61: Psychiatric Epidemiology

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CHAPTER 61 Psychiatric Epidemiology

Albert Yeung, MD, ScD, Raymond W. Kam, MD, MPH

KEY POINTS

• Epidemiology is the study of the distribution and determinants of disease frequency in humans to inform the natural history, service needs, and etiology of illness.
• The frequency of disease can be expressed in different concepts, including cumulative incidence, incidence density, point prevalence, lifetime prevalence, and so on.
• Epidemiological studies frequently rely on assessment instruments to evaluate psychiatric disorders. It is important to first establish the reliability (or consistency) and validity (or truthfulness) of these assessment instruments.
• Based on the recent National Comorbidity Survey (NCS) in the United States, the most common psychiatric disorders were major depression and alcohol dependence, followed by social and simple phobias. Approximately, one in four respondents met criteria for a substance use disorder, one in four for an anxiety disorder, and one in five for an affective disorder in their lifetime.
• Epidemiological studies in the United States and in European countries showed that in general, individuals with a psychiatric disorder under-utilize mental health services. Among those who sought treatment, there was significant delay in seeking help.

OVERVIEW

Epidemiology is the study of the distribution and determinants of disease frequency in man. Epidemiological studies typically examine large groups of individuals, and by providing data on the distribution and frequency of diseases, they help describe the natural history of illness, assess service needs in the community or in special institutions, and shed light on the etiology of illness.

Epidemiology is based on two fundamental assumptions: first, that human disease does not occur at random, and second, that human disease has causal and preventive factors that can be identified through systematic investigation of different populations in different places or at different times. By measuring disease frequency, and by examining who gets a disease within a population, as well as where and when the disease occurs, it is possible to formulate hypotheses concerning possible causal and preventive factors.1

EPIDEMIOLOGICAL MEASURES OF DISEASE FREQUENCY

The frequency of disease or some other outcome within a population group is described using different concepts: the rate at which new cases are observed, or the proportion of a given population that exhibits the outcome of interest.

Incidence refers to the number of new events that develop in a population over a specified period of time (t0 to t1). If this incidence rate is described as the number of events (outcomes) in proportion to the population at risk for the event, it is called the cumulative incidence (CI), and is calculated by the following equation:

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The denominator equals the total number of persons at risk for the event at the start of the time period (t0) without adjustment for any subsequent reduction in the cohort size for any reason, for example, loss to follow-up, death, or reclassification to “case” status. Therefore, CI is best used to describe stable populations where there is little reduction in cohort size during the time period of interest. An example would be a study of the incidence of major depressive disorder (MDD) in a residential program. If, at the beginning of the study, 8 of the 100 residents have MDD, and of the 92 remaining patients, 8 develop MDD over the next 12 months, the CI for MDD would be (8/92 × 100) = 8.7% for this period (i.e., 1 year). Note that the denominator does not include those in the population with the condition at t0, since they are not at risk for newly experiencing the outcome.

When patients are followed for varying lengths of time (e.g., due to loss to follow-up, death, or reclassification to “case” status) and the denominator value representing the population at risk changes significantly, incidence density provides a more precise measure of the rate at which new events occur. Incidence density (ID) is defined as the number of events occurring per unit population per unit time:

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The denominator is the population that is actively at risk for the event, and is adjusted as people no longer belong in that pool. In a study of psychosis, for instance, if a person develops hallucinations and delusions, he or she becomes “a case” and no longer contributes to the denominator. Similarly, a person lost to follow-up would also contribute to the denominator only so long as he or she is being tracked by the study. To illustrate, suppose in a 100-person study of human immunodeficiency virus (HIV) infection, 6 people are lost to follow-up at the end of 6 months, and 4 develop HIV at the end of the third month, the person-years of observation would be calculated as follows: (90 × 1 year) + (6 × 0.5 year) + (4 × 0.25 year) = 94 person-years, and incidence density = (4 cases)/(94 person-years) = 4.26 cases/100 person-years of observation.

