Estimating the Cost of Type 1 Diabetes in the U.S.: A Propensity Score Matching Method
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{"title"=>"Estimating the cost of type 1 diabetes in the U.S.: A propensity score matching method", "type"=>"journal", "authors"=>[{"first_name"=>"Betty", "last_name"=>"Tao", "scopus_author_id"=>"36242379600"}, {"first_name"=>"Massimo", "last_name"=>"Pietropaolo", "scopus_author_id"=>"7004044697"}, {"first_name"=>"Mark", "last_name"=>"Atkinson", "scopus_author_id"=>"35400228700"}, {"first_name"=>"Desmond", "last_name"=>"Schatz", "scopus_author_id"=>"7102923175"}, {"first_name"=>"David", "last_name"=>"Taylor", "scopus_author_id"=>"55475232600"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"359318599", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0011501", "scopus"=>"2-s2.0-77955361316", "pmid"=>"20634976", "sgr"=>"77955361316"}, "id"=>"28ede068-8a85-36f2-a2d8-cd6a7bf0c831", "abstract"=>"BACKGROUND Diabetes costs represent a large burden to both patients and the health care system. However, few studies that examine the economic consequences of diabetes have distinguished between the two major forms, type 1 and type 2 diabetes, despite differences in underlying pathologies. Combining the two diseases implies that there is no difference between the costs of type 1 and type 2 diabetes to a patient. In this study, we examine the costs of type 1 diabetes, which is often overlooked due to the larger population of type 2 patients, and compare them to the estimated costs of diabetes reported in the literature. METHODOLOGY/PRINCIPAL FINDINGS Using a nationally representative dataset, we estimate yearly and lifetime medical and indirect costs of type 1 diabetes by implementing a matching method to compare a patient with type 1 diabetes to a similar individual without the disease. We find that each year type 1 diabetes costs this country $14.4 billion (11.5-17.3) in medical costs and lost income. In terms of lost income, type 1 patients incur a disproportionate share of type 1 and type 2 costs. Further, if the disease were eliminated by therapeutic intervention, an estimated $10.6 billion (7.2-14.0) incurred by a new cohort and $422.9 billion (327.2-519.4) incurred by the existing number of type 1 diabetic patients over their lifetime would be avoided. CONCLUSIONS/SIGNIFICANCE We find that the costs attributed to type 1 diabetes are disproportionately higher than the number of type 1 patients compared with type 2 patients, suggesting that combining the two diseases when estimating costs is not appropriate. This study and another recent contribution provides a necessary first step in estimating the substantial costs of type 1 diabetes on the U.S.", "link"=>"http://www.mendeley.com/research/estimating-cost-type-1-diabetes-propensity-score-matching-method-1", "reader_count"=>72, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>4, "Researcher"=>15, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>2, "Student > Master"=>15, "Other"=>3, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>4, "Researcher"=>15, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>2, "Student > Master"=>15, "Other"=>3, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>14, "Arts and Humanities"=>1, "Business, Management and Accounting"=>1, "Chemistry"=>2, "Economics, Econometrics and Finance"=>6, "Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>4, "Medicine and Dentistry"=>27, "Pharmacology, Toxicology and Pharmaceutical Science"=>3, "Psychology"=>1, "Social Sciences"=>3, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>27}, "Social Sciences"=>{"Social Sciences"=>3}, "Psychology"=>{"Psychology"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>6}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}}, "reader_count_by_country"=>{"United States"=>3, "Ireland"=>1}, "group_count"=>6}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/841065"], "description"=>"a<p>Ninety-five percent confidence intervals in parentheses. ***, **, * indicate the 1 percent, 5 percent and 10 percent significance levels, respectively.</p>b<p>Outcomes do not add up to total because total medical expenditure includes some other factors (home health care, vision, etc.) that are each small.</p>", "links"=>[], "tags"=>["yearly", "expenditure"], "article_id"=>511520, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t006", "stats"=>{"downloads"=>8, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_yearly_medical_expenditure_with_at_least_one_hospital_night_2005_dollars_/511520", "title"=>"Mean yearly medical expenditure with at least one hospital night (2005 dollars).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:37:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/841131"], "description"=>"a<p>All values are percents unless otherwise noted.</p>b<p>Data available for those under 18 years of age only.