Set-Theoretic Formalism for Treating Ligand-Target Datasets

Data on ligand–target (LT) interactions has played a growing role in drug research for several decades. Even though the amount of data has grown significantly in size and coverage during this period, most datasets remain difficult to analyze because of their extreme sparsity, as there is no activity data whatsoever for many LT pairs. Even within clusters of data there tends to be a lack of data completeness, making the analysis of LT datasets problematic. The current effort extends earlier works on the development of set-theoretic formalisms for treating thresholded LT datasets. Unlike many approaches that do not address pairs of unknown interaction, the current work specifically takes account of their presence in addition to that of active and inactive pairs. Because a given LT pair can be in any one of three states, the binary logic of classical set-theoretic methods does not strictly apply. The current work develops a formalism, based on ternary set-theoretic relations, for treating thresholded LT datasets. It also describes an extension of the concept of data completeness, which is typically applied to sets of ligands and targets, to the local data completeness of individual ligands and targets. The set-theoretic formalism is applied to the analysis of simple and joint polypharmacologies based on LT activity profiles, and it is shown that null pairs provide a means for determining bounds to these values. The methodology is applied to a dataset of protein kinase inhibitors as an illustration of the method. Although not dealt with here, work is currently underway on a more refined treatment of activity values that is based on increasing the number of activity classes.

Health management information system (HMIS) data quality and associated factors in Massaguet district, Chad

Background Quality data from Health Management Information Systems (HMIS) are important for tracking the effectiveness of malaria control interventions. However, HMIS data in many resource-limited settings do not currently meet standards set by the World Health Organization (WHO). We aimed to assess HMIS data quality and associated factors in Chad. Methods A cross-sectional study was conducted in 14 health facilities in Massaguet district. Data on children under 15 years were obtained from the HMIS and from the external patient register covering the period January–December 2018. An additional questionnaire was administered to 16 health centre managers to collect data on contextual variables. Patient registry data were aggregated and compared with the HMIS database at district and health centre level. Completeness and accuracy indicators were calculated as per WHO guidelines. Multivariate logistic regressions were performed on the Verification Factor for attendance, suspected and confirmed malaria cases for three age groups (1 to < 12 months, 1 to < 5 years and 5 to < 15 years) to identify associations between health centre characteristics and data accuracy. Results Health centres achieved a high level of data completeness in HMIS. Malaria data were over-reported in HMIS for children aged under 15 years. There was an association between workload and higher odds of inaccuracy in reporting of attendance among children aged 1 to < 5 years (Odds ratio [OR]: 10.57, 95% CI 2.32–48.19) and 5– < 15 years (OR: 6.64, 95% CI 1.38–32.04). Similar association was found between workload and stock-outs in register books, and inaccuracy in reporting of malaria confirmed cases. Meanwhile, we found that presence of a health technician, and of dedicated staff for data management, were associated with lower inaccuracy in reporting of clinic attendance in children aged under five years. Conclusion Data completeness was high while the accuracy was low. Factors associated with data inaccuracy included high workload and the unavailability of required data collection tools. The results suggest that improvement in working conditions for clinic personnel may improve HMIS data quality. Upgrading from paper-based forms to a web-based HMIS may provide a solution for improving data accuracy and its utility for future evaluations of health interventions. Results from this study can inform the Ministry of Health and it partners on the precautions to be taken in the use of HMIS data and inform initiatives for improving its quality.

Implementation Outcomes Assessment of a Digital Clinical Support Tool for Intrapartum Care in Rural Kenya: An Observational Analysis (Preprint)