Prevalence is the proportion of individuals who have a particular disease or outcome at a point or period in time. In most psychiatric studies, “prevalence” refers to the proportion of the population that has the outcome at a particular point in time, and is called the point prevalence:

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In stable populations, prevalence (P) can be related to incidence density (ID) by the equation P = ID × D, where D is the average duration of the disease before termination (by death or remission, for example). At times, the numerator is expanded to include the number of all cases, existing and new, in a specified time period; this is known as a period prevalence. When the period of interest is a lifetime, it is a type of period prevalence called lifetime prevalence, which is the proportion of people who have ever had the specified disease or attribute in their lifetime.

Lifetime prevalence is often used to convey the overall risk for someone who develops an illness, particularly psychiatric ones that have episodic courses, or require a certain duration of symptoms to qualify for a diagnosis (e.g., depression, anxiety, or posttraumatic stress disorder). In practice, however, an accurate lifetime prevalence rate is difficult to determine since it often relies on subject recall and on sampling populations of different ages (not necessarily at the end of their respective “lifetimes”). It is also an overall rate that does not account for changes in incidence rates over time, nor for possible differences in mortality rates in those with or without the condition.

CRITERIA FOR ASSESSMENT INSTRUMENTS

There are a number of concepts that are helpful in the evaluation of assessment instruments. These involve the consistency of the results that the instrument provides, and its fidelity to the concept being measured.

Reliability is the degree to which an assessment instrument produces consistent or reproducible results when used by different examiners at different times. Lack of reliability may be the result of divergence between observers, imprecision in the measurement tool, or instability in the attribute being measured. Interrater reliability (Table 61-1) is the extent to which different examiners obtain equivalent results in the same subject when using the same instrument; test-retest reliability is the extent to which the same instrument obtains equivalent results in the same subject on different occasions.

Table 61-1 Interrater Reliability

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Reliability is not sufficient for a measurement instrument—it could, for example, consistently and reliably give results that are neither meaningful nor accurate. However, it is a necessary attribute, since inconsistency would impair the accuracy of any tool. The demonstration of the reliability of an assessment tool is thus required before its use in epidemiological studies. The use of explicit diagnostic criteria, trained examiners to interpret data uniformly, and a structured assessment that obtains the same types of information from all subjects can enhance the reliability of assessment instruments.

There are several commonly used measures to indicate the degree of consistency between sets of data, which in psychiatry is often used to quantify the degree of agreement between raters. The kappa statistic (κ) is used for categorical or binary data, and the intraclass correlation coefficient (ICC, usually represented as r) for continuous data. Both measures have the same range of values (−1 to +1), from perfect negative correlation (−1), to no correlation (0), to perfect positive correlation (+1). For acceptable reliability, the kappa statistic value of 0.7 or greater is generally required; for the ICC, a value of 0.8 or greater is generally required.

Calculation of the kappa statistic (κ) requires only arithmetic computation, and accounts for the degree of consistency between raters with an adjustment for the probability of agreement due to chance. When the frequency of the disorder is very low, however, the kappa statistic will be low despite having a high degree of consistency between raters; it is not appropriate for the measurement of reliability of infrequent disorders.

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where Po is the observed agreement and Pc is an agreement due to chance. Po = (a + d)/n and Pc = [(a + c)(a + b) + (b + d)(c + d)]/n2. Calculation of the ICC is more involved and is beyond the scope of this text.

Validity is a term that expresses the degree to which a measurement instrument actually measures what it purports to measure. When translating a theoretical concept into an operational instrument that purports to assess or measure it, several aspects of validity need to be accounted for.

For any abstract concept, there are an infinite number of criteria that one might use to assess it. For example, if one wants to develop a questionnaire to diagnose bipolar disorder, one should ask about mood, thought process, and energy level, but probably not whether the subject owns a bicycle. Content validity is the extent to which the instrument adequately incorporates the domain of items that would accurately measure the concept of interest.

Criterion validity is the extent to which the measurement can predict or agree with constructs external to the construct being measured. There are two types of criterion validity generally distinguished, predictive validity and concurrent validity. Predictive validity is the extent to which the instrument’s measurements can predict an external criterion. For instance, if we devise an instrument to measure math ability, we might postulate that math ability should be correlated to better grades in college math courses. A high correlation between the measure’s assessment of math ability and college math course grades would indicate that the instrument can correctly predict as it theoretically should, and has predictive validity. Concurrent validity refers to the extent to which the measurement correlates to another criterion at the same point in time. For example, if we devise a measure relying on visual inspection of a wound to determine infection, we can correlate it to a bacteriological examination of a specimen taken at the same time. A high correlation would indicate concurrent validity, and suggest that our new measure gives valid results for determining infection.

