Predictors Of Resource Use Research Articles

Clinical and socioeconomic predictors of hospital use and emergency department visits among children with medical complexity: A machine learning approach using administrative data

Objectives The primary objective of this study was to identify clinical and socioeconomic predictors of hospital and ED use among children with medical complexity within 1 and 5 years of an initial discharge between 2010 and 2013. A secondary objective was to estimate marginal associations between important predictors and resource use. Methods This retrospective, population-cohort study of children with medical complexity in Alberta linked administrative health data with Canadian census data and used tree-based, gradient-boosted regression models to identify clinical and socioeconomic predictors of resource use. Separate analyses of cumulative numbers of hospital days and ED visits modeled the probability of any resource use and, when present, the amount of resource use. We used relative importance in each analysis to identify important predictors. Results The analytic sample included 11 105 children with medical complexity. The best short- and long-term predictors of having a hospital stay and number of hospital days were initial length of stay and clinical classification. Initial length of stay, residence rurality, and other socioeconomic factors were top predictors of short-term ED use. The top predictors of ED use in the long term were almost exclusively socioeconomic, with rurality a top predictor of number of ED visits. Estimates of marginal associations between initial length of stay and resource use showed that average number of hospital days increases as initial length of stay increases up to approximately 90 days. Children with medical complexity living in rural areas had more ED visits on average than those living in urban or metropolitan areas. Conclusions Clinical factors are generally better predictors of hospital use whereas socioeconomic factors are more predictive of ED use among children with medical complexity in Alberta. The results confirm existing literature on the importance of socioeconomic factors with respect to health care use by children with medical complexity.

Birds optimize fruit size consumed near their geographic range limits.

Animals can adjust their diet to maximize energy or nutritional intake. For example, birds often target fruits that match their beak size because those fruits can be consumed more efficiently. We hypothesized that pressure to optimize diet-measured as matching between fruit and beak size-increases under stressful environments, such as those that determine species' range edges. Using fruit-consumption and trait information for 97 frugivorous bird and 831 plant species across six continents, we demonstrate that birds feed more frequently on closely size-matched fruits near their geographic range limits. This pattern was particularly strong for highly frugivorous birds, whereas opportunistic frugivores showed no such tendency. These findings highlight how frugivore interactions might respond to stressful conditions and reveal that trait matching may not predict resource use consistently.

Uniform distribution elephant herding optimization (UDEHO) based virtual machine consolidation for energy-efficient cloud data centres

Information technology (IT) providers should use cloud-based services due to their flexibility, reliability, and scalability to handle the rising requirement for processing capacity. The maintenance of dependable services between cloud providers and their customers in a cloud environment, on the other hand, depends on Quality of Service (QoS) assurance. Virtual machine (VM) consolidation is nondeterministic polynomial time (NP) hard issue, and numerous heuristic techniques have been suggested to solve it. In this work, the suggested VM consolidation technique takes into account both current and future uniform distribution elephant herding optimization (UDEHO) based VM consolidation approaches for resource utilization via host overload detection (utilization prediction based potential overload detection (UP-POD)) and host underload detection (UP-PUD). A UDEHO method efficiently predicts resource use in the future. Depending on the power utilization and the number of migrations, a power-saving value is advised for identifying under-loaded hosts. Furthermore, the CloudSim toolkit is used to construct and test these techniques using the same experimental parameters. Lastly, the findings demonstrate that the suggested methodologies considerably decrease the number of VM migrations by about 0.073%, the energy usage of about 11%, and SLA violations by 6.15% while retaining QoS guarantees when compared to conventional techniques.

