General Utility Research Articles

Translation and Validation of Brief Adjustment Scale-6 (BASE-6) in Urdu

The current study aimed to translate and validate the Brief Adjustment Scale–6 (BASE-6) to measure the psychological maladjustment in adolescents. The present study further established the psychometric properties and the general utility of the BASE-6. The sample consisted of Adolescents of age (M= 14.61, SD= .968) of class 9th and 10th (N=150) who were recruited through purposive sampling. In this study the process of translation was done by following MAPI guidelines. Through confirmatory factor analysis, the study ascertained that all items in the translated questionnaire effectively aligned with the intended scales. Furthermore, an evaluation of convergent validity underscored the good quality of validity and reliability achieved by the Urdu-translated version of the Brief Adjustment Scale-6 (BASE-6). The results demonstrated a high level of discriminant and convergent validity in the sample. The one-dimensional structure of BASE-6 was confirmed and factorial invariance was established. Therefore, the study provides strong evidence for the utility of the BASE-6 as a valuable instrument for assessing psychological maladjustment in adolescents, with a high level of discriminant and convergent validity.

A modest basic income can benefit a poor majority

Given a general utility and income distribution and positive unemployment benefits, we analyze the impact of a universal basic income (UBI) financed by an income tax and reducing unemployment benefits. With extensive margins only and identical fixed costs of work, we show that UBI can benefit a poor majority. Plausible conditions can ensure similar results with different fixed costs. With both extensive and intensive margins but identical fixed costs, a modest UBI can still benefit a poor majority provided the income weighted tax elasticity of labor supply from intensive margins is less than the non-employment rate, which is usually true.

RWD132 Do Generic Population Utility Scores Accurately Represent Real-World Experienced Health?

RWD132 Do Generic Population Utility Scores Accurately Represent Real-World Experienced Health?

A universal pre-charging method for enhancing transient speed in Organic Electrochemical Transistors

Organic electrochemical transistors (OECT) have shown great potential in diverse applications; however, in many OECTs, their slow transient response has thus far limited their practical use. One reason for the slow response is the complex interplay between lateral and vertical ion transport that has so far been poorly understood. In this work, we study the impact of lateral ion transport on OECT transient response, introduce a robust pre-charging method to manipulate the slow lateral ion transport. This approach leads to quicker ion redistribution and improved switching speeds. We show the general utility of pre-charging method in enhancing the switching speeds across various material systems, characterized by both low and high ion mobilities, and across different device architectures, achieving nearly symmetric speeds for both on-switching and off-switching. Moreover, we showcase the efficacy of the pre-charging method in enabling slow OECTs to capture rapid signals in real-world applications. Our findings present a groundbreaking strategy for enhancing the response times of OECT devices and deepening our understanding of the transient mechanisms in OECT device.

Big data study using health insurance claims to predict multidisciplinary low vision service uptake.

There is a lack of research from high-income countries with various health care and funding systems regarding barriers and facilitators in low vision services (LVS) access. Furthermore, very few studies on LVS provision have used claims data. This study aimed to investigate which patient characteristics predict receiving multidisciplinary LVS (MLVS) in the Netherlands, a high-income country, based on health care claims data. Data from a Dutch national health insurance claims database (2015 to 2018) of patients with eye diseases causing potentially severe visual impairment were retrieved. Patients received MLVS (n = 8766) and/or ophthalmic treatment in 2018 (reference, n = 565,496). MLVS is provided by professionals from various clinical backgrounds, including nonprofit low vision optometry. Patient characteristics (sociodemographic, clinical, contextual, general health care utilization) were assessed as potential predictors using a multivariable logistic regression model, which was internally validated with bootstrapping. Predictors for receiving MLVS included prescription of low vision aids (odds ratio [OR], 8.76; 95% confidence interval [CI], 7.99 to 9.61), having multiple ophthalmic diagnoses (OR, 3.49; 95% CI, 3.30 to 3.70), receiving occupational therapy (OR, 2.32; 95% CI, 2.15 to 2.51), mental comorbidity (OR, 1.17; 95% CI, 1.10 to 1.23), comorbid hearing disorder (OR, 1.98; 95% CI, 1.86 to 2.11), and receiving treatment in both a general hospital and a specialized ophthalmic center (OR, 1.23; 95% CI, 1.10 to 1.37), or by a general practitioner (OR, 1.23; 95% CI, 1.18 to 1.29). Characteristics associated with lower odds included older age (OR, 0.30; 95% CI, 0.28 to 0.32), having a low social economic status (OR, 0.91; 95% CI, 0.86 to 0.97), physical comorbidity (OR, 0.87; 95% CI, 0.82 to 0.92), and greater distance to an MLVS (OR, 0.95; 95% CI, 0.92 to 0.98). The area under the curve of the model was 0.75 (95% CI, 0.75 to 0.76; optimism = 0.0008). Various sociodemographic, clinical, and contextual patient characteristics, as well as factors related to patients' general health care utilization, were found to influence MLVS receipt as barriers or facilitators. Eye care practitioners should have attention for socioeconomically disadvantaged older patients when considering MLVS referral.

