA FORMATS ................................................................................................................................ 24
CATEGORICAL DATA ........................................................................................................................ 24
NUMERICAL DATA............................................................................................................................ 25
3.3. FREQUENCY tables ................................................................................................................... 26
3.4 PERCENTAGES, PROPORTIONS, RATIOS, AND RATES .......................................................... 31
3.5 FIGURES............................................................................................................................................. 35
3.5.1. Bar graph ................................................................................................................................. 36
3.5.3. Histograms .............................................................................................................................. 37
3.5.4. Line Graph ............................................................................................................................... 38
CHECK LIST FOR PRESENTING DATA. ............................................................................................ 40
Ministry of Health -Resource Centre
April 2012
6
SESSION 4: DATA MANAGEMENT AND ANALYSIS: ............................................................................. 42
4.1 DATA ANALYSIS FOR VOLUME OF SERVICES: ........................................................................ 42
4.3 DATA ANALYSIS FOR SERVICE UTILIZATION RATES: .......................................................................... 52
4.4 DATA ANALYSIS FOR MONITORING WORK PLAN: ............................................................................ 52
4.5 .Using HMIS data in Epidemic Detection ................................................................................ 53
4.5 Analysis checklist: ....................................................................................................................... 54
4.5 MAPS ................................................................................................................................................. 55
SESSION 5: DATA INTEPRETATION, SHARING INFORMATION AND PROVIDING FEEDBACK ....... 61
5.1 INTERPRETING DATA ......................................................................................................................... 61
5.2 DATA PRESENTATION, SHARING AND FEEDBACKS: .......................................................................... 63
5.3: LINKING DATA TO ACTION ............................................................................................................... 64
ANNEX 1: DATA MANGEMENT AND USE ACTIVITIES AT VAROUS LEVELS...................................... 69
ANNEX II: LIST OF INDICATORS RELATED TO HEALTH SERVICES PROVIDED AT FACILITY LEVEL
AND HOW THEY CAN BE PRESENTED: .................................................................................................. 73
ANNEX III: INDICATORS WITH SOURCES OF DATA AT THE HEALTH FACILITY AND HOW THE
INFORMATION CAN BE USED:................................................................................................................ 75
6.0.
REFERENCES .................................................................................................................................. 95
Ministry of Health -Resource Centre
April 2012
7
Ministry of Health -Resource Centre
April 2012
8
INTRODUCTION AND ORGANIZATION OF THE GUIDE
GENERAL INTRODUCTION
Health management information is intended to incorporate all the data needed by
policy makers, clinicians and health service users to improve and protect population
health. As the HMIS in Uganda has been evolving, there has been a greater need for
robust health information. The revised HMIS 2010 addressed most of the gaps which
were identified by different stakeholders as the world community and all stakeholders
have turned the attention to meeting Millennium Development Goal (MDG) targets,
and ever increasing resources are going towards preventing and treating high burden
diseases such as HIV and AIDS, Tuberculosis and Malaria. Decision-makers need to be
able to measure whether policies and programs are working, and whether progress is
being made towards the goals that have been set. Donors are also placing more
emphasis on performance, linking the release of funds to performance-based measures.
The World Health Organization (WHO) argues that investment in health management
information systems (HMIS) now could reap multiple benefits, including:
• helping decision makers to detect and control emerging and endemic health
problems, monitor progress towards health goals, and promote equity;
• empowering individuals and communities with timely and understandable healthrelated information, and driving improvements in quality of services;
• strengthening the evidence base for effective health policies, permitting evaluation of
scale-up efforts, and enabling innovation through research;
• improving governance, mobilising new resources, and ensuring accountability in the
way they are used.
As more focus has been put on data availability , both health facility and district
reporting have greatly improved over the past years, however ,reports from
MOH
Planning/resource centre supervisions show that the data collected by the health
facilities using HMIS is not used locally. The strategy to address this gap was to develop
this Data Demand and Use Training Manual which will be used to carry out training to all
health workers to promote data demand and utilization at all levels and strengthening
Ministry of Health -Resource Centre
April 2012
9
data quality control systems.
PURPOSE OF THE MANUAL
This manual is to be used in all HMIS data management and use trainings aiming at
standardizing the training given to all actors involved in planning, implementation and
use of the HMIS data in Uganda.
This manual provides facilitators in Data management and use with user-friendly,
modifiable training components to adapt for different contexts at different levels of
training. The modules can be presented as suggested, or separated to supplement
existing material of a similar topic. Regardless, this guide will assist facilitators in ways to
best incorporate data demand and use concepts into the various health workers
trainings in the different diseases areas. The overall learning objectives of the training
manual include:

Improving the understanding of the role of data in decision making, the context
of decision making, the determinants of data use, and the importance of data
sharing and feedback at all levels .

Building skills for applying data demand and using tools

To build skills in data analysis and interpretation, data presentation, and data
feedback; and

To gain hands-on experience in linking data to the decision-making process at all
levels
HOW TO USE THIS MANUAL
This training manual should be used as the main document for all the trainings
organized by the various stakeholders/partners involved in the HMIS data training, data
managements and use. A copy of the training manual should be given to all trainers /
supervisors at all levels. This manual is for the trainers only. Health workers, Records
assistants or officers participating in training sessions at district level do not need a copy
of the manual. These may be given key message sheets or the health facility data
management and manual for the users.
Ministry of Health -Resource Centre
April 2012
10
Basic assumption:
For any Health worker to be trained in data demand and use, the staff has to be
knowledgeable or was trained in the revised HMIS (2010) and also the HMIS reporting
channel. It is also advisable that this training in Data use can be integrated in the HMIS
Roll out training of the revised HMIS (2010) manuals. The staff trainees should know the
Uganda National Minimum Health Care Package (UNMHCP) at their respective levels.
Training should be held at government institutions and where possible, should be in a
health facility that has been collecting HMIS data for the previous year. This will enable
the participants to have a chance to use that same health facility data in the practical
examples. National training of trainers will use examples of previous computerised data
stored at MOH Resource Centre. The health facility training will be followed up by
intensive, regular supervision at the facilities integrated into the health system support
supervision visits.
Since this manual will be beneficial only if all the trainers fully understand the HMIS
forms which are the source documents for the data, trainers should have copies of HMIS
tools and summary reports submitted to the district and National level for
demonstration purposes. All references have been made to the HMIS forms in the HMIS
health facility manual of 2010.
A list of the training documents required include the following:
a. HMIS health facility manual 2010
b. HMIS district manual 2010
c. MOH-Resource Centre Monitoring and Evaluation manual
d. Standard Operating Procedures for health information
e. HSSIP 2010/11 – 2014/15 (specific indicators packages pages 151-153).
CONTENTS OF THE MANUAL
This manual is composed of five sections:
1: Using data to inform policies & programs,
2. Generating/creating demand for data,
Ministry of Health -Resource Centre
April 2012
11
3. Understanding information flow, data formats and analysis methods,
4. Data management and analysis,
5. Data interpretation, action, sharing information and providing feedback
Each section covers specific issues relating to the HMIS data. This arrangement facilitates
the training activities to be presented in a more organized and logical flow.
The trainers should ensure that they have previously read through this guide to acquaint
themselves with the contents. Trainers should endeavour to follow the instructions
provided for their use in italics and shaded in borders as highlights for training.
Remember: It is critical that all trainers follow this guide closely when delivering the
training. The content has been written carefully and builds on significant experience of
HMIS Trainings by different stakeholders and has been approved by the MOH-RC. Good
trainers are animated, keep their audience engaged, adopt participatory practical
approaches in the training using available HMIS data, and follow the content of this guide
closely.
Ministry of Health -Resource Centre
April 2012
12
OVERVIEW OF REQUIRED TRAININGS
TRAINING MODAL /STRUCTURE:
The CASCADE model training is to be used. Training and sensitization will be carried out
at 3 levels with different objectives for each level (see below). The aim of these trainings
is to ensure all involved understand the scope and importance of their data and use it in
their day today activities for planning and decision making. The National level TOTs
(trained for 2 days at the central level) will cascade the training to the districts level
team. The district TOTs will then do the on job training at the health facilities.
On average, districts with less than 30 facilities can have a maximum of four trainers.
For the districts with more than 30 facilities, a ratio of five (5) health facilities to 1 trainer
will be used
to get the required number of trainers responsible or cascading the
training to lower levels.
The Districts Trainers will be selected /identified in consultation with Biostatistician
and/or
HMIS focal persons of the districts. Trainers will be selected based on training
ability and skill, with Biostatistician, surveillance and HMIS focal person as essential
trainers. These are to be trained for three days at the APPROPRIATE training centre,
followed up by supervision during basic training by the Regional /National level master
trainers when they are cascading the training to the health facilities.
The whole DHT will be involved in the district training of trainers but only selected
people will be picked out to cascade the training to lower levels. The team trained at
the district will cascade the training to the health facilities as an on - job training.
The participants for the activities from Central to health facility are defined (see below).
PERSONEL and REQUIRED TRAININGS:
The course should be delivered to teams from the health facilities or districts level. Each
team should include both data users and data producers. Data users are health
professionals- DHT members, Health Facility In charges, clinicians, midwives, nurses and
other key health decision makers who use data to inform the health delivery system.
Data producers are professionals who acquire and analyze health data and prepare
them for distribution to audiences of users. These include Biostatisticians, malaria focal
persons, HMIS focal persons, records assistant, record officers, data clerks, or researchers
Ministry of Health -Resource Centre
April 2012
13
(at times, this role is also carried out by a data user). The team approach has proven
effective because it ensures that all of those involved understand their respective roles in
data demand and use, and how the roles interact with each other.
Understanding that team training may be cost prohibitive, the next best option is to
provide separate training for data users and data producers from the same levels of the
health systems. This type of training can be conducted as an add-on to previously
scheduled meetings to minimize travel costs. If this option is selected, trainers should
emphasize the links between data users and data producers and in cases where there is
insufficient staff to cover the roles separately, modules should be adapted to ensure the
producer/user understands both roles.
To ensure ongoing functioning of health facilities alongside the training, the health
workers from a particular health facility when the district TOTs are carrying out the on
job training will be trained in two groups, one team for the first allocated days and the
next team in the next allocated days. For example :-The training for a health centre II
which lasts for one day will take two days to have all health workers at that facility
trained , one team trained on Day 1 and the other on the Day 2.
Ministry of Health -Resource Centre
April 2012
14
Table of list of participants art various levels
Training
National /
Regional
Level
Participants
National trainers
DHT trainers/supervisors
The maximum number of
participant per training is
40
Facilitators
Resource centre and
central level partners.
District
Level
DHT , HSD Health InCharge
Records
people
for
hospitals and HSD. not
to exceed 30 participants
National
trainers
trained
in
the
National TOT
Health
facility
Level
All health workers at the
health facility.
districts
biostatistician,
districts HMIS focal
person or HSD HMIS
focal
person
–
sampled supervision
by the National level
master trainers
Objective
To equip the central trainers/supervisors
with appropriate knowledge and skills to
sensitize
and train the DHTS in data
demand and use and oversee /guide the
implementation in the districts .To discuss
the rollout of the training to the lower
health units in the district and ongoing
data ownership and utilisation at the point
of collection.-(Duration 2 days)
To equip the district trainers/supervisors
with appropriate knowledge and skills to
train and supervise the HSD, hospitals and
lower health facilities in data management,
demand and use. equipping the district ,
Hospital and HSD Health workers with skill
of data use an analysis at their level.
(Duration 3 days )
Equip the health workers with skill of data
use at the health facility focusing on
translating data into useful information.
Duration 2-4days depending on the level
of the health facility )
OUTCOMES OF THE TRAINING
After completing of this course, participants at all levels are expected to be able to:
•
Understand the importance of using data locally
•
Analyze the volume and mix of services provided and identify seasonal changes in
disease patterns
•
Graph key demographic characteristics of their catchment population; calculate
targets for key health services as per HSSIP 2011/2015
•
Use HMIS data to monitor the utilization coverage of key services
•
Use HMIS data to monitor stock out of essential drugs
•
Use HMIS and Community Mapping data to monitor the performance of health
facility
Ministry of Health -Resource Centre
April 2012
15
ORIENTATION TO THE WORKSHOP:
Check List during Training in Health Facilities
Health Centre II (1 day)
1.
2.
3.
4.
5.
6.
7.
Uses of data at Health Centre II
data sharing meetings
HMIS data flow
frequency tables
percentage
ratio
bar charts
8.
pie charts
9.
histograms
10.
11.
12.
13.
14.
line graphs
analysis of volume of service
stock level
epidemic
data analysis for service
utilization and coverage
data interpretation and action
Health Centre III (2 days )
1.
2.
3.
4.
5.
6.
7.
uses of data at health
centre III
data sharing meetings
HMIS data flow
frequency tables
percentage
ratio
bar charts
8.
pie charts
9.
histograms
10. line graphs
11. using HMIS data to detect
epidemics
12. analysis of volume of
service
13. stock level
14. feedbacks and action
15. data analysis for service
utilization and coverage
16. data analysis for service
utilization coverage rates
17. data interpretation and
action
Ministry of Health -Resource Centre
Health Centre IV/Hospital
(4 days)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Uses of data all levels
data sharing meetings
HMIS data flow
HMS data use barriers
data formats
frequency tables
percentage
ratios
rates
bar charts
11. pie charts
12. histograms
13. line graphs
14. scatter diagrams
15. map
16. data analysis for service
utilization coverage
18. data analysis for service data
analysis for service utilization
and coverage
19. data analysis for service
utilization coverage rates
20. Data interpretation Data use
supervision
21. linking data to action
22. Providing Feedback
April 2012
16
INTRODUCTION TO THE WORKSHOP / TRAINNING

