What is incidence



(I) What is incidence?

Incidence is a measure of disease that allows us to determine a person's probability of being diagnosed with a disease during a given period of time. Therefore, incidence is the number of newly diagnosed cases of a disease. An incidence rate is the number of new cases of a disease divided by the number of persons at risk for the disease. If, over the course of one year, five women are diagnosed with breast cancer, out of a total female study population of 200 (who do not have breast cancer at the beginning of the study period), then we would say the incidence of breast cancer in this population was 0.025. (or 2,500 per 100,000 women-years of study)

What is prevalence?

Prevalence is a measure of disease that allows us to determine a person's likelihood of having a disease. Therefore, the number of prevalent cases is the total number of cases of disease existing in a population. A prevalence rate is the total number of cases of a disease existing in a population divided by the total population. So, if a measurement of cancer is taken in a population of 40,000 people and 1,200 were recently diagnosed with cancer and 3,500 are living with cancer, then the prevalence of cancer is 0.118. (or 11,750 per 100,000 persons)

What is morbidity?

Morbidity is another term for illness. A person can have several co-morbidities simultaneously. So, morbidities can range from Alzheimer's disease to cancer to traumatic brain injury. Morbidities are NOT deaths. Prevalence is a measure often used to determine the level of morbidity in a population.

(II) Incidence vs. prevalence

Incidence should not be confused with prevalence, which is a measure of the total number of cases of disease in a population, rather than the rate of occurrence of new cases. Thus, incidence conveys information about the risk of contracting the disease, whereas prevalence indicates how widespread the disease is. Prevalence is the ratio of the total number of cases in the total population. Prevalence can also be measured with respect to a relevant subgroup of a population.

For example, consider a disease that takes a long time to cure, and that was spread widely in 2002, but whose spread was arrested in 2003. This disease will have a high prevalence and a high incidence in 2002; but in 2003 it will have a low incidence, although it will continue to have a high prevalence because it takes a long time to cure so the fraction of affected individuals remains high. In contrast, a disease that has a short duration may have a low prevalence and a high incidence. When the incidence is approximately constant for the duration of the disease, prevalence is approximately the product of disease incidence and average disease duration, so prevalence = incidence x duration. The importance of this equation is the relation between prevalence and incidence, for example when the incidence goes up then the prevalence must go up as well.

When studying the etiology of a disease, it is better to analyze incidence rather than prevalence, since prevalence mixes in the duration of a condition, rather than providing a pure measure of risk.

Incidence is a measure of the risk of developing some new condition within a specified period of time. Although sometimes loosely expressed simply as the number of new cases during some time period, it is better expressed as a proportion or a rate with a denominator.

Incidence proportion (also known as cumulative incidence) is the number of new cases within a specified time period divided by the size of the population initially at risk. For example, if a population initially contains 1,000 non-diseased persons and 28 develop a condition over two years of observation, the incidence proportion is 28 cases per 1,000 persons, i.e. 2.8%.

Incidence rate

The incidence rate is the number of new cases per population in a given time period. When the denominator is the sum of the person-time of the at risk population, it is also known as the incidence density rate or person-time incidence rate. In the same example as above, the incidence rate is 14 cases per 1000 person-years, because the incidence proportion (28 per 1,000) is divided by the number of years (two). Using person-time rather than just time handles situations where the amount of observation time differs between people, or when the population at risk varies with time. Use of this measure implicitly implies the assumption that the incidence rate is constant over different periods of time, such that for an incidence rate of 14 per 1000 persons-years, 14 cases would be expected for 1000 persons observed for 1 year or 50 persons observed for 20 years.

When this assumption is substantially violated, such as in describing survival after diagnosis of metastatic cancer, it may be more useful to present incidence data in a plot of cumulative incidence over time, taking into account loss to follow-up, using a Kaplan-Meier Plot.