</p>", "links"=>[], "tags"=>["matching", "matched", "unmatched"], "article_id"=>511588, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t004", "stats"=>{"downloads"=>6, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Propensity_score_matching_assessment_8211_bias_reduction_from_using_a_matched_vs_unmatched_sample_/511588", "title"=>"Propensity score matching assessment – bias reduction from using a matched vs. unmatched sample.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:38:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/840880"], "description"=>"a<p>Ninety-five percent confidence intervals are in parentheses.</p>b<p>Indirect costs are comprised of lost income for those 18 and over.</p>c<p>Total yearly costs are estimated for the 1.1 million T1D patients.</p>d<p>Lifetime costs are the discounted present value of the expected stream of costs over the lifetime.</p>", "links"=>[], "tags"=>["costs", "attributable", "t1d"], "article_id"=>511334, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t011", "stats"=>{"downloads"=>9, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_estimated_costs_attributable_to_T1D_2005_dollars_a_/511334", "title"=>"Summary of estimated costs attributable to T1D (2005 dollars).<sup>a</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:36:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/841260"], "description"=>"a<p>The patient is categorized with T1D if he reports being told by a physician that he has diabetes, is currently using insulin, and is diagnosed at or before age 45. We include patients under age 18 only if they are not considered obese. Patients diagnosed later than age 30 are only included if their BMI is 30 or lower.</p>", "links"=>[], "tags"=>["public health and epidemiology/epidemiology", "public health and epidemiology/health policy", "public health and epidemiology/health services research and economics"], "article_id"=>511720, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t001", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_T1D_sample_size_by_age_of_diagnosis_a_/511720", "title"=>"T1D sample size by age of diagnosis.<sup>a</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:38:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/841172"], "description"=>"a<p>Ninety-five percent confidence intervals in parentheses. ***, **, * indicate the 1 percent, 5 percent and 10 percent significance levels, respectively.</p>b<p>ER visits are likely under-reported. It is possible that a hospital visit that started out as an emergency room visit was reported as simply a hospital visit, and coded that way. Although MEPS does provide a variable that indicates if a hospital visit began with an ER visit, we would not know how to allocate the costs between ER and hospital, so we do not use this variable.</p>c<p>Outcomes do not add up to total because total medical expenditure includes some other factors (home health care, vision, etc.) that are each small.</p>", "links"=>[], "tags"=>["yearly", "expenditure", "patients", "t1d", "matched", "controls"], "article_id"=>511627, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t005", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_yearly_and_total_yearly_medical_expenditure_for_patients_with_T1D_and_matched_controls_2005_dollars_/511627", "title"=>"Mean yearly and total yearly medical expenditure for patients with T1D and matched controls (2005 dollars).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:38:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/840920"], "description"=>"a<p>Ninety-five percent confidence intervals in parentheses. ***, **, * indicate the 1 percent, 5 percent and 10 percent significance levels, respectively.</p>b<p>Income is compared only for those over 18 years of age, but discounted back to the current age.</p>", "links"=>[], "tags"=>["indirect", "costs", "attributed", "t1d", "patients"], "article_id"=>511372, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t010", "stats"=>{"downloads"=>7, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expected_lifetime_medical_and_indirect_costs_attributed_to_T1D_by_current_patients_2005_dollars_a_/511372", "title"=>"Expected lifetime medical and indirect costs attributed to T1D by current patients (2005 dollars).<sup>a</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:37:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/841200"], "description"=>"a<p>The patient is categorized with T1D if he reports being told by a physician that he has diabetes, is currently using insulin, and is diagnosed at or before age 45. We include patients under age 18 only if they are not considered obese. Patients diagnosed later than age 30 are only included if their BMI is 30 or lower.</p>", "links"=>[], "tags"=>["weighted"], "article_id"=>511663, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t002", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sample_sizes_weighted_and_nonweighted_/511663", "title"=>"Sample sizes, weighted and nonweighted.