Standard Operating Procedure - # 20 Data Quality Assurance ...€¦ · Standard Operating Procedure - # 20 Data Quality Assurance and Quality Control Procedures for the Sitakhela

Sitakhela Likusasa Impact Evaluation

Evaluating the Effectiveness of Incentives to improve HIV Prevention

Outcomes for Young Females in Eswatini

Standard Operating Procedure - # 20

Data Quality Assurance and Quality Control

Procedures for the Sitakhela Likusasa Impact Evaluation

Document 20 in a series of 20 Standard Operating Procedures

Version date 10 Aug 2018

Status Final

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Standard Operating Procedure - # 20

Data Quality Assurance and Quality Control

Procedures for the Sitakhela Likusasa Impact Evaluation

NERCHA – National Emergency Research Council on HIV and AIDS authors: Khanyakwezwe Mabuza,

Muziwethu Nkambule, Tengetile Dlamini and Mbuso Mabuza

World Bank authors: Marelize Görgens, Damien de Walque, Andrew Longosz, Sosthenes Ketende; Nigel Herath

and Wendy Heard

IHM Southern Africa authors: Vimbai Tsododo, Tendai Chipepera, Nontobeko Fakudze and Leroy Shongwe

MINISTRY OF EDUCATION AND TRAINING

KINGDOM OF ESWATINI

MINISTRY OF HEALTH National Reference Laboratory, and Swaziland National AIDS Programme (SNAP)

KINGDOM OF ESWATINI

For baseline survey For baseline survey Main study implementation partner SGBV counselling and follow up

1

Table of Contents

Abbreviations and definitions ............................................................................................................. 2

1. Purpose ....................................................................................................................................... 4

2. Data quality assessment approach .............................................................................................. 4

3. Scope and Applicability ............................................................................................................... 4

4. Personnel Roles and responsibilities ........................................................................................... 5

5. General procedures for various types of SL QA/QC activities ...................................................... 8

5.1. Name of person(s) or organization that will be performing the quality assurance ..................... 8

5.2. Details of timing of data QA\QC activities................................................................................. 8

5.3. Preparation needed by Impact Evaluation staff prior to a quality assurance visit ...................... 9

5.4. Details of the QA/QC visits/activities ...................................................................................... 10

5.5. Individual data QA (Self-Review)............................................................................................. 13

5.6. Team QA ................................................................................................................................ 14

7. QA\QC indicators for IHM reporting to the WB......................................................................... 15

8. Survey Solutions Data Quality Assurance Process ..................................................................... 18

6.1. Data management hierarchy .................................................................................................. 18

6.2. Completion of a Survey in the field or by Phone ...................................................................... 18

6.3. Rejecting a survey .................................................................................................................. 18

6.4. Survey completion process ..................................................................................................... 19

6.5. Supervisor accepting/rejecting submitted Surveys .................................................................. 20

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Abbreviations and definitions

Terms Definition

Co-Principal Investigators

Are the three persons (two from the World Bank and one from NERCHA) nominated by their respective institutions to share the responsibility of Principal Investigator of the Sitakhela Likusasa impact evaluation.

Co-Investigators Are researchers working on aspects of the Sitakhela Likusasa impact evaluation, as per agreement from the co-Principal Investigators

Data Management Team

This is the group of individuals who are responsible for cleaning and analyzing data throughout the impact evaluation period. The DMT consists of data-handlers who interact with the data at some point in time during the execution of the impact evaluation (i.e. before and after final impact evaluation results have been published).

Data Use Committee

A governance committee designed to review requests, determines and approves access and use of Sitakhela Likusasa data while ensuring that confidentiality, privacy and integrity of data, and anonymity of participants.

Confidentiality Prevention of disclosure, to other than authorized individuals, of a sponsor’s proprietary information or of a subject’s identity

Data Dictionary This is a codebook that describes how the data that has been collected about the impact evaluation, has been captured in the database, as well as the variable names, labels and all value options and value option definitions. This includes the entities, variables, relationships to other data, values and formats of all data elements throughout the impact evaluation rounds.

Data Management The development, implementation and supervision of policies relating to the management of data. This includes mechanisms to protect the data.

