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Background In regard to health service planning and delivery, the use of information at different levels in the health system is vital, ranging from the influencing of policy to the programming of action to the ensuring of evidence-informed practices. However, neither ownership of, nor access to, good quality data guarantees actual use of these data. For information to be used, relevant data need to be collected, processed and analysed in an accessible format. This problem of underused data, and indeed the absence of data use entirely, is widespread and has been evident for decades. The DHIS2 software platform supports routine health management for an estimated 2.4 billion people, in over 70 countries worldwide. It is by far the largest and most widespread software for this purpose and adopts a holistic, socio-technical approach to development and implementation. Given this approach, and the rapid and extensive scaling of DHIS2, we questioned whether or not there has been a parallel increase in the scaling of improved information use. To date, there has been no rigorous review of the documentation on how exactly DHIS2 data is routinely being used for decision-making and subsequent programming of action. This scoping review addresses this review gap. Methods The five-stage approach of Arksey and O’Malley progressed by Levac et al. and Peters was followed. Three databases (PubMed, Web of Science and Embase) were searched, along with relevant conference proceedings and postgraduate theses. In total, over 500 documents were reviewed and data from 19 documents were extracted. Results Overall, DHIS2 data are being used but there are few detailed descriptions of this usage in peer reviewed or grey literature. We find that, commonly, there exists a centralised versus decentralised pattern of use in terms of access to data and the reporting of data ‘up’ in the system. We also find that the different conceptualisations of data use and how data use is conceptualised are not made explicit. Conclusions We conclude with some suggestions for a way forward, namely: i) the need to document in more detail and share how data are being used, ii) the need to investigate how data were created and who uses such data, iii) the need to design systems based on work practices, and in tandem develop and promote forums in which ‘conversations’ around data can take place.

IntroductionInguinal hernias are repaired using either open or minimally invasive surgical techniques. For both types of surgery it has been demonstrated that a higher annual surgeon volume is associated with a lower risk of recurrence. This present study investigated the volume-outcome implications for recurrence operations, surgical complications, rate of chronic pain requiring treatment, and 30-day mortality based on the hospital volume.Materials and methodsThe data basis used was the routine data collected throughout the Federal Republic of Germany for persons insured by the Local General Sickness Fund “AOK” who had undergone inpatient inguinal hernia repair between 2013 and 2015. Complications were recorded by means of indicators. Hospitals were divided into five groups on the basis of the annual caseload volume: 1–50, 51–75, 76–100, 101–125, and ≥ 126 inguinal hernia repairs per year. The effect of the hospital volume on the indicators was assessed using multiple logistic regression.Results133,449 inguinal hernia repairs were included. The incidence for recurrence operations was 0.95%, for surgical complications 4.22%, for chronic pain requiring treatment 2.87%, and for the 30-day mortality 0.28%. Low volume hospitals (1–50 and 51–75 inguinal hernia repairs per year) showed a significantly increased recurrence risk compared to high volume hospitals with ≥ 126 inguinal hernia repairs per year (odds ratio: 1.53 and 1.24). No significant correlations were found for the other results.ConclusionsThe study gives a detailed picture of hospital care for inguinal hernia repair in Germany. Furthermore, it was noted that the risk of hernia recurrence decreases in line with a rising caseload of the treating hospital.

Return-to-work (RTW) after medical rehabilitation is an important indicator of post-rehabilitation labour market participation, yet evidence on its association with occupation-based job exposure over longer follow-up periods remains limited. This nationwide retrospective cohort study examined the association between job exposure and RTW within 24 months post-rehabilitation using routine administrative data from the German Pension Insurance. The study included 621,695 individuals aged 18–63 years who completed medical rehabilitation between 2014 and 2019. Job exposure was assessed using the Overall Job Exposure Index, integrating physical and psychosocial exposures, and categorised into low, moderate, and high exposure. Initial RTW was defined as at least one month of employment, and stable RTW as at least four consecutive months within 24 months post-rehabilitation. Associations were analysed using Cox proportional hazards regression models with progressive adjustment for sociodemographic, work-related, and health-related factors. Within 24 months, 90.3% achieved initial RTW and 84.2% stable RTW. Higher job exposure was consistently associated with lower RTW rates. In fully adjusted models, moderate and high exposure were associated with a reduced likelihood of initial RTW (HR 0.903 [95% CI 0.897–0.909] and 0.872 [95% CI 0.866–0.878]) and stable RTW (HR 0.878 [95% CI 0.872–0.884] and 0.837 [95% CI 0.830–0.843]), compared with low exposure. These findings provide population-level evidence that occupation-based job exposure is associated with RTW after medical rehabilitation.