Members introduce themselves with a brief background of what they do and . how
they use data

Review of the participants expectations:

Laying the ground rules

Key highlight and facts on data use

Sharing of
the field experience from some participant in regards to data
management and use .(each participants should give examples from their respective
facilities)

Ice breaker
The trainer should put emphasis on working principles for HMIS data management and use.
1.
2.
3.
4.
Any data to be recorded at a service level must have a use (action)
Efforts should be made to make better use of existing data at all levels through
practical analysis and improved presentation of data.
Use of computerization should be encouraged and supported for database
maintenance and report generation.
Improvement in health data generation and use at the various service levels should be
undertaken in support of efforts to improve service task performance and should be
seen as a by-product of such performance improvement.
SETTING THE STAGE:
Highlight to note for facilitators: The collection and reporting of data through the HMIS is useful only if, at every level of
the health system, the data is processed, presented and interpreted so that appropriate
action can be taken to improve the functioning of the health system and the health
status of the population. “Action” does not necessarily mean “change.” If after
thorough analysis, one concludes that things are fine as they are, appropriate action is
to ensure that things continue as they are. Most often, however, a good HMIS will pick
up opportunities for improvement, even when things are going all right in general.
Ministry of Health -Resource Centre
April 2012
17
A routine system will use well-chosen indicators to warn unit heads, health facility Incharge, senior officers, DHT and district councils that certain aspects of the system need
attention.
Analysis of data is a continuum that starts with processing the data:
Looking at the numbers,
Re-organizing them,
Re-grouping them,
Doing calculations to get to ratios and rates, and
calculating pre-defined indicators that reflect progress towards the health set out
objectives and goals.
Next, the data needs to be presented in ways that make it accessible for a large
audience, often only interested in the main trends and findings.
Interpretation of the processed and adequately presented data is best done by those
that face the daily reality represented by the data. The distinction between data
processing, presentation and interpretation is somewhat artificial, and often one goes
through several iterations of the whole process or parts of it before feeling confident
enough to decide on appropriate action.
Ministry of Health -Resource Centre
April 2012
18
MODULE 1: USING DATA TO INFORM POLICIES & PROGRAMS
USES OF DATA TO THE HEALTH SYSTEM
Each member should contribute and probably giving examples to their own health facility
settings this can be an ice breaker.
Group Activity 1:
Divide the team in groups of at least 4 members. Give an Activity of needs
assessment of the use of Health data at different health level and various
stakeholders
1. Health unit level:
Make
discussion of what is the use of data, tailor the
discussion to the specific levels
2. To identify disease patterns at your facility, in the community, district, etc and
identify epidemics, outbreaks and notifiable diseases e.g. cholera, Ebola etc.
3. Helps the in charge of health units to plan for drugs needs , and drug supply
chain management , lead time and time quantification of drugs
4. Plan for human resource management as per patient ratio
5. Leave schedules
of the staff and daily rostra as per patients’ patterns are
identified.
6. Timely essential drug stock outs and malaria endemicity patterns.
7. Provide timely immunization vaccine coverage, quantification , net distribution
plans and all community intervention
8. Important source of information for the year work plans and budgeting purposes.
9. To assess whether the intervention had outcomes and assess impacts
10. Guide implementation of interventions such as malaria control intervention e.g.
health education areas and community outreach plans
Ministry of Health -Resource Centre
April 2012
19
MODULE 2: GENERATING/CREATING DEMAND FOR DATA.
Objectives
1. To understand the determinants of data use at all levels
2. Understand the barriers of data use at various levels.
3. To understand the importance of improving data-informed decision.
4. To shares ways of translating the data use knowledge into practise at various levels
of data collection.
Activity for National and District TOTS:
Facilitators notes:
the trainees have
already highlighted the use of data in section one, the trainer should now understand
why then is the data not used even after the trainees being able to understand that this
data is useful. (i.e. putting knowledge into practice). The
aim of the activity is for the
National/ District TOTs to understand the way for creating demand for data and barriers
of data use at various levels of the health systems and ways of approaching these
barriers such that when they are cascading the training they can share the problems
and practical solutions to the various levels within their districts.
GROUP ACTIVITY 1:
Divide the team in groups of 4 to maximum of 8 members:
1. Each group gives 3-5 ways or strategies which can be used to create demand for data
at various levels. (5-10minutes)
2. In these same groups members should give reasons why this is currently not happening?
(why is HMIS data no used at the point of collection, what are the barriers for data use (510minutes)
3. What are the visible solution to these barriers:
After each exercise an interval is given for at least a group to present to all members.
Alternatively all the exercise can be done at once and presentations done at the end.
DETERMINANTS OF DATA DEMAND: HEALTH FACILITY STAKEHOLDERS’ FORA
The following key activities can drive data demand at the health facility:
Ministry of Health -Resource Centre
April 2012
20
Facilitators notes: This is additional information to the discussion of the trainees in the
above exercise as per health guidelines
DHO / Health facility In Charges are responsible for verification and analysis of
administrative and service delivery reports. The generated reports from the HMIS data
are to be used for health facility performance review and improvement, planning, and
resource mobilization. The Health Information Assistant / Records assistant or
designated person is responsible for submission of the health facility reports to the HSD
and DHO. Each report should be received at the HSD office by the date due.
During the monthly health facility meetings, performance review should focus on the
information from the data generated from the health units, sub districts and districts so
that actions are evidence based. The health facility quarterly assessment reports will be
used for performance review during the quarterly HUMC meetings, whereas the annual
district /sub district performance report will be presented and discussed during the
annual district / Sub County stakeholders’ forum.
During this meeting the forum should also focus on looking at what the data is saying as
per the services provided and how they are utilised – the performance review should
look at things like immunization coverage- why are mothers not bringing the children
for immunization (for example if immunisation is low), do the mother attend ANC, how
is the IPT2 uptake , what is the leading cause of deaths in our community , what can we
do to prevent the diarrhoea outbreak seen as per the health data from the health
facility, etc?
Issues of timeliness, completeness and accuracy of the reports can also be mentioned
when discussing the session of HMIS. There are potential disastrous consequences if
data is missing, poor quality or lately reported. Imagine inaccurate stock reports have
led to under quantification at district/national level, late reports can lead to un noticed
disease out breaks leading to loss of many lives.
Ministry of Health -Resource Centre
April 2012
21
Each health facility in the country has a defined catchment area, for which it is
responsible for coordinating delivery of services to implement the HSSIP. Stakeholders
from the community up to politicians need and should have access to data on which to
base decisions and advise given to the community during their community engagement
platforms on issues like:
(a) Prioritization of household income (e.g. should I buy a mosquito net or use the
money for something else, what is mobile phone ownership more that availability of a
latrine at the household)
(b) Budgeting for programs at the sub-County, Town Council, district and National level.
If the data is not available, analyzed, interpretable and shared with the relevant parties,
wrong decisions can be made with possibly dangerous consequences.
All stakeholders in the catchment area of the facility can come together to discuss health
and health related issues affecting them quarterly each year. In these meetings, data
should be shared.
Group activity 2:
What is the immediate action within your reach which can be done to
improve/promote data use at your level.
Ministry of Health -Resource Centre
April 2012
22
MODULE 3:
HMIS DATA FLOW, DATA FORMATS AND ANALYSIS
METHODS
Objectives:
At the end of this session the learners should be able to:
1. Describe data on terms of frequencies distribution, percentages and proportions
2. Use figures to present data
3. Calculate the frequencies, percentages, proportions, ratios, rates for the major
variables in the top ten diseases.
3.1 DATA FLOW STRUCTURE WITH THE MOH HMIS:
The HMIS involves the following processes: collection of data, processing it for
conversion into useful information, analyzing and discussing it assess the current status
of services and using it to set appropriate strategies and targets for improvement. The
flowchart in Figure 1 shows the different steps in data collection and transmission, as
well as analysis and feedback.
At the level of Health center, health workers keep a record of activities and patients seen
on the provided HMIS forms in all different area( i.e OPD, ANC, pharmacy, etc) , the
records officers/assistant or health in-charge for Health centre II collects monthly
summary reports. This data is submits to the HSD and the HSD submits the aggregated
data for all its health facilities to the districts. The district aggregates all the data from all
the HSD and submits to the National level. These reporting structures are well laid out in
the HMIS health facility manual (2).
Ministry of Health -Resource Centre
April 2012
23
Village Health Teams (VHT)
3.2 DATA FORMATS
In analysing our data, it is important to determine the type of data that we are dealing
with. This is crucial because the type of data used largely determines the type of
techniques that should be used to put this information into use
Some of the variables may have produced numerical data, while other variables
produced categorical data.
CATEGORICAL DATA
There are two types of categorical data: they are nominal or ordinal
Example
NOMINAL DATA
CATEGORIES
SEX
Male , female
Tested
Taken to the lab, not taken to the lab
ORDINAL DATA
CATEGORIES
Ministry of Health -Resource Centre
April 2012
24
Anemia
Severe, moderate ,mild
age group
Under 5, above 5
Level of knowledge
Good , average , poor
NUMERICAL DATA
When do we talk of Numerical data?
We speak of NUMERICAL DATA if data is expressed in numbers. There are two types of
numerical data: Discrete or Continuous.
How do I differentiate discrete data from continuous data?
DISCRETE DATA are a distinct series of numbers.
For example:
DISCRETE DATA
VALUES
Number of malaria cases per day
0, 1, 2, 6, 19, etc.
Number of clinic visits
2, 4, 10, 0, 3, etc.
Number of pregnancies per woman
2, 3, 5, 0, 5, 4, etc.
Number of new attendants per day
40, 45, 100 etc
CONTINUOUS DATA come from variables that can be measured with greater
precision, depending on the accuracy of the measuring instrument, and each
value can increase or decrease without limit.
For example:
a) Height (to 2 decimal point)
b) Temperature (in degree Celsius, OPD register specifically for malaria patients,
patient Form 5)
c) Children’s weight ( in the immunization register)
VALUES
a) 12.12, 9.95, 45.13, 6.99, 28.78, etc,
b) 37.5, 37.8, 39.2, 40.1, 36.9, etc,
Ministry of Health -Resource Centre
April 2012
25
HOW CAN WE PRESENT DATA?
Data can be presented as:
• Frequency distributions
• Percentages, proportions, ratios and rates
• Figures.
• Measures of central tendency
3.3. FREQUENCY tables
Example 3.3.1: categorical data frequency distribution
To identify
the number of females and males patients attending the out patient clinic
at Kasaana H.C IV for the month of December 2010.The results are presented in the
following frequency distribution:
Sex
Frequencies
Male
200
Female
300
Total
500
A frequency distribution is calculated by simply totalling the number of responses in
each category, that is tallying from the OPD register all the male patients. This summary
is also on HMIS form 105.
You should always check that the total number of responses agrees or tallies with the
number of subjects (respondents). Category for missing sex are not allowed in the HMIS
and should be noted as a data quality check.
For example if the total number of patients seen in December was 505, one will expected
that the total of females and males should be 505.
3.3.2. Exercise for the Health facility trainees:
To identify what family planning methods were used by mothers
in Kiryandongo
hospital, Mothers were asked what family planning method they were using. The results
from the Maternal health register are presented in the below:
Ministry of Health -Resource Centre
April 2012
26
Method
Number
Abstinence 16
Condoms
47
Injectables
1
Norplant
1
Pill
35
None
300
Total
400
What can you conclude from the data?
High light discussion answers to exercise 3.1:
By looking at the frequency distribution above you can conclude that 75% or three out
of four of the mothers are not using family planning. For those who are using family
planning methods, Condoms and pills are the most commonly used methods with 47%
and 35% respectively.
Category
Number
Using FP Method
100(25%)
Not using any method Number
300(75%)
Total
400
FP method
Number
Abstinence
16
Condoms
47
Injectables
1
Norplant
1
Pill
35
Total
100
Ministry of Health -Resource Centre
April 2012
27
Example/ exercise 3.3.3:
Health in charge from 148 different public health facilities were asked the following
question during the National support supervision visit to the districts .How often have
you run out of medicines for the treatment of malaria in the past 6months? This was a
closed question with the following possible answers: never, 1 to 2 times a month (rarely),
3 to 5 times a month (occasionally), more than 5 times a month (frequently). The
number of responses in each category was totalled to give the following frequency
distribution:
Categories
Number
of
Health facilities
Never
47
Rarely
71
Occasionally 24
Frequently
6
Total
148
In this example, the data are ORDINAL. The ordering of the categories is important as
eachcategory from top to bottom indicates increasing severity of the problem.
The frequency distribution results indicate that most health facilities
rarely
experience shortages of Anti-malarial drugs, but that it is an occasional problem in
about one sixth of the health facilities and a severe problem in a few.
B. Numerical data frequency distribution
Procedures for making frequency distributions of numerical data are very similar
to those for categorical data, except that now the data have to be grouped in
categories. The steps involved n making a frequency distribution are as follows:
Ministry of Health -Resource Centre
April 2012
28
1. Select groups for grouping the data.
2. Count the number of measurements in each group.
3. Add up and check the results.
When grouping data, the way the groups are selected can affect what the results are
going to
look like. There is little substitute for common sense here, but it may be
necessary to change the
grouping if you suspect the information is being hidden by a poor selection of the
groups.
Example 3.3.2
Seven Health centres of Bukedea district are submitting numbers of malaria cases per
week using the HMIS 033b and the HMIS focal person is to summarise the data.
Compare the health facility and district weekly summaries of the same data as presented
in Table below.
H.F. 1
9 cases
H.F. 2
12
H.F. 3
11
H.F. 4
13
H.F. 5
14
H.F. 6
13
H.F. 7
16
H.F. 1
16 cases
H.F. 2
16
H.F. 3
18
H.F. 4
19
H.F. 5
16
H.F. 6
21
H.F. 7
25
District weekly summary:
Week 1 88 cases
Week 2 131 cases
Ministry of Health -Resource Centre
April 2012
29
H.F. 1
28 cases
H.F. 2
28
H.F. 3
28
H.F. 4
32
H.F. 5
21
H.F. 6
19
H.F. 7
12
Week 3 168 cases
All Health Facilities data show an increasing amount of malaria cases, but the improving
situation showed in health facility 6 and 7 in the third week cannot be reflected in the
weekly summary for the district. It would therefore be better to use the health facility
data if you want to indicate when
and where exactly the numbers of reported
malaria cases started going down/up and which health facility is contributing a bigger
problem.
GROUPING NUMERICALDATA:
How do we group numerical data?
When grouping data the following rules are important:
- The groups must not overlap, otherwise there is confusion concerning in which
group a measurement belongs.
- There must be continuity from one group to the next, which means that there must be
no gaps.
Otherwise some measurements may not fit in a group.
- The groups must range from the lowest measurement to the highest measurement so
that all of the measurements have a group to which they can be assigned.
- The groups should normally be of an equal width, so that the counts in different
Ministry of Health -Resource Centre
April 2012
30
groups can easily be compared.
Sometimes, however, it is valid to choose groups that are of different widths, for
example if you are interested in specific age groups relating to HIV treatment (e.g., less
than 1 year, 1 to 4 years, 5 to 14 years). The advantage we have is that the new HMIS has
standardised the categorised as per need for comparability across health units and
districts.
When you start summarising data it is better to make too many groups than
too few. This is because during data analysis you can combine groups to form new
categories without having to go through all your data again, whereas if you have too
few groups you have to go back to your raw data to make more groups.
A larger number of groups will generally give a more precise picture, but when using
too many groups one can lose the overview.
As a general rule choose round numbers for the lower values of the group limits.
For example:
1.00-9.99, 10.00-19.99, 20.00-29.99, or:
0-4; 5-9, 10-14, etc.
3.4 PERCENTAGES, PROPORTIONS, RATIOS, AND RATES
3.4.1. PERCENTAGE
Instead of presenting data in frequency tables using absolute numbers it is often better
to calculate percentages. Percentages standardise the data, which means that they
make it easier to compare them with similar data obtained from another health
facility or district.
Example 3.4.1
82 health facilities in Wakiso district
are supposed to submit the number of patients
treated for malaria per month. (HMIS 105 – row 27, summary on HMIS 123 for the
district). Data for September 2011 submitted
Ministry of Health -Resource Centre
to the district was summarised. TheHMIS
April 2012
31
focal person presented both the frequency distribution and percentages (or relative
frequencies):
Table3.4.1: Distribution of health facility according to number of patients treated
for malaria in one month
Number of patients
Number of clinicsa
Percentage
0 to 19
25
31%
20 to 39
3
4%
40 to 59
5
6%
60 to 79
11
14%
80 to 99
19
24%
100 to 119
10
12%
120 to 139
4
5%
140 to 159
3
4%
Total
80
100%
a
Data from two health facilities is missing – no reports received.
Note: Usually you do not include missing data in the calculation of percentages.
The frequency of responses in each group is calculated as the percentage of those
study elements for which you obtained data
However, the number of missing data (e.g., heath units which never reported or wards
whose data was not received) is a useful indication of the adequacy of your data
collection. Therefore this number should be mentioned, for example as a note to your
table. As footnote on the above table indicate that 2 health units never submitted
reports.
Facilitators NOTE: Points to note:
Point 1:
The percentage of the cases
Ministry of Health -Resource Centre
April 2012
32