Consider the following example. Say you are looking at a sample population of 225 people, and want to determine the incidence rate of developing HIV over a 10 year period. At the beginning of the study (t=0) you find 25 cases of existing HIV. You follow-up at 5 years (t=5 yrs) and find 20 new cases of HIV. You again follow-up at the end of the study (t=10 yrs) and find 30 new cases. If you were to measure prevalence you would simply take the total number of cases (25 + 20 + 30 = 75) and divide by your sample population (225). So prevalence would be 75/225 = 0.33 or 33%. This tells you how widespread HIV is in your sample population, but little about the actual risk of developing HIV. To measure incidence you must take into account how many years each person contributed to the study, and when they developed HIV. When it is not known exactly when a person develops the disease in question, epidemiologists frequently use the actuarial method, and assume it was developed at a half-way point between follow-ups. For example, at 5 yrs you found 20 new cases, so you assume they developed HIV at 2.5 years, thus contributing (20 * 2.5) 50 person-years. At 10 years you found 30 new cases. These people did not have HIV at 5 years, but did at 10, so you assume they were infected at 7.5 years, thus contributing (30 * 7.5) 225 person-years. That is a total of (225 + 50) 275 person years so far. You also want to account for the 150 people who never had or developed HIV over the 10 year period, (150 * 10) contributing 1500 person-years. That is a total of (1500 + 275) 1775 person-years. Now take the 50 new cases of HIV, and divide by 1775 to get 0.028, or 28 cases of HIV per 1000 population, per year. In other words, if you were to follow 1000 people for one year, you would see 28 new cases of HIV. This is a much more accurate measure of risk than prevalence.

Summary

• 'Incidence' is a specific measure of disease burden in the population and must be distinguished from the prevalence of disease, although the two measures are related.

• Comparing incidence in different groups of people, either by division (ratios) or subtraction (differences), gives us an idea of the effect of (possibly causal) factors on the development of disease.

• Knowing the incidence of a disease is extremely useful for health service planning.

• Measures of incidence are not available for many diseases because the studies required to collect the information (cohort or longitudinal studies) are expensive and difficult to conduct.

In epidemiology, the prevalence of a disease in a statistical population is defined as the total number of cases of the disease in the population at a given time, or the total number of cases in the population, divided by the number of individuals in the population. It is used as an estimate of how common a condition is within a population over a certain period of time. It helps physicians or other health professional understand the probability of certain diagnoses and is routinely used by epidemiologists, health care providers, government agencies and insurers.

Prevalence may also be expressed in terms of subgroups of the population based on different denominator data.

For example, the prevalence of obesity among American adults in 2001 was estimated by the U. S. Centers for Disease Control (CDC) at approximately 20.9%. In plain English, "prevalence" simply means "extent", but in scientific English it means "proportion" (typically expressed as a percentage).

Prevalence is distinct from incidence. Prevalence is a measurement of all individuals affected by the disease within a particular period of time, whereas incidence is a measurement of the number of new individuals who contract a disease during a particular period of time.

To illustrate, a long term disease that was spread widely in a community in 2002 will have a high prevalence at a given point of 2003 (assuming it has a long duration) but it might have a low incidence rate during 2003 (i.e. lots of existing cases, but not many new ones in that year). Conversely, a disease that is easily transmitted but has a short duration might spread widely during 2002 but is likely to have a low prevalence at any given point in 2003 (due to its short duration) but a high incidence during 2003 (as many people develop the disease). As such, prevalence is a useful parameter when talking about long lasting diseases, such as HIV, but incidence is more useful when talking about diseases of short duration, such as chickenpox.

Statistical Measures

Morbidity Frequency Measures

Prevalence Rates

Prevalence (Prevalence Rates)

Prevalence is the proportion of people in a population who have a particular disease at a specified point in time, or over a specified period of time.

• The numerator includes not only new cases, but also old cases (people who remained ill during the specified point or period in time). A case is counted in prevalence until death or recovery occurs.

• This makes prevalence different from incidence, which includes only new cases in the numerator.

