1. USE OF INFORMATION AT THE DISTRICT LEVEL
2nd Annual Health Informatics Summit
04 - 05 October, 2012
Dr Sandhya Ahuja
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2. WHY USE DATA?
Need to know the disease profile-
 epidemiology is the study of prevalence and
determinants of disease.
Need to know the burden of disease—
 So that we know what are the health priorities and their
determinants
Need to know situation in service delivery/access &
utilization of services:
 So that areas/communities which lag behind/have
greater need could be allocated more resources and
inputs.
2

3. SOURCES OF DATA/INFORMATION
External Surveys
Data from Routine Monitoring Systems.
Commissioned Surveys and Studies.
3

4. EXTERNAL SURVEYS
SRS: Sample Registration System
 Birth Rate, Death Rate, IMR, Total Fertility Rate,
NFHS- III- 2005-06- RCH service delivery data
DLHS-III- 2007-08- RCH service delivery data.
UNICEF Coverage evaluation survey- 2009
NSSO- 60th round- cost of health care
Annual Health Survey – 2011 ( Mortality data of
nine High Focussed States, district wise)
Strengths and Limitations of each source
4

5. ROUTINE MONITORING SYSTEMS
Malaria- API, ABER, SPR, SFR, PF rate- by state, district and
even by facility.
Other VBDs- disease prevalence.
Tuberculosis- case detection rates, cure rates, death rates,
Leprosy- New MB cases and cases in children.
IDSP- other communicable disease, disease outbreaks,
Hospital Data: From hospitals which maintain reasonable
case records.
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6. HEALTH MANAGEMENT INFORMATION
SYSTEM
 Mostly pertain to Output indicators- not as useful for
outcomes or for processes. Mostly relate to service
delivery: Indicators of strategy:
 Most process and inputs data would be from programme
reporting- these have to be collected by programme
officers independently.
 Impact/larger health outcome indicators present- but
require greater interpretation- Maternal deaths, infant
deaths, deaths under 5, peri-natal mortality, still births,
6

7. BARRIERS TO USE OF HMIS
1. Perception of reliability- very low.
2. Quality of data – varied, needs interpretation to use.
3. Conversion to indicators, and interpretation of data very
weak.
4. Information not available in easily accessible and usable
form.
5. Clarity on what information would be most useful and
for what purpose is weak.
6. Decentralisation process needs strengthening.
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8. 8

9. ISSUES OF DATA QUALITY
 Completeness of Reporting
◦ Non reporting areas eg corporations, company townships etc.
◦ Non reporting public sector facilities
◦ Non reporting private sector facilities
 Timeliness of Reporting: ( Just leave out data from last one or
two months to improve data quality.)
 Accuracy and Reliability of Reporting.
Primary recording systems /Duplication-/Data definition problems/-
Problems in data entry/aggregation-
Need to build confidence in data – most who
question it have never seen it.
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10. ISSUES OF DATA INTERPRETATION…
 Know which indicators to use – and for what…
 The choice of denominators:
◦ expected population based vs reported- data based.
◦ For population based- updating to current population size-
◦ Uncertain/overlapping catchment area- for example
institutional delivery rate in the headquarters block would
be difficult to estimate- since the DH serves block mainly-
but also the rest of district.
◦ At facility level and in small blocks- use of data elements
rather than indicators may be justified.
 Understanding of indicators and their inherent
characteristics are useful.
10

11. FALSE REPORTING AND FALSIFICATION:
 False reporting: Not as common as expected. Only a 1%
over-reporting at primary level. Also it affects some data
elements more than others.- those highly monitored,
those that beg it- eg no of cases of ANC, no of ANC cases
where BP taken!!!
 Falsification- usually more at district and higher levels.
Though recent trend is to give each block/each facility a
target number for each data element and encourage
reporting accordingly. Also done to compensate for data
quality errors- which really confuses the picture.
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12. 12

