The research team, led by investigators at Sierra Leone’s National Public Health Agency, assembled their cohort from the national District Health Information Software 2 database, the country’s integrated disease surveillance backbone, and then enriched it with patient charts and the original outbreak-investigation forms completed by district response teams. The analysis covered a database extract frozen on 1 July 2026, after the Ministry of Health declared the outbreak over in December 2025. Of the 415 paediatric cases, 411 were laboratory-confirmed, four were classified as probable, and none were clinician-diagnosed only. The age distribution was strikingly young: 188 children, or 45.3 percent, were aged 0 to 4 years, 121 were aged 5 to 9, and 106 were aged 10 to 14. The median paediatric age was just 5 years, and 230 of the 415 children, or 55.4 percent, were male.

The temporal signal was equally telling. National surveillance summaries recorded only three sporadic paediatric cases of mpox-like illness between 2017 and 2024, a period when the disease was largely absent from the country. Notifications then rose sharply from April 2025 and peaked at 37 laboratory-confirmed paediatric cases in the epidemiological week ending 18 May 2025, before declining through June with a smaller resurgence in September and early October. Genomic analyses cited by the authors indicated that the clade IIb G.1 virus had been transmitting locally before public detection, which helps explain how an epidemic that appeared suddenly in adults had already seeded the household settings where young children are most exposed. The paediatric epidemic curve, in other words, was not an independent phenomenon but a mirror of the national outbreak, amplified through domestic contact networks.

Geographically, the burden concentrated in Western Area Urban, the densely populated district containing the capital Freetown, which recorded 195 of the 413 paediatric cases with known district, or 47.2 percent. Yet crude notified paediatric rates told a more nuanced story: Western Area Urban led at 80.2 notified cases per 100,000 estimated children, followed by the northern districts of Koinadugu at 60.5 and Falaba at 30.0. Together, Koinadugu, Falaba, and Karene accounted for 20.1 percent of paediatric cases but only 1.6 percent of cases among people aged 15 and older, a disproportionate paediatric share that the authors flag as a priority for field investigation. They caution, however, that these rates applied a single national age projection to every district, so if northern populations are younger than assumed, their paediatric denominators were underestimated and the rates overestimated. District-specific age denominators and testing data are needed before geographic differences can be read as true incidence.

Clinically, the picture was dominated by fever and rash, each documented in 346 of 415 children, or 83.4 percent, with arm and leg lesions the most commonly reported sites. Secondary bacterial skin infection was documented in 36 children, or 8.7 percent, and one child developed acute respiratory distress syndrome. Care location was recorded for 285 children, of whom 216, or 75.8 percent, received hospital care for isolation, clinical management, or both, while 69 were managed at home. The authors emphasize that hospital admission in this outbreak did not necessarily signal severe disease, because admission frequently served isolation and infection-control purposes in crowded households where home isolation was impractical. Among 162 retrieved patient charts for hospitalized children, the median length of stay was 6 days, with an interquartile range of 4 to 9 days, and admission indication was available in 119 charts: isolation alone in 48, clinical management plus isolation in 62, and clinical management alone in 9.

Outcomes were recorded for 261 children, and three died, a case fatality proportion of 1.1 percent with an exact 95 percent confidence interval of 0.2 to 3.3. The fatal cases shared a concerning pattern of delayed presentation, with onset-to-care intervals of 6, 15, and 27 days, and records described poor feeding or dehydration in two children, secondary bacterial skin infection in one, and moderate acute malnutrition in one. HIV status was unavailable for all three children who died, a gap that carries weight given a companion national analysis from the same team reporting fourfold or higher mpox mortality among people living with HIV during the outbreak. The authors argue that national records should distinguish admission purpose and standardize documentation of severity, treatment, complications, and recovery, because the emergency-era data could not fully separate the clinical course of mpox from the circumstances of delayed or inadequate care.

