Insurance cost and injury characteristics of anterior cruciate ligament injuries in sub-elite football: A population analysis involving 3 years of Australian insurance data.

OBJECTIVES: To investigate the injury characteristics and insurance cost of anterior cruciate ligament injuries in sub-elite football players in New South Wales, Australia. DESIGN: Descriptive epidemiological study. METHODS: Three years of insurance records (2018-2020) was used to describe anterior cruciate ligament injury costs and characteristics. Concomitant injuries and the mechanism of injury were determined by analysing the injury descriptions. Claim characteristics and costs are presented by age group (junior = 7-17 years, senior = 18-34 years, and veteran = 35 + years) and sex. Categorical data (including age-groups and sex) are presented as counts and percentages and analysed using a Chi squared or Fisher's exact test. Cost data are reported as means ±â€¯standard deviation with 95 % confidence intervals. RESULTS: Over the course of three football seasons (2018-2020), 786 anterior cruciate ligament injuries were reported to the injury insurance company. The total insurance cost was AU$3,614,742 with direct injury insurance costs accounting for 36.3 % of the total costs. The mean indirect insurance costs were six-fold higher than direct insurance costs (AU$11,458 vs AU$1914). Isolated injuries had an average cost of $4466 whilst concomitant injuries had an average cost of $4951. Surgical costs are excluded from direct cost calculations. The peak injury count occurred in the first month of all three football seasons, immediately after the pre-season. CONCLUSIONS: Anterior cruciate ligament injuries represent a substantial economic burden to the insurer and individual. The cost data provided can be used for future economic and modelling studies.

Implementation of a spine triage program and its effect on outpatient radiology utilization.

OBJECTIVE: Clinical care pathways designed to triage spinal disorders have been shown to reduce wait times and improve patient satisfaction. The goal of this study was to perform an analysis of outpatient radiology costs before and after the implementation of a spine care triage pathway. METHODS: All imaging orders and surgical procedures were captured in a prospective spine registry for patients referred to the department of neurosurgery within a single academic center between July 1, 2017, and November 3, 2020. A spine triage algorithm was developed and implemented January 1, 2018. Healthcare utilization was recorded for 1 year after the first appointment in the department of neurosurgery. Imaging costs were estimated using publicly available data from the Centers for Medicare and Medicaid Services. Statistical analysis consisted of an independent sample t-test or randomization test for continuous variables and a chi-square test for categorical variables. RESULTS: A total of 3854 patients were included in this study. The mean age was 60 years (50.8% female) and 89.8% had undergone advanced imaging before being referred to the department of neurosurgery. In total, 12.6% of patients were referred with a specific surgical diagnosis (i.e., spinal stenosis, lumbar spondylolisthesis, etc.). During the pretriage phase 1810 patients were enrolled, and there were 2044 patients enrolled after the triage algorithm was implemented. Advanced imaging (CT or MRI) was ordered more frequently by providers before the triage program was initiated, with imaging ordered in 34% (617/1810) of patients pretriage versus 14.8% (302/2044) after the triage pathway was implemented (p < 0.001). The authors calculated a significant reduction in cost associated with reduced radiology utilization. Before triage, the cost of radiology utilization was $85,475/1000 patients compared with $40,107/1000 patients afterward (p < 0.001). The triage program did not change the utilization of surgery (14.6% before, 13.6% after). CONCLUSIONS: Among patients treated after a spinal triage program was implemented in a single neurosurgery department, there was a substantial reduction in the use of advanced imaging and a 50% reduction in cost associated with outpatient radiology utilization. The triage program did not change the rate of spine surgery being performed.

Direct and indirect costs associated with injury in sub-elite football in Australia: a population study using 3 years of sport insurance records.