Construct validity refers to the extent to which the measure assesses the underlying theoretical construct that it intends to measure. This concept is the most complex, and both content and criterion validity point to it. An example of a measure lacking construct validity would be a test for assessing algebra skills using word problems that inadvertently assesses reading skills rather than factual knowledge of algebra. Construct validity also refers to the extent that the construct exists as theorized and can be quantified by the instrument. In psychiatry, this is especially difficult since there are no “gold standard” laboratory (e.g., chemical, anatomical, physiological) tests, and the criteria if not the existence of many diagnoses are disputed. To establish the validity for any diagnosis, certain requirements have been proposed, and include an adequate clinical description of the disorder that distinguishes it from other similar disorders and the ability to correlate the diagnosis to external criteria such as laboratory tests, familial transmission patterns, and consistent outcomes, including response to treatment.

Because there are no “gold standard” diagnostic tests in psychiatry, efforts to validate diagnoses have focused around such efforts as increasing the reliability of diagnostic instruments—by defining explicit and observationally based diagnostic criteria (DSM-III and subsequent versions), or employing structured interviews, such as the Diagnostic Interview Schedule (DIS)––and conducting genetic and outcome studies for diagnostic categories. The selection of a “gold standard” criterion instrument in psychiatry, however, remains problematic.

Assessment of New Instruments

If we assume that a reliable criterion instrument that pro-vides valid results exists, the assessment of a new measurement instrument would involve comparing the results of the new instrument to those of the criterion instrument. The criterion instrument’s results are considered “true,” and a judgment of the validity of the new instrument’s results are based on how well they match the criterion instrument’s (Table 61-2).

Table 61-2 Validity of a New Instrument

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Sensitivity is the proportion of true cases, as identified by the criterion instrument, who are identified as cases by the new instrument (also known as the true positive rate).

Specificity is the proportion of non-cases, as identified by the criterion instrument, who are identified as non-cases by the new instrument (also known as the true negative rate).

For any given instrument, there are tradeoffs between sensitivity and specificity, depending on where the threshold limits are set to distinguish “case” from “non-case.” For example, in the Hamilton-Depression Scale (HAM-D) instrument, the cutoff value for the diagnosis of MDD (often set at 15) would determine whether an individual would be identified as “case” or “non-case.” If the value were instead set at 5, which most clinicians would consider “normal” or not depressed, the HAM-D would be an unusually sensitive instrument (e.g., using a structured clinical interview as the criterion instrument) since most anyone evaluated with even a modicum of depressive thinking would be considered a “case” as would anybody typically considered to have major depression. However, the test would not be especially specific, since it would be poor at identifying those without depression. Conversely, if the cutoff value were set at 25, sensitivity would be low but the specificity high.

In practice, the threshold values in any given evaluation instrument, whether creatine kinase (CK) levels for determining myocardial infarction, the number of colonies on a petri dish to determine infection, or criteria to determine attention-deficit/hyperactivity disorder (ADHD) (e.g., 6 of 9 from group one, 6 of 9 from group two), are chosen to balance the need for both sensitivity and specificity. To improve both these measures without a tradeoff, either the instrument itself or its administration must be improved, or efforts made to ensure maximum stability of the attribute being measured (e.g., administering them concurrently, or in similar circumstances, such as at the same time of day, or a similar clinical setting).

Two other useful measures are the positive predictive value (PPV), the proportion of those with a positive test that are true cases as determined by the criterion instrument. Negative predictive value (NPV) is the proportion of those with a negative test that are true non-cases as determined by the criterion instrument.

Study Designs

There are six basic study types, presented here in the order of their respective ability to infer causality.