The exposure analysis, drawn from 246 classifiable outbreak-investigation forms retrieved for 331 of the 415 children, pointed firmly toward the household. Household or caregiving contact, defined to include a household case, direct care, shared sleeping space, shared bedding or clothing, bathing, or feeding, was documented in 141 of 246 forms, or 57.3 percent. Another 48 children, or 19.5 percent, had several plausible exposures with no single predominant source, 39, or 15.9 percent, reported school, peer, or community contact, 12, or 4.9 percent, reported animal, wildlife, or bushmeat exposure, and 6, or 2.4 percent, reported health-care contact. Notably, eight of the twelve animal-related records came from Koinadugu or Falaba, hinting at rural exposure patterns that differ from the urban household transmission dominating Western Area Urban. The authors are careful to stress that these categories describe documented exposure histories, not proven routes of infection, and that the forms rarely captured linked-case identifiers, timing, or contact testing consistently enough to establish who infected whom.

Vaccination data proved interpretable only for a narrow slice of the cohort. Sierra Leone’s response used the modified vaccinia Ankara-Bavarian Nordic vaccine, or MVA-BN, in a targeted single-dose campaign for selected groups aged 12 years and older, consistent with the World Health Organization’s prequalification of the vaccine for adolescents aged 12 to 17. Among the 54 paediatric cases aged 12 to 14, vaccination status was recorded for only 27, of whom six were vaccinated. Dates, doses, indications, eligibility, and timing relative to exposure or symptom onset were inconsistently documented, so the study could not estimate coverage, access, or effectiveness. The authors note that WHO’s 2024 position paper supports off-label MVA-BN vaccination of children during outbreaks when expected benefits exceed potential risks, and that by September 2026 European authorization for the vaccine, marketed as Imvanex, had extended to children aged 2 years and older on the strength of paediatric immunogenicity data. With 188 children younger than 5 in this cohort alone, they argue that Sierra Leone’s future guidance should define eligibility for exposed younger children under applicable national authorization, with prospective safety and effectiveness monitoring.

The study also surfaced a sensitive clinical and child-protection dimension. After deduplicating surveillance fields, 55 of the 415 children had documented genital or perianal lesions, and the DHIS2 field labelled vaginal lesions was marked for 43 of 185 girls, most often among those aged 10 to 14. The authors are explicit that genital or perianal lesions do not identify the route of infection: plausible explanations include disseminated disease, transfer from another body site, close caregiving contact, contaminated materials, sexual contact, sexual abuse, or simple recording error, and the retrieved records could not distinguish among them. Encouragingly, of 43 records retrieved for these children, 26, or 60.5 percent, documented a child-protection assessment or referral decision, 9 involved referral to social-work or child-protection services, and 3 involved forensic or medico-legal assessment. The authors recommend that clinicians take an age-appropriate exposure history and seek child-protection or medico-legal assessment whenever the history or examination raises concern for abuse.

The broader lesson is one of surveillance architecture. Sex was recorded for all 415 children and district for 99.5 percent, but care location was recorded for only 68.7 percent, final outcome for 62.9 percent, and HIV status for a mere 18.3 percent, while the national database lacked complete fields for lesion count, mucosal involvement, nutritional status, treatment, exposure route, household links, and recovery time. The authors call for DHIS2 to capture epidemiologic links, clinical outcomes, vaccine eligibility and status, and potential exposure settings through standardized fields, supported by detailed investigation forms, and for prospective household and community contact studies to define paediatric transmission pathways. Passive case finding likely missed mild infections and children who never reached care, and the emergency context limited record retrieval, so the true paediatric burden may be higher than reported. What the study establishes beyond doubt is that children were not spared in Sierra Leone’s clade IIb epidemic, and that mpox response plans, from case finding and contact follow-up to clinical care and vaccination assessment, must be designed with children explicitly in view rather than as an afterthought to adult-focused strategies.

