Indirect costs patient- and family-related include all resources consumed during treatment, as well as any resource that contributes to treatment. Patients and their relatives may incur a variety of out-of-pocket costs, such as transportation to health facilities or a live-in caregiver. Variables associated with loss of productivity, such as time spent by patients or their relatives in the search for treatment, time spent on taking part in a health program, work days lost, or reductions in workload should also be assessed as indirect costs. Capital costs are those involved in purchasing the capital assets required by programs.
Equipment, buildings, and land are the most frequent capital costs. Intangibles are costs to which a market value cannot be assigned. These can include suffering, pain, death, and leisure time lost.
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Another important aspect to be considered in costs estimation is the cost perspective - that is, from whose viewpoint expenses are being measured societal, Ministry of Health, employers, insurers, etc. Different viewpoints will include or exclude different costs. For instance, cost of transportation to a health facility may be regarded as a major variable by patients and society, but not by the Ministry of Health. In estimating the costs of a health technology, the amount of each consumed resource should be measured and the total cost should be calculated by multiplying these amounts by the relevant cost or price unit.
Therefore, one should take into account the optimal manner of including costs according to the context in which they are being used. In summary, cost analysis is a three-step process: To clarify the concepts presented in this paper, a cost-effectiveness study from the literature will be used by way of example. It is an effectiveness and cost-effectiveness study comparing 10 antidepressants citalopram, duloxetine, escitalopram, fluoxetine, fluvoxamine, mirtazapine, paroxetine, reboxetine, sertraline and venlafaxine for treatment of patients with moderate to severe depression in the primary care setting.
A multiple treatment comparison meta-analysis was employed to determine the relative efficacy in terms of remission. These rates were then applied in a decision-analytic model in order to estimate costs and quality of life with different treatments at 1 year. The data set included 87 studies with close to 20 patients. The ICER should be interpreted as the additional cost required to achieve an additional unit of health outcome QALY when providing one treatment over another.
Mirtazapine daily dose presented the lowest cost among antidepressants. Regarding effectiveness, the SSRI escitalopram was more likely to provide remission than amitriptyline, citalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline. The results of cost-effectiveness analysis showed that, despite a high acquisition cost, the SSRI escitalopram was associated with greater clinical effectiveness and lower total cost compared with all other treatment strategies. Furthermore, escitalopram was associated with a larger health gain QALYs at 1 year, and therefore dominated the other treatment strategies, as more QALYs are achieved at a lower total cost.
In the cost-effectiveness analysis, escitalopram was the dominant alternative in most comparisons. However, in the comparison between escitalopram and venlafaxine, escitalopram was more effective, but venlafaxine was cheaper. In this situation, calculation of the ICER is indicated. Many statistical programs that can assist in creating a decision tree, as the example shown above in Figure 1 , thus enabling cost-effectiveness analysis.
In this example, the ICER is represented in the numerator by the difference between total costs of both treatments, i. In the denominator, effectiveness is represented by the difference between the QALY of both treatments, i.
Cost-outcome Methods for Mental Health - Google Книги
The results of a CER analysis can only be interpreted by reference to an external standard. This can be done through comparison with other independent interventions, or using a threshold ratio above which a program would no longer be considered cost-effective. Therefore, escitalopram, when compared with venlafaxine, has a CER that is below the national threshold, being a candidate intervention for implementation. For more information, the complete results are described in the original article. Mental health costs can be the result of the behavioral characteristics of individuals with mental disorders, as well as of the response of others to these behaviors.
Mental health problems are defined by their clinical symptoms, but have impact on a variety of dimensions in life. Individuals with mental disorders thus require support from several areas of society, not only the healthcare system. Therefore, mental health costs involve not only the healthcare system but also the systems responsible for social care, housing, employment, criminal justice, and financial support. Expenses associated with health service utilization, inpatient care, and antidepressants or other medications are the main components that cause an economic impact on individuals with mental disorders.
The productivity losses usually associated with mental disorders and the costs of social care by family members or other caregivers also account for a major portion of expenditure on mental health problems. In addition to these variables, there is also the added challenge of taking into account other factors that may be involved, such as the stigma of mental disorders and societal discrimination of individuals with these conditions; certain inherent features of mental disorders, such as self-destructive or dangerous behaviors, suicide, and social exclusion.