13. HMIS IN DISTRICT PLANNING
 Despite problems – more useful than any other existing
data
 Information interpreted in context. Not possible at state/
national level- but block officer, could explain gaps. Great
tool of decentralised programme management, but a very
poor tool for enforcing accountability, or information for
casting policy.
 Could be used for setting targets/outcomes/baselines-
but greater use in understanding patterns across
facilities – with regard to access and quality of care.
Five patterns to look for:
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14. 1. THE GAP BETWEEN WHAT IS REPORTED AND
WHAT IS EXPECTED… INDICATES THOSE NOT
REACHED!!
Punjab- Rupnagar- Home ( SBA & Non SBA)
Punjab- Rupnagar- Home ( SBA & Non SBA)
& Institutional Deliveries against Reported
& Institutional Deliveries against Expected
Deliveries - Apr'11 to Mar'12
Deliveries - Apr'11 to Mar'12
Home Unreport Home
SBA ed SBA
Home 3% Home
3.4% Deliveries Non SBA
Instituti Non SBA 3% 8%
onal 8.1%
(Pvt)
42.6% Instituti
onal Institutio
(Pub) nal
45.9% 86%
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15. TABLES COULD GIVE THE SAME INFORMATION- IF YOU KNOW WHAT
TO LOOK FOR. –
PRINCIPLE: ALWAYS LOOK FOR THE REPORTING GAPS-
BLOCK WISE- SECTOR WISE- AND SECTION WISE.
Punjab- Ropar- Deliveries – HMIS Data - Apr'11 to Mar'12
Total
Apr'11 to Mar'12 Expected Deliveries - Apr'11 to Mar'12 11,536
Population
Institutional ( Pub Total Deliveries Unreported
Home SBA Home Non SBA
& Pvt) Reported Deliveries
379 907 9,952 11,238 298
Total Deliveries Unreported
Home SBA % Home Non SBA% Institutional %
Reported % Deliveries %
3% 8% 86% 97% 3%
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16. Punjab - Ropar - Blockwise Delivery Status Against Reported
Deliveries - HMIS - 2011 - 12
100%
90%
34%
80%
70%
67%
79%
85%
60%
100%
100%
100%
99%
50%
40%
66%
30%
20%
33%
21%
15%
10%
1%
0%
Anandpur BBMB Bhartgarh Chamkaur Kiratpur Nurpur Private Rupnagar
Sahib SDH Nangal Block Sahib (SD) Sahib Bedi Block Institutes DH
Civil Block Ropar
Hospital
Home Deliveries Institutional Deliveries
16

17. Punjab - Ropar - Blockwise Contribution of Deliveries
Against Estimated Deliveries - 2011 - 12
40%
35% 34%
30%
25%
20%
15% 13% 13%
11% 11%
10%
10%
5%
5% 4%
0%
Chamkaur Anandpur Private Nurpur Kiratpur BBMB Bhartgarh Rupnagar
Sahib (SD) Sahib SDH Institutes Bedi Block Sahib Nangal Block DH
Ropar Block Civil
Hospital
17

18. 2. CASE LOADS DISTRIBUTION ACROSS FACILITIES-
1. Which facilities are managing the case loads? For
any given service? How they need to be
strengthened.
2. What is the population that is unable to access
services- what facilities need to be built
up/revitalised?
3. What is the range of services offered? Are there
gaps between service guarantees and what is
available?
This has implications on which facilities to take up for
strengthening and for differential financing …..
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19. Punjab - Roper - Facility Development- Percentage case load
distribution in various group of facilities - 2011 -12
Other State
Private
PHC CHC SDH/DH owned
Facilities
institution
Complicated
deliveries 0.0% 8.4% 40.7% 0.0% 50.9%
managed
C Section
0.0% 8.6% 31.1% 0.0% 60.3%
Deliveries
Sterilisations 47% 32% 14% 0% 6%
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20. Punjab - Ropar – Facility Development - Percentage of different services
Blocks/Facilities- 2011 - 12
BBMB
Kiratpur Private
Anandpur Nangal Bhartgarh Chamkaur Nurpur Rupnagar
Sahib Institutes
Sahib SDH Civil Block Sahib (SD) Bedi Block DH
Block Ropar
Hospital
% delivery
Contribution
against District's 5% 13% 13% 4% 11% 11% 10% 34%
Estimated
deliveries
%C - Section
deliveries against
Institutional(Pub &
31% 26% 26% 0% 13% 10% 20% 34%
Pvt) deliveries
Sex Ratio at
Birth
777 862 991 930 878 834 857 844
% Fully
Immunised ( 0 -11
mnth) against
District's
3% 3% 17% 22% 25% 15% 6% 8%
Estimated Live
Births
% Of FP users
Using Limiting 6.30% 5.20% 8.88% 6.80% 13.14% 10.20% 100.00% 20
7.84%
Methods