Descriptive Studies

The weakest of all study designs, these studies simply describe the health status of a population or of a number of subjects. Case series are an example of descriptive studies, and are simply descriptions of cases, without a comparison group. They can be useful for monitoring unusual patients, and in generating hypotheses for future study. One example is Teicher and colleagues’ 1990 case series of six patients who developed suicidal ideation on fluoxetine, which informed future studies that ultimately led to black box warnings for all antidepressants.2 Case series can also be misleading, though, as in the early 1980s when physicians began describing male homosexuals with depressed immune systems. The use of amyl nitrate–based sexual stimulants was a suspected cause, and studies on the effects of amyl nitrates on the immune system were under way when HIV was discovered.

Ecological Studies

In these types of studies, groups of individuals are studied as a whole, and the overall occurrence of disease (outcome) is correlated with the aggregate level of exposure to a risk factor. The groups being studied can be differentiated by geographical region or by other criteria, such as school, workplace, or clinic. Data are usually collected at different times for different reasons, and do not include data on individuals. Ecological studies are helpful for generating hypotheses, and are generally inexpensive and not time-consuming (since they are often based on data that are routinely published, such as death and disease rates, per capita income, religious affiliation, or food consumption). However, these studies are limited in showing causality because of the lack of individual data, the temporal ambiguity of the data (it is not known if a given risk factor precedes the outcome, for example), and problems with using data in an aggregate form to generalize about individuals. One such study that helped generate a helpful hypothesis included an intercountry comparison of prevalence rates for coronary artery disease (CAD); it showed that CAD was highest in those countries with the highest mean serum cholesterol values. This eventually led to more rigorous studies that confirmed a causal link between cholesterol levels and CAD.

Cross-sectional Studies

Cross-sectional studies examine individuals and determine their case status and risk factor exposures at the same time. Outcome rates between those with exposure can then be compared to those without. Data are collected by surveys, laboratory tests, physical measurements, or other procedures, and there is no follow-up or other longitudinal component. Cross-sectional studies are also called prevalence studies (more precisely, they are point prevalence studies), and, as with ecological studies, are relatively inexpensive and are useful for informing future research. They also aid in public health planning (e.g., determining the number of hospital beds needed) and generating more specific hypotheses around disease etiology by looking at specific risk factors.

As with the previously discussed study types, linking outcome and exposure is problematic. Although the data are collected for individuals, a person’s exposure status may differ from when the disease actually began and when the study was conducted. To illustrate, if smokers tend to quit smoking and start exercising once diagnosed with lung cancer, a cross-sectional study looking at these factors would systematically underestimate the link between smoking and lung cancer, and suggest a link between exercise and lung cancer. Another problem with cross-sectional studies is that point prevalence rates are affected both by the rate at which the outcome develops and by the chronicity of the outcome. For instance, if a given disease has a longer time course in men than in women but identical incidence rates, the point prevalence rate in men would be higher than in women.

Case-Control Studies

In case-control studies, subjects are selected based on whether they have the outcome (case) or not (control), and their exposures are then determined by looking backward in time. For this reason, they are also called retrospective studies, since they rely on historical records or recall. This type of study design is appropriate for rare diseases or for those with long latencies, and they can also be used to study possible risk factors. Problems with case-control studies include recall bias (which occurs if cases and controls recall past exposures differently) and difficulty in selecting controls. Ideally, one wants controls who are exactly matched to the cases in all other exposures except for the risk factor in question (Table 61-3). Thus, controls should be matched for a variety of factors, for example, gender, socioeconomic status (SES), smoking status (unless that is what is being studied), and alcohol use. For case-control studies, an odds ratio is used to determine whether the outcome is more likely in those with the exposure, or in those without one, and it is an approximation of their relative incidence rates.

Table 61-3 Association Between Risk Factors and Outcome in Case-Control and Cohort Studies

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Cohort Studies

In a cohort study, a group of healthy individuals is identified and followed over time to see who develops the outcomes (diseases) of interest and who do not. Exposures to risk factors are assessed over time, and so the sequence between exposure and outcome can be determined, as well as the relationship between different exposures and outcomes. Because neither the subjects nor the researchers know whether and who will develop which outcomes, bias in measuring exposures is avoided. Disadvantages include high cost (both in terms of manpower and time), potential for loss to follow-up, and inefficiency in studying rare diseases. In cohort studies, the association between outcome and exposure is expressed as relative risk, the ratio between the incidence rates in those with the risk factor and those without.