Subject of Research: Paediatric mpox epidemiology during Sierra Leone's 2024 to 2025 clade IIb outbreak

Article Title: Paediatric mpox during Sierra Leone’s 2024 to 2025 clade IIb outbreak: A national surveillance and outbreak-investigation study of 415 children

Article References: Ikoona, E. N., Namulemo, L., Sinnah, M. M., Vandi, M. A., & Sahr, F. (2026). Paediatric mpox during Sierra Leone’s 2024 to 2025 clade IIb outbreak: A national surveillance and outbreak-investigation study of 415 children. PLOS Neglected Tropical Diseases, 20(10), e0014785. https://doi.org/10.1371/journal.pntd.0014785

Image Credits: AI Generated

DOI: 10.1371/journal.pntd.0014785

Keywords: mpox, Sierra Leone, clade IIb, paediatric, children, outbreak investigation, DHIS2, surveillance, MVA-BN vaccine, household transmission, public health, PLOS Neglected Tropical Diseases

The urgency behind the effort is rooted in one of the defining demographic shifts of the twenty-first century. The United Nations projects that by 2050 nearly 68 percent of the world’s population will live in urban areas, with the fastest growth concentrated in South Asia and Africa. In low- and middle-income countries, unplanned urban expansion routinely outpaces infrastructure, producing informal settlements with unsafe housing, poor sanitation, unreliable water, and exposure to flooding, waste pollution, and contaminated air. These clustered environmental and social risks drive diarrheal disease, mosquito-borne infections, and respiratory illness. Yet the people most exposed, including migrants, the urban poor, women, and ethnic minorities, are often invisible in official records, which means municipal planners cannot target resources to the neighborhoods that need them most.

The technical problem the researchers set out to solve is fragmentation. In many cities, health, education, water, civil registration, and planning data sit in separate departmental databases with no interoperability, inconsistent coding, and incompatible geographic coverage. Routine health information systems such as clinic records and vital registration are typically designed for national or rural contexts and rarely capture the plurality of providers in urban areas or the granularity needed at ward level. Periodic household surveys offer snapshots but are infrequent and often miss slum settlements entirely. The result is that a slum cluster with high child mortality or no safe water can remain hidden inside favorable city-wide averages, and planning an immunization campaign or a new clinic becomes an exercise in guesswork.

The study will proceed in three sequential phases. From March to November 2025, the team will conduct formative research, including stakeholder interviews and a policy review, alongside a household census and social mapping. From November 2025 to June 2026, they will develop the portal itself, integrating, validating, and visualizing the data. From July to December 2026, the focus shifts to implementation, user engagement, capacity strengthening, and embedding the portal into municipal decision-making. Budhanilkantha was purposively selected because its diverse and rapidly changing population, including dense informal settlements and migratory groups, makes it an ideal proving ground, while remaining manageable for intensive fieldwork.

The formative phase begins with approximately twelve key informant interviews with municipal officials from departments spanning health, education, civil registration, disaster management, animal health, agriculture, environment, infrastructure, information technology, and women, children and inclusion. Semi-structured interviews conducted in Nepali will explore existing data sources and workflows, priority health issues, and barriers to data sharing. Transcripts will be analyzed thematically using NVivo, with two researchers independently coding an initial subset and resolving discrepancies through discussion. Alongside the interviews, the team will review municipal plans, health policies, and census reports, and compile an inventory of routine data sources, including the national Health Management Information System, civil registration records, and education databases, as well as non-routine sources such as past surveys and geographic information system layers.

Three interlinked primary data collection activities will then fill the gaps that routine systems leave. First, GIS-based social mapping will engage community members, youth volunteers, and ward officials in delineating ward boundaries and marking health providers, water sources, schools, markets, transport routes, garbage dumps, and flood zones, along with informal settlements and even shops selling meat, tobacco, and liquor, all of which are relevant to environmental and behavioral health risks. Second, a door-to-door household census will enumerate every household in Wards 4 and 7, capturing household composition, assets, mortality, healthcare access, risk behaviors, maternal and child health, immunization, and HPV vaccination coverage among adolescent girls. Anthropometric measurements, including height, weight, body mass index, blood pressure, and mid-upper arm circumference in children under five, will be recorded, with enumerators using tablets running electronic forms such as Open Data Kit for daily uploads to a secure database.

Third, the team will assess all eleven public health facilities serving the two wards, including one public hospital, six health posts, and four basic health service centers. Structured tools will capture infrastructure, service readiness, equipment, logistics, the availability of eighteen tracer drugs, and the qualifications and training of health care providers. Performance will be benchmarked against Nepal’s Minimum Service Standards, a nationally endorsed framework of the Ministry of Health and Population. GPS coordinates of each facility will enable spatial analysis, and facility data will be linked with household census data to reveal mismatches between where people live, what they need, and what services actually exist. Preliminary findings already illustrate the value of this approach: Ward 7 hosts a high density of informal settlers but is served by only one under-resourced clinic.