Is a more processed piece of information and better expresses the contribution of
the cases of interest in a denominator.

Enables comparison of the data of one health facility with those of another.
Example of comparison of the data between two health facilities.
For example, if a Health facility has reported 10 cases of pneumonia and another health
facility has reported 30 cases, one may conclude that the second health facility has seen
more cases. But, if the first has seen 400 patients and the second clinic has seen 1200
patients during the same period, the calculation of the percentage of pneumonia cases
shows that the percentage of pneumonia cases in
Both health facilities is the same (i.e., 2.5%).The formula for calculating the percentage of
the cases is: Percentage of the case = Total number of the case x 100 / Total number of
patients
Point 2:
Caution on interpreting percentages if the total number is small!!
One should be cautious when calculating and interpreting percentages if the total
number is small, because, one unit more or less would make a big difference in terms of
percentages. As a general rule, percentages should not be used when the total is less
than 30.
Therefore it is recommended that the number of observations or total cases studied
should always be given together with the percentage.
Example: The Laboratory personnel in Kibaale H.C IV went for leave in the month of July
2011, the total number of blood slides examined for malaria in this Health facility for this
month was recorded as 20. The positive slides were 10 which correspond to 50%. If the
positive had been found to be 11 instead of the 10 due to a mistake in counting, then
the relative frequency would have been 55% which already makes a difference of 5%.
However if the total number of slides examined had been 200, out of which 100 are
positive, (50%), then one slide more or less i.e. (101 0r 99) would not have made a such a
Ministry of Health -Resource Centre
April 2012
33
big difference in terms of percentages
Examples of percentages which can be computed from the HMIS
Number of under malaria cases as a percentage of total malaria cases
Number of lab test as percentage of total suspected malaria cases
Number of malaria positives as percentage of all malaria tests done
Number of IPT2 as a percentage of new ANC attendance
Number of specific family planning methods as percentage of all FP patients seen.
Number of pregnant women tested positive for HIV as percentage of all pregnant
women tested for HIV.
3.4.2. PROPORTIONS
What is a proportion?
A PROPORTION is a numerical expression that compares one part of the study units to
the whole; a proportion can be expressed as a FRACTION or in DECIMALS.
3.4.2.1: Example of proportion
Out of a total of 55 patients diagnosed with malaria in the OPD at Kyamulibwa
H.C.II on a specific day , 22 are adults and 33 are children less than 5 years . We
may say that the proportion of children among the malaria cases is 33/55 or 3/5, which
is equivalent to 0.60.
Note that when a proportion expressed in decimals is multiplied by 100, the value
obtained is percentage. In the example, 0.60 is equivalent to 60%.
3.4.3. RATIOS
What is a ratio?
A RATIO is a numerical expression that indicates the relationship in quantity, amount or
size between two or more parts.
In Example 5 above the ratio of adults with malaria to children with malaria is 22:33, or
2:3
Ministry of Health -Resource Centre
April 2012
34
3.4.4 RATE
What is a rate?
A RATE is the quantity, amount or degree of a disease or event measured over a
specified period of time
Facilitators note:
Mention this for knowledge in health facility II and III training.
To be emphasised/detailed for the district TOTs (Biostatistician HMIS focal person,
Hospital and H.C IV)
Commonly used rates in the health sector are:
Birth Rate
The number of live births per 1000 population over a period
of one year
Death Rate
The number of deaths per 1000 population over a period of
one year
Infant Mortality Rate The number of deaths of infants under one year
(IMR)
deaths of age per 1000 live births over a period of one year
Maternal Mortality Rate The number of maternal pregnancy-related in one
(MMR)
year per 100,000 total births in the same year
Incidence Rate
The number of new cases per population over a
specific period of time (usually a year)
Prevalence Rate
The number of existing cases per population over
a specific period of time (usually a year)
3.5 FIGURES.
From The HMI S many descriptive tables can be generated. It may gain readability if this most
important data is presented in figures .The most frequently used figures for presenting data
includes
• Bar charts
• Pie charts
For categorical data
• Histograms
• Line graphs
for numerical data
• Scatter diagrams
• Maps
Ministry of Health -Resource Centre
April 2012
35
3.5.1. Bar graph
Bar charts are a way of presenting a set of numbers by the length of a bar; they are used
to make comparisons among individual items. They enable the observer to compare the
values visually. The bars are not joined together, but separated by a space. This
diagrammatic arrangement is used when an axis deals with information that is qualitative
or non-continuous in nature, such as different diseases, different facilities, etc....
The categories being compared are conventionally drawn vertically (column charts), but
can be presented horizontally as well. We can draw simple or multiple bar charts. In the
latter, more than one variable is displayed.
Facilitators guide:
Read through example 2 for the trainees: put it up on a flip chart or project it.
We will now look at examples of the above mentioned figures that can be used for
presenting data: The data from example/exercise 4.2.3 can be presented in a bar chart,
using either absolute frequencies or relative frequencies or for percentages.
FIGURE
3.5.1:
BAR
GRAPGH
SHOWING
FREQUENCY
OF
SHORTAGE
OF
ANTIMALARIA DRUGS IN 148 HEALTH FACILITIES SUPERVISED IN THE LAST
QUARTER.
Number of Health facilities
80
70
60
50
40
30
20
10
0
Never
Rarely
Occasionally Frequently
Ministry of Health -Resource Centre
April 2012
36
FIGURE 2 SHOWS A BAR CHART COMPARING THE TOTAL NUMBER OF DELIVERIES,
THE NUMBER OF DELIVERIES WITH COMPLICATIONS AND THE NUMBER OF
DELIVERIES REFERRED, IN DIFFERENT H.C IV OF WAKISO DISTRICT
90
80
70
Number
60
50
40
30
20
10
0
NAMAYUMBA H.C.IV
Total deliveries
WAKISO H.C.IV
KASANGATI H.C.IV
Number of deliveries with complications
NDEJJE H.C.IV
BUWAMBO H.C.IV
Number of deliveries referred
One can also compare the same category in different places or points in time.
3.5.2. Pie charts
When do we use pie charts?
A pie chart can be used for the same set of data, providing the reader with a quick
overview of the data presented in a different form.Pie charts can be used to show the mix
of family planning services provided, the mix of diseases diagnosed, the mix of different
ARI cases, of different Malaria laboratory diagnoses, etc. A pie chart illustrates the relative
frequency of a number of items. All the segments of the pie chart should add up to
100%, so that sphere represents 100%, a hemisphere 50% (half of the pie) and a quarter
25% and so on
3.5.3. Histograms
Numerical data are often presented in histograms, which are very similar to the bar charts
which are used for categorical data. An important difference however is that in a
Ministry of Health -Resource Centre
April 2012
37
histogram the ‘bars’ are connected (as long as there is no gap between the data), whereas
in a bar chart the bars are not connected, as the different categories are distinct entitles.
The data of
3.5.4. Line Graph
What is a line Graph?
Line Graph is used to show the trend of an indicator (e.g. # people served, number of
under five
malaria cases etc) over time.
How do I draw a line Graph?
FACILITATORS GUIDE:
Go through the following steps with a job aide for health centre II and III, for the
H.C IV and hospital this is directed using excel package on the computer.
The
assumption is all national trainer should be technical lead in drawing basic graphs
using excel.
Steps for H.C II and III training: (Grid Job aide provided)
Step 1. Using the line chart template below, label the small boxes below the horizontal
line (called horizontal axis) to correspond to the months of data that your graph will
represent. In this template, the graph can cover a period of 18 months.
Step 2. Now you need to scale the left vertical line (called vertical axis) appropriately and
label the marks on it. Depending on the expected values of your indicator, you need to
determine a maximum to mark the highest limit of your vertical axis. For example, if you
want to plot the trend of OPD patients served in your HF and during the past six month,
if the maximum monthly value has been 600, choose 720 (i.e. 600 + 20%*600) as the
maximum value on the vertical axis. Then, label the vertical axis marks to start at 0 and
finish at the maximum (720 in our example).
Note: if the monthly variation of the indicator is expected to be low, instead of starting at
0,
you can start at a higher value, but you need to keep this point in mind at the time of
Ministry of Health -Resource Centre
April 2012
38
interpretation.
Step 3. Plot the value of your indicator from each month (from the sources that you’ve
already identified, for example total OPD patients (i.e. new attendance and re-attendance
as per the OPD register summarised on the HIMS 105) on the chart. For each month, put a
dot on the graph to show the value of the indicator (total OPD patients served) for each
month.
Step 4.Connect all the dots that you have plotted on the graph. This line will show the
trend of your selected indicator over the past few months. For the next month(s), you only
need to make a new dot showing the new value of your indicator and connect the dot to
the previous month. Continue to do the same for each month to monitor the future trend
of the indicator and to see whether your interventions have been effective in changing
the trend.
Facilitators’ Notes: need to mention that: You can plot two related indicators in one chart and have two different trend lines
in one graph. For instance, you can plot the # OPD cases and<5 cases on the same
graph.
Plotting more than one trend on one graph (if selected appropriately) can give you
additional insight that can facilitate the interpretation of data.
Ministry of Health -Resource Centre
April 2012
39
Exercise: Draw a line graph to present the data below:
Facilitators: The trainer should be aware that the time is the X axis and DPT3, or DPT3
targets or DPT 3 cumulative is the Y-axis. The graph have more than one line plotted on a single
graph be only month.
month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
target
DPT 3
# of
80%
cumulative
DPT3
501
401
401
1002
921
520
1503
1236
315
2004
1651
415
2505
2165
514
3006
2767
602
3507
3177
410
4008
3687
510
4509
4194
507
5010
4506
312
5511
4962
456
6012
5266
304
CHECK LIST FOR PRESENTING DATA.
When using any of the above mentioned tools to present data, check for the following:
1. Do I have a title?
The data presented should have a title. The title must be brief and self-explanatory,
expressing all the information that is being presented. The meaning of the title should be
immediately obvious to readers, without having to refer to a text for an explanation
2. The headings of columns or rows in tables should be clear and concise.
3. Axes of graphs and diagrams should be properly defined and clearly labeled with their
scales. The vertical axis of a graph is known as the Y-axis, or the ordinate, while the
horizontal axis is known as the X-axis, or the abscissa.
Ministry of Health -Resource Centre
April 2012
40
4. Keys or labels are necessary in graphs with more than one line or bar, i.e., when
information on more than one case is presented. The labels identify the different groups
being presented for comparison.
5. Footnotes may be given, where necessary, providing explanatory notes or additional
information.
6. Keep it simple: no graph should attempt to present so much information that is difficult
to comprehend.
7. The data must be presented according to size or importance, chronologically or
geographically.
8. When the tables and charts are prepared, the head of the health facility or in-charge of
OPD should do the checking and the interpretation, as elaborated in the next chapter. The
report, tables and charts should be put on the notice board of the health facility. The
tables and charts that monitor progress for the whole year should be updated every
month and kept on the notice board.
9. Each months / quarter’s tables and charts should be replaced the following
month/quarter and permanently kept in a file if the notice board cannot have all of them
at ago. The notice board should have the most up-to date information for appropriate
planning and decision-making.
Ministry of Health -Resource Centre
April 2012
41
SESSION 4: DATA MANAGEMENT AND ANALYSIS:
Now that we have seen the methods of data analysis, which can be applied to the HMIS,
let us now focus on how we will analyse the data which later link to interpretation of the
findings:
Service Delivery Data Management activities at each level are contained in Annex I:
Needs for this session:
•
One field in HMIS form filled out
•
Catchment area population
•
Map of the district/ sub county / Catchment area map for the Health facility
•
List of villages for H.C II list of parishes for H.C III, lit of sub counties for H.C
IV/HSD , and in case of district Tots, list of HSD).
•
Paper, ruler, pen and calculator for H.C.II & III training, computes for other
levels
Data for the HMIS 105 for the previous six months.
4.1 DATA ANALYSIS FOR VOLUME OF SERVICES:
Source: HMIS form and registers; required for this exercise: the health facility monthly
inpatients data of the last three months, the health facility outpatient data of the three six
months (HMIS 105 and HMIS 108)
Indictors: patient load (health facilities & health posts/out reaches), mix of services,
major patients groups; top ten diseases
Tabulation/presentation of data: line chart (trend analysis), pie chart
Analysis purpose: monitoring improvement and seasonal changes
Objective
Ministry of Health -Resource Centre
April 2012
42
- understand the meaning of performance indicators and select from a list those basic
indicators that are key to monitor delivery of Basic package of health care service at the
various levels
- be able to describe the use of line, bar and pie charts
- use line, bar and pie chart to monitor the basic performance indicators of a health
facility / district (service delivery)
Facilitator’s guide: Ask the participants the following question one at a time and go
through, make sure you note their answers on the chart, write the indicators on the charts
or Project the slide. Remember this is discussion session read out the answers written in
the table i.e. how the data can be used as addition suggestion but probe for the health
workers own thinking and initiatives’ on data use.
Discussion questions
Which of the following indicators do you think are most useful in monitoring the
performance of your health facility?
What other indicators (not listed here) do you think would be key in measuring your
health facility performance?
Relate this to the use of data to the health system in Module 1:
List these indicators electively for applicability in trainings of HC II & H.C III trainees
List all indicators if the training is for levels above H.C III. Examples used will focus on
these indicators. More indicators in Annex III.
Indicator/variable
Source of data
# of OPD served
OPD registers /
# of children less than 5 years summarised on
served in the OPD
HMIS 105
Ministry of Health -Resource Centre
How can the data be used
All levels:
Show utilisation of the health centre
in general.
Identify trends; causes for the current
trends; identify the need for change;
design interventions for change.
April 2012
43
Indicator/variable
Source of data
top ten diseases in the OPD
HMIS 105, OPD
register
# of New ANC & 4th visits
HMIS 105, ANC
register
How can the data be used
Hospitals
For the hospital OPD new attendance
should be less than 10% of all
attendances, as this level is referrals.
OPD Morbidity, as the In-charge ,
there is need Identify top ten most
prevalent diseases treated;
plan for the necessary stock of key
pharmaceuticals; design preventive
interventions and communicate to
the staff team how to approach the
situation , reference the MOH
guidelines necessary etc.
Use of ANC, focus on MCH as per
Reproductive health Division strategic
plan
Access
to
malaria
preventive
measures
Immunisation coverage
Number of LLINs given at HMIS 105, ANC
ANC
register
# of children immunised for HMIS
105,
DPT1, 3 and measles
immunisation
tally sheet, child
register
# new acceptors of family
HMIS 105, Family Family planning coverage, strategies
planning methods
Planning register for improvements
Method mix among
new HMIS 105, Family Adaptive methods , stock refills
acceptors
Planning register
Number of malaria test done
# deliveries carried out at the
health facility
