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Record W2755337532 · doi:10.1007/s41669-017-0057-9

Out-of-Pocket Expenditures, Indirect Costs and Health-Related Quality of Life of Patients with Pulmonary Tuberculosis in Thailand

2017· article· en· W2755337532 on OpenAlexaff
Pimwara Tanvejsilp, Mark Loeb, Jonathan Dushoff, Feng Xie

Bibliographic record

VenuePharmacoEconomics - Open · 2017
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineHealth carePharmacistTuberculosisIndirect costsFamily medicinePulmonary tuberculosisTotal costEmergency medicinePharmacy

Abstract

fetched live from OpenAlex

BACKGROUND: Thailand's hospitals may adopt different supervision approaches to improve tuberculosis (TB) treatment adherence. OBJECTIVE: The aim of this study was to compare out-of-pocket (OOP) expenditures, indirect costs, and health-related quality of life (HRQoL) among TB patients who received pharmaceutical care (pharmacist-led patient education and telephone consultation), home visit, and self-administered therapy (SAT) in Thailand. METHODS: We conducted a prospective study to collect OOP expenditures, indirect costs, and HRQoL from a subsample of 104 adult pulmonary TB patients who started treatment between January and May 2014 in three hospitals. The three sources of data included patient interviews, patient medical records, and the hospital billing database. Patients were followed from January 2014 to March 2015. Relevant OOP expenditures collected during the interviews included (1) healthcare costs and other medications costs (e.g. vitamins, antibiotics, anti-cough) occurring in private healthcare units; and (2) costs of transportation, food, and accommodation. Productivity loss was measured using the self-reported amount of time a patient was unable to work due to TB, travel time to and from the hospital, time spent at the hospital (waiting time, consultation time, and hospitalizations), and time spent accompanying family members on outpatient visits or during hospitalizations. Cost differences among treatment strategies were adjusted for baseline characteristics by generalized linear models (GLMs). All costs were converted to international dollars (I$). RESULTS: A total of 256 eligible patients who started pulmonary TB treatment during the specified period were approached, with 104 patients being included in the analysis (29, 38, and 37 patients receiving pharmaceutical care, home visit, and SAT, respectively). Mean OOP expenditures per patient receiving pharmaceutical care, home visit, and SAT were I$907.56 [confidence interval (CI) I$603.80-I$1269.41], I$148.47 (CI I$109.49-I$194.89), and I$95.35 (CI I$69.11-I$129.63), respectively. The GLM indicated statistically significantly lower OOP expenditures for patients receiving either home visit or SAT (ratio of mean costs 0.247, CI 0.142-0.427; and 0.318, CI 0.187-0.540, respectively) than those receiving pharmaceutical care. Patient's indirect costs for receiving pharmaceutical care, home visit, and SAT were I$1925.68 (CI I$922.06-I$3284.94), I$2393.66 (CI I$1435.01-I$3501.98), and I$833.33 (CI I$453.87-I$1263.45), respectively. The GLM found no statistically significant differences in indirect costs for the home visit and SAT groups (ratio of mean costs 1.904, CI 0.754-4.802; and 0.792, CI 0.289-2.175, respectively) when pharmaceutical care was set as the reference. Mean utility scores [EuroQol five-dimensional three-level (EQ-5D-3L)] at baseline and treatment end were 0.679 and 0.830, 0.713 and 0.905, and 0.708 and 0.913 for patients receiving pharmaceutical care, home visit, and SAT, respectively. CONCLUSION: Pharmaceutical care patients experienced the highest OOP expenditures, compared with home visit and SAT patients. Home-visit patients reached the highest indirect costs and utility score improvements. A large-scale prospective study is required in order to strengthen evidence to support policy making regarding the most efficient use of limited resources for the management of TB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.102
GPT teacher head0.419
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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