Routine health information system data quality in a hill and mountain district of Nepal: a cross-sectional desk review of four years of DHIS2 data from Sankhuwasabha, 2021-2025
This cross-sectional desk review of four years of DHIS2 data from Sankhuwasabha district, Nepal, reveals that while reporting completeness and timeliness are generally high, the data harbor persistent immunization anomalies driven by systematic recording processes and the limitations of validation-rule-dependent consistency checks, underscoring the need to classify indicator pairs against active validation rules before assessing data quality.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer
In the high, rugged terrain of the Himalayas, where villages are scattered across steep valleys and travel can take hours over difficult paths, health workers rely on a digital system to keep track of the care they provide. This system, used by health ministries around the world, acts as a central ledger where every birth, every vaccination, and every visit to a clinic is recorded. The goal is simple: to create a clear picture of community health so that leaders can plan effectively. But for these records to be useful, they must be accurate. If the numbers are wrong, the plans built on them will fail. A major challenge in this work is knowing whether the data is just a collection of numbers or a true reflection of reality. Health officials often check for two things: whether reports are sent in at all, and whether they arrive on time. However, a deeper check is needed to see if the numbers make sense when compared to one another. For instance, if a child receives a first dose of a vaccine, they should logically receive a second and third dose later; if the records show more third doses than first doses, something is wrong with how the information is being written down.
Researchers in the Sankhuwasabha district of Nepal, a region of hills and mountains, decided to look closely at four years of these health records to see if the system was working as intended. They examined data from 132 health facilities, covering everything from maternal care to immunization. Their investigation revealed that the system was doing a good job at one thing: almost every facility was sending in their reports. The records were nearly complete. However, the timing was less reliable. In several remote areas, reports were frequently late, suggesting that the difficulty of the terrain was slowing down the flow of information. But the most surprising discovery came when the researchers looked at the internal logic of the numbers, specifically regarding childhood vaccinations. They found a pattern that defied common sense: the records showed that more children were receiving later doses of vaccines than earlier ones. In the district as a whole, the number of children recorded as receiving the third dose of a vaccine was higher than the number recorded as receiving the first dose. This is impossible in reality, as a child cannot receive a third shot without first receiving the first.
The researchers realized that this strange pattern was not a mistake caused by children moving between villages, which would simply shuffle the numbers around without changing the total count for the whole district. Instead, the error pointed to a systematic issue in how the data was being entered. The study also uncovered a subtle problem with how the health system checks for errors. The software used to collect the data has built-in rules that warn workers if two related numbers do not match. The researchers found that for some pairs of data, these rules were so strict that they forced the numbers to agree, hiding any real recording errors. For other pairs, the software allowed workers to proceed even if the numbers were wrong. This meant that simply seeing two numbers match did not always prove that the data was accurate; it sometimes just proved that the software had forced them to match. The study concluded that while the health workers in this mountainous district were diligent enough to send in their reports, the way they were recording specific details about immunizations needed attention. The data showed a persistent, district-wide pattern where the sequence of vaccine doses was recorded in reverse order, a flaw that standard checks for completeness and timeliness would have missed entirely.
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