Prevalence is most useful for measuring the burden of chronic diseases such as tuberculosis, malaria and HIV in a population. The formula for calculating prevalence is:

 

INCIDENCE AND PREVALENCE RATES

Incidence rate is defined as the number of new cases of a disease in a population over a specified time period.  Prevalence rate measures the number of people in a population who have the disease at a given time.  The formulas for incidence and prevalence rates are:

[pic]

[pic]

While incidence depends only on the number of new cases during a certain time period, prevalence depends on two factors: the number of people who have been ill in the past (previous incidence) and the duration of illness.  Diseases with a long duration will have a larger prevalence than incidence rate, and diseases with a short duration (because of recovery or death) will have a low prevalence.

It would appear intuitively that, if incidence reflects new cases and prevalence reflects existing cases, there should be some quantifiable relationship between them.  A rough estimate of this relationship follows:

Prevalence = Incidence x Duration

This estimate should be used with caution as it will be affected by a number of factors including seasonality of the disease, wide variability in duration of disease, and susceptibility of affected persons to death from other diseases.

Uses of Incidence and Prevalence

Incidence and prevalence rates serve different purposes. Prevalence is important in determining workload needs, measuring the burden of chronic disease, and monitoring public health programs for chronic conditions.  Incidence rates are used for studying causes of both acute and chronic disease, since they are direct indicators of disease risk.  In contrast, high prevalence does not necessarily signify high risk; it may be a result of an increase in life span for individuals with certain diseases.

Health Service Planning

Data on incidence are useful in health service planning. It is important to know the number of new cases of disease which will arise in a population in the future to be able to plan services required and how to deal with them. For example, in India, approximately 2.6 million cataract operations are done each year. In spite of this huge effort, the number of people blind with cataract in the country is increasing. Table 2 shows the results of projections from a population-based study of incidence of blinding cataract in Central India.

This study is very important because it shows that in order for eye health care services to have a realistic chance of dealing with cataract blindness in India, there should be a minimum of at least 4 million cataract operations done each year. This has implications, not only for allocation of health care resources, but also for the realization that more research needs to be done to find ways of preventing or slowing down the progression of cataract.

Epidemic versus Endemic

• Endemic occurrence is defined as "the constant presence of a disease or infectious agent within a given geographical area.  In contrast, epidemic refers to an incidence of illness in excess of normal expectancy in a population.  There is no general rule about the number of cases that must exist for an outbreak to be considered an epidemic.  This level of normal expectancy varies for different diseases and different circumstances.  [pic]

|Navigating the Health Care System |

|Understanding Research Results: The Difference between Prevalence and Incidence |

|This article was first published in Global Campaign News, the newsletter of the Global Campaign for Microbicides. It |

|explains the difference between prevalence and incidence by examining recent research results on microbicides. |

|Several recent articles in Global Campaign News have made reference to “lower than anticipated HIV incidence” during |

|effectiveness trials.  Here, we take a closer look at what incidence means, why so many trials are seeing lower incidence |

|rates than expected, and what the implications are for current and future trials. |

|  |

|Prevalence and incidence are two related, but different measures that describe the distribution of disease in a particular |

|population.  Prevalence is a measure of the number of total cases of a disease in a population at a certain moment in time.|

|For example, among women presenting for screening during a feasibility study at the Mtubatuba site in South Africa, the |

|prevalence of pre-existing HIV infection was 50% (number of HIV positive women/number of total women being screened). |

|  |

|The incidence of disease is the number of new cases occurring in a population over a defined time interval.  Incidence |

|measures how quickly one sees new cases of infection or disease, whereas prevalence describes how many people total in a |

|population are affected, regardless of when they become infected or sick.  At the Mtubatuba site quoted above with a |

|prevalence of 50% among screened women, the HIV incidence among those women enrolled was 12.6 infections per 100 person |

|years.  This means, among every 100 women they followed, 12 to 13 people became infected in the course of one year.  [Note:|

|the term "person-years" is a convention from epidemiology that allows researchers to annualize estimates of infection from |

|individuals followed up for different lengths of time]. |

|  |

|Thus it is possible to have situations of high prevalence but low incidence and vice versa. For example, the incidence of |