For better understanding of the cost dimensions of mental health, these can be divided into two groups. The first group concerns the morbidity costs of mental disorders, and the second, the response costs of mental health problems, that is the costs associated with the responses of others to mental disorders Morbidity costs are those caused by the effects of the symptoms of mental disorders on the productivity of patients and their family members, such as unemployment, absenteeism, impaired work performance, reduced income for the individual, and reduced productivity for the economy; behaviors that result in damage accidents or crime ; and suicide and other premature mortality.
Response costs are those associated with how others respond or react to the symptoms and characteristics of mental disorders As described above, cost-effectiveness analyses are increasingly common in the literature as decision support tools. These analyses are broadly applicable and can be used in a variety of contexts. Searching for treatment efficacy or effectiveness results alone does not suffice for an efficient decision-making process when the demand and cost of mental health programs is involved, whether in publicly or privately funded health systems. Most studies focus on pharmacotherapy and use simple methods; little is invested on economic assessment of psychosocial interventions and cost-effectiveness and cost-benefit analysis.
Based on the assumption that the results of cost-effectiveness studies are not equally applicable to all countries and that pooling these results at a single clearinghouse would be useful to researchers and health administrators alike, in , WHO-CHOICE created a database of cost-effectiveness results divided into 14 WHO sub-regions that have similar epidemiological, infrastructure, and economic characteristics. These topics have included measures for reduction of hazardous alcohol use, 23 cost-effectiveness of schizophrenia treatment, 24 and clinical interventions for reducing the consequences of bipolar disorder, 25 and depression.
The HTA has conducted several economic evaluations in the field of mental health, including assessment of the effectiveness and cost-effectiveness of counseling in patients with chronic depression, 26 new drugs for bipolar disorder, 27 Alzheimer's disease, 28 and treatment of ADHD in children and adolescents. Using principles and methods similar to those of the UK program, the CADTH aims to deliver the information required by health managers for decision making in an efficient and timely manner.
Currently, most investment on cost-effectiveness studies in the field of mental health is still focused in developed countries. It is abundantly clear that investing in these studies provides countrywide benefits and constitutes an intelligent and effective way of guiding government efforts and public resource expenditure. Health economics in mental health in Brazil is a new area, and economic evaluations are still scarce. However, only five studies were Brazilian economic evaluations. Limitations of cost-effectiveness methods in general can also be applied to the mental health area.
Despite being a valuable tool to support decision-making in health policy, cost-effectiveness analysis does not address the ethical issues of allocation of scarce resources. There is no absolute criterion that can be used to recommend an intervention based solely on a cost-effectiveness analysis. Opinions on whether any health intervention is worth a certain amount of money are subject to variations depending on the perspective and values of those who make the value judgment.
Hence, cost-effectiveness analysis is most useful when it is used as one of many inputs to support resource allocation decisions. Consequently, it would be impossible to conduct an economic analysis for every health care intervention. Geographical transferability of economic data can represent a way of making more efficient use of existing studies and may be the only alternative for some countries where information is scarce. International clinical trials are frequently carried out in populations with different genetic, demographic, and cultural characteristics when compared to the majority of the Brazilian population.
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Thus, to achieve recovery is on average almost 4. Next, we investigated associations among indicators. Finally, we compared the ability of indices to discriminate between service providers with stepwise discriminant analysis. The first discriminant analysis evaluated three indicators: Apparently, and in line with the other findings, cost per outcome maximizes separation among the service providers. The discriminant function could classify The second discriminant analysis evaluated outcome, duration and duration per outcome.
This discriminant function classified The prime aim of the present study was to compare performance indicators on how they distinguish between service providers. Specifically, we investigated performance indicators that combine outcome with treatment process variables, such as duration and costs of treatment, as compared to looking at outcome alone. Including cost in the performance indicator does however, amplify the differences among service providers.
With categorical outcomes, combining costs and outcomes augments differences among service providers as well. The service provider with the best results is 4. The results regarding the association among performance indicators reveal that outcome and cost per outcome lead to more similar rankings than outcome and duration per outcome, especially when we consider cost per improved or cost per recovered patient. Performance indices do not overlap completely; rather, they add information onto each other.