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:38:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/841240"], "description"=>"<p><i>Note:</i> The patient is categorized with T1D if he reports being told by a physician that he has diabetes, is currently using insulin, and is diagnosed at or before age 45. We include patients under age 18 only if they are not considered obese. Patients diagnosed later than age 30 are only included if their BMI is 30 or lower.</p>", "links"=>[], "tags"=>["patients", "weighted"], "article_id"=>511697, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t003", "stats"=>{"downloads"=>6, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_Type_1_Diabetes_Patients_by_Current_Age_and_Age_of_First_Diagnosis_Sample_Weighted_Below_/511697", "title"=>"Number of Type 1 Diabetes Patients by Current Age and Age of First Diagnosis (Sample Weighted Below).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:38:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/840957"], "description"=>"a<p>We used a conservative rate of inflation of 3% per year for 2006 and 2007 from the BLS to deflate the ADA figures.</p>b<p>We removed nursing home and hospice costs from the figure since the MEPS does not collect information on the institutionalized population.</p>c<p>This figure was obtained by removing the $26.9 billion attributed to early mortality from the total of $58.2 billion in indirect costs because our methodology concerning early mortality differed significantly. If we left early mortality costs in both the ADA and our calculations, indirect costs for T1D would be 13.9 percent of costs for all diabetes.</p>", "links"=>[], "tags"=>["yearly", "t1d", "ada", "estimates"], "article_id"=>511411, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t012", "stats"=>{"downloads"=>4, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_yearly_costs_T1D_vs_relevant_ADA_all_diabetes_estimates_2005_dollars_/511411", "title"=>"Comparison of yearly costs: T1D vs. relevant ADA all diabetes estimates (2005 dollars).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:37:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/841033"], "description"=>"a<p>Ninety-five percent confidence intervals in parentheses. ***, **, * indicate the 1 percent, 5 percent and 10 percent significance levels, respectively.</p>b<p>Income is compared only for those over 18 years of age, but discounted back to the age of diagnosis.</p>", "links"=>[], "tags"=>["indirect", "costs", "attributed", "t1d", "cohort", "patients"], "article_id"=>511482, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t009", "stats"=>{"downloads"=>3, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expected_lifetime_medical_and_indirect_costs_attributed_to_T1D_by_a_new_cohort_of_patients_2005_dollars_a_/511482", "title"=>"Expected lifetime medical and indirect costs attributed to T1D by a new cohort of patients (2005 dollars).<sup>a</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:37:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/841098"], "description"=>"a<p>*** indicates significance at the 1 percent level, 95 percent confidence intervals in parentheses.</p>b<p>Individual income is estimated for those over 18. Where applicable, income is split evenly between spouses.</p>", "links"=>[], "tags"=>["yearly", "indirect", "costs", "patients", "t1d", "matched", "controls"], "article_id"=>511548, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t007", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_yearly_indirect_costs_for_patients_with_T1D_and_matched_controls_2005_dollars_/511548", "title"=>"Mean yearly indirect costs for patients with T1D and matched controls (2005 dollars).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:38:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/840993"], "description"=>"a<p>Ninety-five percent confidence intervals in parentheses. ***, **, * indicate the 1 percent, 5 percent and 10 percent significance levels, respectively.</p>", "links"=>[], "tags"=>["costs", "attributed"], "article_id"=>511454, "categories"=>["Medicine", "Neuroscience", "Information And Computing Sciences"], "users"=>["Betty Tao", "Massimo Pietropaolo", "Mark Atkinson", "Desmond Schatz", "David Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011501.t008", "stats"=>{"downloads"=>11, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Age_distribution_of_medical_costs_attributed_to_T1D_/511454", "title"=>"Age distribution of medical costs attributed to T1D.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 00:37:27"}

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  • {"unique-ip"=>"148", "full-text"=>"178", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"181", "full-text"=>"211", "pdf"=>"18", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"4", "year"=>"2017", "month"=>"11"}
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  • {"unique-ip"=>"183", "full-text"=>"207", "pdf"=>"24", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"3", "year"=>"2020", "month"=>"3"}
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Relative Metric

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