21. Facility Development - % OPD against Districts's Reported OPD -
Punjab - Roper - 2011-12
Anandpur Sahib
SDH
Rupnagar DH 15.32%
24.38%
Nurpur Bedi Block
6.72%
Kiratpur Sahib
Block Bhartgarh Block
8.73% 4.40%
Chamkaur Sahib
(SD)
19.62%
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22. Facility Development - % Abortions ( Induced/Spontaneous)
against Reported Abortions - Punjab - Ropar - 2011 -12
Rupnagar DH Anandpur Sahib SDH
10% 3%
Nurpur Bedi Block BBMB Nangal Civil
11% Hospital
28%
Kiratpur Sahib Block
20%
Bhartgarh Block
18%
Chamkaur Sahib (SD)
10%
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23. 3. THE RANGE & QUALITY OF DELIVERY SERVICES
Punjab - Ropar - 2011 - 12 Punjab - 2011 - 12
3 ANC check ups 11,920 ( 99% against ANC 3 ANC check ups 4,21,544 (86% against ANC
registration) registration)
Hypertension ( BP > 140/90 ) Hypertension ( BP > 140/90 )
430 15,702
Eclampsia cases managed Eclampsia cases managed
20 286
Hb level<11 (tested cases)
Hb level<11 (tested cases) 2,93,583
9630
Severe anaemia (Hb<7) treated
Severe anaemia (Hb<7) 5,352
265
treated Deliveries 417,720 (89% against estimated
Deliveries 11238 ( 97% against estimated deliveries)
deliveries) C -Section deliveries 62,678( 19% against Institutional
C -Section deliveries 2486( 25% against Institutional Deliveries)
Deliveries) Still Birth
6,212
Still Birth 146
Abortion (spontaneous/induced)
Abortion (spontaneous/induced) 13,069
410
Number of Newborns weighed at birth
Number of Newborns weighed 3,75,653
11,134
at birth Complicated Pregnancies Managed
19,734
Complicated Pregnancies
1370 Blood Transfusion
Managed 2,826
Blood Transfusion 150
Sterilisation
Sterilisation 1837 70,985
RTI/STI Cases RTI/STI Cases
216 (36.7 per lakh OPD) 8,969 (57.2 per lakh OPD)
OPD Visits per capita OPD Visits per capita population
0.9 0.6
population
Infant deaths
Infant deaths 29 2,797
Maternal Deaths 9 Maternal Deaths 172
23

24. Ropar- Punjab - %ge Live Births against Estimated Live Births
across Blocks/facilities - Apr'11 to Mar'12
40%
34%
35%
30%
25%
20%
15% 12% 13%
9% 10% 11%
10% 7%
5%
5%
0%
Chamkaur Sahib (SD)
BBMB Nangal Civil Hospital
Anandpur Sahib SDH
Bhartgarh Block
Rupnagar DH
Private Institutes Ropar
Kiratpur Sahib Block
Nurpur Bedi Block
24

