Presentation1 10.07.12 Ver 2

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  • Main uses of data:Policy level decisions- where are health programmes needed, do we need to invest more money, time, for some goals? Is the strategy working?Information is required in terms of changes in major health outcomes and access to essential services.Information required in terms of burden of diseases and changes in this. Information required on cost of care. Monitoring Function: Are the districts doing their tasks? Is expected outcomes being realized- in terms of health outcomes, service delivery outcomes and in terms of programme milestones/activities. Management Functions: Which districts require more funds? Which require more human resources or infrastructure? Which require closer supervision or capacity building? Which programmes are behind schedules and needs support? e
  • Commissioned Surveys and StudiesCommissioned Surveys and Studies usually have some special purposes such as screening for chikungunea, dengue, or screening for malnutrition among children under five in a community, etc.Use of information from surveys For policy purposesFor accountability-replying to legislature/planning commission/cabinet.For Better management at national, state and to some extent the district level.  Strength High perception of reliability Issues Information available only after a significant time lag.Does not have mortality data.Dis-aggregation to facility/block level not available-essential for district planning. Except for DLHS others do not even have district level data.Limited number of parameters.  
  •  Strength Great tools of decentralisedprogramme management Issues Perception of reliability-very low.Quality of data-varied needs interpretation to use.Conversion to indicators and interpretation of data very weak. RNTCP however has a robust indicator based system. Information not available in easily accessible and usable form.Clarity on what information would be most useful for and for what purpose, is weak.   
  • HMIS data is a reliable source of routine data for use in programme monitoring and management support- though not for policy. Despite data quality issues, HMIS data is more useful than any other existing source of data, as information could be interpreted in context. This is not possible at State or National level-but during a district visit in dialogue with a district or block Officer, one could explain data gaps (if any).  
  • Information Needs for District Level Programme Management. Traditionally in India, this planning had been done at “higher” levels i.e., National or State. Under NRHM, concept of decentralize planning has been given meaning and substance. HMIS plays a crucial role in this. HMIS makes available analytical tools that can generate graphs and charts which can be easily used by healthcare workers at Districts and Blocks. Such analysis makes information more “visible” and meaningful to local staff, who can be encouraged to use every available opportunity to discuss information at meetings, during supervision, in‐service training and most importantly in making Block Health Plan and District Health Action Plan. HMIS is the only available information source and an effective tool of decentralized programme management. This includes both district planning and monitoring. The DLHS is infrequent, available after a large time gap and only yields aggregated district data. Intra-district variations between blocks and facilities which are essential for district level management cannot be obtained from these surveys. The Annual Health Survey may make data available more frequently- but will still not provide the disaggregation that is essential for any meaningful district health management. The DLHS and AHS would thus be useful tools of concurrent programme outcomes evaluation- as against the HMIS which would remain the prime tool of planning, monitoring and management.   HMIS data at the district and intra-district level could be used for 6 objectives:
  • Presentation1 10.07.12 Ver 2