BACKGROUND To support quality of care improvements, iDeliver, a digital clinical support system for maternal and neonatal care, was developed. OBJECTIVE Taking an implementation research lens, we evaluated the adoption and fidelity of iDeliver and assessed the feasibility of its use to provide routine Ministry of Health reports. METHODS We analyzed routinely collected data from the iDeliver implemented at Trans Mara West sub-county Hospital (Kenya), from December 2018 to October 2020. To evaluate its adoption, we assessed the proportion of total facility deliveries over time. To examine the fidelity of iDeliver usage, we studied data completion to assess the plausibility of data entry by care providers during each stage of the labor and delivery workflow and if the usage reflected iDeliver’s envisioned function. We also examined the data completeness of maternal and neonatal indicators prioritized by the Kenyan Ministry of Health. RESULTS 1164 deliveries were registered in iDeliver, capturing 47.3% of the facility’s deliveries over 22 months. Registration improved significantly from 32.3% in the first to 62.2% in the second phase of implementation (P=0.003). Across iDeliver’s workflow, the overall completion rate of all variables improved significantly from 34.1% to 48.0% in the second phase (P<0.001). Data completion was highest for the Discharge-Labor Summary (67.7%) and was lowest for Labor Signs (14.4%). The completion rate of the key Ministry of Health indicators also improved significantly (P<0.001). CONCLUSIONS iDeliver’s adoption and data completeness improved significantly over time. Assessment of iDeliver’ usage fidelity suggested that some features were more easily utilized because providers had time to enter data, versus lower utilization during active childbirth when providers are necessarily engaged with the woman and baby. These insights on the adoption and fidelity of iDeliver usage prompted the team to adapt the application to reflect the users’ culture of use and further improve the implementation of iDeliver. CLINICALTRIAL newborn; neonatal health; maternal health; intrapartum care; labor and delivery; Kenya; digital clinical decision support; health information systems; digital health; implementation research

Evaluation of a hospital-based surveillance system for birth defects in Chennai, India

The Birth Defects Registry of India-Chennai (BDRI-C) was created in 2001 to monitor birth defects and provide timely referrals. Using established guidelines to evaluate surveillance systems, we examined the following attributes of BDRI-C to help strengthen the registry: simplicity, flexibility, data quality, representativeness, acceptability, timeliness, and stability. We reviewed BDRI-C documents, including reporting forms; interviewed key informants; and calculated data completeness, coverage, and reporting time. BDRI-C captured 14% of the births in Chennai April 2013 - March 2014. About 7% of institutions in Chennai registered in BDRI-C, and of those registered, 37% provided data in 2013. Median reporting time was 44 days after birth in 2013. BDRI-C is a useful, simple, flexible, and timely passive birth defects surveillance system; however, improvements can be made to ensure BDRI-C is representative of Chennai, data processing and quality checks are on-going, and the system is acceptable for member institutions and stable.

An Automatic Data Completeness Check Framework for Open Government Data

In recent years, the governments in many countries have recognized the importance of data in boosting their economies. As a result, they are implementing the philosophy of open government data (OGD) to make public data easily and freely available to everyone in standardized formats. Because good quality OGD can boost a country’s economy, whereas poor quality can jeopardize its efficient use and reuse, it is very important to maintain the quality of data stored in open government data portals (OGDP). However, most OGDPs do not have a feature that indicates the quality of the data stored there, and even if they do, they do not provide real-time service. Moreover, most recent studies focused on developing approaches to quantify the quality of OGD, either qualitatively or quantitatively, but did not offer an approach to automatically calculate and visualize it in real-time. To address this problem to some extent, this paper proposes a framework that can automatically assess the quality of data in the form of a data completeness ratio (DCR) and visualize it in real-time. The framework is validated using the OGD of South Korea, whose DCR is displayed in real-time using the Django-based dashboard.

QualiOSM: An Architecture to Improve Data Completeness on OpenStreetMap

OpenStreetMap (OSM) is a large spatial database in which geographic information is voluntarily contributed by thousands of users. In Geographic Information Systems (GIS), and more specifically, in Volunteered Geographic Information (VGI), as in the case of OSM, the issue of data completeness is a constant concern, since users without technical knowledge actively participate in the processes of including, editing and excluding data. Also in thecase of OSM, users can add information to the objects assigning special labels for them. These labels are popularly called tags, and the process of assigning them to objects contributes to improving the attribute completeness, an important metric of data quality. In this context, this article proposes the QualiOSM architecture, which generates an automatic tag adder with the purpose of improving the completeness of address information for OSM objects in Brazil, using the reverse geocoding tools Nominatim, CEP Aberto and the database from Correios. The QualiOSM architecture showed good results for improving the completeness of city, neighborhood and street information in OSM objects, especially in scenarios of large urban centers, where the level of mapping is usually better compared to scenarios in rural or peripheral environments.

IceR improves proteome coverage and data completeness in global and single-cell proteomics

Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

Linking Provider Specialty and Outpatient Diagnoses in Medicare Claims Data: Data Quality Implications

Background With increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data. Objective In our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness. Methods We develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis–specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes). Results A typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset. Conclusion We propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.