RESOURCE UTILIZATION AMONG FAMILY CAREGIVERS: INDIVIDUAL AND NEIGHBORHOOD CHARACTERISTICS

Abstract Resources in the community play a pivotal role in increasing well-being of both care recipients and caregivers and ensuring equitable access to needed resources is a crucial priority for policy and practice. Drawing on the Andersen Behavioral Model, this study explored the longitudinal relationship between individual and neighborhood factors and social resource utilization (i.e., paid caregivers, respite care, support group, training, financial help, or transportation) among family caregivers. Unpaid family caregivers for community-dwelling older adults were identified using data from the National Study of Caregiving and National Health and Aging Trends Study (Time 1: 2015, Time 2: 2017; n=616). Neighborhood characteristics were indicators of the Social Vulnerability Index and provider density at the census-tract level. Poisson regression was used to assess predictors of greater resource use (interpreted as incident rate ratios [IRR] with 95% confidence intervals [CI]). Nearly two-thirds of the sample reported using one or more services at each timepoint. Enabling factors were key predictors of resource use at Time 2, including income above 400% federal poverty level (IRR[CI]=1.53 [1.11,2.09]), better self-rated health (IRR[CI]=1.11 [1.01,1.22]), and resource use at Time 1 (IRR[CI]=1.64 [1.29,2.08]). Need-based predictors included greater frequency of personal care (IRR[CI]=1.13 [1.05,1.22] and longer care duration (IRR[CI]=0.99 [0.98,1.00]). Neighborhood factors were not associated with resource use in this analysis, nor were other need factors including caregiving burden. The findings highlight potential disparities in resource use by income, health status, or experience with systems navigation, with implications for policy and outreach.

Factors Associated With Clinician Self-Reported Resource Use in Acute Care and Ambulatory Pediatrics.

The objective of this study is to determine predictors of resource use among pediatric providers for common respiratory illnesses. We surveyed pediatric primary care, emergency department (ED)/urgent care (UC), and hospital medicine providers at a free-standing children's hospital system. Five clinical vignettes assessed factors affecting resource use for upper respiratory infections, bronchiolitis, and pneumonia, including provider-type, practice location, tolerance to uncertainty, and medical decision-making behaviors. The response rate was 75.3% (168/223). The ED/UC and primary care providers had higher vignette scores, indicating higher resource use, compared with inpatient providers; advanced practice providers (APPs) had higher vignette scores compared with physicians. In multivariate analysis, being an ED/UC provider, an APP, and greater concern for bad outcomes were associated with higher vignette scores. Overall, provider type and location of practice may predict resource use for children with respiratory illnesses. Interventions targeted at test-maximizing providers may improve quality of care and reduce resource burden.

Functional Groups Mask Inter- and Intraspecific Variation in Water Use Strategies in a Seasonally Dry Tropical Forest

Across the karst landscape of Quintana Roo, Mexico, plant access to nutrients and water appears limited by generally shallow soil. However, underlying this surface are heterogenous pockets in bedrock and deeper, stable groundwater, suggesting the potential for specialization by species in accessing soil resources. If species differentially access rock resources, divisions by functional groups may also be expected. In this study, shallow caves provided an opportunity to assess resource use strategies by direct, species-specific root observations coupled with traditional above ground measurements. Utilizing stable isotopes from stems and leaves (δ18O and δ13C), we investigated water access and water use efficiency of trees during the dry season to uncover relationships between rooting habit, tree size, and pre-determined functional groups based on leaf habit and wood density. Functional group membership did not predict measured stable isotope ratios, indicating that functional groups were poor predictors of resource use. We did find evidence for deep water use by select species and larger individuals. Interestingly, as trees became larger, δ13C increased to a threshold but then declined, suggesting increasing vulnerability to water limitation as trees increase in size, consistent with other seasonally dry tropical forests. Our work demonstrates that, although shallow soils likely drive strong resource limitations, co-occurring trees in karst ecosystems employ diverse resource acquisition strategies, suggesting important consequences for community composition and ecosystem function in the face of environmental change.

Parsimonious machine learning models to predict resource use in cardiac surgery across a statewide collaborative

ObjectiveWe sought to several develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors. MethodsAll patients undergoing coronary artery bypass grafting and/or valve operations were identified in the 2015-2021 University of California Cardiac Surgery Consortium repository. The primary end point of the study was length of stay (LOS). Secondary endpoints included 30-day mortality, acute kidney injury, reoperation, postoperative blood transfusion and duration of intensive care unit admission (ICU LOS). Linear regression, gradient boosted machines, random forest, extreme gradient boosting predictive models were developed. The coefficient of determination and area under the receiver operating characteristic (AUC) were used to compare models. Important predictors of increased resource use were identified using SHapley summary plots. ResultsCompared with all other modeling strategies, gradient boosted machines demonstrated the greatest performance in the prediction of LOS (coefficient of determination, 0.42), ICU LOS (coefficient of determination, 0.23) and 30-day mortality (AUC, 0.69). Advancing age, reduced hematocrit, and multiple-valve procedures were associated with increased LOS and ICU LOS. Furthermore, the gradient boosted machine model best predicted acute kidney injury (AUC, 0.76), whereas random forest exhibited greatest discrimination in the prediction of postoperative transfusion (AUC, 0.73). We observed no difference in performance between modeling strategies for reoperation (AUC, 0.80). ConclusionsOur findings affirm the utility of machine learning in the estimation of resource use and clinical outcomes following cardiac operations. We identified several risk factors associated with increased resource use, which may be used to guide case scheduling in times of limited hospital capacity.

