Components of HIV Surveillance: Case Reporting Process

Review of Sessions To Date Overall:  Overview of Case and Second Generation Surveillance  Minimum needs and activities required Country-Specific:  Definition of what you want to know about HIV  Identification of existing data sources  Rapid situational assessment of data, systems, environment

Session Overview  We will discuss: A. Moving prioritized data from collection point to central point B. Using existing data flow systems and structures, or building new ones C. Data management of case data D. Data quality assurance and improvement E. Staffing roles and responsibilities

A. Moving Prioritized Data From Collection Point to Central Point

Moving Data  Case Surveillance can only occur if data are transferred from the point of patient interaction to a central point for data management  Three criteria should be considered:  Format(s) of the data  Method(s) of data transfer  Pathway(s) of data transfer

Moving Data – Format Data Format ProsCons Paper Low cost (?) Rapid implementation Ease of acceptability (?) Human resource needs (high) Less timely reporting Data transfer needs Duplication of effort (?) Electronic Human resource needs (low) Timeliness of reporting Ease of acceptability where systems exist ‘Task Shifting’ (+/-) Higher cost (+/-) Slower implementation Possible need for system/variable expansion Combo May maximize use of existing resources May require some retro-fitting

Moving Data – Method Transfer Method ProsCons Human- Driven Low cost (?) Use of existing pathways Rapid implementation (?) Human resource needs (high) High cost (?) Mapping reporting pathways (?) Less timely reporting Technology- Driven Human resource needs (low) Timeliness of reporting ‘Automation’ Low burden, where systems exist Leveraging of resources – “systems strengthening” (+/-) Slower implementation May guide pathways Possible need for system expansion May require more highly-trained staff Combo May maximize use of existing resources May require some retro-fitting

A. Moving Data - Pathways Site Region Central Site Central Region Points to Consider: Existing Systems Existing Relationships Staffing Resources Capacity of Levels

B. Using Existing Data Flow Systems and Structures – As Is, or With Expansion

Using Existing Systems  Existing Systems Could Include:  EMRs  Has data and existing reporting pathways  Other Disease Reporting Systems  Has existing reporting pathways and staff roles and responsibilities  M&E Systems  Has existing reporting pathways and staff roles and responsibilities  Could be via a regional government office and/or umbrella organization  Other Reporting/Supply Chain Systems  Has existing pathways and staff roles and responsibilities  Could be via a regional government office and/or umbrella organization

Using Existing Systems  Steps Needed: 1. Definition of output  Case surveillance data, format, and frequency of transfer 2. Mandate, and strong partnership  Support of government, funder, implementer, and programs  Policy and procedures put in place (confidentiality, data sharing) 3. Understanding of Systems’ inputs  Where do data come from? In what format? With what frequency? 4. Understanding of Systems’ structure  What is the ‘platform’? Is the platform expandable?

Using Existing Systems  Steps Needed (cont.): 5. Draft model of data transfer and/or extraction  Programming of data fields and/or extraction process  Collaboration with identified staff positions 6. Create master database to receive inputs  Simple system to allow for real-time data management 7. Pilot and testing of the new model and system  Practice data entry, data extraction, and/or data transfer process  Ensure viability of data received 8. Refine systems and processes  Troubleshoot before implementation!

Using Existing Systems  Steps Needed (cont.): 9. Create Standard Operation Procedures and Manuals  Define and document all the steps and the requirements 10. Create training materials for those involved  Cover all points that will ensure buy-in and success 11. Prepare for system roll-out  Train involved staff  Sign data sharing and confidentiality agreements with all required 12. Launch system  Support, monitor, support, monitor, refine, support, monitor, refine

Expanding Systems  Expanded Systems Could Include:  Paper-based system  Electronic system  Server-based or Web-based  Dual paper/electronic system  National system  All possible inputs  Representative system  All ART sites; all government sites; etc.