25. Punja - Ropar - % Women receiving post partum check-up within 48
hours after delivery against total deliveries - 2011 - 12
100%
90% 88%
80%
72% 72%
70%
60% 57%
50%
40%
30%
22% 23% 23%
20%
10%
0%
Bhartgarh Block
Kiratpur Sahib Block
Chamkaur Sahib (SD)
Nurpur Bedi Block
BBMB Nangal Civil Hospital
Rupnagar DH
Anandpur Sahib SDH
25

26. LIST OF INDICATORS USED IN DIST. ANALYSIS
 OPD/IPD
 Total OPD cases and per capita OPD attendance
 IPD as percentage of OPD
 Operation major as percentage of total OPD
 Operation minor as percentage of total OPD
 AYUSH as percentage of total OPD
 Dental procedures done as percentage of total OPD
 Adolescent counseling services as percentage of OPD
 Lab
 Hb test conducted as percentage of OPD
 Hb<7gm as percentage of Hb tested
 HIV test conducted as percentage of OPD
 HIV positive as percentage of HIV tested
 Blood Smear Examined as percentage of Population
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27. Female
Male RTI/STI Total RTI/STI
RTI/STI per
per lakh OPD per lakh OPD
lakh OPD
Anandpur
30.27 143.49 173.76
Sahib SDH
Kiratpur Sahib
60.97 133.75 194.72
Block
Nurpur Bedi
109.82 377.98 487.79
Block
Rupnagar DH 214.15 877.01 1,091.16
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28. 4. OUTREACH SERVICES – ACHIEVEMENT BY BLOCK/
BY SECTOR
What is the extent of population coverage- where are the
gaps? Eg ANC
What is the quality of outreach care?
Is it too few immunisation points/VHNDs planned, or many
sessions being missed? Or adverse facility to
VHND/immunisation points ratio or sub-centers without
staff?
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29. OUTREACH SERVICE INDICATORS
 ANC
 ANC Registration against Expected Pregnancies
 ANC Registration in First trimester against Total ANC
registration/ Expected pregnancies
 3 ANC Checkups against ANC Registrations
 TT1 given to Pregnant women against ANC Registration
 100 IFA Tablets given to Pregnant women against ANC
Registration
 Hypertension cases detected against ANC registration
 Eclampsia cases managed against ANC registration
 Percentage of ANC moderately anemic (Hb<11) against ANC
registration
 Percentage of ANC severe anemia treated (Hb<7) against ANC
registration
 Postpartum Care
 PNC within 48 hours as percentage of reported delivery
 PNC between 48hours to 14 days as percentage of
reported delivery 29

30. OUTREACH SERVICES INDICATORS
 Immunization
 BCG given against Expected Live Births
 OPV3 given against Expected Live Births
 DPT3 given against Expected Live Births
 Measles given against Expected Live Births
 Fully Immunized Children against Expected Live Births- by sex and
totals
 Percentage of immunisation sessions held against planned
 Percentage of immunisation sessions attended by ASHA against
sessions held
 Family Planning :
 All Methods Users ( Sterilizations(Male &Female)+IUD+ Condom
pieces/72 + OCP Cycles/13)
 Percentage of sterilizations against reported FP Methods
 Percentage of IUD Insertions against reported FP Methods
 Percentage of Condom Users against reported FP Methods
 Percentage of OCP Users against reported FP Methods
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31. 4. COMMUNITY LEVEL INTERVENTIONS.
Functionality of ASHAs( immunisation sessions
attended, paid for JSY)
Effectiveness of ASHAs: BF in first hour, newborn
weighing efficiency.
Health Practices in the community
JSY payments.
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32. MONITORING ASHA PROGRAMME:
Output indicator Process Indicator Data source
and frequency
% of Institutional JSY payment to Mother/ To ASHA HMIS
delivery+ % of home
SBA delivery
proportion of pregnant women who ASHA divas/
had a birth plan monthly
proportion of pregnant women who ASHA divas-
were streamed appropriately for a monthly
complication.
% of pregnant women Immunisation sessions held as % HMIS
who received three of required/planned
ANCs Attending immunisation day
Quality of ANC-cases HMIS
of HT detected, anemia
detected, severe
anemia treated
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33. MONITORING ASHA PROGRAMME
Output Indicator Process Indicator Data Source
% Newborns Breastfed % of newborns visited by ASHAs- within first HMIS + AD
in first hour hour.
% of LBW % of newborn weighed in the last month HMIS+ AD
% of newborns % of newborns who received full complement HMIS+ AD
referred /admitted as of visits
sick % of newborns referred as sick.
% of ASHAs who made visit to last three
newborns in their area.
% of children admitted % of children with diarrhoea who got ORS HMIS+ AD
for ARI % of children who got appropriate care for ARI
% of children severe % of children or pregnant women with fever
dehydration in for whom testing was done
diarrhoea
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34. LIST OF INDICATORS USED IN COMMUNITY CARE
ANALYSIS
Births & Neonates Care
Live Births Reported against Estimated Live Births
New born weighed against Reported Live Births
New born weighed less than 2.5 kgs against newborns
weighed
New born breastfed within one hr of Birth against Reported
live Births
Sex Ratio at Birth
JSY
 JSY incentives paid to mothers as percentage of reported delivery
 For home delivery
 For institutional delivery
 For private institutional delivery
34