Randomized controlled trial of a brief online self‐compassion intervention for mothers of infants: Effects on mental health outcomes

To test the effectiveness of a brief self-compassion intervention in improving mental health outcomes for mothers of infants. A randomized controlled trial study design was used. A community sample of mothers of infants (<2 years) completed measures of self-compassion, fears of compassion, psychological flexibility, depression, anxiety, stress, symptoms of posttraumatic stress, and infant feeding experiences. Mothers randomized to intervention received access to online self-compassion resources, and 248 mothers (intervention n = 94, waitlist-control n = 154) completed postintervention assessment 8 weeks later. Overall, 62.8% (n = 59) of intervention participants accessed the resources per-protocol, and lower fear of compassion scores predicted resource use. At postintervention, mothers who used the resources had improved scores for posttraumatic stress symptoms (95% confidence interval [CI] = 0.31-5.47, p = .028), depression (95% CI = 0.15-2.01, p = .023), self-compassionate action (95% CI = 0.41-3.45, p = .012), and engagement with compassion from others (95% CI = 0.22-5.49, p = .034) compared to waitlist-control. Fears of compassion moderated intervention effectiveness. There were no effects on other outcome variables. Findings support the potential effectiveness of interventions based on compassion-focused therapy to improve maternal mental health.

The frugal entrepreneur: A self-regulatory perspective of resourceful entrepreneurial behavior

The present research complements extant perspectives of resourcefulness, which assert that resourceful behaviors arise out of responses to environmental constraints, by developing a model illustrating that entrepreneurs self-impose constraints on resource acquisition and deployment for differing reasons. Specifically, we introduce a novel conceptualization of frugality and differentiate it from self-control to develop a set of hypotheses that frugality predicts resource use behaviors based on long-held preferences (e.g., effectuation and bricolage) and self-control predicts resource use behaviors based on known end states or goals (e.g., causation and pre-commitments). After accumulating evidence of reliability and validity for a new measure of frugality contextualized for entrepreneurship research, the results support our self-regulatory theoretical framework. Our study contributes to research on resourcefulness by making multiple theoretical insights, and we outline numerous future research opportunities for applying the construct of frugality to explain entrepreneurial behavior.

Point-of-Care Urinary Biomarker Testing for Risk Prediction in Critically Injured Combat Casualties

Risk prediction is important during combat operations because resources are limited and triage decisions must be rapid and accurate. We evaluated 2 point-of-care urinary biomarker tests for risk prediction in combat casualties. This was an observational cohort study of critically injured military personnel admitted to Craig Joint Theater Hospital in Afghanistan from October 2012 to December 2013. We collected urine within 3 hours of admission and measured urinary biomarkers with NephroCheck and a neutrophil gelatinase-associated lipocalin dipstick (NGALds) to evaluate their ability to predict a combined end point of need for renal replacement therapy or death. Odds ratios (ORs) were calculated and receiver operator characteristic curves were generated for both tests. A total of 89 patients were included for analysis. The median Injury Severity Score was 18 and the combined end point occurred in 12 (13.5%) patients. NephroCheck was not associated with the combined end point (OR 1.56; 95% CI 0.81 to 3.03; p= 0.19) and the area under the curve of the receiver operator characteristic curve was 0.65. The NGALds was highly associated with the combined end point (OR 4.93; 95% CI 2.18 to 11.14; p < 0.001) and the area under the curve of the receiver operator characteristic curve was 0.84. The NGALds remained significantly associated with the combined end point in a logistic regression model that included Injury Severity Score as a covariate (OR 4.10; 95% CI 1.74 to 9.67; p=0.001). Measurement of urinary biomarkers with an NGALds, but not NephroCheck, predicts poor outcomes in combat casualties. An NGALds is a simple urine dipstick that could be deployed to combat zones to prioritize aeromedical evacuation, help with triage decisions, and predict resource use.