Expanding Systems  Steps Needed: 1. Definition of output 2. Mandate, and strong partnership 3. Definition of System’s inputs 4. Definition of System’s structure 5. Draft model of data collection and transfer 6. Create master database to receive inputs 7. Pilot and testing of the new model and system 8. Refine systems and processes 9. Create Standard Operation Procedures and Manuals 10. Create training materials for those involved 11. Prepare for system roll-out 12. Launch system

Value of Web-based Systems  Accessible any time of day  Statistics are instant and data downloads are always the most current versions  Program updates do not need to be distributed to users since the program “lives” on the server  The only program users need is a web browser  User interfaces utilizes standard website controls that people are accustomed to  Multiple users can access the system at the same time from different locations  System can be accessed from anywhere in the world with internet access

C. Management of Case Data

Managing Data  Data Management includes such questions:  Are the correct data collected  Are the correct data entered  Are the correct data in the system  Are the correct data cleaned  Are the correct data usable  Are the correct data available for use  Data Management must happen at every level:  Site level  Regional level  National level  Data Management must include  Feedback loops Site Region Central

Managing Data – Site Level At the site level, attention should be paid to:  Clear definitions and support  Are there clear policies and procedures?  Do staff know what they are supposed to do?  May include data collection, data entry, data validation, data transmission, etc.  System function  Are data being collected and inputted into the system?  Are staff doing their work?  Are staff supported to do their work?  Data use  Can sites access their data?  Do sites get feedback on their data?  Do sites use their data?

Managing Data – Regional Level At the regional level, attention should be paid to:  Clear definitions and support  Are there clear policies and procedures?  Do staff know what they are supposed to do?  May include data collection, data entry, data validation, data cleaning, data deduplication, data transmission, etc.  System function  Are staff doing their work?  Are staff supporting the sites?  Are staff supported to do their work?  Data use  Can regions access their data?  Do regions get feedback on their data?  Do regions use their data?

Managing Data – National Level At the national level, attention should be paid to:  Clear definitions and support  Are there clear policies and procedures?  Do staff know what they are supposed to do?  May include data collection, data entry, data validation, data cleaning, data deduplication, data transmission, etc.  System function  Are staff doing their work?  Are staff supporting the sites? The regions?  Are staff supported to do their work?  Data use  Is there a clean national data set?  Do regions and sites get feedback on their data?  Are national data being used?

Managing Data – Tools and Materials

Managing Data – Special Considerations  1. Patient Identification  What it is  A unique way to identify each case (person)  Why it is important  Patient identification is important if we want to have a unique count of persons infected with HIV  Patient identification allows patient tracking over time  Each event is entered into the system to determine if it is a unique (new) record. Some will be new cases; some will be an update to an existing patient in the system. Updates include:  Transition from HIV to AIDS  A pregnancy  A visit to a different clinic system  A death

Managing Data – Special Considerations  2. Data Deduplication  What it is  An evaluation and assessment of each case entered into the system to determine if it is a unique (new) case, or if it is an update on an existing patient in the system  Why it is important  Deduplication is important if we want to have a unique count of persons infected with HIV  Deduplication, and matching records to the source file, allows patient tracking over time

Managing Data – Special Considerations  2. Data Deduplication  How it can be done  Manually or automatically  Cases are matched by certain selected criteria:  Unique ID Code - Each record needs one for each patient  Ideally, people have national identifiers (and they are used!) before a record is entered  More often a unique identifier must be established from some combination of common demographic information  The more unique, the more certainty that records are for the same person  Combination of other variables that are somewhat unique:  NameParents Names  Date of BirthLocation of Birth Location of Residence  Records that match are appended to each other to track over time

Managing Data – Special Considerations StepRecords must match on exactly on 1First name, Last name, Year of birth, Month of birth, Sex, Patient code 2same as (1) without Patient code 3same as (1) with just with first four letters of Frist name 4 First name, Last name, Year of HIV diagnosis, Institution, Data source, Patient code 5same as (4) without Patient code 6First name, Last name, Year of HIV diagnosis, Town of birth, Patient code 7same as (6) with Mother’s name  We try to do it automatically first:  If records do not meet the criteria, they are subset and reviewed by qualified personnel. We refer to this as manual review.  The manual process is manageable if done in a timely manner. Otherwise, a backlog could develop.  Data Deduplication Example: Haiti System