Undiagnosed HIV infection remains substantial in key population subgroups including adolescents, older adults, and men, driving ongoing transmission in sub-Saharan Africa. We evaluated the impact, safety, and costs of community-led delivery of HIV self-testing (HIVST), aiming to increase HIV testing in underserved subgroups and stimulate demand for antiretroviral therapy (ART). This cluster-randomised trial, conducted between October 2018 and July 2019, used restricted randomisation (1:1) to allocate 30 group village head clusters in Mangochi district, Malawi to the community-led HIVST intervention in addition to the standard of care (SOC) or the SOC alone. The intervention involved mobilising community health groups to lead the design and implementation of 7-day HIVST campaigns, with cluster residents (≥15 years) eligible for HIVST. The primary outcome compared lifetime HIV testing among adolescents (15 to 19 years) between arms. Secondary outcomes compared: recent HIV testing (in the last 3 months) among older adults (≥40 years) and men; cumulative 6-month incidence of ART initiation per 100,000 population; knowledge of the preventive benefits of HIV treatment; and HIV testing stigma. Outcomes were measured through a post-intervention survey and at neighboring health facilities. Analysis used intention-to-treat for cluster-level outcomes. Community health groups delivered 24,316 oral fluid-based HIVST kits. The survey included 90.2% (3,960/4,388) of listed participants in the 15 community-led HIVST clusters and 89.2% (3,920/4,394) of listed participants in the 15 SOC clusters. Overall, the proportion of men was 39.0% (3,072/7,880). Most participants obtained primary-level education or below, were married, and reported a sexual partner. Lifetime HIV testing among adolescents was higher in the community-led HIVST arm (84.6%, 770/910) than the SOC arm (67.1%, 582/867; adjusted risk difference [RD] 15.2%, 95% CI 7.5% to 22.9%; p < 0.001), especially among 15 to 17 year olds and boys. Recent testing among older adults was also higher in the community-led HIVST arm (74.5%, 869/1,166) than the SOC arm (31.5%, 350/1,111; adjusted RD 42.1%, 95% CI 34.9% to 49.4%; p < 0.001). Similarly, the proportions of recently tested men were 74.6% (1,177/1,577) and 33.9% (507/1,495) in the community-led HIVST and SOC arms, respectively (adjusted RD 40.2%, 95% CI 32.9% to 47.4%; p < 0.001). Knowledge of HIV treatment benefits and HIV testing stigma showed no differences between arms. Cumulative incidence of ART initiation was respectively 305.3 and 226.1 per 100,000 population in the community-led HIVST and SOC arms (RD 72.3, 95% CI -36.2 to 180.8; p = 0.18). In post hoc analysis, ART initiations in the 3-month post-intervention period were higher in the community-led HIVST arm than the SOC arm (RD 97.7, 95% CI 33.4 to 162.1; p = 0.004). HIVST uptake was 74.7% (2,956/3,960), with few adverse events (0.6%, 18/2,955) and at US$5.70 per HIVST kit distributed. The main limitations include the use of self-reported HIV testing outcomes and lack of baseline measurement for the primary outcome. In this study, we found that community-led HIVST was effective, safe, and affordable, with population impact and coverage rapidly realised at low cost. This approach could enable community HIV testing in high HIV prevalence settings and demonstrates potential for economies of scale and scope. Clinicaltrials.gov NCT03541382.