|new cases of diabetes in a population may be only 1 per 1000 people per year (or 0.01% annually) but the prevalence of |

|diabetes in a population could be 8 percent.  The .01% incidence estimate includes only people who were newly diagnosed |

|with diabetes this year whereas the prevalence estimate includes these people as well as those already living with diabetes|

|who were diagnosed in the past.  |

|  |

|Likewise is it possible to have pockets of high HIV incidence (e.g. high rates of new HIV infections among intravenous drug|

|users) in settings with an overall lower rate of HIV prevalence (e.g. less than 1 percent overall). Significantly, most |

|measures of infection quoted by UNAIDS, National AIDS control authorities, and in the media are measures of HIV prevalence,|

|not incidence.   |

|  |

|Regrettably, there is no cheap, easy way to derive accurate estimates of HIV incidence.  The most reliable way to establish|

|incidence is to enroll HIV negative women in a cohort study and evaluate, using repeat HIV tests, the number of HIV |

|infections that occur over time.  For example, the Microbicide Development Programme conducted cohort “feasibility” studies|

|to determine the incidence of HIV in the different populations being considered for inclusion in their current phase III |

|trial of two concentrations of Pro2000. They found HIV incidence rates ranging from a low of 3.5 per 100 person-years in |

|Mwanza, Tanzania to a high of 12.6 per 100 person-years in Mtubatuba, South Africa. |

|Cohort studies, however, are expensive to run and delay the start date of a potential trial for at least 6 months to a year|

|while incidence data is being collected.  In the interest of speed, some trial sponsors have tried to estimate the likely |

|incidence in their participant population based on past prevalence estimates or data from previous studies about the |

|observed ratio of prevalent to incident cases.  As recent trial closures demonstrate, however, such approaches can yield |

|misleading results.  The field is currently discussing the pros and cons of different strategies for estimating the |

|incidence of HIV. |

|  |

|Researchers in the Ghana Savvy trial estimated, for example, that there would be at least five infections per 100 person |

|years of follow up in the placebo group (an HIV incidence rate of 5%), and that they would observe at least 66 incident |

|infections during the trial. However, halfway through the study, an interim analysis found that only 17 total |

|sero-conversions had occurred: nine on placebo and eight on Savvy. This translates into an HIV incidence of only 1.0% for |

|Savvy and 1.1% for the placebo. |

|This incidence was dramatically lower than anyone anticipated, and the trial was closed on the recommendation of the Data |

|Safety and Monitoring Board. Given the low rate of incident HIV infection observed, the DSMB concluded it would not be |

|possible to recruit enough participants to answer the question of Savvy’s effectiveness. |

|  |

|There are several possible explanations for the lower than expected incidence rates in effectiveness trials.  The first is |

|that the original estimate of HIV incidence could have been inaccurate, especially if not based on cohort data.  As noted |

|above, accurate estimates of incidence are difficult to come by.  Also incidence can shift dramatically over time, |

|especially in populations where men and women migrate frequently. |

|  |

|High rates of pregnancy among trial participants may also have contributed to lower rates of HIV acquisition.  According to|

|a presentation by Dr. Wes Rountree at M2006, women in the Ghana Savvy trial who discovered they were pregnant changed their|

|sexual behavior in ways that reduced their risk of HIV.  They engaged in sex less often, had fewer unprotected sex acts, |

|and fewer partners.  These behaviour changes together with high rates of pregnancy could partially account for the low rate|

|of HIV incidence observed in the Savvy study. |

|  |

|Finally, just by participating in a prevention trial of this sort, a participant’s risk of HIV acquisition may be |

|diminished.  Participants are getting the best available safer sex counseling and support, which is reinforced with every |

|clinic visit.  Participants also receive treatment for other sexually transmitted infections, which in turn indirectly |

|decreases their risk of acquiring HIV.  Thus, a good HIV prevention study itself can dramatically lower HIV incidence among|

|participants.  This is great news for the trial communities, but makes it more difficult to determine whether the candidate|

|product is effective. |

|  |

|To address this issue, recruitment strategies are being modified to increase the likelihood of enrolling women at highest |