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Examples of cohort studies include the Framingham Heart Study (which has followed generations of residents from Framingham, Massachusetts) and the Nurses Health Study (which has followed a national sample of nurses with annual questionnaires). One classic study in Britain followed 35,445 British physicians and found that among smokers, the incidence of lung cancer was 1.30 per 1,000, but only 0.07 for nonsmokers.3 The relative risk was therefore 1.30/0.07 = 18.6, indicating that smokers had more than 18 times the risk of developing lung cancer than nonsmokers.

Randomized Controlled Trials

Randomized controlled trials (RCTs) are a type of cohort study in which the exposure is controlled by the researchers. Like standard cohort studies, a population who has yet to develop the outcome(s) of interest is defined; then, unlike standard cohort studies, the subjects are randomly assigned to different exposures. In these trials, the exposure is usually a treatment, such as a medication, or an intervention, such as counseling or a behavioral program. Those randomized to nonexposure may receive a placebo, or treatment as usual for the community. Multiple outcomes can be studied, from cessation of psychosis, reduction of depressive or anxious symptoms, to side effects or other adverse outcomes. RCTs, also known as experimental studies, are the gold standard of epidemiological research.

RCTs retain all the advantages of standard cohort studies, but because the exposure is randomized, the causal link between the exposure and the outcome is much stronger. Disadvantages are the same as for other cohort studies, but also include ethical issues around assigning subjects to treatment or nontreatment, as well as issues around adequate blinding and creation of appropriate placebo controls. Many times in psychiatric research, blinding is impossible, as with studies of psychotherapy, and defining an adequate placebo or “nonexposure” is difficult.

DEVELOPMENT OF ASSESSMENT TOOLS

Case Definition

In 1972, Cooper and colleagues4 published a United States/United Kingdom study that showed high variability in the diagnosis of psychotic disorders. It highlighted the need for having explicit operational criteria for case identification. The development of such diagnostic criteria with the publishing of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM-III) in 1980 represented a notable step toward increasing the reliability and validity of psychiatric diagnoses.

Standardized Instruments for Case Assessment

The clinical interview is generally used to diagnose psychiatric illness. However, differences in personal styles and theoretical frameworks, among other factors, can affect the process and conclusions of a psychiatric interview. To increase interrater reliability, a number of standardized interview instruments have been developed. The first was the Present State Examination (PSE), initially used in the International Pilot Study of Schizophrenia sponsored by the World Health Organization (WHO). The PSE was designed for use by psychiatrists or experienced clinicians, however, so its use in larger epidemiological studies was impractical. In 1978, epidemiologists at the National Institute of Mental Health (NIMH) began developing a comprehensive diagnostic instrument for large-scale, epidemiological studies that could be administered by either laypeople or clinicians. The result was the Diagnostic Interview Schedule (DIS), which used the then newly published DSM-III (1980), and elements of other research instruments, including the PSE, the Renald Diagnostic Interview (RDI), the St. Louis criteria, and the Schedule for Affective Disorders and Schizophrenia (SADS). The DIS has been used extensively in the United States and many other countries for surveys of psychiatric illness. Over time, the DIS has undergone revisions, first to incorporate DSM-III-R and then DSM-IV diagnoses. The WHO and the NIMH have also jointly developed the Composite International Diagnostic Interview (CIDI) that is structurally similar to the DIS and provides both ICD-10 and DSM-IV diagnoses.

CONTEMPORARY STUDIES IN PSYCHIATRIC EPIDEMIOLOGY

The Baseline National Comorbidity Survey (NCS)

The NCS, conducted between 1990 and 1992, was the first national survey of mental disorders in the United States. Face-to-face structured diagnostic interviews were administered by nonclinicians to a representative sample of all people living in households within the continental United States. The 8,098 NCS respondents were selected from over 1,000 neighborhoods in over 170 counties distributed over 34 states, and assessed with a modified CIDI.