Background Recent malaria control efforts in mainland Tanzania have led to progressive changes in the prevalence of malaria infection in children, from 18.1% (2008) to 7.3% (2017). As the landscape of malaria transmission changes, a sub-national stratification becomes crucial for optimized cost-effective implementation of interventions. This paper describes the processes, data and outputs of the approach used to produce a simplified, pragmatic malaria risk stratification of 184 councils in mainland Tanzania. Methods Assemblies of annual parasite incidence and fever test positivity rate for the period 2016–2017 as well as confirmed malaria incidence and malaria positivity in pregnant women for the period 2015–2017 were obtained from routine district health information software. In addition, parasite prevalence in school children ( Pf PR 5to16 ) were obtained from the two latest biennial council representative school malaria parasitaemia surveys, 2014–2015 and 2017. The Pf PR 5to16 served as a guide to set appropriate cut-offs for the other indicators. For each indicator, the maximum value from the past 3 years was used to allocate councils to one of four risk groups: very low (< 1% Pf PR 5to16 ), low (1− < 5% Pf PR 5to16 ), moderate (5− < 30% Pf PR 5to16 ) and high (≥ 30% Pf PR 5to16 ). Scores were assigned to each risk group per indicator per council and the total score was used to determine the overall risk strata of all councils. Results Out of 184 councils, 28 were in the very low stratum (12% of the population), 34 in the low stratum (28% of population), 49 in the moderate stratum (23% of population) and 73 in the high stratum (37% of population). Geographically, most of the councils in the low and very low strata were situated in the central corridor running from the north-east to south-west parts of the country, whilst the areas in the moderate to high strata were situated in the north-west and south-east regions. Conclusion A stratification approach based on multiple routine and survey malaria information was developed. This pragmatic approach can be rapidly reproduced without the use of sophisticated statistical methods, hence, lies within the scope of national malaria programmes across Africa.

Background The heterogeneity of sepsis represents a significant challenge to the development of personalized sepsis therapies. Sepsis subtyping has therefore emerged as an important approach to this problem, but its impact on clinical practice was limited due to insufficient molecular insights. Modern proteomics techniques allow the identification of subtypes and provide molecular and mechanistical insights. In this study, we analyzed a prospective multi-center sepsis cohort using plasma proteomics to describe and characterize sepsis plasma proteome subtypes. Methods Plasma samples were collected from 333 patients at days 1 and 4 of sepsis and analyzed using liquid chromatography coupled to tandem mass spectrometry. Plasma proteome subtypes were identified using K-means clustering and characterized based on clinical routine data, cytokine measurements, and proteomics data. A random forest machine learning classifier was generated to showcase future assignment of patients to subtypes. Results Four subtypes with different sepsis severity were identified. Cluster 0 represented the most severe form of sepsis, with 100% mortality. Cluster 1, 2 and 3 showed a gradual decrease of the median SOFA score, as reflected by clinical data and cytokine measurements. At the proteome level, the subtypes were characterized by distinct molecular features. We observed an alternating immune response, with cluster 1 showing prominent activation of the adaptive immune system, as indicated by elevated levels immunoglobulin (Ig) levels, which were verified using orthogonal measurements. Cluster 2 was characterized by acute inflammation and the lowest Ig levels. Cluster 3 represented the sepsis proteome baseline of the investigated cohort. We generated an ML classifier and optimized it for the minimum number of proteins that could realistically be implemented into routine diagnostics. The model, which was based on 10 proteins and Ig quantities, allowed the assignment of patients to clusters 1, 2 and 3 with high confidence. Conclusion The identified plasma proteome subtypes provide insights into the immune response and disease mechanisms and allow conclusions on appropriate therapeutic measures, enabling predictive enrichment in clinical trials. Thus, they represent a step forward in the development of targeted therapies and personalized medicine for sepsis.

Background Routinely collected health data are increasingly used to generate real-world evidence for therapeutic decision-making. Their use, however, depends on the expectations of multiple stakeholders. Clinicians require clinically interpretable analyses, pharmaceutical stakeholders need robust evidence on effectiveness and safety, patient advocacy groups emphasize transparency, privacy, and meaningful outcome measures, and statisticians focus on bias control, reproducibility, and methodological rigor. Without explicit consideration of these perspectives, analyses risk being fragmented, misaligned with end-user needs, or lacking transparency. Aligning these perspectives early in the design of routine data analyses therefore remains a central challenge. Methods We developed a stakeholder-inclusive conceptual framework for modeling routine health data, through expert panel discussions, an interdisciplinary workshop and targeted literature examples. The synthesis focused on four stakeholder perspectives: clinicians, pharmaceutical industry, patient advocates, and statisticians. To illustrate how stakeholder priorities can be translated into analytical strategies, we reviewed selected applications of multistate models (MSMs) in routine health data settings. Results The conceptual framework links stakeholder-specific priorities, methodological requirements and identifies shared needs for analyses that are clinically meaningful, transparent, reproducible, and able to represent patient pathways, intermediate events, treatment trajectories, disease progression, safety outcomes, and patient-reported measures. While the framework is intended to be applicable across various analytical approaches MSMs are used here to illustrate how these diverse requirements can be operationalized in practice. They can capture longitudinal health processes, competing events, recurrent or intermediate states, and state-specific outcomes while retaining an interpretable graphical structure, and the reviewed examples show their applicability across different research questions using routine health data. Beyond specific methodological choices, clinical research relies fundamentally on statistical expertise. The framework also highlights that the statistician’s role varies with the complexity of the research question, ranging from consultation on standard analyses to adaptation or development of advanced methods. Conclusions The stakeholder-inclusive framework provides methodological guidance for designing analyses of routine health data that are clinically meaningful, scientifically rigorous, and socially acceptable. By aligning the research question with the intended perspective from the beginning, it supports more robust and transparent evidence generation, with multistate models serving as a flexible tool to operationalize this integration.