Data Governance The overall agreements in terms of data management, data security, dissemination, data use, and data access. The DUC is responsible for developing a data governance policy – and this SOP on data management will be referenced in the Sitakhela Likusasa Data Governance policy

Standard Operating Procedure (SOP)

Detailed written instructions designed to achieve uniformity of the performance of a specific function

Data Quality Assurance

Activities which focus on detecting and preventing data defect or data quality issues. The operational techniques and activities undertaken within the Quality Assurance system to assess whether the requirements for quality of the data have been fulfilled.

Data Quality Control

Real-time monitoring and review of data to verify the accuracy and validity by impact evaluation team involved in the research.

WB World Bank

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IHM Institute for Health Measurement

NERCHA The National Emergency Response Council on HIV and AIDS

DQC/QC Data quality control/Quality control

QA Quality control

coPI Co-Principal Investigator

coI Co-Investigator

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1. Purpose

Data Quality Assurance (QA) is planned and systematic activity implemented as part of a quality system to

ensure that quality requirements (validity) of the data generated during the research will be fulfilled. Data

Quality Control (QC) is a real-time review (monitoring) of data to verify the accuracy and validity by

Impact Evaluation staff involved in the research.

Due to the scale of the Sitakhela Likusasa Project, it is evident that there is dire need for standard and systematic data quality assurance (QA) and quality control (QC) processes and practices across all levels of data flow –from data collection to data reporting. The purpose of this standard operational procedure (SOP) is to stipulate the routine, standardized and systematic procedures that are to be followed by the Impact Evaluation staff within the Sitakhela Likusasa project.

The SOP will minimize variation and promote data quality through consistent and systematic manner application of all tools for data collection, management, analysis and reporting within the project, even if there are temporary or permanent changes in terms of personnel who are trained to conduct the survey and handle data.

Furthermore, the aim of this document is to outline the specific steps and requirements for monitoring data quality and act immediately to address issues.

2. Data quality assessment approach

The following is a typical data quality approach:

1. Identify which data items need to be assessed for data quality, typically this will be data items

deemed as critical to the intervention and associated evaluation reporting

2. Assess which data quality dimensions to use and their associated importance

3. For each data quality dimension, define values or ranges representing good and bad quality data.

Please note, that as a data set may support multiple requirements, a number of different data quality

assessments may need to be performed

4. Apply the assessment criteria to the data items

5. Review the results and determine if data quality is acceptable or not

6. Where appropriate take corrective actions e.g. clean the data and improve data handling processes

to prevent future recurrences

7. Repeat the above on a periodic basis to monitor trends in data quality

3. Scope and Applicability This SOP applies to all stakeholders in the project who are generating and handling data at all levels of the Sitakhela Likusasa project – most notably the research and data collection teams, contracting staff, the coPIs and coIs (Sitakhela Likusasa ‘Impact Evaluation team’). The procedures outlined in this SOP are applicable to the handling and management of data at all levels of Sitakhela Likusasa project data flow framework. This SOP also references the Data Management Procedures (SOP8), Endline Field Manual

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(SOP10) and the Data Governance Policy (SOP12), which stipulates data curatorship duties and data ownership agreements.

4. Personnel Roles and responsibilities

In accordance to ethical consideration and Good Clinical Practice appropriately trained and qualified individuals are responsible for the overall conduct of the study, handling of the data, verifying the data, and conducting statistical analyses. Their responsibilities are classified in table 2. The classification of roles and responsibilities are geared at ensuring that best practices of data collection and management are adhered to, to deliver reliable and high-quality data necessary for valid Impact Evaluation results. The following table depicts the breakdown of basic roles and responsibilities needed to ensure the collection, handling, managing and storage of high-quality data. Table 3 includes names and QA\QC responsibilities of all personnel in the study. Table 2: Personnel overall responsibilities

Personnel Personnel role QA/QC Responsibility

Impact Evaluation Staff at IHM

Impact Evaluation staff with who work directly or indirectly with participants and have Participant file documentation responsibilities, e.g. Research Assistants (RA), Help desk clerks

Conduct self-review of data and documentation quality.

Correct data and documentation issues after supervisor, PI or team lead review.

Adhere to SOPs and performance objectives.

Supervisor(s) and PIs

Supervisors, who manage impact evaluation staff and who have primary responsibility and accountability for overall data and documentation quality

Train impact evaluation team on participants file documentation, data collection, and data quality assurance responsibilities.