The most important CIDI modifications involved the use of diagnostic stem questions, which were a small number of initial questions to assess core features of psychiatric disorders. Follow-up questions would only be asked when the subject responded positively. Another innovation of the NCS was the use of a two-phase clinical interview design for patients with evidence of schizophrenia or other nonaffective psychoses. Because prior studies had shown that these types of patients could not provide reliable self-reports, they were reinterviewed and diagnosed by experienced clinicians using a structured clinical interview.

In order to collect information on nonrespondents to the study, the NCS also systematically evaluated about one-third of nonrespondents using telephone interviews. Using the results of the nonresponse survey, the NCS study was able to adjust for the bias due to the lower rates of survey participation, especially among patients with anxiety disorders.

The NCS General Findings

DSM-III-R disorders were more prevalent than had been expected. About 48% of the sample reported at least one lifetime disorder, and 30% of respondents reported at least one disorder in the 12 months preceding the interview. The most common disorders were major depression and alcohol dependence, followed by social and simple phobias. As a group, substance use and anxiety disorders were more prevalent than affective disorders, with approximately one in four respondents meeting criteria for a substance use disorder in their lifetime, one in four for an anxiety disorder, and one in five respondents for an affective disorder (Table 61-4).

Table 61-4 Lifetime and 12-Month Prevalence Estimates for Psychiatric Disorders, NCS Results

  Lifetime Prevalence Estimate (%) 12-Month Prevalence Estimate (%)
Major depression 17.1 10.3
Mania 1.6 1.3
Dysthymia 6.4 2.5
Generalized anxiety disorder 5.1 3.1
Panic disorder 3.5 2.3
Social phobia 13.3 7.9
Simple phobia 11.3 8.8
Agoraphobia without panic 5.3 2.8
Alcohol abuse 9.4 2.5
Alcohol dependence 14.1 7.2
Drug abuse 4.4 0.8
Drug dependence 7.5 2.8
Antisocial personality disorder 2.8 —
Nonaffective psychosis* 0.5 0.3

* Nonaffective psychosis: schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, and atypical psychosis.

Source: Adapted from Tsuang and Tohen (2002).5

There were no differences by gender in the overall prevalence of psychiatric disorders. For individual disorders, men were more likely than women to have substance use disorders and antisocial personality disorder, whereas women were more likely than men to have anxiety and affective disorders (with the exception of mania).

NCS-Replication Survey (NCS-R)

The NCS-R was conducted between 2001 and 2003 and involved a new sample of 10,000 respondents in the same nationally representative sampling segments as the baseline NCS. Lifetime prevalence estimates of DSM-IV disorders per the NCS-R are as follows: anxiety disorders, 28.8%, mood disorders, 20.8%, impulse-control disorders, 24.8%, substance use disorders, 14.6%, and any disorder, 46.4%. First onset of mental illness was found most often in childhood to early adulthood, with one-half of all cases starting by age 14 years and three-fourths by age 24 years.

Mental Health Services Utilization

From the NCS to the NCS-R, there was no change in the overall prevalence of mental disorders, but the rate of treatment increased in the past decade. Among patients with a psychiatric disorder, 20.3% received treatment between 1990 and 1992 compared to 32.9% between 2001 and 2003 (p < 0.001). Nevertheless, most patients with mental disorders in the NCS-R study still did not receive treatment. For those who did, there was significant delay, ranging from 6 to 8 years for mood disorders and 9 to 23 years for anxiety disorders. The unmet need of mental health services has been greatest in traditionally underserved groups (including elderly persons, racial-ethnic minorities, residents of rural areas, and those with low incomes or without insurance).

The European Study of the Epidemiology of Mental Disorders (ESEMeD) Project

The ESEMeD is a cross-sectional epidemiological study, conducted between January 2001 and August 2003, that assessed the psychiatric epidemiology of 212 million noninstitutionalized adults from Belgium, France, Germany, Italy, the Netherlands, and Spain.6 Individuals were assessed in-person at their homes using computer-assisted psychiatric interview (CAPI) instruments, and data from 21,425 respondents were collected. A stratified, multistage, clustered area, probability sample design was used to analyze the data.