Knowledge, attitude and practices of energy utilisation behaviours: a study of residential building occupants

PurposeThis study aims to assess residential energy consumption knowledge, attitudes, and practices in Abuja Municipality, providing insights for effective conservation strategies, reducing costs and mitigating environmental impact.Design/methodology/approachData for this study was collected through a cross-sectional survey conducted among a representative sample of the Nigerian population between February and April 2021. A total of 462 questionnaire responses were collected and subsequently analysed using SPSS. Descriptive statistics, including frequency count, percentages, mean, and standard deviation, were calculated. Additionally, inferential statistics were performed using Chi-Square analysis, with significant level set at p = 0.05 to draw meaningful conclusions from the data.FindingsThe study results indicate that out of the total respondents, 244 individuals (67.4%) demonstrated a profound knowledge of and good practice in energy utilisation. In comparison, 118 individuals (32.6%) exhibited poor knowledge and practice in energy saving. Moreover, the findings reveal a significant association between the sociodemographic factors of the respondents, building type, and their overall practice in energy utilisation. Statistical analysis shows significant? 2 values for each case: 8.563 (p = 0.003), 66.736 (p = 0.000), 60.866 (p = 0.000), 23.487 (p = 0.000), 37.877 (p = 0.000), and 92.334 (p = 0.000), respectively, where p < 0.05. These results highlight the importance of considering sociodemographic profiles and building characteristics when assessing general energy utilisation practices.Originality/valueThe research offers valuable insights into Nigerian energy usage behaviours and attitudes towards energy saving in residential buildings, contributing significantly to the knowledge base.

Study on structural, morphological, elastic and electrical properties of ZnO nanoparticles for electronic device applications

The present study investigates the synthesis and analysis of zinc oxide nanoparticles (ZnO NPs) intended for use in electronic devices. ZnO NPs with an average crystallite size of 33 nm and a hexagonal wurtzite structure were produced by the combustion process. Scanning Electron Microscopy (SEM) revealed a densely packed, spherical morphology. Elastic properties, studied through the General Utility Lattice Program (GULP) and Elastic Tensor Analysis (ELATE), reveal the ductile nature of ZnO NPs. Optical studies exhibited a characteristic absorption peak at 366 nm, with a calculated optical band gap of 2.36 eV. Additionally, efforts are being made to determine refractive index (n), electronic polarizability (α) and optical susceptibility (χ). Electrical properties, including dielectric behaviour, AC conductivity, and I–V characteristics, demonstrated the influence of relaxation phenomena and revealed non-ohmic conduction. The dielectric studies show that dielectric constant, dielectric loss, and impedance are frequency and temperature dependent. Richardson-Schottky emission was identified as the predominant conduction mechanism, highlighting the potential of ZnO NPs in electronic devices.

Optical properties of Ba-impurity defect clusters in KH2PO4 crystals

Potassium dihydrogen phosphate (KDP) is widely used in electro-optical switches and inverter crystals in inertial confinement fusion (ICF). The early studies suggested that defect clusters are one of the main reasons why the laser-induced damage threshold (LIDT) is far lower than the theoretical value. However, it is not clear what type of defect clusters cause this phenomenon. The BaK defect cluster models have been determined by simulation using General Utility Lattice Programs (GULP). We found that BaK+VH and BaK+VK cause a dramatic distortion on H-O bonds with maximum values of 29.5 % and 27.12 %, respectively. By analyzing the configuration coordinate diagram and optical spectra, we believe that both defect clusters can promote ionization through defect-assisted "2+1" photon absorption, which leads to free electrons more easily exceeding the critical value for electron avalanche breakdown. Our study will offer insight into the effects of defect clusters in KDP crystals.