# lab confirmed TB cases who
started treatment
# of HIV tests done
HMIS 105, Lab
register
HMIS
055A
HMIS
105,
maternity
register
Rates of presumptive treatments,
laboratory utilisation and training
needs in laboratory
Need to identify the current trend in
H.F deliveries and whether the trend
should change; design interventions
for change; monitor effectiveness of
the interventions
HMIS 106a, TB Access to TB care, diagnosis
register
HMIS 105, HCT Access to HIV care, laboratory
register HMIS- utilisation and training needs in
055b
laboratory
Ministry of Health -Resource Centre
April 2012
44
Indicator/variable
Number of families visited by
Health assistant or number of
community out reaches done
Source of data
How can the data be used
Health assistant
activity reports.,
HMIS 105
Quick example:
HMIS form 123 reported for the month of July2009 show data as below
District
Suspected Malaria cases
Under five
Soroti
3042
2452
Amuria
1045
982
Kaberamaido
800
685
Kumi
2096
1045
Present this data on a graph
Group work:
Put the class into groups and have the list of the HMIS forms data for the previous 6
months. If there is more than one person from a particular health facility, and the Health
facilities are not more than 10, each health facility would work on their set of indicators
and present it out to the class.
Exercises
Note: For doing these exercises you will need to have the HMIS105 and/or HMIS 108 of
your health facility for the last six months. For the district, they require HMIS 123 and 124
for the last six months
Use the provided templates and grids for constructing line, pie and bar charts to present
the appropriate indicators if the trainees do not have computers (specifically for
training at HC II & III
Note to the facilitator move around to the team and ensure groups pick the appropriate
presentation i.e. type of graph for the indicators. Since the indicators are many, you can
Ministry of Health -Resource Centre
April 2012
45
opt for each group to present work on other indicators. However ensure that at least two
groups are working on the same set of indicators to compare the reasoning for under
performance.
For the HSD/Hospital they opt to use computers so it will not take a lot of time.
1- Using a chart, plot the trend of OPD patients served in your health facility over the
past six month. In the same chart plot the trend of under five OPD patients served during
the same period.
The group should pick the appropriate chart,
Facilitators note: Bar and line graph are the most appropriate to present the two
indicators on one chart.
(Probing questions)
 How do these trends look like? Decreasing, increasing, constant or fluctuating?
 Do you think this trend is acceptable or you need to do something to change it?
 What are the most likely causes of the current trend?
 If you need to change the trend, what interventions should be adopted?
 Which one of these interventions is within your control at the health facility level? How
are you going to share this information with your staff?
 Which one of them is beyond your control at the health facility level and what needs
to be done so that you can get the necessary support?
2- Repeat the above exercise for the new acceptors of family planning services.
Using a pie chart, indicate the share of each family planning method among the new
acceptors.
(Probing questions)
 Which method is most commonly used? Which method is used the least?
 Which method is used less than you expected? Which method is used more than
expected?
Ministry of Health -Resource Centre
April 2012
46
 What is the most likely underlying cause for these conditions?
 Do you think you need to bring any changes to the share of any of the methods?
 If you need to change this pattern, what interventions should be adopted?
 Which one of these interventions is within your control at the health facility level?
How are you going to share this information with your staff?
 Which one of them is beyond your control at the health facility level and what
needs to be done so that you can get the necessary support?
4.2 DATA ANALYSIS FOR SERVICE UTILIZATION COVERAGE:
- Use demographic and target group data, HMIS and baseline household survey data to
monitor the utilization coverage for EPI, antenatal care, deliveries by skilled personnel and
family planning services
- Be able to identify trends and needs for change in the current status and analyze most
likely causes
- Design interventions to make changes happen
Discussion questions
The below charts demonstrate the performance of a health facility on DPT3 vaccination in
its catchment areas.
- On which HMIS form and where exactly can you collect the number of DPT3 doses
administered to under 1 children?
- In the first chart, explain what does each of the following mean?
• The vertical axis and its values
• the bar graph
• the line graph
• How do you think the line and bar graph are related?
Ministry of Health -Resource Centre
April 2012
47
Example 4.2.3: Use the data below to create a line graph of monthly DPT3 coverage
and targets for Nakasongola district.
#
of
#
of
#
of
month
DPT3
month
DPT3
month
DPT3
Jan
401
May
514
Sep
507
Feb
520
Jun
602
Oct
312
Mar
315
Jul
410
Nov
456
Apr
415
Aug
510
Dec
304
Total number of children under 1 year of age: 7512
National DPT3 coverage target: 80%.
1. Use the national DPT3 coverage target, or the specific target for your facility if you
have it. We assume the target is to completely immunize 80% of children under one
year of age (or 80 children out of every 100 children).
2. Get your facility’s data. You need a denominator. In the case of DPT3 immunization,
this will be the number of children under one year of age in your catchment area. There
are several ways to obtain this denominator. For our example, If Nakasongola district the
<1 population in 2012= 7512children.
3. Convert/calculate the local annual target for your facility as follows:
Ministry of Health -Resource Centre
April 2012
48
Number of children <1 multiplied by target = your facility’s annual target, using a target
of 80%: (7512 X 80/100) = 6010 children to be vaccinated in a year (i.e. the health facility
target
4. Calculate the local monthly target for your facility, using the annual target, as follows:
Local ANNUAL target divided by / 12 months = your HMIS MONTHLY target.
6010 divided by 12 = approximately 501 children per month, to be vaccinated for DPT3.
5. Draw the X and Y-axis (see line graph example):X-axis = 12 months, labeled “Jan to
Dec“(Bottom, horizontal line of graph). Y-axis = labeled Children Immunized (Left hand
side, vertical line).
6. Place total number of children <1 (7512) at the top of the Y axis and complete the scale
down to zero (the corner of the graph).
7. Mark the point for your annual local target (6010) up on the graph above the month of
December.
This is the number of children that you plan to have immunized with DPT3 by the end of
the year.
8. Trace a broken line from 0 children at the beginning point in January, through to your
local target point marked at the end of the year (December). This is your “Target Line”,
which you need to achieve in order to reach your target.
9. In your frequency table in the left upper corner, ADD the number of newly immunized
Children of each month to the previous month’s total number. The number of children
vaccinated each month is taken from the HMIS 105, Section “child immunization”, which
Ministry of Health -Resource Centre
April 2012
49
is the total number of children 0 – 11 months vaccinated with DPT3. Adding the number
to the previous month’s number gives you the “cumulative” total.
10. Plot the graph accordingly, labeling dots with the cumulative number of new + old
children immunized. Connect the dots with an un-broken line. Starting from the end of
January, plot the ACTUAL number of children immunized. Make the dot and label that
point with the actual number of children immunized.
11. Each month monitor your facility’s performance against the target. If the target is NOT
being met, decide on immediate actions to be taken by nurse in charge of immunization
of health assistant staff.
12. Finally, put a Title on your graph that explains what the graph is showing (example:
DPT3 Target Monitoring, Nakasongola district, 2011). Also include the Source: of the data
(which HMIS form or register), the Author (s): and the Date the data was compiled.
Computerised excel system for district, H.C IV and Hospital: Using excel after you
have entered the data, click on insert tab on excel menu, click the line graph under
the chart tab, click the line graph type of your choice, you can adjust insert titles,
adjust the legend according)
Facilitators note: Demonstrate this on the computer as appropriate for the trainees
to get this done with a computer.
DPT 3 immunisation coverage for Nakasonogla district: 2011:
Ministry of Health -Resource Centre
April 2012
50
DPT3 Immunization Coverage
Nakasongola distrcit,2011
Population <1 7512
National DPT3 Target 80%
Annual Local DPT3 Target 6010
Monthly Local Target 501
month
target 80%
# of DPT3
/123
reported from HMIS 105
Jan
501
401
401
Feb
1002
520
921
Mar
1503
315
1236
Apr
2004
415
1651
May
2505
514
2165
Jun
3006
602
2767
Jul
3507
410
3177
Aug
4008
510
3687
Sep
4509
507
4194
Oct
5010
312
4506
Nov
5511
456
4962
Dec
6012
304
5266
DPT 3 cumulative
7000
Repoted number of DPT3
6000
5000
target 80%
DPT 3 cumulative
4000
3000
2000
1000
0
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
Month
Figure
Line Chart: Immunization Coverage Nakasongola district, data source
health unit HMIS 105-2011
Ministry of Health -Resource Centre
April 2012
51
4.3 DATA ANALYSIS FOR SERVICE UTILIZATION RATES:
Objective
o Be able to identify the high priority area and identify priorities for intervention
o To be familiar with ten key indicators
o Be able to create and describe the use of Bar chart
Which of the following indicators are the most useful in monitoring the performance of
your health facility?
• % women of reproductive age (15-49 years) who are using (or partner is using) a
contraceptive method
• % women of reproductive age (15-49 years) who can identify at least two forms of
modern contraception
• % births attended by a skilled birth attendant
• % children >= 1 year and < 2 years fully immunized (DPT3)
• % children >= 1 year and < 2 years who received Vitamin A therapy
• % children 0 to 6 months exclusively breastfed
• % mothers receiving PNC after delivery
• % mothers attending at least one ANC visit
• % mother receiving TT injections
• % mothers reporting appropriate behaviour for treating a sick child
4.4 DATA ANALYSIS FOR MONITORING WORK PLAN:
The HMIS data can be used to monitor the achievements as per he set targets in the Work
plan
Below is the number of children immunised a DPT3 in Buginyanya H.C III in 2 in 2004 as
reported on HMIS 105 Under CHILD IMMUNISATION section. The health facility had set a
target as per column A and the number immunised in column B, column c obtains the
cumulative DPT3 by adding the previous month results to the current month.
Ministry of Health -Resource Centre
April 2012
52
Year
Cumulative DPT3
Targeted (A)
Jan
12
13
13
Feb
24
17
30
Mar
36
25
42
Apr
48
35
60
May
60
33
68
Jun
72
55
88
Jul
84
45
100
Aug
96
91
136
Sep
108
59
150
Oct
Nov
Dec
120
132
144
120
225
200
175
No of children given
DPT3 (B)
Cumulative DPT3
Attained (C)
179
75 195
132 207
Cumulative DPT3 Targeted
Cumulative DPT3 Attained
150
125
Cases
100
75
50
25
0
Months
4.5 .Using HMIS data in Epidemic Detection
Epidemic detection requires well kept data and monition from the previous two years to
have a trend. Below is and example of trend ongoing for Bukhalu H.C III as collected on
Ministry of Health -Resource Centre
April 2012
53
HMIS 105 OPD diagnosis – epidemic prone diseases section .Data was plotted was from
Jan 2001 to November 2003.
20
18
16
14
Cases
12
10
8
6
4
2
0
Months
The in-charge should use the information and prompt the following questions?
 What is the most likely underlying cause for this epidemic?
 If you need to change this pattern, what interventions should be adopted
immediately?
 Which one of these interventions is within your control at the health facility level?
How are you going to share this information with your staff?
 Which one of them is beyond your control at the health facility level and what needs
to be done so that you can get the necessary support?
4.5 Analysis checklist:
Facilitator’s notes: go through this checklist as a way of interesting the trainees that
analysis is not business as usual but activity done for a purpose, and to make a positive
Ministry of Health -Resource Centre
April 2012
54
change in the way the health activities are done, and influence decisions at all levels.
Several steps in the process of using data should be applied systematically when looking at
data. Depending on the situation, some of these steps may be reduced in scope and time,
particularly when action is urgent and timely interventions important.
The main steps of data analysis are reflected in the following questions:
1. Are the data accurate? Do the data measure what they are supposed to measure? Were
the calculations made correctly?
2. Are the data reliable? Are you confident that you could come up with the same numbers
if you were to review the source registers and documents again?
3. Do any of the numbers cause you immediate concern or alarm even before doing any
additional calculations? Sometimes you might find a figure that seems way out of line
with all of the others! Did someone enter an extra 0 – a common mistake that
inflates122 numbers? Are too many priority drugs out of stock? Does the number of
emergency purchases seem too high?
4. How do the numbers compare to previous time periods? Is there a trend towards
improvement even if the numbers are not as good as you had hoped? Drawing a simple
graph on a wall chart can help you identify such trends.
4. How do the numbers compare between/among geographic areas? Are some areas more
successful than others? Do some areas need extra help attracting new clients or
reevaluating their procurement methods? Does more training or IEC work need to be
done for BHC staff or community?
5. If the reports identify specific problem areas, what might be the causes? Are certain
factors external to the program possibly responsible for some of the problems (e.g.,
economic problems of clients)? What are the internal causes? (e.g., poor estimation of drug
requirements, delays in processing orders). Think also about changes in community
characteristics, such as an aging population or decreasing pool of potential new patients.
4.5 MAPS
Ministry of Health -Resource Centre
April 2012
55
Maps provide the advantage of visual presentation of the geographical distribution of
cases.
Thus, it is easier to identify the location of cases to get an idea of how communicable
diseases are spreading. For example, diarrhea cases may be seen as a cluster in one area. If
there is a map of the catchment area of the health facility available, certain cases--especially
EPI target diseases--can be marked by pins.
Community mapping has been used successfully in several countries as a useful tool for
non-literate health workers to monitor the health status of families in their catchment area.
At region level and national levels, coverage rates by district quickly let planners distinguish
between well-served and less well-served areas and populations.
Exercise 4: Data analysis and use
For HSD, hospitals and district level trainees
Case study Activity 1: The DHT has come to supervise a HC III, the MFP checks the stores
stock cards for the number of doses given out in the previous month. The stock cards were
found filed in the box file placed in the in charge’s desk ; the stock cards show that 200
blue , 412 green; 200 red , 400 brown doses of ACT were released from the stores in that
month , however the total number of doses available is noted as 30000 on the stock card.
All Coartem
different pack
are
available in stock as per the stock card records, on
checking the HMIS register it show only 1200 malaria cases were seen in that month
1. Explain how you would use the data to advise the health unit stock management
system.
2. As part of the H.C III management team, explain the procedures you would take to
address these gap.
Case study Activity 2: The VHT report show 183 VHTs reported out of the 220 in the
Ntwetwe H.C IV catchment area, the VHT supervisor has complied data from these VHT and
the total number of children treated were 540. Only 320 were followed up after 3 days and
Ministry of Health -Resource Centre
April 2012
56
only 120 had taken the medication given. The VHT report also shows that 104 of the
caretakers own mosquito net but 24 had
slept under a mosquito net the day before
reporting for treatment.
State clearly what useful information and malaria indicators can be computed from this
data.
Discuss within your group how this data can be effectively utilized to improve the health
education and fill in the gaps of the VHT system.
Key points to highlight during assessment:

% of VHT reporting

Follow up rate

Estimate of proportion of children with a net

Estimate of net use

Health education of adherence , was the right information given during treatment
seeking, are the CMDs still competent enough, refresher course of CMDs , when were
they last supervised?
Ministry of Health -Resource Centre
April 2012
57
Group work /exercise
Case study Activity 3: HSD, HOSPITALS and District
This exercise is to highlight that Useful interests of data can be got from the initial
stage of eye bowling and suspected error checks could be investigated.)
Data on the malaria laboratory tests are done for six month in 2008 in a particular health
facility were as follow:
Jan
Feb
MARCH
APRIL
MAY
JUNE
112
245
99
2886
145
185
What can you say about this data??
Cross Tabulation
Age by laboratory results among malaria suspected cases
Age group
Malaria results
+ve
-ve
total
Under 5 year
120
25
145
Above five years
65
60
125
Total
185
85
270
What are the useful percentages in relation to malaria? Column percentages or row
percentage?
calculate test positivity rate among children
calculate test positivity rate among adults
Ministry of Health -Resource Centre
April 2012
58
Trainers Guide: Direct the trainees to fast have a Data overview and feel by first thinking
through what are the numerator and denominator, which percentage are more
important?
Example of for HC II & III data use:
Consider 420 outpatients who have been recorded in the OPD register. Among these
clinical diagnosis column shows 320 have been suspected to be having malaria .if the
disease of interest is malaria then the proportion of malaria cases is
It’s further found out that of the 420,210 were children under five and of the 320 malaria
suspects 183 were children.
Present this data in a table. Of age group by malaria case
Age group
Suspected
malaria
with Not suspected
to be having
malaria
Under five
183
b
5 years and above
a
c
Total
320
210
100
420
Find a, b, c
Age group by malaria confirmed cases
Calculate the proportion of malaria suspected cases per each age group:
a) under five year and
b) Those aged 5 years and above
Ministry of Health -Resource Centre
April 2012
59
Of the 320 suspected malaria cases, 200 were taken to the laboratory for confirmation.
Calculate the malaria test ratio or lab test ratio.
From the laboratory register, 83 were found to be positive. Obtain the test positivity
ratio or Proportion of positives.
What would be the excess malaria treatment doses used if the laboratory tests were not
done i.e. only clinical diagnosis was used.
Facilitators guide: The trainees can refer to Annex 2 for more information on each
indicators and how they can be presented and analysed. Print it out for each
person.
Ministry of Health -Resource Centre
April 2012
60
SESSION 5: DATA INTEPRETATION, SHARING INFORMATION AND
PROVIDING FEEDBACK
Objectives:

Understand the importance of feedback in program improvement and
management

List potential barriers to providing feedback

Identify priority decisions and programmatic questions

Link decisions/questions with potential data sources

Create a time-bound plan for using data in decision making
Consider how to improve feedback mechanisms in participants’ each level.
Facilitators guide: Asks for group participation. Before moving on to the next session,
ask participants if everything was clear and if they would like to discuss any of the
concepts or topics further.
5.1 INTERPRETING DATA
Data is usually interpreted in relation to a specific indicator:
Indicators
An indicator is an indirect measure of an event or change in a situation; it is a structured
way to recognize patterns of data. When studying complex situations or events, it is
often impossible or impractical to study each of the many factors that contribute to it.
Measurement of carefully selected indicators (see also Annex 1 – Indicators will allow
getting a good idea about the event or situation. If the indicator changes, we conclude
Ministry of Health -Resource Centre
April 2012
61
that the event of the situation has changed accordingly. Indicators could be discussed
under data processing as well: the indicators we chose tell us how to rearrange data to
present them in a more understandable way.
Did you recognize any data patterns?
The following patterns should immediately be looked for.
Spikes are unusual or sharp increases in the number of cases. Spikes can indicate an
outbreak of a disease. At clinic level you may notice spikes even during your work while
you are examining and tallying the patients. In such cases, you should not wait for the
end of the month. You should inform the authorities and start investigation and action
immediately. Line graphs and frequency tables are useful for identifying and recording
spikes.
Clusters are groupings of cases by time period, area, age and sex groups, etc. This can
indicate an epidemic or endemic that is limited to a certain place, group of people and
period of time. Comparison of frequency and relative frequency of cases in different age
and/or sex groups in a clinic and between/among different clinics can be helpful in this
regard. Comparison of the top 10 cases in different health facilities is also helpful
as well as pie, line and vertical bar charts.
Trends are gradual increases or decreases in cases over time. Look carefully for trends,
since they may occur so gradually that change is not observed. Identify trends by
looking at the number of cases that have occurred and comparing them to the number
reported in previous months and previous years (data of several years are needed for
studying and learning trends as they are different in different areas). Trends are very
useful to assess service coverage, making sure that the necessary stocks in required
amounts are available to be used and timing of the leave schedules appropriately
Ministry of Health -Resource Centre
April 2012
62
in the year. The number of services delivered should increase over time until the target
is reached. The line charts are extremely useful for this purpose.
Seasonal variations. Regular changes occurring according to the time of year. The
incidence of measles, diarrhea, ARI and malaria, for example, varies seasonally. Increased
accessibility of facilities due to seasonal weather changes can explain a suddenly
increased number of patient visits. A sudden spike or drop may be due to seasonal
variation. Line charts are particularly helpful for displaying and identifying seasonal
variations.
Refer to Annex III on details of indicators and how they can be used
/interpretation.
5.2 DATA PRESENTATION, SHARING AND FEEDBACKS:
1. Discuss reports with colleagues when they are first prepared. Include discussions of
reports and structure of presentation (even preliminary findings) in regular staff
meetings. Others can offer useful insights into why specific conditions are getting better
or worse and have helpful suggestions about changes which could be made.
The two main ways of summarizing data are by using tables and charts or graphs.
A table is the simplest way of summarizing a set of observations. A table has rows and columns
containing data, which can be in the form of absolute numbers or percentages, or both.
Charts and graphs are visual representations of numerical data and, if well designed, will convey
the general patterns of the data.
The presenter should mind the audience the data is to be presented as this will determine
whether the visual presentation will convey the message more at the tables or the combination.
Ministry of Health -Resource Centre
April 2012
63
If no structures report formats are provided, the presenter should pick the data to be
presented relating to the priorities of the data sharing meeting. This can involve
consultation with other National level officers, DHT members or health unit staff.
Facilitators guide: It should be noted that HMIS data or health data is not only for the
records people, and the HMIs focal person /biostatisticians in or national data bank but
for all stakeholders in the health system. The more we get all members involved in data,
the more they will be interested in the information from it and the more utilization of
the data generated once it is shared.
2. Provide feedback to those who have sent reports or data from other units. Doing this
can be as simple as acknowledging the receipt of the report, and responding to any
issues that require your action. Better still, you can develop a routine mechanism to
feedback some analysis of key indicators on reports you receive from all facilities. This
should help them compare their performance with that of other reporting facilities.
5.3: LINKING DATA TO ACTION
There is need to understand how to link the data we have with our programmatic
questions and the decisions we need to make. Once the analysis is made, there is
need to
o Identify priority decisions and programmatic questions ,
o Link decisions/questions with potential data sources
o Create a time-bound plan for using data in decision making.
In the previous sessions, we discussed many of the concepts and tools, anaylsis
methods that can facilitate data use in your setting.
Ministry of Health -Resource Centre
April 2012
64
Facilitators Note : Now let’s discuss the practical aspect of data use. How can you manage
to build data use into your work? How do you ensure that data use becomes part and parcel
of your day-to-day duties? The answer is to PLAN for it.
These may be decisions and questions around:

Program monitoring, planning, and improvement

advocacy needs.( e.g increase the doses of malaria treatment in a Health center II
medicine kit, number of staff required for immunization outreach etc may
require advocacy with evidence based data at Health center II level to be sent to
the upper levels )

Program management or operations issues based on the information obtained
from the data.
One needs to link the decisions and questions with data and then creates a timebound plan for decision making. It is also critical to involve others in your work
because the best decisions are made with stakeholder involvement.
For the data use to be effective , one needs to identify the ddecision makers and
stakeholders with potential interest in your data and the information you have
obtained from the interpretation, decisions / actions that the stakeholder makes,
Questions to which the stakeholder requires answers and When the decision will be
made

One needs to know “What Are Decisions?”

The following should be notes :

Choices that can lead to action

All decisions are informed by questions
Ministry of Health -Resource Centre
April 2012
65

All questions should be based on data
For example: Some decisions may require Allocation of resources across districts
/ facilities,Revising VHT program approaches to emphasize testing every fever
case, develop and institute PMTC policies on ANC clinics , Hire and allocate staff
to facilities
In some contexts, a decision cannot be identified before a key programmatic or policy
question is answered. Or decision makers may have a question about their program for
which they need an answer. It is the answer to this question that may provide the
evidence that some kind of action needs to be taken to either improve or realign
services. In these cases, we focus on identifying ‘programmatic questions,’ as opposed
to decisions.
Examples of programmatic questions:

What percentage of HIV+ pregnant women in care actually are delivering in
health facilities?

What percentage of clients starting ART are lost to follow-up?

Are the number of family planning clients decreasing?

What percentage of pregnant patients who are HIV+ actually are receiving ART?
Facilitator notes: In some cases, it may be helpful to have participants brainstorm a
list of programmatic questions and not use the list above. As participants identify
questions (or as you go through the list above), talk about what data could answer
the questions, as well as what decisions might result from the data.
Ministry of Health -Resource Centre
April 2012
66

For example, if the monthly report data reveal that only 20% of women are
delivering in health facilities, ask the participants to brainstorm solutions. Identify
at least one decision that would need to be made.
Lets use the PMTC T example to clearly see how we can derive actions:
1. Are we reaching testing targets in PMTCT?
2) Do we have sufficient test kits?
3) What is the nurse-to-client ratio in the clinic?
let’s assume that the analysis informed us that the PMTCT program was no where close
to meeting its targets for the year, the nurse-to-client ratio was far below acceptable,
and there were sufficient test kits to reach programmatic targets. After this information
was discussed with key stakeholders and interpreted in the context of the PMTCT
program, the in-charge or DHT decided that additional PMTCT counsellors would
improve program performance. What steps would be taken.
For Linking Data with Action to be effective one needs to:
1) Creates a time-bound plan for data-informed decision making by setting dates by
which data should be reviewed in relation to key programmatic questions and upcoming
decisions.
2) Encourages greater use of existing information by identifying existing data
resources and linking that information with the programmatic questions that need
answers to support evidence-based decision making.
Last, it provides you with a data-informed decision-making ‘record’ so that you can—
Ministry of Health -Resource Centre
April 2012
67
3) Monitor the use of information in decision making — Provides a timeline for
conducting analyses and making decisions.
Facilitator’ notes

It should be noted that Building data use into your work takes planning and dedicated
time