35. 5. HEALTH OUTCOMES- MORTALITY
( COULD ALSO AND LOW BIRTH WEIGHT.
Maternal Deaths and their causes
Child deaths and their causes
Perinatal mortality rate- neonatal mortality rate
and still birth rates.
Deaths in all age groups.
Low birth weight.
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36. PROMOTING USE OF INFORMATION:
Present it in CHMO review meetings- and with programme officers in a
session called ― Conversations over data‖
Make it readily available to all programme officers- keep meeting and
distributing.
Make it available on the web-site
Respond to requests - Reduce information service delivery time to less
than 30 minutes
Disseminate it along with books/ training manuals etc.
Call for its use in making PIPs.
HMIS Analysis of all states & districts are available on NHSRC website
http://www.nhsrcindia.org/hsd_hmis_data.php
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37. BARRIERS TO INFORMATION USE
HMIS personnel see themselves as eyes/data entry
hands of the administrators above or at that
level- not as assistance (brains?)of the service
providers and lower level managers .
HMIS personnel see accountability function- do not
see themselves as service providers.
 Need for HMIS personnel to see themselves as information
service providers : the programme officers become
clientiele- they would ply the latter with information.
 Need for HMIS personnel to promote (market) the value and
use of the information provided.
 Need for HMIS personnel to see feedback forms as the
central output of the system.- not sending up- but sending
down- that is what decentralisation is about!!
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38. NEED TO PERCEIVE WHAT IS USEFUL.
The identification of areas of low RCH service delivery and
its links with programme design.
Need to find out which sub-centers or PHCs conduct
delivery
Which facilities have poor coverage.
The power to understand the needs, customize the
application and deliver the report.
Whose task is this? Programme officers or HMIS managers?
38

39. NEED REFORMS IN PUBLIC HEALTH MANAGEMENT..
Differential Financing: Funding goes to facilities according
to the volume of cases, range of cases seen and the
quality of care. Blended Grants- Baseline grant plus
Additional Performance Based Grant. Would need to
build in equity considerations.
Human Resources Deployment and incentivisation.
Area focussed Behaviour change communication and
demand side/community side investments: eg of Malaria/
Kala-azar.
39

40. SUPPLEMENTARY COMMISSIONED
STUDIES
• Cluster Sample Surveys- for validation/triangulation
• Qualitative Studies- for understanding determinants of
poor coverage of services eg home delivery, high
malaria, no deaths/high deaths.
• Qualitative studies for understanding high prevalence of
diseases,
• Exit interviews and sample surveys for understanding
costs of care.
• Hospital Based Epidemiology – case –control studies for
understanding determinants and risk factor and patterns
of disease
40

41. You can have data without information,
but you cannot have information
without data.
Daniel Keys Moran
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42. THANK YOU
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