Association of general anxiety and pediatric dental preventive examination utilization, National Survey of Children's Health, 2021.

Many factors influence preventive dental health service access for children. The objective of this research was to examine one factor, general anxiety, in accessing at least one preventive dental examination in the past 12 months in children with special healthcare needs (CSHCN) and children without special healthcare needs (CWSHCN). National Survey of Children's Health (NSCH) 2021 were obtained for this cross-sectional research. Chi-square and logistic regression analyses were used to determine association of anxiety and past 12-month preventive dental examinations. The sample included 10493 CSHCN, and 35675 CWSHCN. Overall, 72.7% had past 12-month preventive dental examinations, and 9.9% had a healthcare provider indicate they had general anxiety. CSHCN with anxiety, CWSHCN with anxiety, and CSHCN without anxiety were more likely to have a past 12-month preventive dental examination visit than CWSHCN without anxiety (Adjusted Odds Ratios: 1.86, 1.39, 1.32, respectively). Our results suggest children with general anxiety (both CSHCN and CWSHCN) are more likely to have had at least one regular preventive dental visit within the past 12 months than CWSHCN and without general anxiety. There is a need for further understanding the relationship of general anxiety and dental health to improve the health of all children. CWSHCN without anxiety need individualized, comprehensive care with enough time, attention, instruction, and rewards to demonstrate to parents/guardians the importance of making routine preventive dental examinations a priority for their child.

How to Select an Objective Function Using Information Theory

AbstractIn machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information‐theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well‐suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of “maximizing information and general utility” extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data.

Incremental Trainable Parameter Selection in Deep Neural Networks.

This article explores the utilization of the effective degree-of-freedom (DoF) of a deep learning model to regularize its stochastic gradient descent (SGD)-based training. The effective DoF of a deep learning model is defined only by a subset of its total parameters. This subset is highly responsive or sensitive toward the training loss, and its cardinality can be used to govern the effective DoF of a model during training. To this aim, the incremental trainable parameter selection (ITPS) algorithm is introduced in this article. The proposed ITPS algorithm acts as a wrapper over SGD and incrementally selects the parameters for updation that exhibit the maximum sensitivity toward the training loss. Hence, it gradually increases the DoF of the model during training. In ideal cases, the proposed algorithm arrives at a model configuration (i.e., DoF) optimum for the task at hand. This whole process results in a regularization-like behavior induced by a gradual increment of the DoF. Since the selection and updation of parameters is a function of the training loss, the proposed algorithm can be seen as a task and data-dependent regularization mechanism. This article exhibits the general utility of ITPS by evaluating it on various prominent neural network architectures such as CNNs, transformers, recurrent neural networks (RNNs), and multilayer perceptrons. These models are trained for image classification and healthcare tasks using the publicly available CIFAR-10, SLT-10, and MIMIC-III datasets.

Defect Structures of Rare Earth-Doped Lutetium Oxide and Impacts of Li Co-Dopant

Defect complexes consisting of point defects induced by the doping of rare earth elements (Nd, Er) into lutetium oxide (Lu2O3) host were investigated with respect to defect formation energies and defect configurations using atomistic simulations with General Utility Lattice Program (GULP). The site preferences of the substitutional point defects of the dopants and the occupation between the two available cationic sites, the 8b and 24d sites, were analyzed. Additionally, the impacts of Li on the doping of rare earth elements into Lu2O3 were revealed from the viewpoints of energy and structure. Dopant pairs in the nearest neighbor configurations (8b + 8b), (8b + 24d), and (24d + 24d) were considered. The results contribute to the understanding of structures of defects in rare earth-doped Lu2O3.

Factors Influencing Drug Prescribing for Patients with Hospitalization History in Circulatory Disease: Patient Severity, Composite Adherence, and Physician-Patient Relationship - A Retrospective Cohort Study.