Review results of self-review and spot check select

cases.

Communicate findings to team and IHM project manager

Support impact evaluation team in meeting SOPs and performance objectives.

Hold impact evaluation team accountable for addressing data quality issues.

IHM project manager

IHM project manager, who oversees the Supervisor(s)

Holds direct responsibility and is accountable for implementing the SOPs and achieving performance objectives.

Makes necessary changes to program policies and design based on data QC review results.

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Personnel Personnel role QA/QC Responsibility

- QA\QC coordinator I

- IHM team member

- Coordinate all QA/QC activities and makes sure that QA/QC activities are conducted as stipulated

- Collates all QA\QC reports and submits to the WB (with copies to NERCHA?)

- QA\QC coordinator II

- WB team member

- Verifies that all QA/QC reports are produced by IHM - Coordinates review of QA/QC reports by the WB team -

Table 3: Personnel and responsibilities

Role Responsibility in terms of Data QA/QC

Co-Principal Investigator - Review QA/QC reports

- Conduct QA\QC visits

- Meet with Impact Evaluation Lead to review and discuss QA\QC reports

- Deal with issues relating to breach of protocol

Co-Investigator - Conduct QA/QC visits

- Report to PIs

- Participate in QA/QC meetings

Contractor Project Director

- Review QA/QC reports

- Participate in QA\QC review meetings at IHM

- Reports to World Bank

Contractor Project Manager

- Supervise Impact Evaluation team

- Conduct QA/QC visits

- Report to Contractor Project Director and to World Bank


Contractor Data Manager - Supervise Impact Evaluation team

- Conduct QC tasks

- Report to Impact Evaluation Lead


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CAPI officer - Supervise Impact Evaluation team

- Conduct QC tasks



Contractor Database Administrator

- Conduct QC tasks



Contractor Project Assistant

- Manage QC activity calendar

- Support QA/QC coordinator


Contractor Field Supervisor

- Supervise field teams and assist with field work

Contractor Research assistant

- Conduct QA\QC self-review

- Reports to QA\QC coordinator

Contractor Biomedical Staff

- Conduct QA\QC self-review


Medical Supervisor - Conduct QA\QC self-review


Data analysts - Conduct QA\QC self-review

- Conduct QA visits

- Conduct and supervise QC

- Reports to Impact Evaluation Lead

QA\QC coordinator II - Conduct QA\QC self-review

- Conduct QA/QC visits and review

- Conduct and supervise QA\QC


QA\QC coordinator II - Conduct QA\QC self-review

- Conduct QA/QC visits and review

- Conduct and supervise QA\QC


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5. General procedures for various types of SL QA/QC activities

5.1. Name of person(s) or organization that will be performing the quality assurance There will two QA/QC coordinators/champion from IHM, and from the WB. The QA/QC coordinators will take a lead in conducting and reporting on QA\QC results. They may appoint other team members to conduct and report QA\QC activities.

Table 4: Roles and responsibilities of QA\QC coordinators

Role Responsibilities

IHM QA\QC coordinators

- Coordinate all QA\QC activities at IHM level - Implement changes following issues identified by QA\QC review - Enforce QA\QC activities schedule - Prepare QA\QC reports for supervisors and Impact Evaluation Lead

WB QA\QC coordinators

- Coordinate all QA\QC activities at WB level - Conduct QA visits - Conduct QC review - Review QA\QC reports - Enforce QA\QC activities schedule - Prepare QA\QC reports for supervisors and Impact Evaluation Lead

5.2. Details of timing of data QA\QC activities Monthly data QA\QC reports should be prepared and submitted to supervisors monthly. Reports covering

a previous calendar month are due on every first Wednesday of the month. Daily QA\QC self-review

reports are due on the same day. Table 5 below describes the reports, timing, and responsible personnel

Table 5: Report types and timing of QA/QC activities

Timing Report type and content Personnel responsible

Reports to

Monthly

-Due every first Wednesday of the

QA\QC monthly reports - List of all QA\QC findings -Remedial action recommendations


Supervisors and Impact Evaluation

-

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Timing Report type and content Personnel responsible

