Dataset Information

Estimating utility weights and quality-adjusted life year loss for colorectal cancer-related health states in Korea.

ABSTRACT: We aimed to assess utility weight of health states associated with colorectal cancer (CRC) that reflect the societal preference of the Korean population and to estimate the quality-adjusted life year (QALY) loss with CRC. We recruited 607 individuals from the Korean population; they were surveyed via face-to-face computer-assisted interviews. The participants evaluated each CRC-associated health state using standard gamble. Utility weight for each health state was calculated as the possibility of full health restoration. Moreover, we estimated total QALY loss due to CRC in Korean individuals aged ?30 years in 2013. To calculate QALY due to morbidity, we yielded utility weights and used epidemiologic data of CRC on severity from the National Cancer Control Institute. QALY loss due to mortality was calculated using mortality of CRC and life expectancy data from the Korean Statistical Information Service. The highest and lowest utility weights were assigned to "adenomatous polyps" and "metastatic colon cancer", respectively. Total QALY loss due to CRC in Korea was 173,662; these patients were more likely to be men or be included in the 70-74-year age group. These utility weights may be useful for conducting cost-utility studies of cancer screening for CRC and for measuring disease burden with QALY.

SUBMITTER: Lee JY

PROVIDER: S-EPMC5514107 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Estimating utility weights and quality-adjusted life year loss for colorectal cancer-related health states in Korea.

Lee Jin Yong JY Ock Minsu M Jo Min-Woo MW Son Woo-Seung WS Lee Hyeon-Jeong HJ Kim Seon-Ha SH Kim Hyun Joo HJ Lee Jong Lyul JL

Scientific reports 20170717 1

We aimed to assess utility weight of health states associated with colorectal cancer (CRC) that reflect the societal preference of the Korean population and to estimate the quality-adjusted life year (QALY) loss with CRC. We recruited 607 individuals from the Korean population; they were surveyed via face-to-face computer-assisted interviews. The participants evaluated each CRC-associated health state using standard gamble. Utility weight for each health state was calculated as the possibility o ...[more]

PMID: 28717246

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Project description:PurposeThe goal of this study is to develop an approach for estimating nationally representative quality-adjusted life-year (QALY) loss from injury and poisoning conditions using data collected in the Medical Expenditure Panel Survey (MEPS) and the National Health Interview Survey (NHIS).MethodsThis study uses data from the 2002-2015 NHIS and MEPS surveys. Injuries were identified in the MEPS medical events file and through self-reporting of medical conditions. We restricted our model to 163,731 adults, for which we predict a total of 294,977 EQ-5D scores using responses to the self-administered questionnaire. EQ-5D scores were modeled using age, sex, comorbidities, and binary indicators of the presence and duration of injury at the time of the health status questionnaire. These models consider nonlinearity over time during the first 3 y following the injury event.ResultsInjuries are identified in MEPS using medical events that provide a reasonable proxy for the date of injury occurrence. Health-related quality of life (HRQL) decrements can be estimated using binary indicators of injury during different time periods. When grouped into 29 injury categories, most categories were statistically significant predictors of HRQL scores in the first year after injury. For these groups of injuries, mean first-year QALY loss estimates range from 0.005 (sprains and strains of joints and adjacent muscles, n = 7067) to 0.109 (injury to nerves and spinal cord, n = 71). Fewer estimates are significant in the second and third years after injury, which may reflect a return to baseline HRQL.ConclusionThis research presents both a framework for estimating QALY loss for short-lived medical conditions and nationally representative, community-based HRQL scores associated with a wide variety of injury and poisoning conditions.HighlightsThis research provides a catalog of nationally representative, preference-based EQ-5D score decrements associated with surviving a large set of injuries, based on patient-reported health status.Mean first-year QALY loss estimates range from 0.005 (sprains and strains of joints and adjacent muscles, n = 7067) to 0.109 (injury to nerves and spinal cord, n = 71).This article presents a novel methodology for assessing quality-of-life impacts for acute conditions by calculating the time elapsed between injury and health status elicitation. Researchers may explore adapting these methods to study other short-lived conditions and health states, such as COVID-19 or chemotherapy.

Project description:ObjectivesThe purpose of this research is to understand the thought processes that underpin responses to stated preference approaches for eliciting quality of life, in particular the standard gamble.MethodsWe utilize standard gamble preference elicitation survey techniques to elicit quality-adjusted life year weights for two reduced health states: chronic severe depression and total blindness. After the survey, we conduct open-ended qualitative interviews with respondents to determine their thought processes while taking the surveys and to shed light on what their quality-adjusted life year weight is capturing. Survey responses were coded and analyzed for themes in NVivo, the results of which were then formalized in the terminology of decision sciences.ResultsThe qualitative results of the cognitive interviews present systematic evidence for a type of cognitive bias present in standard gamble quality-adjusted life year weight elicitation, which has not been previously highlighted and which we call treatment bias. We define this treatment bias as the consideration of salient treatment alternatives correlated with a reduced health state, when these alternatives are not explicitly posed in the question. Our formalization of this cognitive behavior demonstrates that treatment bias will always bias the elicited health state utility of treating the illness in question downward.ConclusionThe treatment bias highlighted in this study has implications for economic evaluation when comparing treatment for illnesses where alternative treatments are widely publicized versus those that are not. For example, comparing the effectiveness of treating depression versus arthritis may be biased against depression if advertisements for anti-depressants are more widely viewed by survey respondents than advertisements for arthritis treatments. We propose a statement to be imbedded in all questionnaires regarding stated preference elicitation of quality-adjusted life year weights in order to correct for this bias in future stated preference surveys.

Project description:BackgroundLife expectancy is increasingly incorporated in evidence-based screening and treatment guidelines to facilitate patient-centered clinical decision-making. However, life expectancy estimates from standard life tables do not account for health status, an important prognostic factor for premature death. This study aims to address this research gap and develop life tables incorporating the health status of adults in the United States.MethodsData from the National Health Interview Survey (1986-2004) linked to mortality follow-up through to 2006 (age ≥ 40, n = 729,531) were used to develop life tables. The impact of self-rated health (excellent, very good, good, fair, poor) on survival was quantified in 5-year age groups, incorporating complex survey design and weights. Life expectancies were estimated by extrapolating the modeled survival probabilities.ResultsLife expectancies incorporating health status differed substantially from standard US life tables and by health status. Poor self-rated health more significantly affected the survival of younger compared to older individuals, resulting in substantial decreases in life expectancy. At age 40 years, hazards of dying for white men who reported poor vs. excellent health was 8.5 (95% CI: 7.0,10.3) times greater, resulting in a 23-year difference in life expectancy (poor vs. excellent: 22 vs. 45), while at age 80 years, the hazards ratio was 2.4 (95% CI: 2.1, 2.8) and life expectancy difference was 5 years (5 vs. 10). Relative to the US general population, life expectancies of adults (age < 65) with poor health were approximately 5-15 years shorter.ConclusionsConsiderable shortage in life expectancy due to poor self-rated health existed. The life table developed can be helpful by including a patient perspective on their health and be used in conjunction with other predictive models in clinical decision making, particularly for younger adults in poor health, for whom life tables including comorbid conditions are limited.

Dataset Information

Estimating utility weights and quality-adjusted life year loss for colorectal cancer-related health states in Korea.

Publications

Estimating utility weights and quality-adjusted life year loss for colorectal cancer-related health states in Korea.

Similar Datasets

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