With countries promoting generic drug prescribing, their growth may plateau, warranting further investigation into the factors influencing this trend, including physician and patient perspectives. Additional strategies may be needed to maximize the switch to generic drugs while ensuring healthcare system sustainability, focusing on factors beyond mere low cost. Emphasizing affordability and clarifying other prescription considerations are essential. This study aimed to provide initial insights into how patient severity, composite adherence, and physician-patient relationships impact generic switching. This study utilized a long-term retrospective cohort design by analyzing data from a national healthcare database. The population included patients of all ages, primarily older adults, who required primary-to-tertiary preventive actions with a history of hospitalization for cardiovascular diseases (CVDs) (International Classification of Diseases, Tenth Revision) during April 2014 to March 2018 (4 years). We focused on switching to generic drugs, with temporal variations in clinical parameters as independent variables. Lifestyle factors (smoking and drinking) were also considered. Adherence was measured as a composite score comprising 11 elements. The physician-patient relationship was established based on the interval between physician change and prescription. Logistic regression analysis and propensity score matching (PSM) were employed, along with complementary analysis of physician-patient relationships, proportion of days covered (PDC), and adherence for a subset of the population. The study included 48,456 patients with an average follow-up of 36.1±8.8 months. The mean age was 68.3 ± 9.9 years, body mass index was 23.4 ± 3.4 kg/m2, systolic blood pressure was 131.2 ± 15.0 mmHg, low-density lipoprotein cholesterol (LDL-C) was 116.6 ± 29.3 mg/dL, hemoglobin A1c (HbA1c) was 5.9% ± 0.8%, and serum creatinine was 0.9 ± 0.8 mg/dL. Logistic regression analysis revealed significant associations between generic switching and systolic blood pressure (odds ratio [OR], 0.996, 95% confidence interval [CI]: 0.993-0.999), serum creatinine levels (OR, 0.837, 95% CI: 0.729-0.962), glutamic oxaloacetic transaminase levels (OR, 0.994, 95% CI: 0.990-0.997), PDC score (OR, 0.959, 95% CI: 0.948-0.970), and adherence score (OR, 0.910, 95% CI: 0.875-0.947). Additionally, generic drug rates increased with improvements in the HbA1c level band and smoking level (P<.01, P<.001). The group with a superior physician-patient relationship after PSM had a significantly higher rate of generic drug prescribing (51.6±15.2%) than the inferior relationship group (47.7±17.7%) (P<.001). Although physicians' understanding influences the choice of generic drugs, patient condition (severity) and adherence also impact this decision. For example, improved creatinine levels are associated with generic drug choice, while stronger physician-patient relationships correlate with higher rates of generic drug utilization. These findings may contribute to the appropriate prescription of pharmaceuticals if the policy diffusion of generic drugs begins to slow down. Thus, preventing serious illness while building trust may result in clinical benefits and positive socio-economic outcomes.

Partially observable discrete-time discounted Markov games with general utility

In this paper, we investigate partially observable zero sum games where the state process is a discrete time Markov chain. We consider a general utility function in the optimization criterion. We show the existence of value for both finite and infinite horizon games and also establish the existence of optimal polices. The main step involves converting the partially observable game into a completely observable game which also keeps track of the total discounted accumulated reward/cost.

Linearized Models of the Coupled Rotorcraft Flight Dynamics and Acoustics for Real-Time Noise Prediction

This article demonstrates the linearization of the coupled rotorcraft flight dynamics and aeroacoustics to provide realtime acoustic predictions in generalized maneuvering flight. To demonstrate the methodology, the study makes use of a nonlinear simulation model of a generic utility helicopter (PSUHeloSim) that is coupled with an aeroacoustic solver based on a marching cubes algorithm. A periodic equilibrium of the coupled rotorcraft flight dynamics and acoustics is first found at a desired flight condition using a modified harmonic balance trim solution method. Next, the nonlinear time-periodic dynamics are linearized about that periodic equilibrium and transformed into an equivalent higher order linear time-invariant system in harmonic decomposition form. Composite aeroacoustic measures are included as an output of this system. To speed up runtime and make control design tractable, the order of these harmonic decomposition models is reduced via residualization to an 8-state model where the states are representative of the rigid-body dynamics of the aircraft. This 8-state model is shown to provide accurate acoustic response predictions for small-amplitude pilot inputs and to abate runtime by a factor of approximately 104, thus enabling acoustic predictions in generalized maneuvering flight that are significantly faster than real time. The 8-state model is subsequently used to demonstrate the use of linear system tools for the dynamic analysis of the coupled rotorcraft flight dynamics and acoustics.