|risk of HIV infection. Since younger women are often at the highest risk for new HIV infection, current efficacy trials are|

|focusing on recruiting younger  participants. Trial groups and sponsors  are also working  to explore the  use of new |

|assays and surveillance techniques to better estimate HIV incidence during screening and/or through pilot studies, in order|

|to arrive at estimates of HIV incidence that are as accurate as possible.    |

|  |

|For more information, and sources for this article, see:  |

|Smart, T.  Microbicides 2006: Are the microbicide clinical efficacy studies big enough? NAM’s Wednesday, May |

|10, 2006.  |

|Skoler, S., Peterson, L., Cates, W. Our Current Microbicide Trials: Lessons Learned and To Be Learned, The Microbicide |

|Quarterly, Jan-March 2006, Vol 4. No. 1.  |

|Written by: The Global Campaign for Microbicides |

|Last revised: Nov 2006 |

| |

|< Return to Navigating the Health Care System Overview |

 

Prevalence and Costs of Chronic Disease in a Health Care System Structured for Treatment of Acute Illness1

1. James H. Thrall, MD

+ Author Affiliations

1. 1From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114. Received October 14, 2004; revision requested November 10; revision received December 9; accepted December 28. Address correspondence to the author (e-mail: jthrall@).

 

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© RSNA, 2005

Chronic illnesses account for 70% of deaths and for the expenditure of over 75% of direct health care costs in the United States, according to the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services (1). Direct costs are now estimated at over $1.5 trillion (2). Indirect costs of chronic diseases, in the form of lost productivity and nonreimbursed personal costs, add several more hundreds of billions of dollars each year. In a landmark study published in 1996, Hoffman et al (3) reported that in 1990 90 million people in the United States lived with a chronic disease or condition and 39 million people had more than one such condition. Extrapolating from these and other data, the Centers for Disease Control and Prevention estimated that as many as 25 million Americans have a chronic condition that is disabling (1). Although the literature does not support a single uniform definition for chronic disease, recurrent themes include the non–self-limited nature, the association with persistent and recurring health problems, and a duration measured in months and years, not days and weeks (3,4).

Since the prevalence of chronic diseases increases with age, increased longevity is a major contributor to the high and steadily rising prevalence of chronic diseases and the aggregate costs of care for people with them. At the turn of the 19th century, the life expectancy at birth for people in the United States was just over 47 years (5). One century later, life expectancy had increased to 77 years, an astonishing 30-year, or 64%, increase. The number of people in the country over 65 years of age increased from 3 million to 35 million (6).

Substantial contributions to increased longevity have come from advances in medicine, especially reduced infant mortality and the treatment and prevention of infectious diseases. Other important contributions have come from advances in public health measures, including improved sanitation and purification of water supplies. With people living longer, many diseases and conditions such as arthritis; cardiovascular ailments; and neurodegenerative diseases, including Alzheimer disease, have time to manifest.

Moreover, many diseases that were fatal in the past, such as type I diabetes, acquired immunodeficiency syndrome, and a number of cancers, have been converted to chronic conditions with prolonged courses and resulting in substantially improved life expectancy. This phenomenon has also contributed to the increase in the prevalence of chronic disease. Some of the diseases that have been converted from acutely fatal to manageable chronic conditions are very costly to treat over their full courses.

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Prevalence of Chronic Disease

Input data for estimates of the prevalence of chronic disease come from a variety of sources, including data from population surveys and reviews of insurance claims, which typically record the reasons for which patients have sought care and/or the diagnoses associated with the care episode. The ninth revision of the International Classification of Diseases, Clinical Modification (ICD-9-CM) (7) is probably the most widely used recording instrument in health status surveys. ICD-9-CM coding is also used widely in the processing of insurance claims. Since each disease or reason for care delivery has associated ICD-9-CM codes, the respective prevalence of diseases can be readily determined for any given data source that uses the ICD-9-CM system.

