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The article highlights a drastic revision of official COVID-19 statistics in Mexico, particularly in Oaxaca, resulting from a change in epidemiological surveillance methodology. By restricting official reporting exclusively to RT-PCR tests conducted at sentinel health units, the federal government significantly reduced the recorded number of confirmed cases and deaths compared to previous data that included rapid tests. This methodological shift demonstrates how data transparency can be compromised by selective reporting standards, leading to substantial discrepancies between historical estimates and current official figures. This case underscores the critical importance of open data principles regarding methodological consistency and completeness. When governments alter data collection criteria retroactively without clearly distinguishing between different verification methods, it obscures the true historical impact of public health crises. The exclusion of non-PCR data from official indicators means that public understanding and policy evaluation are based on an incomplete picture, potentially misleading stakeholders about the actual scale of the pandemic’s effect on society and healthcare systems. The relevance to open data lies in the necessity for full transparency and accessibility of all underlying datasets, not just aggregated final statistics. Open data initiatives must advocate for the publication of raw data alongside processing methodologies to allow independent verification. Ensuring that all data sources, including alternative testing methods, remain accessible prevents the loss of historical context and enables researchers and citizens to accurately assess public health trends, fostering trust in institutional reporting and supporting evidence-based decision-making.

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Published on 2024-03-13

Open Data

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