Cognitive ability and cognitive style: finding a connection through resource use behavior

The goal of this study was to investigate cognitive style (the visualizer–verbalizer dimension) and cognitive ability (spatial and verbal abilities) in terms of corresponding resource use behavior. The study further examined the potential link between cognitive style and cognitive ability based on observable behavior. A total of 67 university students participated in the study by completing an online survey containing a series of questionnaires, tests, and tasks, which assessed their cognitive style, cognitive ability, and resource use behavior. Multinomial logistic regression analyses revealed that cognitive style in general predicts resource use behavior. The findings also showed that spatial ability, particularly lower spatial ability, predicts resource use behavior. This study thus contributes to the literature with theory-based, empirical evidence that cognitive ability is reflected in cognitive style. This study further provides information needed to better understand the interplay between individuals’ cognitive style and cognitive ability and how these may be addressed in the design and implementation of learning environments.

Predicting resource use in microbes

Metabolic Modeling![Figure][1] A scanning electron micrograph of Escherichia coli bacteria, a workhorse for molecular and synthetic biology PHOTO: MARTIN OEGGERLI/SCIENCE SOURCE A bacterial cell is a microscopic, self-replicating factory that hosts thousands of interconnected chemical reactions. Metabolism varies with input materials and environment and can be manipulated in the lab to yield desired products. Bulovic et al. created a package of tools to automate construction of models of metabolite flux in bacteria. A key step is optimization of parameters using experimental data, but the predictions were robust even when limited information was available. The authors model a strain of Escherichia coli engineered to fix carbon, demonstrating that such models can be useful planning tools for synthetic biology. Metab. Eng. 55 , 12 (2019). [1]: pending:yes

Comparing resource use between paediatric emergency department visits by triage level

ObjectiveThe majority of paediatric ED visits result in discharge but little is known about what ED resources are deployed for these visits. The goal of this study was to understand...

Extreme flooding decreases stream consumer autochthony by increasing detrital resource availability

Abstract Extreme disturbances, those high magnitude events that are statistically rare in a particular system, may affect consumer resource use through multiple mechanisms, such as differential consumer mortality and modified resource availability and quality. Documenting the ecological importance of these rare events is difficult, but essential, as the frequency of extreme events is predicted to increase under many climate change scenarios. We quantified changes in stream insect resource use following intense, nonseasonal flooding in the Rocky Mountains of northern Colorado during September 2013. We hypothesised that taxonomic identity, functional feeding group membership and disturbance‐caused alterations in resource quantity and quality (C:N ratios) would determine consumer reliance on autochthonous and allochthonous resources and community structure following the disturbance. In summer 2014, we collected basal resources and stream insects for carbon, nitrogen and deuterium bulk stable isotope analysis, and community composition. Basal resource quantity and quality (C:N ratios) were collected at seven headwater streams along a 2013 flood intensity gradient. Using stable isotope mixing models, we analysed consumer autochthony. We also used baseline community composition data from 2011 to compare functional feeding group abundance prior to and following the flood events. We found that consumer resource use was primarily associated with detrital resource quantity, which was positively correlated with disturbance intensity and elevation. Functional feeding group membership did not predict resource use. However, consumers in functional feeding groups did experience differential mortality following flooding. Herbivore relative abundance significantly declined along the disturbance gradient, and predator relative abundance generally declined across all streams from 2011 to 2014. Our results suggest that changes in resource quantity from extreme disturbances can be associated with shifts in consumer resource use. Following this flood event, detritus standing stocks increased, resulting in a concomitant increase in consumer reliance on allochthonous sources.

The Characteristics and Trends of Socioeconomic Metabolism in China

SummaryChina has become the country with the largest resource use and has high levels of waste emissions that pose a great management challenge. To provide more details about environmental problems and to find effective solutions, this article analyzed the scale, structure, and trend of the socioeconomic metabolism in China during the period 1992–2014 based on economy‐wide material flow accounts (EW‐MFA), and predicted resource use during the period of the 13th Five‐Year Plan. The results of this study show that the scale of China's socioeconomic metabolism in China increased more than twofold, during 1992–2014. However, after 2011, with the economic slowdown, the growth rates of total material requirement (TMR), direct material input (DMI), and domestic processed output (DPO) began to decrease. China may reach an inflection point, but this point will probably not be approached before the year 2020. Material recycling (MR) has played an important role in improving resource productivity, improving it by 92.52 renminbi per tonne in 2014. Metallic minerals and fossil fuels are the main sources of hidden flow. Carbon dioxide (CO2) emissions, construction waste, and agricultural emissions have become the major sources of DPO. Because of the 13th Five‐Year Plan, China may slow the growth rate of DMI and may save 10.26 gigatonnes of resources during 2015–2020. Resource productivity is predicted to increase by 15.91%. Imports and MR may play more important roles. These suggestions are made: (1) strengthening the recycling system; (2) stronger policies, especially in metallic mineral and fossil fuels; (3) developing management systems for CO2 emissions, construction waste, and agricultural emissions; and (4) adjusting China's economic structure.