The technical architecture of the portal rests on common identifiers and geocoding. Each household receives a unique ID, with individual-level IDs nested within it, allowing records to be linked across datasets where consent permits, or linked spatially through shared geographic identifiers where it does not. Routine government data will be standardized and aligned with ward boundaries using a comprehensive data dictionary, and automated pipelines through APIs or periodic batch uploads will refresh the portal monthly or quarterly. Built on the open-source DHIS2 platform enhanced with GIS modules, the portal will feature interactive dashboards, mapping layers for household density, infrastructure, water and sanitation coverage, and health outcomes, hotspot analytics, custom report generation, and role-based access controls to protect privacy. A user-centered design approach, with participatory workshops, prototype testing, and feedback loops involving municipal officials and community representatives, is intended to ensure usability and long-term adoption.

Data security receives careful attention throughout. All datasets will be stored in password-protected systems with access restricted to a designated municipal focal person and authorized team members, with routine quality checks for missing, duplicate, and out-of-range values, and geospatial information that could identify individual households will never be publicly displayed at an identifiable level. Ethical approval was granted by the Nepal Health Research Council and the University of Leeds, and the study is part of the Community-led Responsive and Effective Urban Health System research consortium funded by UK aid through the Foreign, Commonwealth and Development Office.

The implications extend well beyond two wards in Kathmandu. Because the architecture is built on common identifiers and flexible GIS layers, the framework can be extended to additional wards and municipalities, with indicators tailored to local contexts such as air quality sensors or traffic accident hotspots. The researchers envision future integration of environmental sensors, urban mobility data, and predictive modeling for disease outbreaks, potentially evolving into a community-facing platform with public health alerts and citizen reporting. By demonstrating that participatory mapping, a household census, and facility surveys can be fused with routine administrative data into a living analytical tool, the project offers a proof of concept for equity-focused urban health governance, aligning local action with Sustainable Development Goal 11 on sustainable cities. For the millions of urban residents across low- and middle-income countries whose lives and needs are currently missing from the data, this Nepali pilot suggests that being counted may be the first step toward being served.

Subject of Research: Development of an integrated urban health data portal to strengthen municipal health systems in Nepal

Article Title: Study protocol for developing an integrated urban health data portal to strengthen urban health systems in Nepal

Article References: Kakchapati, S., Sharma, N., Baral, J., Maharjan, S., Mainali, S., Marasini, S., Elsey, H., Lamichhane, B., Karki, S., Bhattarai, A., Sharma, S., Shrestha, G., Karki, S., Ebenso, B., Hicks, J., Dawkins, B., Koirala, B., Dahal, K. P., Lama, A., & Baral, S. C. (2026). Study protocol for developing an integrated urban health data portal to strengthen urban health systems in Nepal. Discover Cities, 3(1), Article 200. https://doi.org/10.1007/s44327-026-00377-y

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00377-y

Keywords: urban health, data integration, Nepal, GIS, health informatics, household census, informal settlements, health equity, DHIS2, municipal governance, Kathmandu, study protocol

Mental health disorders represent a substantial and growing share of the global burden of disease, yet in many low- and middle-income countries the health systems responsible for detecting and treating these conditions operate largely blind. Routine health information systems, the administrative machinery through which clinics report diagnoses, cases, and service delivery to district and national authorities, are the foundation of any evidence-based response. When those systems fail to capture mental health data, planners cannot estimate need, allocate medication, deploy specialists, or evaluate interventions. The quality of routine mental health reporting has long been recognized as particularly weak in resource-constrained settings, but the question of whether internationally supported training programs improve that quality has remained poorly understood. The new study set out to answer it directly.