Reports to

month -Implemented remedial action and date

Daily QC\QA daily report -Daily self-review -Syncing of Survey Solutions forms

Impact Evaluation staff

IHM coordinators

Daily

QC\QA serious event report -Loss of documents containing participant’s identifiable data -Failure or breach of QC procedures -Failure of data skip patterns and logical checks -Failure of electronic data collection forms -Failure to access documents which are necessary for day to day activities as a result of system or hardware failure -Failure to sync survey solutions forms

Failure to archive impact evaluation documents correctly


Supervisors and Impact Evaluation Lead

Two working days following QA\QC site visit

QA\QC site visit report - List of all QA\QC findings -Remedial action recommendations -Implemented remedial action and date

Supervisors and WB coordinators

Supervisors and Impact Evaluation Lead

5.3. Preparation needed by Impact Evaluation staff prior to a quality assurance visit

To confirm data is valid and correct it is necessary to cross check against the original record. This is called the source data. So to confirm participant’s school registration and attendance, for example, records entered in the SEAV form must be checked against submitted school registers, and a participant’s withdrawal must be accompanied by a completed and signed withdrawal form; OOSY payment to have the required documents indicating proof of registration and the school/institution bank account details, along with a proof of payment from the bank to the institution (after payment is made by IHM). All impact evaluation documents, forms and data bases should be up to date prior to a visit. All QA measures to be made against documented procedures outlined in the relevant SOPs. A room or quiet desk should be booked for the use during the visit. The impact evaluation team should be aware of the planned visits and be able to make available the necessary time and assistance. Some QA visits might be an announced.

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5.4. Details of the QA/QC visits/activities The objective of each QC\QA visit may vary depending on findings reported in daily and monthly reports,

recommendations from previous visits, prescribed by the Impact Evaluation Lead, or a combination of all

the above. Tables 6 and 7 provide QC and QA activities, procedures and tasks to be performed by a data

quality reviewer.

Table 6: QC activities and procedures list

QC activity Procedures

Task

Completed

Corrective Measure

Taken

Name/

Initials Date

Supporting

Documents (List

Document

Name)

Date

Data Gathering, Input, and Handling Checks

Check for

transcription

errors in data

input and

reference.

Cross-check a sample of school and STU

registration, school attendance, raffle

and education incentive attendance

entered in electronic data files against

paper records such as proof of

payment, for transcription errors.

Check built automated checks, such as

computational checks for calculations,

or range checks for input data work as

intended.

Check the

integrity of SQL

server

database files.

Confirm that the appropriate data

processing steps are correctly

represented in the database.

Confirm that data relationships are

correctly represented in the database.

Ensure that data fields are properly

labeled and have the correct design

specifications.

Ensure that adequate documentation of

database

Check for

consistency in

data between

categories.

Identify parameters (e.g., activity data,

constants) that are common to multiple

categories and confirm that there is

consistency in the values used for these

parameters in the emissions/removal’s

calculations.

Check that the

flow of Check that education and raffle

incentive, as well as OOSY intervention

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participant’s

data along

processing

steps is correct

and observed.

data collected from various sources are

prepared and entered in appropriate

forms

Check that there are no lost education

registers before and after data

extraction and entry

Data Documentation

Review of

internal

documentation

and archiving.

Check that every primary scanned or

electronic document has a reference ID

via document upload form (BI) and

stored on OneDrive

Check that all primary documents are

archived and stored to facilitate

detailed review

Check that the archive is closed and

retained in secure place following

completion of the review

Calculation Checks

Check

methodological

and data

changes

resulting in

recalculations.

Check for temporal consistency in time

series input data for variables such as,

but not limited to history of pregnancy,

age at first sex, highest education level,

and date of birth or data within

academic calendar.

Check for consistency in the

algorithm/method used for calculations

of key indicators throughout the time

series.

Check

completeness Confirm that estimates are reported for

key indicators such HIV incidence and

treatable STI prevalence are

reproducible from baseline onwards

For raffle rounds, check that there is a

final/terminal outcome for every

randomly selected participant for a

given raffle round. This includes

treatable STI results, indication is

selected as a winner or not and

payment of raffle incentive to those

who won.

