Understanding health data falsification and its drivers among Nigerian health workers
Health data falsification, the fabrication or alteration of health data, reduces the quality of health data and limits decision-making. Health workers can potentially engage in health data falsification. Thus, understanding health data falsification and its drivers among health workers is pivotal. F...
| Autores principales: | ; ; ; |
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| Tipo de documento: | Electrónico Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| En: |
Crime, law and social change
Año: 2025, Volumen: 83, Número: 1 |
| Acceso en línea: |
Volltext (lizenzpflichtig) |
| Journals Online & Print: | Gargar...
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| Verificar disponibilidad: | HBZ Gateway |
| Palabras clave: |
| Sumario: | Health data falsification, the fabrication or alteration of health data, reduces the quality of health data and limits decision-making. Health workers can potentially engage in health data falsification. Thus, understanding health data falsification and its drivers among health workers is pivotal. Falsifying health data is a problem within the Nigerian health system. However, there is no comprehensive information on the issue and its drivers among health workers in the country. Employing Vian’s theory of corruption in the health sector, the authors designed a qualitative online survey to address this gap. Fifty-three health workers were selected across primary, secondary, and tertiary health facilities between January and February 2024. Participants responded to five open-ended questions informed by theory and literature. The data were collected until data saturation occurred. Following this, thematic analysis was performed. Findings reveal aspects of health data that are falsified in the country. These include data on mortality, morbidity, vaccination, medical history, medical negligence, laboratory test results, and outpatient attendance. The findings also uncover drivers of health data falsification. In line with the theoretical framework, the drivers are grouped into opportunity, pressure, and rationalisation drivers. Opportunity drivers include the lack of monitoring or supervision, manual approach to health data capturing, and health workers’ limited knowledge regarding the importance of accurate health data. Pressure drivers include performance pressure, high workload on health workers, personal and organisational benefits, and fear of threats from work supervisors. The rationalisation driver is political leadership failure. While the findings are of immediate relevance to the Nigerian context, they may also be relevant to other countries where corruption is a problem in the health sector. Thus, governments, health service managers, data improvement teams, and relevant stakeholders should consider these findings when developing interventions to tackle health data falsification. |
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| ISSN: | 1573-0751 |
| DOI: | 10.1007/s10611-024-10194-2 |
