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EcommScripts.Buybox : null var Modal = EcommScripts ? EcommScripts.Modal : null if (weHasBrowserSupport && Buybox && Modal) { var modalID = "ecomm-modal_" + timestamp + "_" + index var modal = new Modal(modalID) modal.domEl.addEventListener("close", close) function close() { form.querySelector("button[type=submit]").focus() } var cartURL = "/cart" var cartModalURL = "/cart?messageOnly=1" form.setAttribute( "action", formAction.replace(cartURL, cartModalURL) ) var formSubmit = Buybox.interceptFormSubmit( Buybox.fetchFormAction(window.fetch), Buybox.triggerModalAfterAddToCartSuccess(modal), function() { form.removeEventListener("submit", formSubmit, false) form.setAttribute( "action", formAction.replace(cartModalURL, cartURL) ) form.submit() } ) form.addEventListener("submit", formSubmit, false) document.body.appendChild(modal.domEl) } else { console.log("binding failed:", weHasBrowserSupport, EcommScripts) } } } })() Fig. 1Table IFig. 2Table II NotesThere has been a movement away from using the term ‘indirect’ costs in COI studies, as this term is also commonly used for overhead and other shared costs in patient-level costing of healthcare services within an accounting framework.[2]It is noted that intangibles are regarded as burdens (on the effects side of the equation) in other aspects of health economic analysis.To locate such a guide, the clinical literature from PubMed was searched on 8 September 2010 (no date restrictions applied) using the keywords ‘Cost of illness[mh] OR Illness cost*[tiab] OR Costs of disease*[tiab] OR Cost of disease*[tiab] OR Sickness cost*[tiab] OR Cost of sickness[tiab] OR Costs and cost analysis[mh:noexp] OR economic burden[tiab]’ in combination with the terms ‘Guideline[pt] OR Guidance[tiab] OR Guideline*[tiab] OR Guide[tiab] OR Understanding[tiab] OR how to[tiab]’. This wide search of the literature found no general guide to COI studies amongst the 3102 references generated.However, COI studies should calculate the excess costs that are caused by the disease in question, i.e. the costs for a person with the disease compared with a like person without the disease. The baseline costs required for costeffectiveness analysis of an intervention are the total (not just excess) costs for people with the disease who have not had the intervention, which are then compared with the costs for people with the disease who have the intervention.This practice has its roots in the theory of human capital developed during the 1960s, and is parallel to valuing physical capital on the basis of its rental income.[51]Since there is a tendency for certain chronic conditions to occur concurrently, due largely to confounding factors, this is clearly a theoretical proposition only.A third approach, developed to estimate healthcare costs of smoking in the US[66,67,79] could also be applied for complex health problems. This combination method estimates the per-person mean fraction of healthcare resource use that can be attributed to the disease of interest from person-level data, and then applies these fractions to known expenditure aggregates of relevant components of healthcare service to estimate aggregate attributable costs. Unlike the PAF approach, this does not restrict the analysis to those co-morbidites known to be causally related to the health problem of interest.Coller et al.[79] found that the higher costs of non-related treatment for smokers than for non-smokers may be responsible for around a third of smoking-attributable healthcare expenditure.i.e. \(PAF = pdj\left[ {{{RRj - 1} \over {RRj}}} \right]\) where pdj is the age-adjusted prevalence of the health problem of interest among the subpopulation who have the attributable condition j, and RRj is the age-adjusted RR of condition j occurring among cases of the health problem of interest compared with a control group.[58,89] A modified formula should also be used for health risks that are measured on a continuous scale.[55]A possible exception is rare longitudinal person-level studies that track health and resource use for a cohort over time.ReferencesRice DP. Estimating the cost-of-illness. 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Moss) with the title ‘Effective strategies to reduce the costs of overweight and obesity to South Australia’. The authors acknowledge advice and guidance provided by Christian Gericke during the course of the project. The authors have no conflicts of interest to declare.Author informationAuthors and AffiliationsDiscipline of Public Health, School of Population Health and Clinical Practice, The University of Adelaide, Level 9, 10 Pulteney Street, Adelaide, South Australia, 5005, AustraliaAllison Larg & John R. MossAuthorsAllison LargView author publicationsYou can also search for this author in PubMed Google ScholarJohn R. MossView author publicationsYou can also search for this author in PubMed Google ScholarCorresponding authorCorrespondence to Allison Larg.Rights and permissionsReprints and PermissionsAbout this articleCite this articleLarg, A., Moss, J.R. Cost-of-Illness Studies. Pharmacoeconomics 29, 653–671 (2011). https://doi.org/10.2165/11588380-000000000-00000Download citationPublished: 23 September 2012Issue Date: August 2011DOI: https://doi.org/10.2165/11588380-000000000-00000">
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Cost-of-illness studies: a guide to critical evaluation