Trend checks Check if there any unusual or

unexplained trends noticed for key

variables in each module of the midline,

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MOMP and endline questionnaires

Data security

Check that all tablets used for

communications and data collection

are passcode enabled and locked

Check that tables and other electronic

devices such as laptops, memory sticks,

and desktop computers are password

protected

Check that all electronic devices are

wiped of data before being disposed or

reassigned

Table 7: QA activities and procedures list

QA Activity Procedures

Task

Completed

Corrective Measure

Taken

Name/

Initials Date

Supporting

Documents

(List Document

Name)

Date

Data Gathering, Input, and Handling Checks

Check that

relevant SOPs

are being

adhere to

For each activity, check that the

relevant SOP procedures are adhered

to

Check that the movement of

participant’s data among processing

steps is correct and observed.

Check that there are no lost education

registers before and after data

extraction and entry

Check that education incentive data

collected from various sources are

prepared, meet the required standards

and conditions, and entered in

appropriate forms

Check and confirm that education

original data documents are

appropriately stored and archived

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WhatsApp

messages,

email, and text

messages

Check that these are securely archived

Data Documentation

Review of

internal

documentation

and archiving

Check that primary document uploaded

on the SQL server and one drive folder

has a reference ID and PID or other

identifiers

Check that all primary documents are

archived and stored to facilitate

detailed review

Check that the archive is closed and

retained in secure place following

completion of the review

Data security

Check that all tablets and telephones

used for communications and data

collection are passcode enabled and

locked

Check that tablets and other electronic

devices such as laptops, memory sticks,

and desktop computers are password

protected

Check that all electronic devices are

wiped of data before being disposed or

reassigned

5.5. Individual data QA (Self-Review) Every impact evaluation staff member to conduct a daily self-review to make sure that whatever they

have done would meet all standards stipulated in relevant SOPs. For example, RAs should check that all

tablets, phones, printed participants list, and notebooks are stored in orderly and secure manner at the

end of their working shift, and that there are no loose papers left in a working area. The work area and

project documentation need proper close out when RAs or FWs leave and resign

Frequency: daily.

Purpose:

1. To ensure quality data and documentation practices early.

2. To facilitate communication between Impact Evaluation Staff and Supervisor(s) regarding both

data and documentation quality.

3. To reduce the number of cases requiring second-level data QA.

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5.6. Team QA

Included a QA in team meeting agenda and discuss any issue identified, solutions and changes prevent

future assurances of events which may affect data quality.

Frequency: Biweekly Team Meeting

Purpose:

1. Connect data and documentation quality to quality of intervention delivery.

2. Support team approach to data quality; highlight examples of quality data.

3. Show areas where improvements maybe needed.

Supervisor(s)

i. Discuss each case’s data and documentation quality. Identify actions needed to address gaps or errors (such as blank columns for demographics or outcome data).

ii. Discuss overall team trends, scores, and goals for upcoming period. Email summary to Impact Evaluation Lead.

Impact Evaluation staff

i. Email QA\QC issues identified to Supervisor prior to meeting so they can be compiled into single document to be discussed during team QA.

ii. Compete follow-up actions identified above.

iii. During the next meeting, provide updates on actions taken for each case.

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7. QA\QC indicators for IHM reporting to the WB The format of QA\QC report will vary depending on the type of assessment. Table 6.1. Raffle round 8

# Task Who will do it

Indicators to be reported to the WB Additional requirements/task description

1 Supervisor approval of survey solutions & confirmation of activities undertaken

IHM core team & help desk staff

i. Cumulative number of forms approved

Numbers to match existing records in survey solutions form(s) Reviews to be conducted within 24/48? Hours This excludes those who were not reached during raffle fieldwork

ii. Number of forms rejected/sent back to RAs

iii. Number of forms not approved within expected time frame

iv. 10% of RR8 participants who were not selected as winners called by Help Desk to see if they were tested under the raffle.

v. 10% of RR8 winners called by Help desk to see if they were paid their winnings

vi. Compare original winner selection file to those paid as raffle winners

2 Verification trich results through confirmatory testing

NRL with IHM providing samples & IHM AFM/BM

i. Cumulative number of completed confirmatory tests

Numbers to match existing records in survey solutions form(s) Written report explaining that discordant results have been discussed with relevant participant

ii. Cumulative number of discordant results

iii. Number of follow up visits concluded to those participants with discordant results

3 Verification of follow up testing for STI positive participants

IHM AFM/BM

i. Cumulative number and proportion of completed follow-up tests

Numbers to match existing records in survey solutions form(s)

ii. Cumulative number of positive follow-up tests

A report describing steps taken and number refusal of treatment if any

IHM helpdesk iii. List PIDs, date of the call and outcome whether follow-up was done within 2 weeks or not