    1. 1. USE OF INFORMATION AT THE DISTRICT LEVEL2nd Annual Health Informatics Summit 04 - 05 October, 2012 Dr Sandhya Ahuja 1
    2. 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 determinantsNeed 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. 3. SOURCES OF DATA/INFORMATIONExternal SurveysData from Routine Monitoring Systems.Commissioned Surveys and Studies. 3
    4. 4. EXTERNAL SURVEYSSRS: Sample Registration System Birth Rate, Death Rate, IMR, Total Fertility Rate,NFHS- III- 2005-06- RCH service delivery dataDLHS-III- 2007-08- RCH service delivery data.UNICEF Coverage evaluation survey- 2009NSSO- 60th round- cost of health careAnnual Health Survey – 2011 ( Mortality data of nine High Focussed States, district wise)Strengths and Limitations of each source 4
    5. 5. ROUTINE MONITORING SYSTEMSMalaria- 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. 5
    6. 6. HEALTH MANAGEMENT INFORMATIONSYSTEM 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. 7. BARRIERS TO USE OF HMIS1. 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. 7
    8. 8. 8
    9. 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. 9
    10. 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. 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. 11
    12. 12. 12
    13. 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: 13
    14. 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 - Apr11 to Mar12 Deliveries - Apr11 to Mar12 Home Unreport Home SBA ed SBA Home 3% Home 3.4% Deliveries Non SBAInstituti Non SBA 3% 8% onal 8.1% (Pvt) 42.6% Instituti onal Institutio (Pub) nal 45.9% 86% 14
    15. 15. TABLES COULD GIVE THE SAME INFORMATION- IF YOU KNOW WHATTO LOOK FOR. –PRINCIPLE: ALWAYS LOOK FOR THE REPORTING GAPS-BLOCK WISE- SECTOR WISE- AND SECTION WISE. Punjab- Ropar- Deliveries – HMIS Data - Apr11 to Mar12 Total Apr11 to Mar12 Expected Deliveries - Apr11 to Mar12 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% 15
    16. 16. Punjab - Ropar - Blockwise Delivery Status Against Reported Deliveries - HMIS - 2011 - 12100%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. 17. Punjab - Ropar - Blockwise Contribution of Deliveries Against Estimated Deliveries - 2011 - 1240%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. 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 ….. 18
    19. 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 institutionComplicated deliveries 0.0% 8.4% 40.7% 0.0% 50.9% managed C Section 0.0% 8.6% 31.1% 0.0% 60.3% DeliveriesSterilisations 47% 32% 14% 0% 6% 19
    20. 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 Contributionagainst Districts 5% 13% 13% 4% 11% 11% 10% 34% Estimated deliveries %C - Section deliveries againstInstitutional(Pub & 31% 26% 26% 0% 13% 10% 20% 34% Pvt) deliveries Sex Ratio at Birth 777 862 991 930 878 834 857 844 % FullyImmunised ( 0 -11 mnth) against Districts 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. 21. Facility Development - % OPD against Districtss 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% 21
    22. 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% 22
    23. 23. 3. THE RANGE & QUALITY OF DELIVERY SERVICES Punjab - Ropar - 2011 - 12 Punjab - 2011 - 123 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 15,702Eclampsia 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 265treated Deliveries 417,720 (89% against estimatedDeliveries 11238 ( 97% against estimated deliveries) deliveries) C -Section deliveries 62,678( 19% against InstitutionalC -Section deliveries 2486( 25% against Institutional Deliveries) Deliveries) Still Birth 6,212Still Birth 146 Abortion (spontaneous/induced)Abortion (spontaneous/induced) 13,069 410 Number of Newborns weighed at birthNumber of Newborns weighed 3,75,653 11,134at birth Complicated Pregnancies Managed 19,734Complicated Pregnancies 1370 Blood TransfusionManaged 2,826Blood Transfusion 150 SterilisationSterilisation 1837 70,985RTI/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 0.6population Infant deathsInfant deaths 29 2,797Maternal Deaths 9 Maternal Deaths 172 23
    24. 24. Ropar- Punjab - %ge Live Births against Estimated Live Births across Blocks/facilities - Apr11 to Mar1240% 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. 25. Punja - Ropar - % Women receiving post partum check-up within 48 hours after delivery against total deliveries - 2011 - 12100%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. 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 26
    27. 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 SDHKiratpur Sahib 60.97 133.75 194.72 Block Nurpur Bedi 109.82 377.98 487.79 BlockRupnagar DH 214.15 877.01 1,091.16 27
    28. 28. 4. OUTREACH SERVICES – ACHIEVEMENT BY BLOCK/BY SECTORWhat is the extent of population coverage- where are the gaps? Eg ANCWhat 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? 28
    29. 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. 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 30
    31. 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 communityJSY payments. 31
    32. 32. MONITORING ASHA PROGRAMME:Output indicator Process Indicator Data source and frequency% of Institutional JSY payment to Mother/ To ASHA HMISdelivery+ % of homeSBA 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 % HMISwho received three of required/plannedANCs Attending immunisation dayQuality of ANC-cases HMISof HT detected, anemiadetected, severeanemia treated 32
    33. 33. MONITORING ASHA PROGRAMMEOutput Indicator Process Indicator Data Source% Newborns Breastfed % of newborns visited by ASHAs- within first HMIS + ADin first hour hour.% of LBW % of newborn weighed in the last month HMIS+ AD% of newborns % of newborns who received full complement HMIS+ ADreferred /admitted as of visitssick % 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+ ADfor ARI % of children who got appropriate care for ARI% of children severe % of children or pregnant women with feverdehydration in for whom testing was donediarrhoea 33
    34. 34. LIST OF INDICATORS USED IN COMMUNITY CAREANALYSIS 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. 35. 5. HEALTH OUTCOMES- MORTALITY( COULD ALSO AND LOW BIRTH WEIGHT.Maternal Deaths and their causesChild deaths and their causesPerinatal mortality rate- neonatal mortality rate and still birth rates.Deaths in all age groups.Low birth weight. 35
    36. 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-siteRespond to requests - Reduce information service delivery time to less than 30 minutesDisseminate 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 36
    37. 37. BARRIERS TO INFORMATION USEHMIS 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!! 37
    38. 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 deliveryWhich 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. 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. 40. SUPPLEMENTARY COMMISSIONEDSTUDIES• 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. 41. You can have data without information,but you cannot have informationwithout data.Daniel Keys Moran 41
    42. 42. THANK YOU 42

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