OBJECTIVES: To determine the direct and indirect costs of injuries in sub-elite footballers in New South Wales (NSW), Australia, stratified by injury location, type, sex and age groups. DESIGN: Descriptive epidemiological study. METHODS: A de-identified insurance database containing three seasons (2018-2020) of football injuries in NSW was used to determine injury costs. Injuries were coded using the Orchard Sports Injury and Illness Classification System. Claim costs are presented by age group (Junior = 7-17 years, Senior = 18-34 years, and Veteran = 35+ years), sex and injury location and type. Cost data are reported as means ±â€¯standard deviation (SD) with 95 % Confidence Intervals (CI). RESULTS: There were 4145 total injury claims, totalling AU$13,716,173, at a mean cost of $3309 (95 % CI 3042-3577) per injury. Joint sprains accrued the largest costs ($6,665,938) with knee injuries accounting for just under half of the total costs of all injuries over the three-season period (49.1 %). ACL injuries accounted for 26.2 % of total costs with a high mean cost per injury ($4564 SD ±â€¯346) alongside lower limb fractures ($4787 SD ±â€¯425) and tendon ruptures ($4659 SD ±â€¯1053). Despite only 22.5 % of injuries accruing indirect costs, these costs accounted for 70.2 % of the total cost ($9,623,665) with the mean indirect cost per injury being ten-times higher than the mean direct cost per injury ($10,337 vs. $987, respectively). CONCLUSIONS: Knee injuries (mainly ACL ruptures), joint sprains, fractures and tendon ruptures are the costliest injuries in sub-elite football in NSW. With effective preventative measures available, there is potential to reduce injury rates and subsequent costs.

Economic Impact of Diagnostic Imaging in the Workup of Uncomplicated Bell's Palsy.

Objective Our primary objective is to identify the costs associated with imaging in the diagnostic workup of uncomplicated Bell's palsy. Our secondary objective is to identify a dollar amount spent on extraneous diagnostic testing on a state and national level. Design and Setting Retrospective chart analysis was performed at our tertiary care medical center between 2007 and 2018. International Statistical Classification of Diseases-10 code G51.0 was used to identify patients with Bell's palsy seen by the senior author. A total of 163 patients were divided into two groups: those having received imaging and those diagnosed without imaging. The imaging group was then further subdivided by imaging modality: computed tomography (CT) only, magnetic resonance imaging (MRI) only, or both. There was a total of 138 scans in 115 patients. To quantify the amount spent by insurance companies or patients on these scans, net expected pay (NEP) for each modality was used as a representation of cost. The NEP for a CT was $618. The NEP for an MRI was $1,119. The NEP for both scans was $1,737. We extrapolated our results to a state and national level. Main Outcome Measurements Cost of workup; state and national economic burden. Results Extrapolating our data, we forecast that in Illinois and in the United States, over $2 million and $53 million, respectively, are spent on unnecessary imaging. Conclusion By highlighting an unnecessary financial burden, our study provides concrete evidence to support the American Academy of Otolaryngology's recommendation that clinicians should not perform routine imaging studies when diagnosing uncomplicated Bell's palsy.

Role of data from cost and other economic analyses in healthcare decision-making for HIV, TB and sexual/reproductive health programmes in South Africa.

An increasing focus on the use of the results of cost analyses and other economic evaluations in health programme decision-making by governments, donors and technical support partners working in low- and middle-income countries is accompanied by recognition that this use is impeded by several factors, including the lack of skills, data and coordination between spheres of the government. We describe our experience generating economic evaluation data for human immunodeficiency virus, tuberculosis and sexual/reproductive health programmes in South Africa alongside the results of a series of in-depth interviews (IDIs) among decision-makers within the South African government and implementing organizations (data users) and producers of economic evaluations (data producers). We summarize results across (1) the process of implementing a new intervention; (2) barriers to the use of cost data and suggested solutions and (3) the transferability of experiences to the planned South African implementation of universal health coverage (UHC). Based on our experience and the IDIs, we suggest concrete steps towards the improvement of economic data use in the planning and the establishment of structures mandated under the transition to UHC. Our key recommendations include the following: (1) compile a publicly available and regularly updated in-country cost repository; (2) increase the availability of programmatic outcomes data at the aggregate level; (3) agree upon and implement a set of primary decision criteria for the adoption and funding of interventions; (4) combine the efforts of health economics institutions into a stringent system for health technology assessments and (5) improve the link between national and provincial planning and budgeting.