Epidemiology

Using DSM-IV criteria, about one in four respondents reported a lifetime history of any mental disorder. MDD, specific phobia, alcohol abuse, and dysthymia were the most common mental disorders, with estimated lifetime prevalence rates of 12.8%, 7.7%, 4.1%, and 4.1%, respectively. The lifetime prevalence of other mental disorders was low (less than 3%). About 10% of respondents were diagnosed, with any mental disorder in the 12 months preceding the diagnostic interview, MDD, and specific phobia being the most common with prevalence estimates (3.9% and 3.5%, respectively). Only 0.7% of respondents reported a history of an alcohol abuse disorder in the preceding 12 months.

Under-utilization of Health Services

The ESEMeD results suggest that the use of health services is limited among individuals with mental disorders in the European countries studied. Of the participants with a mental disorder in the preceding 12 months, only 25.7% consulted a formal health service during that period. The rates were higher for individuals with a mood disorder (36.5%) or anxiety disorder (26.1%). Among those who had used formal health services in the previous 12 months, approximately two-thirds had contacted a mental health professional. Psychotropic drug utilization was generally low (32.6%) by United States standards, with only 21.2% of those diagnosed with MDD in the preceding 12 months having received antidepressants during that period.

EPIDEMIOLOGY OF MAJOR PSYCHIATRIC DISORDERS

Schizophrenia

Epidemiology

Based on the NCS, the lifetime prevalence rate for schizophrenia is 1.4 per thousand, and 6.9 per thousand for the five nonaffective psychoses (schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, and atypical psychoses).

Risk Factors

Genetic loading is a robust risk factor for schizophrenia (Table 61-5). The prevalence of schizophrenia in a monozygotic twin of a schizophrenia patient is 50%, and 15% in a dizygotic twin. The prevalence for a child with two schizophrenic parents is 46.3%, and 12.8% for a child with one schizophrenic parent.

Table 61-5 Prevalence of Schizophrenia in Specific Populations

Population Prevalence (%)
General population 0.3
First-degree relatives of parents of schizophrenic patients 5.6
Children with one schizophrenic parent 12.8
Dizygotic twins of a schizophrenic patient 15.0
Children of two schizophrenic parents 46.3
Monozygotic twins of a schizophrenic patient 50.0

Other risk factors of schizophrenia include being a member of a lower social class, being unmarried, having birth complications, and being born during the winter months. The inverse relationship between social class and schizophrenia may be a result of social impairment and downward social drift rather than the cause of the illness. Studies have also shown that stressful life events, high levels of “expressed emotions” (critical and overprotective behavior and verbalizations toward the family member with schizophrenia), and substance use can precipitate psychotic episodes.

Bipolar I Disorder

Epidemiology

The baseline NCS reported a lifetime prevalence of 1.6% for a manic episode and a 1-year prevalence of 1.3% for a manic episode.

Risk Factors

Both Epidemiology Catchment Area (ECA) and NCS studies showed that there is no gender difference in the prevalence of bipolar disorder. Some studies conducted before 1980 showed a higher prevalence of bipolar disorder in the upper socioeconomic classes, but there has been no evidence for that in recent studies. Bipolar I disorder occurs at much higher rates in first-degree biological relatives of persons with bipolar I disorder than it does in the general population. Family, adoption, and twin studies clearly support the evidence that bipolar disorder is genetically transmitted. The lifetime risks of suffering from bipolar disorder in relatives of bipolar probands are 40% to 70% in monozygotic co-twins, 5% to 10% in first-degree relatives, and 0.5% to 1.5% in non–blood-related individuals. Individuals who are cohabitating, divorced, or never married are more likely to suffer from bipolar disorder compared to those who are married. Both the ECA and the NCS studies found that there were no race differences in the prevalence of bipolar disorder.

Co-morbidities

Individuals with bipolar disorder have a high prevalence of co-morbid psychiatric disorders. In both ECA and NCS studies, about 60% of patients with bipolar disorder had co-morbid substance abuse. Co-morbid alcoholism was three times higher than in those with depression, and substance use was 6.6 higher in those with bipolar disorder than it was in the general population.

Major Depression

Epidemiology

The baseline NCS reported a 1-year prevalence for MDD of 10.3% and a lifetime prevalence of 17.1%. Studies from different nations have reported significant variations in the prevalence of major depression. However, it is not clear whether cross-national differences in the prevalence of major depression were due to variations in risk factors or to methodological differences across studies.