Author(s): Larg A, Moss JR

Abstract

Cost-of-illness (COI) studies aim to assess the economic burden of health problems on the population overall, and they are conducted for an ever widening range of health conditions and geographical settings. While they attract much interest from public health advocates and healthcare policy makers, inconsistencies in the way in which they are conducted and a lack of transparency in reporting have made interpretation difficult, and have ostensibly limited their usefulness. Yet there is surprisingly little in the literature to assist the non-expert in critically evaluating these studies. This article aims to provide non-expert readers with a straightforward guide to understanding and evaluating traditional COI studies. The intention is to equip a general audience with an understanding of the most important issues that influence the validity of a COI study, and the ability to recognize the most common limitations in such work.

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There has been a movement away from using the term ‘indirect’ costs in COI studies, as this term is also commonly used for overhead and other shared costs in patient-level costing of healthcare services within an accounting framework.[2]

It is noted that intangibles are regarded as burdens (on the effects side of the equation) in other aspects of health economic analysis.

To locate such a guide, the clinical literature from PubMed was searched on 8 September 2010 (no date restrictions applied) using the keywords ‘Cost of illness[mh] OR Illness cost*[tiab] OR Costs of disease*[tiab] OR Cost of disease*[tiab] OR Sickness cost*[tiab] OR Cost of sickness[tiab] OR Costs and cost analysis[mh:noexp] OR economic burden[tiab]’ in combination with the terms ‘Guideline[pt] OR Guidance[tiab] OR Guideline*[tiab] OR Guide[tiab] OR Understanding[tiab] OR how to[tiab]’. This wide search of the literature found no general guide to COI studies amongst the 3102 references generated.

However, COI studies should calculate the excess costs that are caused by the disease in question, i.e. the costs for a person with the disease compared with a like person without the disease. The baseline costs required for costeffectiveness analysis of an intervention are the total (not just excess) costs for people with the disease who have not had the intervention, which are then compared with the costs for people with the disease who have the intervention.

This practice has its roots in the theory of human capital developed during the 1960s, and is parallel to valuing physical capital on the basis of its rental income.[51]

Since there is a tendency for certain chronic conditions to occur concurrently, due largely to confounding factors, this is clearly a theoretical proposition only.

A third approach, developed to estimate healthcare costs of smoking in the US[66,67,79] could also be applied for complex health problems. This combination method estimates the per-person mean fraction of healthcare resource use that can be attributed to the disease of interest from person-level data, and then applies these fractions to known expenditure aggregates of relevant components of healthcare service to estimate aggregate attributable costs. Unlike the PAF approach, this does not restrict the analysis to those co-morbidites known to be causally related to the health problem of interest.

Coller et al.[79] found that the higher costs of non-related treatment for smokers than for non-smokers may be responsible for around a third of smoking-attributable healthcare expenditure.

i.e. \(PAF = pdj\left[ {{{RRj - 1} \over {RRj}}} \right]\) where pdj is the age-adjusted prevalence of the health problem of interest among the subpopulation who have the attributable condition j, and RRj is the age-adjusted RR of condition j occurring among cases of the health problem of interest compared with a control group.[58,89] A modified formula should also be used for health risks that are measured on a continuous scale.[55]

A possible exception is rare longitudinal person-level studies that track health and resource use for a cohort over time.

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Acknowledgements

The authors thank three anonymous referees for helpful comments on an earlier draft.

Financial support was provided by the South Australian Department of Health through its Strategic Health Research Program, grant number SHRP 9881 (Chief Investigators: C.A. Gericke and J. Moss) with the title ‘Effective strategies to reduce the costs of overweight and obesity to South Australia’. The authors acknowledge advice and guidance provided by Christian Gericke during the course of the project. The authors have no conflicts of interest to declare.

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Discipline of Public Health, School of Population Health and Clinical Practice, The University of Adelaide, Level 9, 10 Pulteney Street, Adelaide, South Australia, 5005, Australia

Allison Larg & John R. Moss

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Correspondence to Allison Larg.

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Larg, A., Moss, J.R. Cost-of-Illness Studies. Pharmacoeconomics 29, 653–671 (2011). https://doi.org/10.2165/11588380-000000000-00000

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Published: 23 September 2012

Issue Date: August 2011

DOI: https://doi.org/10.2165/11588380-000000000-00000

Referred From: http://link.springer.com/10.2165/11588380-000000000-00000

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