Estimating the quantile medical cost under time-dependent covariates and right censored time-to-event variable based on a state process.

Estimating the medical costs from disease diagnosis to a terminal event is of immense interest to researchers. However, most of existing literature on such research focused on the estimation of cumulative mean function (CMF) for history process. In this paper, the combined scheme of both inverse probability of censoring weighting (IPCW) technique and longitudinal quantile regression model is used to develop a novel procedure to the estimation of cumulative quantile function (CQF) based on history process with time-dependent covariates and right censored time-to-event variable. The consistency of proposed estimator is derived. The extensive simulation study is conducted to investigate the performance of the estimator given in this paper. A medical cost data from a multicenter automatic defibrillator implantation trial (MADIT) is analyzed to illustrate the application of developed method.

National cost study versus hospital cost accounting for organ recovery cost assessment in a French hospital group.

BACKGROUND: The choice of cost data sources is crucial, because it influences the results of cost studies, decisions of hospital managers and ultimately national directives of policy makers. The main objective of this study was to compare a hospital cost accounting system in a French hospital group and the national cost study (ENC) considering the cost of organ recovery procedures. The secondary objective was to compare these approaches to the weighting method used in the ENC to assess organ recovery costs. METHODS: The resources consumed during the hospital stay and organ recovery procedure were identified and quantified retrospectively from hospital discharge abstracts and the national discharge abstract database. Identified items were valued using hospital cost accounting, followed by 2010-2011 ENC data, and then weighted using 2010-2011 ENC data. A Kruskal-Wallis test was used to determine whether at least two of the cost databases provided different results. Then, a Mann-Whitney test was used to compare the three cost databases. RESULTS: The costs assessed using hospital cost accounting differed significantly from those obtained using the ENC data (Mann-Whitney; P-value < 0.001). In the ENC, the mean costs for hospital stays and organ recovery procedures were determined to be €4961 (SD €7295) and €862 (SD €887), respectively, versus €12,074 (SD €6956) and €4311 (SD €1738) for the hospital cost accounting assessment. The use of a weighted methodology reduced the differences observed between these two data sources. CONCLUSIONS: Readers, hospital managers and decision makers must know the strengths and weaknesses of each database to interpret the results in an informed context.

An Analysis of Cancer Registry Cost Data: Methodology and Results.

The Centers for Disease Control and Prevention initiated an economic analysis of the National Program of Cancer Registries (NPCR) in 2005 to estimate the true economic costs of operating a cancer registry, identify costs associated with registry activities, and evaluate the factors that may affect the efficiency of registry operations. We developed a Web-based NPCR cost assessment tool (NPCR-CAT) to collect activity-based cost data from all 48 NPCR registries. We collected data on registry funding, actual expenditures, and factors that may affect the efficiency of operating a central cancer registry. Key lessons learned during data collection and analysis include the importance of working closely with registry staff and balancing the need for standardized data elements with an understanding of individual registry characteristics. Our findings and lessons can be adapted to develop costing tools for other surveillance systems and cancer control programs, both domestically and internationally.

Tweedie distributions for fitting semicontinuous health care utilization cost data.

BACKGROUND: The statistical analysis of health care cost data is often problematic because these data are usually non-negative, right-skewed and have excess zeros for non-users. This prevents the use of linear models based on the Gaussian or Gamma distribution. A common way to counter this is the use of Two-part or Tobit models, which makes interpretation of the results more difficult. In this study, I explore a statistical distribution from the Tweedie family of distributions that can simultaneously model the probability of zero outcome, i.e. of being a non-user of health care utilization and continuous costs for users. METHODS: I assess the usefulness of the Tweedie model in a Monte Carlo simulation study that addresses two common situations of low and high correlation of the users and the non-users of health care utilization. Furthermore, I compare the Tweedie model with several other models using a real data set from the RAND health insurance experiment. RESULTS: I show that the Tweedie distribution fits cost data very well and provides better fit, especially when the number of non-users is low and the correlation between users and non-users is high. CONCLUSION: The Tweedie distribution provides an interesting solution to many statistical problems in health economic analyses.