Managing Data – Special Considerations  3. Data Validation  What it is  A review of data to see that what is submitted is accurate  Examples:  Are the report dates more recent than the last data transfer?  Does everyone have a birthdate?  How many fields are completely empty?  Why it is important  Speaks to the quality of the data  People make mistakes. The wrong file can be uploaded, data can be deleted, or records can be shifted.  How it can be done  Chart review (sub-set) vs. submitted data  Record review (sub-set) vs. submitted data

D. Data Quality Assurance and Improvement

Quality Assurance and Improvement  Quality Assurance (QA) allows one to assess the quality of the system and the data to:  Implement improvement activities  Speak to the strength of the resulting data  Quality Improvement (QI) occurs from the QA process, and allows one to refine and improve the system and data  Quality Improvement should be an ongoing activity: “continuous quality improvement” (CQI)

Quality Assurance and Improvement  PDSA Model

Quality Assurance and Improvement  QA and QI can be self-defined, per the system and environment, but should consider:  Data Quality  Do the data in the master system match those from the sites?  Data Completeness  Are all variables expected received?  Timeliness of Data  Are data received in a timely manner (one week vs. one month)  System Representativeness  Do data in the system represent the country, or a sub-set?

Quality Assurance and Improvement  Sample Processes from Haiti – Site Level

Quality Assurance and Improvement  Sample Processes from Haiti – Regional Level

Quality Assurance and Improvement  Sample Processes from Haiti – National Level MESI – weekly data share “Surveillance”-Online MESI-offline – weekly data share “Surveillance”-Offline National EMR – monthly data share GHESKIO EMR – monthly data share HAITI (clean) HIV/AIDS Case Surveillance Database MoH MESI ITECH MESI Automated and Manual Intra- and Inter- System Case Deduplication Surveillance Loop: - trend reports - process reports - quality reports PIH EMR – monthly data share Automated quality data entry flags Intra-EMR system duplication feedback Site-level data quality and completeness feedback

Quality Assurance and Improvement  Sample 11-Step Processes from Haiti – National Level

Quality Assurance and Improvement  Two Imperatives:  Data Quality Feedback given to those inputting the data  Support given for Data Quality Improvement, at the source Site Region Central

D. Staff Roles and Responsibilities

What are the Personnel Needs?  System Management and Oversight  Site/Clinic-level surveillance lead  District or provincial surveillance lead  National surveillance lead  Data Management  Data entry clerks at appropriate level (depending on where data are entered)  National-level data manager  Define Roles and Responsibilities:  Data quality  Cleaning and merging data  Data reports  IT support

Roles and Responsibilities: Site Level  Needs:  Complete forms for newly diagnosed cases  Complete forms for changes in clinical status  Complete forms at death of HIV-infected persons  Submit forms to the next level per the reporting chain, maintaining confidentiality  Record each instance of case reporting on a patient’s clinical record to the surveillance programme  Who will do this?  Dedicated staff or additional role/task shifting?

Roles and Responsibilities: Regional Level  Needs:  Receive, review, manage HIV case reports in a timely manner  Ensure that case reports are filled out completely, accurately and clearly; provide training and TA as needed  Follow-up on cases of epidemiologic importance  Implement quality improvement initiatives  Are case reports complete, with quality data?  Are all cases reported?  Compile and clean data  Disseminate data  Who will do this?  Dedicated staff or additional role/task shifting?

Roles and Responsibilities: National Level  Needs:  Develop and operationalize guidelines on HIV case reporting  Train and assist sub-national surveillance programs, including facility-level personnel  Maintain a complete and accurate HIV case database that is secure, with access limited to authorized personnel  Analyse, interpret and disseminate HIV case reporting data  Assess the performance of the surveillance programs by monitoring surveillance activity  Provide overall guidance and training for sub-national programs  Who will do this?  Dedicated staff or additional role/task shifting?

Thank You Working Together to Plan, Implement, and Use HIV Surveillance Systems