Risk Factors

Risk factors for MDD include being female, having a history of depressive illness in first-degree relatives, having prior episodes of major depression or other psychiatric disorders, having a 2-week period of two concurrent depressive symptoms, and being divorced.

Depression as the Cause of Disability

Depression has been shown more likely to cause disability than are other chronic medical conditions. According to the Global Burden of Disease Study conducted by the WHO in 1990, major depression ranked as the fourth most disabling disease in the world, after lower respiratory infections, diarrhea diseases, and perinatal diseases.

Generalized Anxiety Disorder

Epidemiology

The baseline NCS reported a 1-year prevalence for generalized anxiety disorder (GAD) of 3.1% and a lifetime risk for of 5.1%.

Risk Factors

Based on the ECA study, the 1-year prevalence of GAD was significantly higher in females, African Americans, and persons under 30 years of age.

Panic Disorder

Epidemiology

The Baseline NCS reported a lifetime prevalence rate of 3.5%, and 1-year prevalence rate of 2.3% for panic disorder. In the NCS-R study, respondents were assessed for DSM-IV panic attacks, as well as for panic disorder with and without agoraphobia. Lifetime prevalence estimates were 22.7% for isolated panic without agoraphobia, 0.8% for panic attack with agoraphobia but without panic disorder, 3.7% for panic disorder without agoraphobia, and 1.1% for panic disorder with agoraphobia.

Risk Factors

Women and persons under age 65 are at higher risk of having panic disorder. The differences among Hispanics, non-Hispanic whites, and blacks are small. The only social factor related to panic disorder is a recent history of divorce or separation. Panic disorder most commonly develops in young adulthood; the mean age of presentation is about 25 years, but both panic disorder and agoraphobia can develop at any age.

Social Burdens of Panic

While the major societal burden of panic is caused by panic disorder and panic disorder with agoraphobia, isolated panic attacks also have high prevalence and meaningful role impairment. Compared to patients with common chronic medical conditions (including hypertension, diabetes, heart disease, arthritis, and chronic lung problems), patients with panic disorder were found to have lower health-related quality of life.

Alcohol Abuse and Dependence

Epidemiology

The baseline NCS reported a lifetime prevalence for alcohol abuse and alcohol dependence of 9.4% and 14.1%, respectively. The yearly prevalence rates for alcohol abuse and dependence were 2.5% and 7.2%, respectively. Alcoholism is one of the most common psychiatric disorders. The prevalence is higher among institutionalized individuals, particularly those in correctional facilities.

Risk Factors

Genetic history is an important risk factor. The risk of alcoholism in any first-degree family members of an alcoholic is increased seven times. Twin studies also support genetic transmission of alcoholism; it is higher for men and for alcohol dependence than it is for alcohol abuse. Other risk factors include male gender, younger ages, being separated or divorced, having a low educational level, and having a low occupational level and income.

Co-morbidity

In both ECA and NCS studies, alcoholics frequently had co-morbid drug abuse and dependence, antisocial persona-lity disorder (in men), mania, schizophrenia, and anxiety disorders (in women). Alcoholism is also associated with accidents, violence, cardiovascular diseases, and cirrhosis. Women who use alcohol during pregnancy may cause growth, morphologic, and neurological deficits in their offspring.

REFERENCES

1 Hennekens CH, Buring JE;1987, Little, Brown Boston 73-100

2 Teicher MH, Glod C, Cole JO. Emergence of intense suicidal preoccupation during fluoxetine treatment. Am J Psychiatry. 1990;147:207-210.

3 Doll R, Hill AB. Mortality in relation to smoking: ten years’ observations of British doctors. Br Med J. 1964;1(5395):1399-1410. 1(5396):1460-1467

4 Cooper JE, Kendell RE, Gurland BJ, et al. Psychiatric diagnosis in New York and London. London: Oxford University Press, 1972.

5 Tsuang MT, Tohen M. Textbook in psychiatric epidemiology, ed 2, New York: Wiley; 2002:343-479.

6 ESEMeE/MHEDEA 2000 Investigators. Prevalence of mental disorders in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr Scand. 2004;109(suppl 420):21-27.

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