The research team, led by Mithun Kumar Jha and Binita Kumari Paudel of Purbanchal University School of Health Sciences, conducted an explanatory mixed-methods study in twenty-eight primary health care facilities in Parsa District in the southern plains of Nepal. Fourteen of the facilities had staff trained under mhGAP, the WHO program designed to scale up services for mental, neurological, and substance use disorders by equipping non-specialist health workers with evidence-based protocols. The other fourteen had no mhGAP training. The quantitative arm of the study involved a retrospective analysis of twelve months of routine mental health reporting data drawn from Nepal’s Health Management Information System, which runs on the open-source District Health Information Software 2 platform, or DHIS2, used by dozens of countries worldwide. The qualitative arm consisted of in-depth interviews with eleven health workers, analyzed through the Consolidated Framework for Implementation Research, a widely used implementation science framework for identifying barriers and facilitators to putting evidence-based programs into practice.

The quantitative results were unambiguous and, in places, startling. Timeliness of reporting, meaning whether facilities submitted their monthly reports on schedule, was impressively high: every single mhGAP-trained facility achieved one hundred percent timeliness, and the untrained facilities were close behind at 96.4 percent. But completeness, the proportion of expected mental health data actually reported, was zero percent in both groups. Not a single facility in either arm of the study achieved complete mental health reporting over the twelve-month period, and most facilities reported no mental health cases at all across the entire year. Accuracy fared no better. The researchers found no consistency between the paper registers kept at the facilities and the data entered into DHIS2, meaning that in none of the twenty-eight facilities could the digital record be verified against the primary source documents. Because timeliness was analyzed as a continuous indicator rather than a categorical one, the authors note it was not subjected to formal statistical comparison, but the completeness and accuracy findings alone paint a bleak picture.

Perhaps the most telling contrast in the study was between structural readiness and functional performance. The mhGAP-trained facilities did show higher structural preparedness: they had trained personnel, and presumably the nominal capacity to identify and manage priority mental health conditions under the WHO protocols. Yet this structural advantage translated into no measurable improvement in the reporting domain. Whether a facility had received mhGAP training or not, its performance in routine mental health reporting was rated as poor. The authors conclude that the facilities lack functional routine reporting systems for mental health, and that training alone, however well designed, does not create the operational machinery needed to move data from a clinic register to a national database in a complete and accurate form.

The qualitative interviews, analyzed through the CFIR lens, help explain why this gap persists. Health workers described a system in which mental health remains peripheral to everyday primary care practice, with weak integration into the regular health information workflow. Without supportive supervision to reinforce training, without system-level coordination between the mental health program and the health management information system, and without mental health indicators being genuinely embedded in routine reporting expectations, the knowledge gained in mhGAP trainings had nowhere to go. A health worker may know how to diagnose depression or psychosis using a WHO protocol, but if no one checks whether those diagnoses are recorded, if the reporting forms and DHIS2 data elements are not aligned, and if supervisors never review the mental health column of the monthly report, the diagnosis simply never enters the record.

The implications reach far beyond Parsa District. The mhGAP program has been implemented in more than a hundred countries and is the centerpiece of global efforts to address the enormous treatment gap for mental, neurological, and substance use disorders, a gap that is widest precisely in the low- and middle-income countries where specialist psychiatrists are scarce. The program’s logic is elegant: train generalist health workers to deliver evidence-based care, and specialist-level treatment can be scaled without waiting for specialists. But the Nepal study exposes a critical dependency in that logic. Care that is delivered but not recorded is invisible to the health system. It cannot be counted, funded, resupplied, or improved. If routine reporting remains at zero completeness, then even successful mhGAP implementation at the clinical level would be undetectable in national statistics, and the program’s true coverage would be unknowable.

The study also carries a broader lesson for the rapidly digitizing world of global health data. Nepal’s adoption of DHIS2 represents a significant technical achievement, and the near-perfect timeliness scores show that facilities are capable of disciplined, punctual reporting when the system demands it. The failure is therefore not one of infrastructure or motivation but of functional integration: the mental health component of the information system has not been wired into the routines, supervision structures, and accountability mechanisms that make other reporting streams work. This distinction between structural readiness and functional performance is one that implementation scientists have emphasized for years, but it is rarely quantified so starkly. A trained workforce and a functioning software platform are necessary conditions for good data, and the Nepal results demonstrate they are nowhere near sufficient.