This paper includes a comprehensive literature review of sludge composition data from wastewater treatment plants. 722 data sets from 249 sources were used to establish typical ratios between COD and solids-based parameters and to verify rule-of-thumb values, respectively. Confirmation of these typical ratios can also be accomplished by using biochemical composition data. It is shown that a correlation between data from proteins, lipids and carbohydrates analysis can be related to COD/VSS ratios. Finally, using the findings from the literature review, the organic and inorganic conversion factors of COD fractions in activated sludge models are adjusted to solids-based parameters. It was shown that with the adjustments of the factors and a partition of the particulate inert fraction into a fraction assigned to the influent and a fraction assigned to the endogenous products, a better agreement with the ratios of COD/VSS in the individual sludge streams can be established.

Background: Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high priority to understand and address the causes of disease. Objective: This study aims to provide an outline of the use of genomic data alongside routinely collected data in health research to date. As this field prepares to move forward, it is important to take stock of the current state of play in order to highlight new avenues for development, identify challenges, and ensure that adequate data governance models are in place for safe and socially acceptable progress. Methods: We conducted a literature review to draw information from past studies that have used genomic and routinely collected data and conducted interviews with individuals who use these data for health research. We collected data on the following: the rationale of using genomic data in conjunction with routinely collected data, types of genomic and routinely collected data used, data sources, project approvals, governance and access models, and challenges encountered. Results: The main purpose of using genomic and routinely collected data was to conduct genome-wide and phenome-wide association studies. Routine data sources included electronic health records, disease and death registries, health insurance systems, and deprivation indices. The types of genomic data included polygenic risk scores, single nucleotide polymorphisms, and measures of genetic activity, and biobanks generally provided these data. Although the literature search showed that biobanks released data to researchers, the case studies revealed a growing tendency for use within a data safe haven. Challenges of working with these data revolved around data collection, data storage, technical, and data privacy issues. Conclusions: Using genomic and routinely collected data holds great promise for progressing health research. Several challenges are involved, particularly in terms of privacy. Overcoming these barriers will ensure that the use of these data to progress health research can be exploited to its full potential.

Background Chronic obstructive pulmonary disease (COPD) causes significant morbidity and mortality worldwide. Estimation of incidence, prevalence and disease burden through routine insurance data is challenging because of under-diagnosis and under-treatment, particularly for early stage disease in health care systems where outpatient International Classification of Diseases (ICD) diagnoses are not collected. This poses the question of which criteria are commonly applied to identify COPD patients in claims datasets in the absence of ICD diagnoses, and which information can be used as a substitute. The aim of this systematic review is to summarize previously reported methodological approaches for the identification of COPD patients through routine data and to compile potential criteria for the identification of COPD patients if ICD codes are not available. Methods A systematic literature review was performed in Medline via PubMed and Google Scholar from January 2000 through October 2018, followed by a manual review of the included studies by at least two independent raters. Study characteristics and all identifying criteria used in the studies were systematically extracted from the publications, categorized, and compiled in evidence tables. Results In total, the systematic search yielded 151 publications. After title and abstract screening, 38 publications were included into the systematic assessment. In these studies, the most frequently used (22/38) criteria set to identify COPD patients included ICD codes, hospitalization, and ambulatory visits. Only four out of 38 studies used methods other than ICD coding. In a significant proportion of studies, the age range of the target population (33/38) and hospitalization (30/38) were provided. Ambulatory data were included in 24, physician claims in 22, and pharmaceutical data in 18 studies. Only five studies used spirometry, two used surgery and one used oxygen therapy. Conclusions A variety of different criteria is used for the identification of COPD from routine data. The most promising criteria set in data environments where ambulatory diagnosis codes are lacking is the consideration of additional illness-related information with special attention to pharmacotherapy data. Further health services research should focus on the application of more systematic internal and/or external validation approaches.