Developing a standardized healthcare cost data warehouse.

BACKGROUND: Research addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients. METHODS: The warehouse is based on a National Institutes of Research-funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries. RESULTS: We describe the two institutions' administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request. CONCLUSION: The resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information. After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request.

Developing and testing a cost data collection instrument for noncommunicable disease registry planning.

BACKGROUND: This article reports on the methods and framework we have developed to guide economic evaluation of noncommunicable disease registries. METHODS: We developed a cost data collection instrument, the Centers for Disease Control and Prevention's (CDC's) International Registry Costing Tool (IntRegCosting Tool), based on established economics methods We performed in-depth case studies, site visit interviews, and pilot testing in 11 registries from multiple countries including India, Kenya, Uganda, Colombia, and Barbados to assess the overall quality of the data collected from cancer and cardiovascular registries. RESULTS: Overall, the registries were able to use the IntRegCosting Tool to assign operating expenditures to specific activities. We verified that registries were able to provide accurate estimation of labor costs, which is the largest expenditure incurred by registries. We also identified several factors that can influence the cost of registry operations, including size of the geographic area served, data collection approach, local cost of living, presence of rural areas, volume of cases, extent of consolidation of records to cases, and continuity of funding. CONCLUSION: Internal and external registry factors reveal that a single estimate for the cost of registry operations is not feasible; costs will vary on the basis of factors that may be beyond the control of the registries. Some factors, such as data collection approach, can be modified to improve the efficiency of registry operations. These findings will inform both future economic data collection using a web-based tool and cost and cost-effectiveness analyses of registry operations in low- and middle-income countries (LMICs) and other locations with similar characteristics.

Double robust estimator of average causal treatment effect for censored medical cost data.

In observational studies, estimation of average causal treatment effect on a patient's response should adjust for confounders that are associated with both treatment exposure and response. In addition, the response, such as medical cost, may have incomplete follow-up. In this article, a double robust estimator is proposed for average causal treatment effect for right censored medical cost data. The estimator is double robust in the sense that it remains consistent when either the model for the treatment assignment or the regression model for the response is correctly specified. Double robust estimators increase the likelihood the results will represent a valid inference. Asymptotic normality is obtained for the proposed estimator, and an estimator for the asymptotic variance is also derived. Simulation studies show good finite sample performance of the proposed estimator and a real data analysis using the proposed method is provided as illustration. Copyright © 2016 John Wiley & Sons, Ltd.

Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?

Cost and effect data often have missing data because economic evaluations are frequently added onto clinical studies where cost data are rarely the primary outcome. The objective of this article was to investigate which multiple imputation strategy is most appropriate to use for missing cost-effectiveness data in a randomized controlled trial. Three incomplete data sets were generated from a complete reference data set with 17, 35 and 50 % missing data in effects and costs. The strategies evaluated included complete case analysis (CCA), multiple imputation with predictive mean matching (MI-PMM), MI-PMM on log-transformed costs (log MI-PMM), and a two-step MI. Mean cost and effect estimates, standard errors and incremental net benefits were compared with the results of the analyses on the complete reference data set. The CCA, MI-PMM, and the two-step MI strategy diverged from the results for the reference data set when the amount of missing data increased. In contrast, the estimates of the Log MI-PMM strategy remained stable irrespective of the amount of missing data. MI provided better estimates than CCA in all scenarios. With low amounts of missing data the MI strategies appeared equivalent but we recommend using the log MI-PMM with missing data greater than 35 %.

Estimating unbiased economies of scale of HIV prevention projects: a case study of Avahan.