Dependence Stage and Pharmacoeconomic Outcomes in Patients With Alzheimer Disease.

The level of assistance patients with Alzheimer disease (AD) require in their care may be an important predictor of resource use, costs of care, and quality of life. The Dependence Scale (DS), a measure of care-assistance required, was used to estimate costs of care and quality of life of patients with AD categorized into 6 dependence stages based upon the summated item scores of the DS. Data were derived from a 3-year, noninterventional study of 132 patients with probable AD (ages, 50 to 85 y) and caregiver dyads. We investigated the association between DS scores and health care costs, health-related quality of life (HRQoL), caregiver burden and estimated annual costs and HRQoL for 6 dependence stages in adjusted models. DS scores were significant predictors of health care costs, HRQoL, and caregiver burden. The estimated annual health care costs and a measure of HRQoL (EuroQoL-5D) ranged from $11,418 and 1.00 for those at very mild dependence stage to $101,715 and 0.26 for those at very severe dependence stage. DS scores classified into 6 dependence stages provides a useful method to estimate unique levels of care-associated costs and health utilities for pharmacoeconomic evaluations of new treatments for AD.

Predicting resource use in ant species and entire communities by studying their morphological traits: Influence of habitat and subfamily

I investigated whether the morphological traits of Neotropical ants can be used to infer food resource use by individual species and by entire communities, and whether these relationships are related to habitat type and/or by morphological differences between ant subfamilies. I attracted ants using food baits that represented different ecological processes (e.g. predation, granivory, detritivory, nectarivory) in five habitat types along a land-use gradient (from forests to gardens). I assessed ant activity at the baits and characterized 64 species from six subfamilies according to their food use and community-level resource exploitation intensity in the different habitats. Next, I performed a Mantel test to reveal the relationships between 13 morphological measures and resource use at the species level. I then used ant clades (i.e. subfamily) and habitat to rank the ants along three axes of variation in relation to their morphology and food resource use. Finally, I tested whether associations existed between the community-level exploitation intensity for such resources and the distribution of morphological trait values using the “4th-corner” analysis. Morphological traits were closely linked to the species’ ability to exploit different resources. These relationships were affected by subfamily and, to a lesser extent, by habitat type. The characterization of trait sets for entire communities was not useful, however, in predicting the intensity of the exploitation of different types of resources in varying environmental conditions. I conclude that morphological traits are accurate predictors of the ecology of species, but they should be used with caution when trying to understand community-level patterns.

Cats, connectivity and conservation: incorporating data sets and integrating scales for wildlife management

Summary Understanding resource selection and quantifying habitat connectivity are fundamental to conservation planning for both land‐use and species management plans. However, data sets available to management authorities for resource selection and connectivity analyses are often highly limited and fragmentary. As a result, measuring connectivity is challenging, and often poorly integrated within conservation planning and wildlife management. To exacerbate the challenge, scale‐dependent resource use makes inference across scales problematic, resource use is often modelled in areas where the species is not present, and connectivity is typically measured using a source‐to‐sink approach, erroneously assuming animals possess predefined destinations. Here, we used a large carnivore, the leopard Panthera pardus, to characterise resource use and landscape connectivity across a vast, biodiverse region of southern Africa. Using a range of data sets to counter data deficiencies inherent in carnivore management, we overcame methodological limitations by employing occupancy modelling and resource selection functions across three orders of selection, and estimated landscape‐scale habitat connectivity – independent of a priori source and sink locations – using circuit theory. We evaluated whether occupancy modelling on its own was capable of accurately informing habitat connectivity, and identified conservation priorities necessary for applied management. We detected markedly different scale‐dependent relationships across all selection orders. Our multi‐data, multi‐scale approach accurately predicted resource use across multiple scales and demonstrates how management authorities can more suitably utilise fragmentary data sets. We further developed an unbiased landscape‐scale depiction of habitat connectivity, and identified key linkages in need of targeted management. We did not find support for the use of occupancy modelling as a proxy for landscape‐scale habitat connectivity and further caution its use within a management context. Synthesis and applications. Maintaining habitat connectivity remains a fundamental component of wildlife management and conservation, yet data to inform these biological and ecological processes are often scarce. We present a robust approach that incorporates multi‐scale fragmentary data sets (e.g. mortality data, permit data, sightings data), routinely collected by management authorities, to inform wildlife management and land‐use planning. We recommend that management authorities employ a multi‐data, multi‐scale connectivity approach – as we present here – to identify management units at risk of low connectivity.