Finally, discriminant analyses revealed that the combined indices of cost per outcome and duration per outcome maximized separation among service providers. Apparently, efficiency discriminates better between service providers than outcome only and yields a more informative and useful performance indicator.
The cost per outcome indicator allows for a straightforward ranking of the service providers regarding their efficiency. However, cost per outcome is by itself a rather abstract concept and not very appealing from a clinical perspective. Looking at the cost of improvement or recovery is less abstract and a more informative way to look at efficiency.
Yet, a potential drawback of looking at outcome categorically is that statistical power to find differences among groups diminishes Fedorov et al. At the service provider level duration and cost are negatively associated with outcome. Service providers who provide shorter and less expensive treatments also tend to have better outcome in terms of pre-to-posttreatment change in severity of symptoms. For example, service provider 8 is procuring the highest cost and is also the one with the lowest improvement stats.
Consequently, service provider 8 is consistently last in the rankings based on the ratio of cost per outcome across the indices. On the other hand, service provider 2 has the least costs while showing the highest number of improved patients and is consistently first in the ranking based on the ratio of cost per outcome across indices. By and large, the service providers with the highest change scores the best results also treat shorter and procure lower costs, despite case-mix correction of these indicators.
At the patient level, irrespective of service provider, the association between outcome and duration or cost is no longer found. A daring explanation for this disparity in findings is that the association between duration or cost with outcome is not contingent upon patient characteristics, but rather on service provider characteristics. Quite consistently, the service providers who rank first and second have better results than the other service providers, both in posttreatment outcome and in the proportion of recovered or merely changed patients.
In contrast, the service provider who ranked last had the lowest scores on all three outcome indices. It is unknown which factors cause variation in efficiency and outcome. Service providers differ in the kind of treatments that are offered e. For instance, some service providers do not offer inpatient treatment whereas others do. Service providers who do not provide inpatient treatment may refer more severe patients out which may boost their results.
Organizational differences between service providers will also influence treatment duration, cost, and outcome. Termination of treatment can be difficult, especially when insufficient improvement has been achieved, as it runs counter to the professional ethos of caring therapists Cohen et al. Yet, continuing an unproductive treatment is not beneficial to the patient either and diminishes the efficiency of care. Comparing outcomes between service providers calls for caution and prudence given the observational nature of the present data. Firstly, service providers used different outcome measures and had different rates for the completeness of pre-to-post assessments.
Furthermore, nonrandom missingness of outcome data e. Also, service providers differ in the treatment modalities they offer, with some providing at least some inpatient service, inducing substantial divergence in cost, and potentially influencing the type of patients referred to them. In addition, service providers vary substantially regarding the patient population they serve, their so-called case mix.
Although we did correct all indicators of outcome, duration and cost for known case mix differences among service providers Iezzoni ; Warmerdam et al. Relevant case mix variables may have been overlooked or are still unknown. For instance, prior episodes of psychopathology or prior treatments may be an impediment to good outcome, but this information was not available. The only way to fully rule out the influence of known and unknown pretreatment differences between compared populations is a true experimental design with randomization Hulley et al.
As no randomization of patients to service providers has taken place in this observational study, results should be interpreted with outmost caution regarding comparative performance of service providers. Only a truly experimental study design will yield conclusive answers to the question of how cost or duration of treatment and outcome are interrelated and whether duration of treatment is dependent on patient or service provider characteristics.
The main strength of this study is the use of real life data, not acquired in a clinical trial with selected patients, but under the daily circumstances of clinical care provision. This allows to reach conclusions that are based on real-life clinical practice and increases our confidence in the generalizability of the results regarding the usefulness of indicators.
A further strength of this study is that it is based on a large dataset. The dataset consists of the treatment trajectories of over patients from eight mental health service providers. It constitutes a representative mix of integrated service providers and providers who specialize in only outpatient treatment. Patients from various age, gender, diagnostic, educational and socio-economic groups were included representing the outpatient population well. In addition, from a statistical point of view, the sample size ensures adequate statistical power for the required analyses.