Data should be linked to specific decisions so as to facilitate use

Relevant stakeholders should be involved in each step of the process so that they
appreciate its relevancy.
For District Level training Examples:
The health worker was complaining of heavy workload. The health unit has 2 midwives,
2 clinician (1 in charge, 1 laboratory assistant, 4 nurses) who claim they cannot handle
the workload. The health facility has no records officer and the in charge rarely sends
monthly report to the HSD. The last report sent to the HSD three months ago shows the
OPD attendance of 1004 patients in the month of May approximating to 82 patients per
day with no report on impatient data at all .(no HMIS 108). The unit has piles of ANC
register in the stores and OPD counter books as stationery in the In-charge’s office but
are not filled.
You have headed a team to supervise this health facility describe how you can help this
in-charge be able to manage this health unit and be able to convince the DHT that he
needs more staff. State both immediate and long-term actions.
Ministry of Health -Resource Centre
April 2012
68
ANNEX 1: DATA MANGEMENT AND USE ACTIVITIES AT VAROUS LEVELS
Service Delivery Data Management activities at various levels [1]
At National Level
The Biostatistician at the Resource Centre is responsible for:
•
Receiving all district data (including those from the national referral hospitals.
•
Ensuring entry of all district data (including those from the national referral hospitals)
onto the District Health Information System (DHIS) software package.
•
Analyzing the quality of all reports received and ensuring follow-up in case of
incompleteness, problems with validity, as well as delays.
•
Compiling all reports from the districts & NRHs into a single national report using
the DHIS software.
•
Preparing an analysis of the data for discussion during the SMER TWG meetings and
sector performance review meetings for decision-making.
•
Providing quarterly feed-back to the districts / NRHs.
•
Disseminating weekly IDSR reports to all stakeholders and community.
•
Disseminating quarterly district performance reports at sector review meetings.
At District Level
The District Biostatistician, or, where this position in not filled, the HMIS focal person, is
responsible for:
•
Receiving all health unit data (including those from the general and referral
hospitals).
Ministry of Health -Resource Centre
April 2012
69
•
Entering all health unit data (including those from the general and referral hospitals)
onto the District Health Information System (DHIS) 2 software package.
•
Analyzing the quality of all HMIS reports received and ensuring follow-up in case of
incompleteness, problems with validity, as well as delays.
•
Compiling all reports from the units into a single district report using the DHIS 2
software.
•
Preparing an analysis of the data for discussion by the DHT for decision-making and
participating in the DHT discussion.
•
Forwarding the DHIS 2 report electronically to the RC. If not possible, deliver the
physical report by the 28th day of the following month.
•
Providing quarterly feed-back on data management to the health units.
•
Disseminating quarterly district assessment reports to DHMT.
•
Disseminating annual district performance report to District stakeholders’ forum.
At Health Sub District Level
The HSD Health Information Assistant is responsible for:
•
Receiving all health unit data (including those from the private providers) in the HSD.
•
Entering all health unit data (including those from the private providers) into the
HSD databank.
•
Analyzing the quality of all HMIS reports received and ensuring follow-up in case of
incompleteness, problems with validity, as well as delays.
•
Compiling all reports from the units into a single HSD report.
Ministry of Health -Resource Centre
April 2012
70
•
Preparing an analysis of the data for discussion by the HSD Team for decisionmaking and participating in the discussion;
•
Forwarding the HSD report to the DHO by the 14th day of the following month;
•
Providing quarterly feed-back on data management to the health units.
•
Disseminating quarterly HSD assessment reports to HSD Team.
•
Disseminating annual district performance report to HSD stakeholders’ forum.
At Health Facility Level
All Health Providers including those from the private and community (VHT) are
responsible for:
•
Collecting patient data using relevant patient forms.
•
Compiling relevant patient data from patient forms and entering it into the patient
registers on a daily basis.
The Health Information Assistant (HIA) or Medical Records Officer, or, where there is no
HIA or MRO, the Health Unit In Charge or a designated person is responsible for:
•
Regularly compiling relevant patient data from patient registers and community
(VHT) into the health facility HMIS database.
•
Analyzing the quality of all patient registers and community reports received and
ensuring follow-up in case of incompleteness, problems with validity, as well as
delays.
•
Compiling all reports from the sections/units / departments into a single health
facility report using the health facility HMIS database.
Ministry of Health -Resource Centre
April 2012
71
•
Plotting monthly performance on the displayed monitoring graphs.
•
Preparing an analysis of the data for discussion within the health facility for decisionmaking methods and participating in the discussions.
•
Forwarding or delivering the health facility report to the HSD/DHO by the 7 th day of
the following month; In case of IDSR data weekly reports should be forwarded every
Monday.
•
Providing quarterly feed-back on data management to the sections / units/
departments and community (VHTs).
•
Disseminating of monthly performance during monthly facility meetings.
•
Dissemination of quarterly facility assessment reports to the HUMC / Hospital Board.
•
Dissemination of annual facility performance reports to the Sub County forum.
At Community Level
All Community Health Providers (VHT) are responsible for:
•
Collecting patient/client, or activity data using relevant forms.
•
Compiling data from the relevant forms and entering it into the VHT Register on a
regular basis.
•
Compiling relevant data from the VHT register into the VHT Report.
•
Preparing an analysis of the data for discussion within the VHT for decision-making
methods and participating in the discussions.
•
Forwarding or delivering the VHT report to the nearest health Centre by the 5th day
of the following month;
Ministry of Health -Resource Centre
April 2012
72
•
Disseminating quarterly performance report data to Parish Committee.
ANNEX II: LIST OF INDICATORS RELATED TO HEALTH SERVICES PROVIDED AT
FACILITY LEVEL AND HOW THEY CAN BE PRESENTED:
Summary of the list of indicators related to health services provided at facility level and
how they can be analyzed:
Reporting
% of reports received (received reports table)
Health problems
Number of new cases in all age and sex groups (frequency table)
% of new cases in all age and sex groups (percentage table)
% of positive slides for malaria (percentage table)
Health problems in children < 5 years
Number of new cases in children < 5 years (frequency table for children < 5 years)
% of new cases in children < 5 years (frequency table for children < 5 years)
Number of neonates delivered < 2500 g (frequency table)
% of neonates delivered < 2500 g (percentage table)
Health problems in different age and sex groups
Number of new cases in all age and sex groups (frequency table)
% of new cases in different age and sex groups (percentage table)
Quality of practices
% of re-attendance (percentage table for re-attendance)
% of pneumonia among all ARI cases (percentage tables)
% of acute bloody diarrhea among all diarrheal cases (percentage tables)
Ministry of Health -Resource Centre
April 2012
73
% of blood tests in malarial cases (percentage tables)
Reproductive health services
Number of deliveries (frequency table)
% of assisted deliveries among all deliveries (percentage table)
Number of new antenatal visits (frequency table)
Number of repeat antenatal visits (frequency table)
% of 2nd antenatal visits out of new antenatal visits (percentage table)
Number of other antenatal visits (frequency table)
% of other antenatal visits out of new antenatal visits (percentage table)
Number of women who came for at least one postpartum visit (frequency table)
% of women that delivered who came for postpartum visit (percentage table)
% of postpartum visits out of new antenatal visits (percentage table)
Number of maternal deaths (frequency table)
Number of neonatal deaths (frequency table)
Number of stillbirths (frequency table)
Utilization of health services
Total number of OPD visits (frequency table)
Percentage of different age and sex groups (pie chart)
Ministry of Health -Resource Centre
April 2012
74
ANNEX III: INDICATORS WITH SOURCES OF DATA AT THE HEALTH FACILITY AND HOW THE INFORMATION CAN
BE USED:
INDICATOR
DEFINITION
HOW TO USE IT
Numerator: Number of total
OPD
attendance
attendance
OPD
RATES
(i.e.
and
new
re-
UTILIZATION attendance).
Information on the service delivery system and the use of the
Denominator:
Catchment service by the community.
population for the year in
question
or
catchment
population/4 for the quarter.
Numerator:
Number
of
patient bed days in a given
BED
RATE
OCCUPANCY period (e.g. quarter or year) x
100
Denominator:
Number
of
beds in institution x Number
The bed occupancy rate gives the average percentage of
occupied beds during the period under review(usually one year).
The bed occupancy rate should ideally be 80% or more. Two sets
of parameters determine the occupancy rate: the need for service,
and the service delivery factors.
Ministry of Health -Resource Centre
April 2012
75
INDICATOR
DEFINITION
HOW TO USE IT
of days in time period under
review
It is estimated that in areas of high malaria transmission, 23% of
deaths among children under 5 years are attributable to malaria
Numerator:
Number
of
deaths in children under 5 due
to malaria in health unit(s)
MALARIA
-
CASE (x100)
FATALITY RATE IN Denominator:
CHILDREN UNDER 5
Number
of
cases of diagnosed malaria
among
children
admitted to health
unit(s).
under
5
and 11% in areas with medium transmission intensity based on
findings from verbal autopsy study in western Uganda. The under
5 mortality of 159/1000 live births (DHS 1995) in
Uganda translates into a malaria specific mortality of 37/1000 in
high malaria endemic areas and
18/1000 in low endemic areas, to a total of 70,000-110,000 child
deaths annually. The case fatality rate is probably more closely
related to quality of service than the incidence rate. Even if the
number of deaths is too small for the CFR to have statistical
significance,
each
death
should
prompt
an
investigation
regarding case management, referral, treatment failure and other
related factors.
Ministry of Health -Resource Centre
April 2012
76
INDICATOR
DEFINITION
Numerator:
HOW TO USE IT
Number
of
deaths due to pneumonia in
health unit(s) ( x 100)
ARI-PNEUMONIA
CASE FATALITY RATE
Denominator:
cases
Number
of
of Same as malaria above
diagnosed
pneumonia admitted to health
units(s)
Numerator:
Number
of
deaths due to diarrhea in
DIARRHOEA
FATALITY RATE
CASE health units (x 100)
Denominator:
Number
of
cases of diagnosed diarrhea
cases admitted to health units
CUMULATIVE
INCIDENCE
SEXUALLY
An increasing case fatality rate should lead to an
analysis that includes review of the availability of drugs and
supplies and of the rational use of pharmaceuticals. The
possibility of missed outbreak of cholera or dysentery should not
be dismissed
Numerator: Number of new The Sexually Transmitted Infections that are captured using HMIS
OF cases of STIs in health units (x 105 are categorized as;
1000)
Urethral Discharges, Transmitted Infection due to Gender Based
Ministry of Health -Resource Centre
April 2012
77
INDICATOR
DEFINITION
HOW TO USE IT
TRANSMITTED
Denominator:
INFECTIONS
aged above fifteen years old in STI incidence should be monitored by comparing its value with
Population Violence, and
the catchment area.
Other Sexually Transmitted Infections
that of the same time period in the previous year. Investigation
and action should follow an increase of 15% from the earlier time
period. To follow time trends in a single area, counts of STD cases
may be monitored instead of incidence. Ranking the village
incidence rates can identify priority areas for STD interventions,
particularly prevention. Interventions include IEC campaigns, as
well as promotion of the use of condoms.
Numerator: Number of cases
of TB notified annually x 100%.
TB
RATE
NOTIFICATION Denominator:
Expected
number of TB cases for the
catchment population in the
same period.
CASE NOTIFICATION Numerator (N): Total number NTLP Quarterly treatment outcome report forms.
Ministry of Health -Resource Centre
April 2012
78
INDICATOR
DEFINITION
RATE (CNR)
of all new TB cases and relapse
smear
HOW TO USE IT
positive
TB
cases
(D):
Total
notified
per year
Denominator
population in the catchment
area.
Case notification rate = N/D X
100,000
Numerator:
reported
NUMBER
CASES
OF
OF
NEW Flaccid
Number
cases
of
Paralysis
of
Acute AFP is a notifiable disease and a single case should be
among investigated with follow-up at all levels of the
ACUTE people less than 15 years of
FLACCID PARALYSIS
age x 100000.
Denominator:
service delivery system. AFP may only be confirmed as
poliomyelitis after investigation and
Population confirmation following the WHO criteria.
aged less than 15 years.
Ministry of Health -Resource Centre
April 2012
79
INDICATOR
DEFINITION
HOW TO USE IT
Progress is monitored monthly at the health unit graphically. At
Numerator: Number of new health
Health
Sub-Districts,
Districts
and
MOH
clients at antenatal clinic (x headquarters, the proportion of new pregnancies attended
ANTENATAL
ATTENDANCE
COVERAGE
units,
100)
Denominator:
should be analyzed quarterly and annually.
Number
of When the attendance is lower than targeted, reconsider the
estimated pregnancies in the strategies.
catchment population
Identify
the
districts/parishes/villages
with
low
attendance. Contact the community and find a solution with
them: e.g. intensified information or organize an outreach.
At
health
units,
health
sub-districts,
districts
and
MOH
Numerator: Number of TT2, headquarters, the proportion of new pregnancies attended
TT3, TT4 and TT5 doses given should be analyzed quarterly and annually.
TETANUS
COVERAGE
TOXOID to pregnant Women (x 100)
Denominator:
Number
The coverage of pregnant women attending Antenatal Clinic with
of TT vaccine is an indicator of quality
expected pregnancies in the of care. Ideally, we would also want to know the percentage of
same period
women in the entire country whose pregnancies (and therefore
newborns) are protected. Overall coverage of the population of is
Ministry of Health -Resource Centre
April 2012
80
INDICATOR
DEFINITION
HOW TO USE IT
estimated
at around 90% of pregnant women
COVERAGE
OF
PREGNANT WOMEN
WITH PRESUMPTIVE
TREATMENT
FOR
(IPT)
MALARIA
USING S-P
Numerator:
Number
pregnant women receiving 2
doses of S-P in a given period
(x 100)
Denominator:
Number
of
new ANC attendances in a
given period
Numerator:
PERCENTAGE
of
Number
of
OF deliveries in health facilities (x
DELIVERIES TAKING 100)
PLACE IN A HEALTH
Denominator:
FACILITY
expected
Number
births
catchment population
in
of
the
Coverage of pregnant women with malaria prevention is a
national preventive target. At heath Units, Health Sub-Districts,
Districts and MOH headquarters, the proportion of women
attending for Antenatal Clinic who receive two doses of Fansidar
should be analyzed quarterly and annually. National Target is
80% and each heath unit should strive for that target.
The number of deliveries in maternity is monitored monthly as a
national preventive target. Progress is monitored monthly at
health Unit and reported. If deliveries decline while ANC new
clients do not, it
is necessary to find out why and correct the situation. This may
include decreasing or eliminating user
fees for delivery.
Ministry of Health -Resource Centre
April 2012
81
INDICATOR
DEFINITION
HOW TO USE IT
CAESAREAN
SECTION
RATE Numerator:
(NUMBER
Number
of
OF Caesarian section in Hospitals
CAESAREAN
