Eberechukwu Onukwugha1, Jacquelyn McRae2, Alex Kravetz3, Stefan Varga4, Rahul Khairnar3, C Daniel Mullins3. 1. Department of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, 220 Arch Street, Baltimore, MD, 21201, USA. eonukwug@rx.umaryland.edu. 2. College of Pharmacy, Mercer University, Atlanta, GA, USA. 3. Department of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, 220 Arch Street, Baltimore, MD, 21201, USA. 4. School of Pharmacy, Notre Dame of Maryland University, Baltimore, MD, USA.
Abstract
INTRODUCTION: Cost-of-illness (COI) studies provide policy-relevant information for cross-country, longitudinal, and other cost comparisons. Prior studies have called for standardization in COI methods. We investigated trends, identified factors associated with variation in COI estimation methods, and characterized reporting of heterogeneity in COI estimates. METHODS: The review of COI studies was implemented following (i) a structured search of PubMed, SCOPUS and EMBASE; (ii) a review of abstracts; (iii) a full-text review; and (iv) classification of articles according to six COI estimation methods: Sum_All Medical, Sum_Diagnosis Specific, Matched, Regression, Other_Total and Other_Incremental. Descriptive and multivariable regression analyses were conducted. RESULTS: Of the 993 studies included in the full-text review, 186 (18.7 %) were Sum_All Medical, 458 (46.1 %) were Sum_Diagnosis Specific, 96 (9.7 %) were Matched, 97 (9.8 %) were Regression, 70 (7.1 %) were Other_Incremental, and 68 (6.9 %) were Other_Total. Compared with the early period, publications in the middle and late period were associated with lower odds of using Sum_All Medical compared with Sum_Diagnosis Specific (adjusted odds ratio [AOR]middle 0.14; 95 % CI 0.07-0.28; AORlate 0.44; 95 % CI 0.29-0.67). Overall, 640 articles (64 %) reported COI estimates across patient groups defined by patient-level factors, while 247 articles (25 %) reported COI estimates across patient groups defined by non-patient-level factors. CONCLUSION: The disease-specific total costing method (Sum_Diagnosis Specific) was most commonly used and its use increased over the time period covered by this review. The investigation of subgroup heterogeneity in COI estimates represents an area for future research.
INTRODUCTION: Cost-of-illness (COI) studies provide policy-relevant information for cross-country, longitudinal, and other cost comparisons. Prior studies have called for standardization in COI methods. We investigated trends, identified factors associated with variation in COI estimation methods, and characterized reporting of heterogeneity in COI estimates. METHODS: The review of COI studies was implemented following (i) a structured search of PubMed, SCOPUS and EMBASE; (ii) a review of abstracts; (iii) a full-text review; and (iv) classification of articles according to six COI estimation methods: Sum_All Medical, Sum_Diagnosis Specific, Matched, Regression, Other_Total and Other_Incremental. Descriptive and multivariable regression analyses were conducted. RESULTS: Of the 993 studies included in the full-text review, 186 (18.7 %) were Sum_All Medical, 458 (46.1 %) were Sum_Diagnosis Specific, 96 (9.7 %) were Matched, 97 (9.8 %) were Regression, 70 (7.1 %) were Other_Incremental, and 68 (6.9 %) were Other_Total. Compared with the early period, publications in the middle and late period were associated with lower odds of using Sum_All Medical compared with Sum_Diagnosis Specific (adjusted odds ratio [AOR]middle 0.14; 95 % CI 0.07-0.28; AORlate 0.44; 95 % CI 0.29-0.67). Overall, 640 articles (64 %) reported COI estimates across patient groups defined by patient-level factors, while 247 articles (25 %) reported COI estimates across patient groups defined by non-patient-level factors. CONCLUSION: The disease-specific total costing method (Sum_Diagnosis Specific) was most commonly used and its use increased over the time period covered by this review. The investigation of subgroup heterogeneity in COI estimates represents an area for future research.
Authors: Grégoire de Lagasnerie; Anne-Sophie Aguadé; Pierre Denis; Anne Fagot-Campagna; Christelle Gastaldi-Menager Journal: Eur J Health Econ Date: 2017-02-11
Authors: Scott D Grosse; Xu Ji; Phyllis Nichols; Samuel H Zuvekas; Catherine E Rice; Marshalyn Yeargin-Allsopp Journal: Psychiatr Serv Date: 2020-10-20 Impact factor: 3.084