Multiphysics Missing Data Synthesis: A Machine Learning Approach for Mitigating Data Gaps and Artifacts

Abstract The presence of gaps and spurious nonphysical artifacts in datasets is a nearly ubiquitous problem in many scientific and engineering domains. In the context of multiphysics numerical models, data gaps may arise from lack of coordination between modeling elements and limitations of the discretization and solver schemes employed. In the case of data derived from physical experiments, the limitations of sensing and data acquisition technologies, as well as myriad sources of experimental noise, may result in the generation of data gaps and artifacts. In the present work, we develop and demonstrate a machine learning (ML) meta-framework for repairing such gaps in multiphysics datasets. A unique “cross-training” methodology is used to ensure that the ML models capture the underlying multiphysics of the input datasets, without requiring training on datasets free of gaps/artifacts. The general utility of this approach is demonstrated by the repair of gaps in a multiphysics dataset taken from hypervelocity impact simulations. Subsequently, we examine the problem of removing scan artifacts from X-ray computed microtomographic (XCMT) datasets. A unique experimental methodology for acquiring XCMT data, wherein articles are scanned multiple times under different conditions, enables the ready identification of artifacts, their removal from the datasets, and the filling of the resulting gaps using the ML framework. This work concludes with observations regarding the unique features of the developed methodology, and a discussion of potential future developments and applications for this technology.

Clinical activity in general practice before sarcoma diagnosis: an Australian cohort study.

Increased time to diagnosis in sarcoma is associated with poor prognosis and patient outcomes. Research is needed to identify whether opportunities to expedite the diagnosis of sarcoma in general practice exist. To examine pre-diagnostic GP clinical activity before sarcoma diagnosis. An Australian retrospective cohort study using hospital registry data (Australian Comprehensive Cancer Outcomes and Research Database [ACCORD]) linked to two primary care datasets (Patron and MedicineInsight). The frequency of general practice healthcare utilisation events (general practice attendances, prescriptions, blood test, and imaging requests) were compared in 377 patients with soft tissue sarcoma (STS) and 64 patients with bone sarcoma (BS) in the year pre-diagnosis. Poisson regression models were used to calculate monthly incidence rate ratios (IRR) for the 24 months pre-diagnosis and estimate inflection points for when healthcare use started to increase from baseline. In the 6 months pre-diagnosis, patients with sarcoma had a median of 3-4 general practice attendances, around one-third had a GP imaging request (33% [n = 21] BS and 36% [n = 134] STS), and approximately one in five had multiple imaging requests (19% [n = 12] BS and 21% [n = 80] STS). GP imaging requests progressively increased up to eight-fold from 6 months before sarcoma diagnosis (IRR 8.43, 95% confidence interval [CI] = 3.92 to 18.15, P<0.001) and general practice attendances increased from 3 months pre-diagnosis. Patients with sarcoma have increased GP clinical activity from 6 months pre-diagnosis, indicating a diagnostic window where potential opportunities exist for earlier diagnosis. Interventions to help identify patients and promote appropriate use of imaging and direct specialist centre referrals could improve earlier diagnosis and patient outcomes.

Mid-infrared trace detection with parts-per-quadrillion quantitation accuracy: Expanding frontiers of radiocarbon sensing

Detection sensitivity is a critical characteristic to consider during selection of spectroscopic techniques. However, high sensitivity alone is insufficient for spectroscopic measurements in spectrally congested regions. Two-color cavity ringdown spectroscopy (2C-CRDS), based on intra-cavity pump-probe detection, simultaneously achieves high detection sensitivity and selectivity. This combination enables mid-infrared detection of radiocarbon dioxide ([Formula: see text]CO[Formula: see text]) molecules in room-temperature CO[Formula: see text] samples, with 1.4 parts-per-quadrillion (ppq, 10[Formula: see text]) sensitivity (average measurement precision) and 4.6-ppq quantitation accuracy (average calibrated measurement error for 21 samples from four separate trials) demonstrated on samples with [Formula: see text]C/C up to [Formula: see text]1.5[Formula: see text] natural abundance ([Formula: see text]1,800 ppq). These highly reproducible measurements, which are the most sensitive and quantitatively accurate in the mid-infrared, are accomplished despite the presence of orders-of-magnitude stronger, one-photon signals from other CO[Formula: see text] isotopologues. This is a major achievement in laser spectroscopy. A room-temperature-operated, compact, and low-cost 2C-CRDS sensor for [Formula: see text]CO[Formula: see text] benefits a wide range of scientific fields that utilize [Formula: see text]C for dating and isotope tracing, most notably atmospheric [Formula: see text]CO[Formula: see text] monitoring to track CO[Formula: see text] emissions from fossil fuels. The 2C-CRDS technique significantly enhances the general utility of high-resolution mid-infrared detection for analytical measurements and fundamental chemical dynamics studies.

