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Accurate and reliable data are key when it comes to decision-making in the health system. The generation of such data relies on staff recording correct information, but what happens when there are strong incentives to falsify data?

This qualitative study published in BMJ Global Health aimed to understand reasons why healthcare providers intentionally falsify maternal and newborn health data in two regions of Ethiopia. In Ethiopia the Health Management Information System (HMIS) collects data predominantly related to service delivery at all levels of the health system. Improving the quality and utility of HMIS data is a priority for the Ministry of Health of Ethiopia. Despite this, poor data quality has been identified as a major system weakness and increased focus has been placed on the need to improve HMIS data quality and prevent data falsification.

The IDEAS-led study team conducted in-depth interviews with a range of staff in hospitals, health centres and associated health posts in Oromia and Amhara regions. All participating health facilities were part of a large-scale quality improvement (QI) initiative. Results from the interviews show that although study participants were hesitant to report personally falsifying data they do report it to be common practice and had experienced it in other health facilities or had been told about it by other health workers.

This study finds, underpinned by system’s focus on numbers, four main reasons drives intentional data falsification:

  1. System level incentives to falsify data: the system’s focus on numbers also leads facilities to exaggerate their performance to ensure they rank highly compared to others or even achieve material gains for facilities that appear to perform better.
  2. System level disincentives to report accurate data: study participants reported pressure from a higher level to report inaccurate data.
  3. Individual level incentives to falsify data: on an individual level the study found incentives for staff to report false data including gain of tangible benefits and access to educational opportunities.
  4. Individual level disincentives to falsity data: the individual level disincentives included fear of implications of not complying with request from above and being blamed for poor performance

Findings from this study point to the need for data quality improvement frameworks to reflect on the reasons for data falsification identified through this study and the need to disentangling rewards and punishment from performance reports based on HMIS report.


Abiy Seifu Estafinos

School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia

Dorka Woldesenbet Keraga

School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia

Rediet Gezahegn

Department of Reproductive, Family and Population Health, School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia

Abiyou Kifle

Institute for Healthcare Improvement, Addis Ababa, Ethiopia

Profile picture of Fanny Procureur
Fanny Procureur

Research Fellow in social sciences (UCL)