and Level IV Health Centres x
SECTIONS PER 100 100
DELIVERIES WITHIN Denominator:
THE CATCHMENT
AREA
OF
deliveries
Number
within
of
the
THE catchment population
This indicator is intended to measure whether or not Hospitals
and Level IV Health Centers are fulfilling their role in providing a
service, namely caesarian section, which cannot be provided at
lower levels of the health system. At level IV Health Units, HSD,
District and National Level, analysis is recommended on an
annual basis.
HEALTH FACILITIES)
Numerator:
Number
of
children under one year of age
BCG
VACCINATION
COVERAGE
who
have
received
BCG
immunization (x
delivery
and
providers’
delivery
procedures
should
be
investigated. Activities to increase accessibility to BCG at birth
include increasing facility based deliveries, vaccinating the baby
100)
Denominator:
If the coverage is falling, the availability of vaccine, health service
Number
of
children under one year in the
before discharge of the mother and assuring availability of the
vaccines.
Ministry of Health -Resource Centre
April 2012
82
INDICATOR
DEFINITION
HOW TO USE IT
catchment area.
Numerator:
Number
of
children under 1 year of age
DPT-HepB+Hib
VACCINATION
COVERAGE
3
who have received 3 doses of At health unit, DPT-HepB+Hib coverage should be calculated,
DPT-HepB+Hib
analyzed and graphed monthly and quarterly. At health sub-
vaccination ( x 100)
districts,
Denominator:
Number
districts,
and
national MOH headquarters, DPT-
of HepB+Hib coverage should be calculated quarterly and annually.
children under 1 year in the
catchment area.
Numerator:
Number
of At the health unit, measles immunization coverage should be
children under 1 year of age calculated, analyzed and graphed monthly, and quarterly. At
MEASLES
who have received the 1st health sub-districts, districts, and national MOH headquarters,
VACCINATION
dose of measles
COVERAGE
Vaccine before age 1 (at 9 and annually. Measles vaccination coverage should be compared
measles coverage should be calculated and graphed quarterly
months of age) (x 100)
Denominator:
Number
to the measles incidence rate (use case based measles
of surveillance data) to confirm that the vaccine is conveying
Ministry of Health -Resource Centre
April 2012
83
INDICATOR
DEFINITION
HOW TO USE IT
children under 1 year
immunity. If the number of cases is increasing (or indeed not
declining over previous years), this may indicate a diminished
effectiveness of the vaccine. An investigation into possible
breakdowns in the
cold chain should be investigated.
This indicator is for nutritional surveillance of young children in
Numerator:
children
UNDERWEIGHT
PREVALENCE
Number
at
of
measles
immunization with weight for
AT age below the bottom line
MEASLES
(x 100)
IMMUNISATION
Denominator: Total number
of children weighed at measles
vaccination
the service area. Each month the estimate is for a different group
of children (those receiving measles vaccinations). There could
easily be some seasonal variations since this age group of
children is extremely vulnerable to food shortages.
It is most important during times of disaster (floods, droughts)
but it is an indication of general economic well being in the area,
and therefore trends over years will also be important to
document.
Comparison of locations: Priority areas for nutrition interventions,
particularly prevention and modification in treatment protocols,
Ministry of Health -Resource Centre
April 2012
84
INDICATOR
DEFINITION
HOW TO USE IT
can be identified by ranking the areas’ prevalence rates. This can
be done through a register review, which identifies the villages or
parishes with the highest prevalence. At health units, health subdistricts, districts, and MOH headquarters, the proportion of
children below two standard deviations from the bottom line is
analyzed quarterly and annually. Children who are identified in
this category should be followed up through outreach programs.
PERCENTAGE
VILLAGES
VILLAGE
OF
WITH
HEALTH
TEAMS
PERCENTAGE
VILLAGES
Numerator:
Number
of
villages with Village Health
Teams x 100
Denominator:
Number
villages in catchment area
OF Numerator:
Number
of
The indicator should be analyzed quarterly at health unit, HSD
level, District and National levels.
Special effort should be made to help form VHT in villages where
active community participation in health does not exist.
Community Health Workers can assist in formation and
functioning of these committees.
of Emphasis should be placed on raising the awareness in the
WITH villages with known access to population of the relationships between their health and
ACCESS TO WATER
safe water x 100
surroundings. Collaboration with relevant agencies for the
Ministry of Health -Resource Centre
April 2012
85
INDICATOR
DEFINITION
Denominator:
HOW TO USE IT
Number
of promotion of safe water should be made at all levels. This
villages in catchment area
indicator is monitored quarterly and annually at health unit,
Health sub-district and District levels.
PERCENTAGE
PLANNED
OF
Numerator:
Number
of
outreach activities conducted
OUTREACH
Denominator:
ACTIVITIES
CONDUCTED
Number
of
outreach activities planned
This indicator monitored monthly at health units, HSD and
Districts levels. It will monitor the availability of staff and
implementation of Outreach activities t from the health unit staff
to a community to conduct preventive promotion activities such
as immunization, growth monitoring, family planning,
The HSD team and Health Unit staff should make efforts to avoid
NUMBER
TRAINED
OF Numerator:
of drop outs from those who have been trained for these voluntary
VILLAGE trained Village health Teams
HEALTH TEAMS PER Denominator:
VILLAGE
Number
Number
positions;
regular
meetings
with
CHWs/VHTs
and
public
of recognition of their importance can help reduce drop outs.
villages in the catchment area
This indicator is monitored quarterly at health units, Health subdistricts, district and National levels. It is important to work with
Community-Based Health Care programmers so that the support
Ministry of Health -Resource Centre
April 2012
86
INDICATOR
DEFINITION
HOW TO USE IT
is optimized to the benefit of all. CHWs can be very useful to
create an effective patient/client follow-up system. The In-charge
can prepare a list of outpatients who do not return for continuing
treatment (e.g. TB), or children who late for immunizations, or
women who need to come for the next Tetanus booster. The
CHW can visit those persons to remind them of their
appointment.
At health units, health sub-districts, districts and National level,
Numerator: Number of health stock-outs of drugs is monitored monthly : Essential drugs for
PERCENTAGE
OF units reporting no stock outs monitoring the Health Sector Strategic and Investment Plan
FACILITIES
WITHOUT
of any of the six essential (HSSIP) are First Line
ANY drugs in a
STOCK-OUTS FOR
given time period
ESSENTIAL DRUGS
Denominator:
health units
Drug for Malaria, Cotrimoxazole tablets, Measles vaccines,
Fansidar,Depo-provera and ORS sachets.
Number
of Monthly monitoring of the stock conditions is done through the
HMIS reports received at the MOH Resource Centre.
An adequate stock level is a level between the maximum and the
Ministry of Health -Resource Centre
April 2012
87
INDICATOR
DEFINITION
HOW TO USE IT
minimum. A stock out of any of the essential medicines should
not happen at any one time in any health facility as it is an
indication of inadequate stock management, or of an unplanned
extremely large increase in use, or routine misuse of the
commodity. In normal circumstances, the Balance on Hand
should not fall below the minimum. If
this happens, then if an order has already been placed, ensure
that it will arrive before a stock out occurs. If an order has not
been placed, then an emergency order should be made.
Minimum and maximum values may need to be adjusted. The
stock control system may be one cause of the problem, but
health unit in-charges should also consider the possibility of,
pilferage, prescription habits of staff, and changes in disease
patterns.
Measures should be taken to correct the problem found. In the
eyes of the general public the availability of drugs in health
Ministry of Health -Resource Centre
April 2012
88
INDICATOR
DEFINITION
HOW TO USE IT
institutions is key for the confidence in the health system.
At Health Units and Health Sub districts, staff workload by type of
service provided is analyzed quarterly and annually. At District
Numerator:
contacts
Total
client
(disaggregated
by
OPD, antenatal/postnatal care,
DAILY STAFF WORK immunizations
LOAD
and
family
planning)
Denominator:
Number
of
clinic days (disaggregated by
type of service as above)
and
MOH
headquarters
total
staff
daily
workload
(not
disaggregated by services) is analyzed quarterly and annually. The
number of attendance per staff member per clinic day is a
measure of the workload. From this you can also calculate how
much time a clinician spends with each patient/client (by dividing
the hours in usual clinic day by the number of attendance per
staff member per clinic day). If the available time is too short, the
quality of care declines. Since a Health worker who has a low
work load will never complain, this will not be noticed unless
calculations are done. An unfair distribution of workload will
undermine staff relations.
ON-TIME
HEALTH Numerator:
Number
of Routinely, the In-charge should be observing the recording of
Ministry of Health -Resource Centre
April 2012
89
INDICATOR
DEFINITION
MANAGEMENT
monthly reports received on information for all the services the health unit provides and
INFORMATION
or before the due date during making corrections as needed. In addition, at the end of a
SYSTEMS
the time period
reporting period, the aggregation of the totals for reporting must
(HMIS) REPORTING
(x 100)
be done correctly. If late reporting is a general problem, the
Denominator:
HOW TO USE IT
Number
of health sub district should take measures to speed up reporting.
reports expected during the Support supervision may provide an opportunity to encourage
time period
on-time reporting and to collect reports.
Ministry of Health -Resource Centre
April 2012
90
a) PMTCT
1. Number of pregnant women tested for HIV
2. Number of pregnant women tested HIV positive
3. Number of HIV positive pregnant women given ARVs by regimen
4. Proportion of babies born to HIV positive mothers who tested HIV positive
5. Proportion of HIV positive mothers who accessed family planning services
b) ART
1. Number of new patients enrolled in HIV care at Facility during the quarter
2. Number of pregnant women enrolled into care during the quarter.
3. Cumulative Number of individuals on ART ever enrolled in HIV care at facility
4. Number of HIV positive patients active on pre-ART Care
5. Number of HIV positive cases who received CPT at last visit in the quarter
6. Number eligible patients not started on ART
7. Number of new patients started on ART at facility during the quarter
8. Number of pregnant women started on ART at facility during the quarter
9. Cumulative Number of individuals on ART
10. Number of HIV positive patients assessed for TB at last visit in the quarter
11. Number of HIV positive patients started on TB treatment during the quarter
12. Number of people accessing ARVs for PEP
c) VCT
1. Number of clients received Pre test counseling
2. Number of clients Tested
3. Number received their HIV results
Ministry of Health -Resource Centre
April 2012
91
4. Number of clients Received HIV results for the first time in this financial year
5. Number of clients Counseled and tested as a couple
6. Number Received results as a couple
7. Number of Discordant results
8. Number of TB Suspect
9. Number of clients Started on CPT
10. Number of clients who received HCT for PEP
11. Number of clients Linked to Care
Ministry of Health -Resource Centre
April 2012
92
ANNEX IV:
Section 2 – Generating Demand for Data
Activity 1 – Case Study Review
Instructions:
 Select a reporter.
 Read the case study assigned to your group (either the first or the second case
study) and answer the following questions (45 minutes):
o What prompted the data use undertaking?
o What was the decision made?
o What types of data were used to make the decision?
o What was the outcome of the decision?
o What can you comment on such kind of data collection arrangements of
partners I the district.
 Report back. (10 minutes per group)
Case study #1
MUJAP
The MUJAP in wakiso from January 2009 and by September 2011 was assisting 12
service sites (Local Partner Treatment Facilities, or LPTF) to provide antiretroviral therapy
(ART). As of the end of September 20011, the program had tested 103,685 individuals
for HIV, enrolled more than 7,840 people in care, and started 3,852 clients on ART. Each
LPTF provides services at a health facility (either a hospital or health center IV) and
supports community outreach. MUJAP utilizes a system known as IQ Chart to collect
patient data necessary for its ART programs. IQ Chart is an electronic patient
management and monitoring system that allows service sites to collect, store, and
analyze patient-level data.
In May 2011, an IQ Chart report showed that a large number of clients were missing
their scheduled appointments for antiretroviral (ARV) drug pick-up. The central office
discussed the findings with each site’s clinical team. The initial reaction from the clinical
teams was that the number of patients who allegedly missed appointments was inflated;
they did not believe that so many patients had missed their ARV appointments. To
determine the true cause, the clinical teams requested an investigation to determine if
this was a data quality issue, or if many patients were in fact missing appointments.
MUJAP had a multifaceted problem to solve. If the quality of data was poor, the
Ministry of Health -Resource Centre
April 2012
93
organization needed to reinvest in training service site staff in data collection
techniques; if the data quality was sufficient, it needed to address the fact that many
people were missing their appointments.
Using patient data already collected and stored, the office generated a list of names and
addresses of every patient marked as more than 20 days overdue for an ARV pick-up.
The lists were given to clinicians and community coordinators for individual follow-up
and verification at both the facility and the patient’s home. It was determined that
patient records were indeed correct. This resolved the issue of data quality of existing
medical records.
As a result, MUJAP instituted new procedures to strengthen support services to ARV
patients. Each community volunteer was assigned eight ART clients whom he/she was
expected to visit weekly. New forms were designed for the volunteers to report on their
clients, and monthly meetings were scheduled with the community coordinators to
share and discuss the information collected and their experiences in supporting clients.
With improved increased availability of data on ARV client progress, it was possible to
provide a higher standard of care. Every site now monitors weekly ARV pick-up, CD4
testing, and care support. With improved data available at the service sites, clinicians
now use the reports to identify problematic patients. Community volunteers then follow
up with these patients to ensure that they continue with the drug regimen. As a result of
these program improvements, the quality of life of program beneficiaries has improved.
The number of patients lost to follow-up has dropped significantly. Anecdotal reports
suggest that community volunteers have become more visible in their communities,
which has encouraged other PLHIV to join the program and seek HIV-related services.
Ministry of Health -Resource Centre
April 2012
94
6.0. REFERENCES
1. Health Sector Strategic and Investment Plan, 2010/11 – 2014/15.
2. HMIS Health facility manual 2010
3. HMIS District Manual 2010
4. MOH- indicator for monitoring indices and the Health Sector Strategic and
Investment Plan In Uganda , The User indicator Manual
5. Standard Operating Procedures for health information
6. MEASURE evaluation data use training materials.
7. Designing and Conducting Health systems Research projects Volume 2, Part 2, WHO
2005
Ministry of Health -Resource Centre
April 2012
95