Use of a severity indicator as a predictor of the use of hepatic transplantation resources.

To evaluate the use of a severity indicator for end-stage liver disease as a predictor of resource use in a teaching hospital in São Paulo. Descriptive, retrospective study, classifying independent variables in seven key dimensions: identification/risk rating; length of stay/use of advanced life support; imaging; clinical analysis; special procedures; blood products in the intensive care unit; and in the operating room. The frequencies were analyzed by linear regression analysis of variance to detect relevance due to the dependent variable (severity indicator) in 76 cases seen in 2013. Among the variables studied, those that presented relevance due to the functional risk score were laboratory measurements of bilirubin, amylase, transaminase, blood count, creatine phosphokinase (p<0.05), hemotherapy procedures fresh frozen plasma (FFP) and platelet concentrate (p<0.05), and Doppler echocardiography image (p<0.07). Given the results/objective of this study, it is concluded that the indicator presents a potential predictive capability in the use of postoperative resources of liver receptors in the size, clinical analysis, images and hemotherapy dimensions. Avaliar o uso do indicador de gravidade para doenças hepáticas terminais como fator preditivo do uso de recursos em um hospital-escola de São Paulo. Estudo descritivo, retrospectivo, classificando variáveis independentes em sete dimensões principais: identificação/classificação de risco; tempo de permanência/uso de suporte avançado à vida; exames de imagem; análises clínicas; procedimentos especiais; hemoderivados em unidade de terapia intensiva; e em centro cirúrgico. As frequências foram analisadas por regressão linear com análise de variância para detecção de relevâncias face à variável dependente (indicador de gravidade) em 76 casos atendidos em 2013. Dentre as variáveis estudadas, apresentaram relevância em função do escore de risco funcional as dosagens laboratoriais de bilirrubina, amilase, transaminase, hemograma, creatinofosfoquinase (p<0,05), procedimentos hemoterápicos plasma fresco congelado (PFC) e concentrado de plaquetas (p<0,05), e imagem Ecodoppler (p<0,07). Face aos resultados/propósito do estudo conclui-se que o indicador apresenta potencial capacidade preditiva no uso de recursos pós-operatórios de receptores de fígado nas dimensões, análises clínicas, imagens e hemoterapia.

Understanding pain among older persons: Part 2—the association between pain profiles and healthcare utilisation

While polypharmacy and multimorbidity predict healthcare utilisation among older people, the influence of differing pain profiles on healthcare utilisation is unclear. To compare healthcare utilisation between people with different pain profiles. Baseline data from The Irish LongituDinal study on Ageing (TILDA), a population-representative cohort study involving over 8,171 community living people resident in Ireland aged 50 or over, was used. Following the creation of four novel pain profiles, variables relating to healthcare utilisation were compared across the different profiles using chi-square tests and logistic regression. Healthcare utilisation differed across the four pain profiles, with pain being an independent predictor of resource use. Pain profiles 3 and 4 had higher use of general practitioner (GP) care and outpatient visits than people with no pain and pain that had less impact. The odds of being a frequent GP attender increased across pain profiles, with those in profile 4 being almost three times as likely to be frequent attenders compared to those with no pain (adjusted odds ratio (OR) = 2.79; 95% CI 2.74, 2.83). People in profile 4 were almost twice as likely to have a hospital outpatient visit compared to people with no pain (adjusted OR = 1.75; 95% 1.73, 1.78). Healthcare utilisation differed between the four pain profiles, with people in profiles 3 and 4 having greater usage of primary and secondary healthcare resources. Pain profile membership was a significant independent predictor of the utilisation of GP care and hospital outpatient visits.