Governments and donors are investing considerable resources on HIV prevention in order to scale up these services rapidly. Given the current economic climate, providers of HIV prevention services increasingly need to demonstrate that these investments offer good 'value for money'. One of the primary routes to achieve efficiency is to take advantage of economies of scale (a reduction in the average cost of a health service as provision scales-up), yet empirical evidence on economies of scale is scarce. Methodologically, the estimation of economies of scale is hampered by several statistical issues preventing causal inference and thus making the estimation of economies of scale complex. In order to estimate unbiased economies of scale when scaling up HIV prevention services, we apply our analysis to one of the few HIV prevention programmes globally delivered at a large scale: the Indian Avahan initiative. We costed the project by collecting data from the 138 Avahan NGOs and the supporting partners in the first four years of its scale-up, between 2004 and 2007. We develop a parsimonious empirical model and apply a system Generalized Method of Moments (GMM) and fixed-effects Instrumental Variable (IV) estimators to estimate unbiased economies of scale. At the programme level, we find that, after controlling for the endogeneity of scale, the scale-up of Avahan has generated high economies of scale. Our findings suggest that average cost reductions per person reached are achievable when scaling-up HIV prevention in low and middle income countries.

Collecting and analysing cost data for complex public health trials: reflections on practice.

BACKGROUND: Current guidelines for the conduct of cost-effectiveness analysis (CEA) are mainly applicable to facility-based interventions in high-income settings. Differences in the unit of analysis and the high cost of data collection can make these guidelines challenging to follow within public health trials in low- and middle- income settings. OBJECTIVE: This paper reflects on the challenges experienced within our own work and proposes solutions that may be useful to others attempting to collect, analyse, and compare cost data between public health research sites in low- and middle- income countries. DESIGN: We describe the generally accepted methods (norms) for collecting and analysing cost data in a single-site trial from the provider perspective. We then describe our own experience applying these methods within eight comparable cluster randomised, controlled, trials. We describe the strategies used to maximise adherence to the norm, highlight ways in which we deviated from the norm, and reflect on the learning and limitations that resulted. RESULTS: When the expenses incurred by a number of small research sites are used to estimate the cost-effectiveness of delivering an intervention on a national scale, then deciding which expenses constitute 'start-up' costs will be a nontrivial decision that may differ among sites. Similarly, the decision to include or exclude research or monitoring and evaluation costs can have a significant impact on the findings. We separated out research costs and argued that monitoring and evaluation costs should be reported as part of the total trial cost. The human resource constraints that we experienced are also likely to be common to other trials. As we did not have an economist in each site, we collaborated with key personnel at each site who were trained to use a standardised cost collection tool. This approach both accommodated our resource constraints and served as a knowledge sharing and capacity building process within the research teams. CONCLUSIONS: Given the practical reality of conducting randomised, controlled trials of public health interventions in low- and middle- income countries, it is not always possible to adhere to prescribed guidelines for the analysis of cost effectiveness. Compromises are frequently required as researchers seek a pragmatic balance between rigor and feasibility. There is no single solution to this tension but researchers are encouraged to be mindful of the limitations that accompany compromise, whilst being reassured that meaningful analyses can still be conducted with the resulting data.

Weighted quantile regression for analyzing health care cost data with missing covariates.

Analysis of health care cost data is often complicated by a high level of skewness, heteroscedastic variances and the presence of missing data. Most of the existing literature on cost data analysis have been focused on modeling the conditional mean. In this paper, we study a weighted quantile regression approach for estimating the conditional quantiles health care cost data with missing covariates. The weighted quantile regression estimator is consistent, unlike the naive estimator, and asymptotically normal. Furthermore, we propose a modified BIC for variable selection in quantile regression when the covariates are missing at random. The quantile regression framework allows us to obtain a more complete picture of the effects of the covariates on the health care cost and is naturally adapted to the skewness and heterogeneity of the cost data. The method is semiparametric in the sense that it does not require to specify the likelihood function for the random error or the covariates. We investigate the weighted quantile regression procedure and the modified BIC via extensive simulations. We illustrate the application by analyzing a real data set from a health care cost study.