Em ONC Toolkit Appendix 3 Ensuring Data Quality

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Em. ONC Toolkit Appendix 3 Ensuring Data Quality for Monitoring and Evaluation

Em. ONC Toolkit Appendix 3 Ensuring Data Quality for Monitoring and Evaluation

Objectives • To define high-quality data • To discuss the characteristics of goodquality data

Objectives • To define high-quality data • To discuss the characteristics of goodquality data • To describe the ethical considerations in collecting data for M&E

What Are Data? • Facts or information, especially when examined and used to find

What Are Data? • Facts or information, especially when examined and used to find out things or to make decisions • The plural of datum Oxford Advanced Learner's Dictionary

What Are High-Quality Data? Data are of high quality if they are fit for

What Are High-Quality Data? Data are of high quality if they are fit for their intended use in operations, decisionmaking, and planning. Source: Juran, J. M. (1964). Managerial breakthrough. New York, N. Y. , USA: Mc. Graw -Hill.

What Are High-Quality Data? Data are of high quality if they are fit for

What Are High-Quality Data? Data are of high quality if they are fit for their intended use in operations, decisionmaking, and planning. Source: Juran, J. M. (1964). Managerial breakthrough. New York, N. Y. , USA: Mc. Graw -Hill.

Importance of High-Quality Data • High-quality information is an important resource for the health

Importance of High-Quality Data • High-quality information is an important resource for the health sector in planning, managing, delivering, and monitoring high quality, safe, and reliable health care • The Ministry of Health recommends that data quality review fora (meetings, workshops, conferences) be held periodically at each appropriate level (national, county / subcounty, facility, and community).

Dimensions of High-Quality Data 1. Accuracy 2. Precision 3. Completeness 4. Reliability 5. Timeliness

Dimensions of High-Quality Data 1. Accuracy 2. Precision 3. Completeness 4. Reliability 5. Timeliness 6. Relevance 7. Ethical integrity

Accuracy • Data should provide a clear representation of the activity/interaction • Data should

Accuracy • Data should provide a clear representation of the activity/interaction • Data should be in sufficient detail • Data should be captured once only, as close to the point of activity as possible • Data sources: Direct visual inspection, interview, review of medical records • Accuracy can be ascertained by supervision and verification

Precision • The number of observations (e. g. , health facilities or medical records)

Precision • The number of observations (e. g. , health facilities or medical records) sampled should be sufficient to estimate measures to an acceptable level of refinement • The fewer the number of observations, the lower the precision • Need to balance precision with the logistical challenge of increasing the number of observations sampled

Accuracy versus Precision Accuracy: The tendency of a measurement to center around the true

Accuracy versus Precision Accuracy: The tendency of a measurement to center around the true value Precision: The refinement or exactness of a measurement

Completeness • Missing data are impossible to interpret • Leaving a field blank does

Completeness • Missing data are impossible to interpret • Leaving a field blank does not equate to absence of the indicator being accessed or “not applicable” Y N Does not equate to Y N

Reliability • Data collection processes must be clearly defined and stable to ensure consistency

Reliability • Data collection processes must be clearly defined and stable to ensure consistency over time • Standard operating procedures enhance consistency in process of data collection across locations and over time

Timeliness • Data should be collected and recorded as quickly as possible after the

Timeliness • Data should be collected and recorded as quickly as possible after the event or activity • Data should remain available for the intended use within a reasonable or agreed period • Delayed transmission of data reduces data’s utility for decisionmaking

Relevance • Data should be relevant for the purposes for which they are used

Relevance • Data should be relevant for the purposes for which they are used • Data requirements should be clearly specified and regularly reviewed to reflect any change in needs • The amount of data collected should be proportional to the value gained from them

Ethical integrity • Appropriate authorization is essential • Data from medical records must be

Ethical integrity • Appropriate authorization is essential • Data from medical records must be handled with confidentiality and extracted data must be made anonymous • Data storage must be secure and only accessible to authorized people • Highest standards of integrity are required among those collecting data. Falsification of data may result in serious consequences. [Group to discuss possible consequences of data falsification. ]

References • Government of Kenya. (2014). Kenya health sector data quality assurance protocol. Nairobi,

References • Government of Kenya. (2014). Kenya health sector data quality assurance protocol. Nairobi, Kenya: Ministry of Health, Afya. Info Project. • Government of Kenya. (2015). Addendum to Kenya health sector data quality assurance protocol (2014) data quality review: Guidelines for conducting data quality reviews at all levels. Nairobi, Kenya: Ministry of Health, Afya. Info Project.

MEASURE Evaluation PIMA is funded by the U. S. Agency for International Development (USAID)

MEASURE Evaluation PIMA is funded by the U. S. Agency for International Development (USAID) through associate award AID-623 -LA-12 -00001 and is implemented by the Carolina Population Center at the University of North Carolina at Chapel Hill, in partnership with ICF International; Management Sciences for Health; Palladium; and Tulane University. The views expressed in this presentation do not necessarily reflect the views of USAID or the United States government. www. measureevaluation. org/pima

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