A Cost Function Analysis of Shigellosis in Thailand


Arthorn Riewpaiboon, Department of Pharmacy, Faculty of Pharmacy Mahidol University, 447 Sri Ayutthaya Road, Ratchathevi, Bangkok Thailand 10400. E-mail: pyarp@mahidol.ac.th


Objective:  The purpose of this study was to develop a cost function model to estimate the public treatment cost of shigellosis patients in Thailand.

Methods:  This study is an incidence-based cost-of-illness analysis from a provider's perspective. The sample cases in this study were shigellosis patients residing in Kaengkhoi District, Saraburi Province, Thailand. All diarrhea patients who came to the health-care centers in Kaengkhoi District, Kaengkhoi District Hospital and Saraburi Regional Hospital during the period covering May 2002 to April 2003 were tested for Shigella spp. The sample for our study included all patients with culture that confirmed the presence of shigellosis. Public treatment cost was defined as the costs incurred by the health-care service facilities arising from individual cases. The cost was calculated based on the number of services that were utilized (clinic visits, hospitalization, pharmaceuticals, and laboratory investigations), as well as the unit cost of the services (material, labor and capital costs). The data were summarized using descriptive statistics. Furthermore, the stepwise multiple regressions were employed to create a cost function, and the uncertainty was tested by a one-way sensitivity analysis of varying discount rate, cost category, and drug prices.

Results:  Cost estimates were based from 137 episodes of 130 patients. Ninety-four percent of them received treatment as outpatients. One-fifth of the episodes were children aged less than 5 years old. The average public treatment cost was US$8.65 per episode based on 2006 prices (95% CI, 4.79, and 12.51) (approximately US$1 = 38.084 Thai baht). The majority of the treatment cost (59.3%) was consumed by the hospitalized patients, though they only accounted for 5.8% of all episodes. The sensitivity analysis on the component of costs and drug prices showed a variation in the public treatment cost ranging from US$8.29 to US$9.38 (−4.20% and 8.43% of the base-case, respectively). The public treatment cost model has an adjusted R2 of 0.788. The positive predictor variables were types of services (inpatient and outpatient), types of health-care facilities (health center, district hospital, regional hospital), and insurance schemes (civil servants medical benefit scheme, social security scheme and universal health coverage scheme). Treatment cost was estimated for various scenarios based on the fitted cost model.

Conclusion:  The average public treatment cost of shigellosis in Thailand was estimated in this study. Service types, health-care facilities, and insurance schemes were the predictors used to predict nearly 80% of the cost. The estimated cost based on the fitted model can be employed for hospital management and health-care planning.