The authors call for comprehensive system-level interventions that go well beyond training. These would include regular supportive supervision that explicitly reviews mental health reporting, coordination between the mental health program and the health management information system so that indicators, registers, and digital data elements are aligned, and genuine integration of mental health into the routine reporting expectations placed on every primary health care facility. Such measures are less glamorous than international training workshops, but the evidence suggests they are where the real work lies. Without them, the study warns, reporting performance may not improve no matter how many workers are trained.

For a world preparing to confront a mental health crisis that the burden-of-disease data only partially captures, the message from Nepal is uncomfortable but valuable. The invisible fraction of the mental health burden, the cases that never appear in any register or dashboard, may be far larger than global statistics suggest, not because the cases do not exist but because the systems meant to see them are not looking. Fixing that will require treating mental health data not as an optional add-on to primary care but as a core, supervised, and audited function of every health facility, in Nepal and in every country that hopes to close the mental health gap.

Subject of Research: Routine mental health reporting performance in primary health care facilities in Nepal and the effect of WHO mhGAP training

Article Title: Mental health reporting performance in primary health care: a comparative mixed-methods study of mhGAP-trained and untrained health facilities in Nepal

Article References: Jha, M. K., & Paudel, B. K. (2026). Mental health reporting performance in primary health care: a comparative mixed-methods study of mhGAP-trained and untrained health facilities in Nepal. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15782-7

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15782-7

Keywords: mental health, mhGAP, Nepal, primary health care, DHIS2, health information systems, WHO, low- and middle-income countries, implementation science, CFIR, health services research, reporting completeness

The research, published in BMC Health Services Research, was led by Daniel Gudina, Gudeta Ayele, Behailu Hawulte and Ibsa Mussa from the School of Public Health at Haramaya University’s College of Health and Medical Sciences. The team set out to answer a deceptively simple question: what determines whether health workers in public facilities actually use the data flowing through DHIS2? This question matters because data utilization is one of the key functions of any health information system. When performance monitoring teams can read and interpret their own data, they can identify gaps in immunization coverage, spot outbreaks earlier, allocate staff and supplies more rationally and ultimately improve the quality of health service delivery. When they cannot, or do not, the entire investment in digital infrastructure risks becoming an expensive exercise in data entry without insight.

To investigate, the researchers conducted an institutional-based cross-sectional study between June 1, 2020 and July 31, 2020. They used a stratified sampling technique to select 220 participants working in public health facilities across the two administrative areas of eastern Ethiopia. Data collection relied on a structured questionnaire complemented by an observational checklist, allowing the team to capture both what health workers reported about their practices and what could be verified on the ground. The completed questionnaires were thoroughly checked, coded and entered into Epi-data 3.1 before being transferred to Stata 14 for statistical analysis. To identify the factors associated with data utilization, the researchers applied a binary logistic regression model, treating results as statistically significant when the p value fell below 0.05 with 95 percent confidence intervals.

The headline finding is sobering. Overall utilization of data from DHIS2 stood at about 45 percent, with a 95 percent confidence interval ranging from 39 to 50 percent. That figure falls well below the national recommended threshold, which calls for more than two-thirds of health facilities to be actively using their routine health information data for decision making. In other words, even in a system where the software has been deployed and staff have been trained to feed it, a majority of facilities are not translating the numbers into action. The study also notes that prior research across Ethiopia has reported significant variability in DHIS2 data utilization, suggesting that this is not a local anomaly but a systemic challenge with roots that vary from place to place.

What, then, separates the facilities that use their data from those that do not? The regression analysis identified four independent determinants, and each one is revealing. The first is attitude. Health workers who held a favorable attitude toward data use were roughly three times more likely to utilize DHIS2 data, with an adjusted odds ratio of 3.0 and a 95 percent confidence interval of 1.90 to 8.62. Attitude, in this context, reflects whether staff believe that reviewing data is worth their time, whether they see it as relevant to their daily work and whether they feel that acting on evidence is part of their professional identity. A health worker who views monthly data review as a bureaucratic chore will behave very differently from one who sees it as a diagnostic tool for improving services.

