Components of HIV Surveillance: Case Reporting Process

Review of Sessions To DateOverall:

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 dataD. Data quality assurance and improvementE. 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

Pros Cons

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

MethodPros Cons

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 systemServer-based or Web-based

Dual paper/electronic system

National systemAll possible inputs

Representative systemAll ART sites; all government sites; etc.

Expanding Systems Steps Needed:

1. Definition of output2. Mandate, and strong partnership3. Definition of System’s inputs4. Definition of System’s structure5. Draft model of data collection and transfer6. Create master database to receive inputs7. Pilot and testing of the new model and system8. Refine systems and processes9. Create Standard Operation Procedures and Manuals10. Create training materials for those involved11. Prepare for system roll-out12. 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: Name Parents Names Date of Birth Location of Birth Location of Residence

Records that match are appended to each other to track over time

Managing Data – Special Considerations

Step Records must match on exactly on

1First name, Last name, Year of birth, Month of birth, Sex, Patient code

2 same as (1) without Patient code3 same as (1) with just with first four letters of Frist name

4First name, Last name, Year of HIV diagnosis, Institution, Data source, Patient code

5 same as (4) without Patient code

6First name, Last name, Year of HIV diagnosis, Town of birth, Patient code

7 same 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/AIDSCase

SurveillanceDatabase

MESI

ITECHMESIAutomated and

ManualIntra- 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