Abstract P368: Differences in Perceived General Health and Health Care Utilization by Race and Ethnicity During the COVID Pandemic: The C4R-MESA Study

Background: The COVID-19 pandemic reshaped individuals' lives with potential impacts on perceptions of their general health and healthcare utilization. Objective: Examine patterns of self-reported general health perceptions and routine healthcare utilization during the pandemic, and assess differences by race and ethnicity, in a community-based sample of US adults. Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) assessed the impact of the pandemic on participants via questionnaires designed by the Collaborative Cohort of Cohorts for COVID-19 Research (C4R) Study. For the question, “How does your general health compare to before the pandemic?”, responses of “worse” were compared to “about the same” or “better.” For the question, “Since March 2020, did you have to delay or miss out on any healthcare services?”; “yes” responses were compared to “no.” Associations with race and ethnicity were tested using logistic regression models, adjusting for covariates. Results: Of 1929 MESA participants (age 75±8 years; 55% female; 40% White; 23% Hispanic; 22% Black; 15% Chinese), worse general health during the pandemic was reported by 15% of participants and 32% reported missed or delayed healthcare services. Race and ethnicity were not associated with worse general health ( Table ). Black (vs White) participants were less likely to report having missed or delayed healthcare (aOR:0.56;95%CI:0.37-0.84). There were no significant differences in missed or delayed healthcare for Chinese and Hispanic participants compared to White participants. Conclusion: These findings suggest that 1 in 7 adults experienced worsening general health and 1 in 3 missed or delayed healthcare during the pandemic. However, race and ethnicity were not associated with perceived general health and associations with missed/delayed healthcare were limited. Longer-term effects of the pandemic on general health and utilization, and related health disparities, are being evaluated in this prospective cohort.

Utilization of routine health data and its determinants among healthcare workers in public health facilities of harari region, eastern Ethiopia

BackgroundRoutine health information is the pillar of the planning and management of health services and plays a vital role in effective and efficient health service delivery, decision making, and program improvement. Little is known about evidence-based actions to successively advance the use of information for decision making. Therefore, this study aimed to assess the level and determinants of routine health data utilization among health workers in public health facilities in the Harari region, Ethiopia.MethodsAn institutional-based cross-sectional study design was used from June 1 to July 31, 2020. A total of 410 health care providers from two hospitals and five health centers were selected using a simple random sampling technique. Data were collected through a structured questionnaire complemented by an observational checklist. The collected data were thoroughly checked, coding, and entered into Epi-data version 4.6 before being transferred to Stata version 14 for analysis. Frequency and cross-tabulations were performed. To measure factors associated with routine use of health data, bivariate and multivariate logistic regression analyzes were performed. The odds ratio with a 95% CI was calculated, and then a p-value of less than 0.05 was considered significant.ResultThe general utilization of routine health data was 65.6%. The use of routine health data was significantly associated with healthcare workers who had a positive attitude towards data [AOR = 4 (2.3–6.9)], received training [AOR = 2.1 (1.3–3.6)], had supportive supervision [AOR = 3.6 (2.1–6.2)], received regular feedback [AOR = 2.9 (1.7–5.0)] and perceived a culture of information use [AOR = 2.5 (1.3–4.6)].ConclusionsSixty percent of health professionals had used routine health data utilization. Training, supervision, feedback, and the perceived culture of information were independently associated with the use of routine health data utilization. Therefore, it is critical to focus on improving data utilization practices by addressing factors that influence the use of routine health data.