The second determinant is perceived self-competence. Workers who felt confident in their ability to interpret and apply the data were nearly three times more likely to use it, with an adjusted odds ratio of 2.9 and a confidence interval of 1.14 to 7.38. This finding speaks to a well-documented problem in health information systems across low- and middle-income countries: staff may be trained to enter data but not to analyze it. DHIS2 offers dashboards, pivot tables and visualization tools, but these features are only useful to people who understand what the outputs mean and how they connect to programmatic decisions. Self-doubt, in this environment, becomes a silent barrier. A nurse or health officer who feels incompetent with data will avoid opening the very reports that could guide their work, and the avoidance reinforces the incompetence in a self-perpetuating cycle.

The third and strongest single factor was perceived data quality. Health workers who believed the data in the system were accurate, complete and timely were more than four times as likely to use them, with an adjusted odds ratio of 4.4 and a confidence interval of 1.76 to 10.9. This is perhaps the most intuitive of the findings, and also the most troubling. If staff suspect that the numbers in DHIS2 are riddled with errors, duplicates or gaps, they will reasonably distrust any conclusion drawn from them. Data quality and data use are locked in a feedback loop: poor quality suppresses use, and without use there is little incentive or feedback mechanism to correct quality problems. Breaking that loop requires deliberate investment in data verification, feedback to data enterers and a culture in which accuracy is valued and rewarded rather than assumed.

The fourth determinant was supportive supervision. Facilities whose staff received supportive supervision were more than four times as likely to use DHIS2 data, with an adjusted odds ratio of 4.3 and a confidence interval of 1.48 to 12.45. Supportive supervision, in the language of the Performance of Routine Health Information System framework, known as PRISM, means supervisors who do more than inspect forms. They review data with frontline staff, help troubleshoot technical problems, encourage discussion of trends and connect the numbers to concrete service improvements. The PRISM framework, which underpins much of the conceptual thinking in this field, holds that technical, behavioral and organizational determinants together shape whether routine health information systems deliver value. The Ethiopian findings map neatly onto that framework: attitude and self-competence are behavioral determinants, perceived data quality is a technical one, and supportive supervision is organizational.

The implications for policy are direct. Ethiopia’s Federal Ministry of Health has invested substantially in DHIS2 as the backbone of its routine health information system, and the platform is expected to increase the utilization of health data nationwide. But the study’s results suggest that software deployment alone does not close the gap between data availability and data use. Interventions should target the four determinants identified: building favorable attitudes toward evidence-based practice, strengthening the analytical self-competence of health workers through practical mentorship rather than one-off training, assuring and communicating data quality so that staff trust what they see, and institutionalizing supportive supervision so that every facility benefits from regular, constructive engagement around its own numbers. The performance monitoring teams that exist in Ethiopian facilities could become the natural vehicle for this work, provided they are equipped and encouraged to function as intended.

There is also a broader lesson for the global health informatics community. DHIS2 is now used in dozens of countries, and the dream of a digital health information backbone is closer to reality than ever. Yet the eastern Ethiopia study is a reminder that the last mile of any information system is human. A dashboard nobody opens is indistinguishable from a filing cabinet nobody opens. The researchers, whose work was financially supported by the Doris Duke Charitable Foundation as part of the Capacity Building and Mentorship Program project, with no funder role in study design or interpretation, obtained ethical clearance from Haramaya University’s Institutional Health Research Ethics Review Committee in accordance with the Helsinki II declaration. Their message to health systems everywhere is clear: to unlock the value of digital health data, invest as much in confidence, trust and supervision as in servers and software. Until the people closest to the data believe in its quality and in their own ability to act on it, the numbers will keep flowing, and the decisions will keep waiting.

Subject of Research: Determinants of District Health Information System 2 data utilization among health workers in public health facilities in eastern Ethiopia

Article Title: Determinants of district health information system 2 data utilization in public health facilities in Harari Regional States and Dire Dawa City Administration, Eastern Ethiopia

Article References: Determinants of district health information system 2 data utilization in public health facilities in Harari Regional States and Dire Dawa City Administration, Eastern Ethiopia. (n.d.). https://doi.org/10.1186/s12913-026-15620-w

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15620-w

Keywords: DHIS2, health information systems, data utilization, Ethiopia, public health facilities, health informatics, supportive supervision, data quality, evidence-based decision making, routine health information systems, Haramaya University, performance monitoring