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Econometric estimation of country-specific hospital costs.

ABSTRACT: Information on the unit cost of inpatient and outpatient care is an essential element for costing, budgeting and economic-evaluation exercises. Many countries lack reliable estimates, however. WHO has recently undertaken an extensive effort to collect and collate data on the unit cost of hospitals and health centres from as many countries as possible; so far, data have been assembled from 49 countries, for various years during the period 1973-2000. The database covers a total of 2173 country-years of observations. Large gaps remain, however, particularly for developing countries. Although the long-term solution is that all countries perform their own costing studies, the question arises whether it is possible to predict unit costs for different countries in a standardized way for short-term use. The purpose of the work described in this paper, a modelling exercise, was to use the data collected across countries to predict unit costs in countries for which data are not yet available, with the appropriate uncertainty intervals.The model presented here forms part of a series of models used to estimate unit costs for the WHO-CHOICE project. The methods and the results of the model, however, may be used to predict a number of different types of country-specific unit costs, depending on the purpose of the exercise. They may be used, for instance, to estimate the costs per bed-day at different capacity levels; the "hotel" component of cost per bed-day; or unit costs net of particular components such as drugs.In addition to reporting estimates for selected countries, the paper shows that unit costs of hospitals vary within countries, sometimes by an order of magnitude. Basing cost-effectiveness studies or budgeting exercises on the results of a study of a single facility, or even a small group of facilities, is likely to be misleading.

SUBMITTER: Adam T

PROVIDER: S-EPMC156022 | biostudies-literature | 2003 Feb

REPOSITORIES: biostudies-literature

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Econometric estimation of country-specific hospital costs.

Adam Taghreed T Evans David B DB Murray Christopher JL CJ

Cost effectiveness and resource allocation : C/E 20030226 1

Information on the unit cost of inpatient and outpatient care is an essential element for costing, budgeting and economic-evaluation exercises. Many countries lack reliable estimates, however. WHO has recently undertaken an extensive effort to collect and collate data on the unit cost of hospitals and health centres from as many countries as possible; so far, data have been assembled from 49 countries, for various years during the period 1973-2000. The database covers a total of 2173 country-yea ...[more]

PMID: 12773218

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Project description:BACKGROUND:Almost all countries without complete vital registration systems have data on deaths collected by hospitals. However, these data have not been widely used to estimate cause of death (COD) patterns in populations because only a non-representative fraction of people in these countries die in health facilities. Methods that can exploit hospital mortality statistics to reliably estimate community COD patterns are required to strengthen the evidence base for disease and injury control programs. We propose a method that weights hospital-certified causes by the probability of death to estimate population cause-specific mortality fractions (CSMFs). METHODS:We used an established verbal autopsy instrument (VAI) to collect data from hospital catchment areas in Chandpur and Comilla Districts, Bangladesh, and Bohol province, the Philippines, between 2011 and 2014, along with demographic covariates for each death. Hospital medical certificates of cause of death (death certificates) were collected and mapped to the corresponding cause categories of the VAI. Tariff 2.0 was used to assign a COD for community deaths. Logistic regression models were created for broad causes in each country to calculate the probability of in-hospital death, given a set of covariate values. The reweighted CSMFs for deaths in the hospital catchment population, represented by each hospital death, were calculated from the corresponding regression models. RESULTS:We collected data on 4228 adult deaths in the Philippines and 3725 deaths in Bangladesh. Short time to hospital and education were consistently associated with in-hospital death in the Philippines and absence of a disability was consistently associated with in-hospital death in Bangladesh. Non-communicable diseases (excluding stroke) and stroke were the leading causes of death in both the Philippines (33.9%, 19.1%) and Bangladesh (46.1%, 21.1%) according to the reweighted method. The reweighted method generally estimated CSMFs that fell between those derived from hospitals and those diagnosed by Tariff 2.0. CONCLUSIONS:Statistical methods can be used to derive estimates of cause-specific probability of death in-hospital for Bangladesh and the Philippines to generate population CSMFs. In regions where hospital death certification is of reasonable quality and routine verbal autopsy is not applied, these estimates could be applied to generate cost-effective and robust CSMFs for the population.

Project description:Interventions: Elective or emergent colonic resection, defined using the procedural ICD-10 codes: 32000-00 Limited excision of large intestine with formation of stoma 32000-01 Right hemicolectomy with formation of stoma 32000-02 Laparoscopic limited excision of large intestine with formation of stoma 32000-03 Laparoscopic right hemicolectomy with formation of stoma 32003-00 Limited excision of large intestine with anastomosis 32003-01 Right hemicolectomy with anastomosis 32003-02 Laparoscopic limited excision of large intestine with anastomosis 32003-03 Laparoscopic right hemicolectomy with anastomosis 32004-00 Subtotal colectomy with formation of stoma 32004-01 Extended right hemicolectomy with formation of stoma 32004-02 Laparoscopic subtotal colectomy with formation of stoma 32004-03 Laparoscopic extended right hemicolectomy with formation of stoma 32005-00 Subtotal colectomy with anastomosis 32005-01 Extended right hemicolectomy with anastomosis 32005-02 Laparoscopic subtotal colectomy with anastomosis 32005-03 Laparoscopic extended right hemicolectomy with anastomosis 32006-00 Left hemicolectomy with anastomosis 32006-01 Left hemicolectomy with formation of stoma 32006-02 Laparoscopic left hemicolectomy with anastomosis 32006-03 Laparoscopic left hemicolectomy with formation of stoma 32009-00 Total colectomy with ileostomy 32009-01 Laparoscopic total colectomy with ileostomy 32012-00 Total colectomy with ileorectal anastomosis 32012-01 Laparoscopic total colectomy with ileorectal anastomosis The procedures were performed at the Austin Hospital, a tertiary hospital with colorectal expertise, over the period of January 2013 and June 2018. Patients were followed-up for 30 days following discharge from the index admission. If no subsequent readmissions occurred within this period, follow-up ceased. If a patient was readmitted within the 30-day period, then the readmission was included, and followi Primary outcome(s): Costs of complications (as a composite outcome) following Colon resections. Costs of complications defined as cost of patients with complications less the cost of patients without complications (ie by comparison of patients with and without complications). All costs related to the index admission for colon resection and any consequent readmissions are included. Costs related to preoperative course are not considered. Allocation of costs will be calculated using an activity-based costing methodology. Only in-hospital costs are considered, with both direct and indirect costs assessed to produce a total cost for each patient. Costs for each patient will be obtained from the Austin Health Department of Finance, which maintain an itemised prospective database of all hospital expenses related to each patient.[Costs will be calculated from day of surgery to hospital discharge. Readmissions are considered within 30 days of discharge from the index admission.] Study Design: Purpose: Natural history;Duration: Longitudinal;Selection: Defined population;Timing: Retrospective

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Econometric estimation of country-specific hospital costs.

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Econometric estimation of country-specific hospital costs.

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