Omitting them has likely affected the results. The excluded long treatments occurred predominantly in service providers with on average longer treatments. Therefore, the present results may be a too conservative representation of the true differences between service providers, as outliers were mostly excluded in service providers with poorer results.
A limitation of the study is that comparison of service providers is hampered, as their outcomes may be confounded by some patient characteristics we did not correct for, such as previous psychopathology and treatment history. Furthermore, the study is limited to short term outcome, assessed immediately after the conclusion of treatment. Future studies should broaden the scope by following how patients fare in the period after conclusion of treatment.
It would be interesting to investigate how cost-effectiveness and relapse rates are associated, as more extensive and expensive treatments may be offset by better results in the long run. We have included waiting time prior to the treatment in the treatment duration. Service providers vary considerably in waiting time. The average per service provider ranges from 2. One could argue against our choice to include waiting time, since being on a waitlist is not the same as undergoing treatment.
Yet, from the perspective of the patient a long waiting time is adverse and does prolong the time till improvement or recovery. Thus, for a fair comparison between service providers, it is in our opinion correct to have included waiting time in the duration of treatment variable. It should be stressed that the purpose of the present study was not a formal cost-effectiveness or cost benefit analysis, but rather a comparison of patient-oriented performance indicators.
Different conclusions may be drawn when effectiveness and efficiency are approached from the broader societal perspective, which would imply the gathering of extra data regarding cost of illness, financial benefits of treatment and gains in quality adjusted life years van Agthoven et al.
For future studies, it would be useful to examine the applicability of the effectiveness and efficiency indicators on inpatient treatment, as the present study concerned predominantly outpatient treatments. Furthermore, the source of differences in efficiency among treatment providers should be investigated further, to examine whether these differences are associated to demographic features, institutional culture or factors related to the treatment process, as the latter two are key to quality improvement and reducing clinical variance.
There was quite some variation in treatment duration. Future studies could examine the association between duration of treatment and outcome in more detail. For example, by investigating whether patients who require lengthier treatments differ in baseline characteristics from those who require less treatment or whether the lengthiest treatments are predominantly treatment failures.
In times of rising health care costs and finite budgets for health care, the quest for more efficient MHS delivery is opportune and swift positive results are beneficial to our patients. In The Netherlands, most service providers offer a standard treatment regime of weekly or biweekly sessions and are—for logistic reasons—not inclined to attune the session frequency to the momentary needs of patients.
More flexibility, for instance by offering more intensive treatment in the initial phase or a more varied treatment menu group therapy, e-health applications and blended forms may yield speedier, better, and more lasting results. In addition, Eric O.
Warmerdam, and Eric O. Oudejans and Masha Spits work for Mark Bench. Informed consent with ROM was obtained by most participating MHS providers from all individual participants included in the study. The Dutch Central Medical Ethical Committee CCMO has ruled that Dutch Law regarding research with humans does not apply to the collection of anonymized information and, consequently, providing SBG with this data and analyzing anonymized data for the present study does not require additional informed consent from participants.
National Center for Biotechnology Information , U. Administration and Policy in Mental Health. Adm Policy Ment Health.
Published online Jul Warmerdam , 1 S. Oudejans , 3 M. Spits , 3 P. Dingemanse , 4 S. Houben , 7 W. Kuyck , 8 E.
Noorthoorn , 1, 9 M. Nugter , 10 S. Robbers , 11 and G. Abstract Assessing performance of mental health services MHS providers merely by their outcomes is insufficient. Performance indicator, Benchmarking, Treatment outcome, Costs, Duration. Introduction The need to systematically gather data regarding process and outcome in health care to improve quality was first pointed out by Ellwood , who suggested the creation of databases with treatment and outcome data by assessing patients at regular intervals. Methods Outline of the Study Anonymized data were obtained in an observational study in which performance of MHS providers was compared.
Table 1 Characteristics of the patients from the MHS providers. Open in a separate window. Davis , Tricia B. He earned a Ph. His research interests are in the psychosocial and pharmacological treatments for schizophrenia, and in the clinical practices, organization, management, cost, and effectiveness of mental health services.
She earned a Ph. Her research interests include the cost-effectiveness of mental health services and methods of estimating the outcome preferences of persons with schizophrenia and of related stakeholders.