Chapter 004. Screening and Prevention of Disease (Kỳ 2) pps

Chapter 004. Screening and Prevention of Disease (Kỳ 2) Methods of Measuring Health Benefits It is not practical to perform all possible screening procedures. For example, screening for laryngeal cancer in smokers is not currently recommended. It is necessary to examine the strength of evidence in favor of screening measures relative to the cost and risk of false-positive tests. For example, should ultrasound be used to screen for ovarian cancer in average-risk women? It is currently estimated that the unnecessary laparotomies triggered by finding benign ovarian masses would actually cause more harm than the benefit derived from detecting the occasional curable ovarian cancer. A variety of endpoints are used to assess the potential gain from screening and prevention interventions: 1. The number of subjects screened to alter the outcome in one individual. It is estimated, for example, that 731 women ages 65–69 would need to be screened by dual-energy x-ray absorptiometry (DEXA) and then treated appropriately to prevent one hip fracture from osteoporosis. 2. The absolute and relative impact of screening on disease outcome. A meta-analysis of Swedish mammography trials (ages 40–70) found that ~1.2 fewer women per thousand would die from breast cancer if they were screened over a 12-year period. By comparison, ~3 lives per 1000 might be saved from colon cancer in a population (ages 50–75) screened with annual FOBT over a 13-year period. Based on this analysis, colon cancer screening may actually save more women's lives than mammography. The impact of FOBT (8.8/1000 versus 5.9/1000) might be stated either as 3 lives per 1000 or as a 30% reduction in colon cancer death; thus, it is important to consider both the relative and absolute impact on numbers of lives saved. 3. The cost per year of life saved is used to assess the effectiveness of many screening and prevention strategies. Typically, strategies that cost <$30,000– 50,000 per year of life saved are considered "cost-effective" (Chap. 3). For example, using alendronate to treat 65-year-old women with osteoporosis approaches this threshold of approximately $30,000 per year of life saved. 4. Increase in average life expectancy for a population. Predicted increases in life expectancy for various screening procedures are listed in Table 4-2. It should be noted, however, that the life-expectancy increase is an average that applies to a population and not to an individual. In reality, the vast majority of the screened population does not derive any benefit and possibly incurs a slight risk from false-positive results. A small subset of patients, however, will benefit greatly from being screened. For example, Pap smears do not benefit the 98% of women who never develop cancer of the cervix. However, for the 2% who would develop localized cervical cancer, Pap smears may add as much as 25 years to their lives. Some studies suggest that a 1-month gain of life expectancy is a reasonable goal for a population-based preventive strategy. Table 4-2 Estimated Average Increase in Life Expectancy for a Population Screening Procedure Average Increase Mammography: Women, 40–50 years 0–5 days Women, 50–70 years 1 month Pap smears, age 18–65 2–3 months Screening treadmill for a 50-year- old (asymptomatic) man 8 days PSA and digital rectal exam for a man >50 years Up to 2 weeks Getting a 35-year-old smoker to quit 3–5 years Beginning regular exercise for a 40-year- old man (30 min 3 times a week) 9 months to 2 years Note: PSA, prostate-specific antigen. . Chapter 004. Screening and Prevention of Disease (Kỳ 2) Methods of Measuring Health Benefits It is not practical to perform all possible screening procedures. For example, screening. consider both the relative and absolute impact on numbers of lives saved. 3. The cost per year of life saved is used to assess the effectiveness of many screening and prevention strategies. Typically,. absorptiometry (DEXA) and then treated appropriately to prevent one hip fracture from osteoporosis. 2. The absolute and relative impact of screening on disease outcome. A meta-analysis of Swedish mammography