The 12 month prevalence for any mental disorder was 31%. Anxiety, depression, and somatoform disorders were most common (any mood disorder: 11.9%; unipolar depression: 10.7%; any anxiety disorder: 14.5%; phobias: 12.6%; any somatoform disorder: 11.0%). Eating disorders, obsessive-compulsive disorder, and illegal drug abuse had the lowest rates (0.3%, 0.7%, and 0.7%). Prevalence of possible psychosis was 2.6%. Comorbidity rates ranged from 44% (alcohol abuse) to 94% (generalised anxiety), with 60.5% of individuals with mental disorders having a single diagnosis. Most disorders emerged at a young age (median age for lifetime disorders was 20 years); depression and psychosis started later (medians of 31 and 37 years). Increased rates of mental illness were associated with being female, single, low social class, or of poor health. Forty per cent of participants received “at least minimal intervention” and this rate depended on comorbidity (single disorder 30%, 76% for high comorbidity). Show Mental disorders are highly prevalent in the adult German population with rates similar to other national studies. Disorders start early in life and are often comorbid. Response rate of the GHSCS was 61.4% (n = 7124). After exclusion of subjects >65 years and 50% of screen negatives, the sample size for the GHS-MHS was 4773. A response rate of 87.6% resulted in a total of 4181 participants in the GHS-MHS. The paper also reported selected one month and lifetime prevalence rates. This paper reports on a large German government sponsored survey of the prevalence of somatic and mental disorders in the general population, along the lines of the well known Epidemiologic Catchment Area Study in the United States.1 Particular strengths of the study were the concurrent screening for both psychiatric and somatic morbidities, the assessment of four week, 12 month, and lifetime prevalence, and the wide range of disorders considered, including many anxiety disorders, somatoform disorders, substance abuse disorders, and psychiatric disorders secondary to medical conditions. The results were similar to those found in other large studies. Twelve month prevalence of any DSM disorder was 31%, with female sex, low socioeconomic status, and medical comorbidities all associated with a higher prevalence of mental disorders. Mood, anxiety, and somatoform disorders predominated. Some prevalence findings were difficult to accept, such as the very low 0.3% prevalence of eating disorders. There are a number of implications for clinical practice. Medical morbidity was strongly associated with mental disorders, with odds ratios of 1.9 to 4.0, suggesting that non-psychiatric clinicians should expect to see large numbers of these disorders in the medically ill population. Although the study was conducted in Germany, a country with a well established, largely socialised mental health system, utilisation rates were low compared with the USA, with only 40% of people with at least one current disorder receiving any intervention. This suggests that underutilisation may depend in part on factors such as stigma, lack of insight, or failure to screen for mental disorders, rather than access problems. In a striking and unfortunate omission, all people over the age of 65 were excluded from the study “because the psychometric properties of the CIDI, the interview used in the study, have not yet been satisfactorily established for use in older populations”. The authors missed a valuable opportunity to investigate phenomena such as late onset depression, or to explore medical/psychiatric comorbidity in the population likely to show the strongest associations.2 This limits the applicability of their findings to the whole population.
Screening refers to the application of a medical procedure or test to people who as yet have no symptoms of a particular disease, for the purpose of determining their likelihood of having the disease. The screening procedure itself does not diagnose the illness. Those who have a positive result from the screening test will need further evaluation with subsequent diagnostic tests or procedures. Why do we do screening?The goal of screening is to reduce morbidity or mortality from the disease by detecting diseases in their earliest stages, when treatment is usually more successful. Examples of Screening Tests:Pap smear, mammogram, clinical breast exam, blood pressure determination, cholesterol level, eye examination/vision test, and urinalysis. What are sensitivity and specificity?Sensitivity and specificity are measures of a test's ability to correctly classify a person as having a disease or not having a disease. Sensitivity refers to a test's ability to designate an individual with disease as positive. A highly sensitive test means that there are few false negative results, and thus fewer cases of disease are missed. The specificity of a test is its ability to designate an individual who does not have a disease as negative. A highly specific test means that there are few false positive results. It may not be feasible to use a test with low specificity for screening, since many people without the disease will screen positive, and potentially receive unnecessary diagnostic procedures. It is desirable to have a test that is both highly sensitive and highly specific. This is frequently not possible. Typically there is a trade-off. For many clinical tests, there are some people who are clearly normal, some clearly abnormal, and some that fall into the gray area between the two. Choices must be made in establishing the test criteria for positive and negative results. What is predictive value?The probability of having the disease, given the results of a test, is called the predictive value of the test. Positive predictive value is the probability that a patient with a positive (abnormal) test result actually has the disease. Negative predictive value is the probability that a person with a negative (normal) test result is truly free of disease. Predictive value is an answer to the question: If my patient's test result is positive, what are the chances that my patient does have the disease? Predictive value is determined by the sensitivity and specificity of the test and the prevalence of disease in the population being tested. (Prevalence is defined as the proportion of persons in a defined population at a given point in time with the condition in question.) The more sensitive a test, the less likely an individual with a negative test will have the disease and thus the greater the negative predictive value. The more specific the test, the less likely an individual with a positive test will be free from disease and the greater the positive predictive value. When the prevalence of preclinical disease is low, the positive predictive value will also be low, even using a test with high sensitivity and specificity. For such rare diseases, a large proportion of those with positive screening tests will inevitably be found not to have the disease upon further diagnostic testing. To increase the positive predictive value of a screening test, a program could target the screening test to those at high risk of developing the disease, based on considerations such as demographic factors, medical history or occupation. For example, mammograms are recommended for women over the age of forty, because that is a population with a higher prevalence of breast cancer. What criteria should be considered for an effective screening program?
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