Phone calls to random 10% of STI positive participants to determine whether follow up was done within 2 weeks of initial positive result

Table 6.2. Education incentives

# Task Who will Indicators to be reported to Additional requirements/ task

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do it the WB description

1 Verify 10% of education incentive documentation received every round

IHM assistant field manager

i. Report of education incentive data quality assurance

30% should be for STU and 70% should be for basic education

2 Paper trail check for 7 in school and 3 in STU randomly selected participants & confirmation calls

IHM assistant field manager

ii. List of selected PIDs, date of paper trail check, outcome of QA check, corrective measures taken in terms of discrepancies

Confirm that: data on registers and school attendance documents match data entered in relevant forms; that STU information matches the data entered in relevant data files; that education incentive payment and confirmation of payment documents match data entered in relevant forms Call selected participants to confirm if they received their money

Table 6.3 Endline data collection

# Task Who will do it Indicators to be reported to the WB

Additional requirements/task description

1 Survey Solutions-based verification of all data

IHM DQA officers (8 will be appointed for the 15 weeks of endline data collection)

i. Cumulative number of forms approved

Included in Survey Solutions and reports available from there ii. Number of forms

rejected/sent back to RAs

2 Report of issues encountered by Supervisors

8 Supervisors i. Report of issues encountered, and corrective measures taken if any

Approval/rejection weekly reports with issues encountered During first 2 weeks of Endline data collection – should be daily reviews and meetings to deal with implementation issues if relevant.

3 Reports by Project Manager

Project Manager i. Reports by supervisors Supervisors weekly checklists to be completed by supervisors every week During first 2 weeks of Endline data collection – should be daily reviews and meetings to deal with implementation issues if relevant.

5 Field visit report 8 Supervisors i. Field visit report Report for every field visit to be prepared separately, using the supervisor field visit checklist

6 Withdrawals 8 Supervisors ii. Cumulative number of -

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withdrawals

iii. Cumulative proportion of signed withdrawal forms

iv. Cumulative proportion of uploaded withdrawal forms

-

v. Number of signed withdrawal forms within the reporting period

Scanned of all pages of withdrawal pages

7 Field tracing Field tracing teams i. List of PIDs and reason for field tracing

ii. List of PIDs from previous reporting period, reason for field tracing and outcome of field tracing

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8. Survey Solutions Data Quality Assurance Process

Data management hierarchy

HQ accounts – WB Team

Supervisor accounts – IHM Field manager and assistant field managers and IHM data team

Interviewer accounts – IHM RAs

6.2. Completion of a Survey in the field or by Phone

1) Review of completed surveys ON tablets by the RAs – to check and make sure that there were no

skipped questions or sections that should have been completed, then clicking ‘sync’ button to submit to supervisors for review/approval

2) IHM (Field managers and assistant field managers) to review submissions using their supervisor accounts and supervisor android applications on tablets. They will then either approve, or reject submissions.

a. If they approve a submitted form, it is then ready for review by the WB. b. If they reject a submitted form, the form is then sent back to the responsible RA to

amend the flagged questions before resubmitting.

3) The WB will be responsible for FINAL approval of forms. Using the HQ accounts, they will log into Headquarters and review all approved forms from IHM. If there are any forms that require further changes, they will reject. The forms then get sent back to the RAs tablets for action before re-submitting.

6.3. Rejecting a survey Rejection of a submitted survey could be due to one or more of the following reasons:

- Incomplete questions - Incomplete sections - Incorrect entries:

o Typographical errors including numbers and figures

As we progress with data collection using the Endline Survey, there will be a guide developed that will

indicate specific questions and sections of the survey that could be problematic and which should be

reviewed carefully.

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6.4. Survey completion process

RA Completes Survey

RA Submits Survey

IHM Supervisor decides:

Is survey complete?

IHM Supervisor

reviews Survey

IHM Supervisor

reviews survey

IHM supervisor decides: should

questionnaire be accepted?

WB checks questionnaire:

should it be approved?

WB decides: should

questionnaire be approved?

WB admin does final

check and data exports

daily

NO

NO

NO

YES

YES

YES

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6.5. Supervisor accepting/rejecting submitted Surveys

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