The National Health and Nutrition Examination Survey (8) and the Medical Expenditure Panel Survey (formerly, National Medical Expenditure Survey) (9) are two important surveys that provide nationally representative information about disease prevalence and costs for the entire population. Data on Medicare claims provide extensive information for persons over 65 years of age. Insurance data for younger populations are fragmented between carriers and are more difficult to access.

The sample sizes required for adequate statistical sampling and the complexity and cost of obtaining survey data preclude comprehensive annual surveys or studies of insurance claims data. As a consequence many, if not most, published estimates of disease prevalence are based on extrapolations from periodically available survey data to account for changes over time in factors such as population growth and changing age distribution.

Given the number of challenges in obtaining timely high-quality input data and the need to extrapolate to fill in time gaps, estimates of disease prevalence should probably be regarded as broadly indicative and directional rather than precise. Nonetheless, the magnitude of the population burden of chronic disease is eye opening and encompasses every organ system.

The American Heart Association, drawing on a number of data sources, estimated that a total of 64.4 million Americans have one or more types of cardiovascular disease (10). Hypertension alone, defined as a systolic pressure above 140 mm Hg and/or diastolic pressure greater than 90 mm Hg, accounts for afflictions in 50 million people (10). Coronary heart disease affects 13.2 million people, manifesting as acute myocardial infarction in 7.8 million and as chest pain syndromes in 6.8 million (some people experience both, which accounts for the higher sum of the components vs the overall prevalence) (10). Five million people live with congestive heart failure, and 4.8 million have strokes each year (10). Cardiovascular diseases, including stroke, accounted for around 40% of all deaths in the United States in 2001 and were considered a contributing factor in another 20% (11).

Another category of chronic disease with a very high prevalence is arthritis, which is estimated to afflict 50–70 million Americans (12,13). The lower end of the range includes people with physician-diagnosed arthritis, and the higher end comes from population surveys in which people were asked to report symptoms of joint disease. Disability due to arthritis and back pain is substantial (1). Arthritis and back pain account for over one-third of all non–mental illness–related disability among persons over the age of 15 years in the United States (1). A report issued by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (12) noted that the number of Americans with some form of documented arthritis will increase by 50% by the year 2020, owing to the aging of the population, and that there will be an increasing burden not just to individuals but to the economy.

Other numerically important chronic diseases are asthma (14), with an estimated 15 million individuals affected, and diabetes (15,16), with an estimated 17–18 million people affected, including almost 6 million who have not been formally diagnosed. Both of these diseases are associated with substantial disability.

Chronic neurodegenerative diseases are also widely prevalent. These conditions are often challenging to diagnose correctly and are among the most difficult to manage because of their effect on both patients and families. An estimated 4 million Americans have Alzheimer disease (17), a condition that robs people of their ability to remember and reason and therewith steals their human identity. Parkinson disease (18) affects 1.5 million people in the United States.

Blindness and hearing loss are chronic conditions that both will increase with the aging of the population. A longitudinal study by Lee et al (19) in 20 325 representative Medicare beneficiaries demonstrated an increasing prevalence of three major eye diseases—macular degeneration, glaucoma, and diabetic retinopathy—over a 9-year period of study. Half of the surviving cohort had at least one of the diseases.

Mental disorders are also often difficult to diagnose or even to classify as chronic or acute. The Surgeon General of the United States estimated (20) that 19% of the population manifests evidence of a mental disorder within a given year, 3% have addictive and mental disorders, and 6% have addictive disorders alone, for a total of approximately 30% of the total population. Severe depression is a major cause of disability and lost days from work.

Direct and Indirect Costs Associated with Chronic Disease

In the study by Hoffman et al (3), the costs of caring for patients with chronic diseases were projected for the year 1990. The estimated total cost for care of patients with chronic diseases was $659 billion, divided between direct costs of $425 billion and indirect costs of $234 billion, a ratio of just under 2:1.

Direct costs of care were determined (3) by reviewing insurance payments and other payments to individual providers and provider organizations for care episodes and payments for the purchase of prescribed medicines and other medical equipment or supplies. All costs of care for a person with chronic disease were considered chronic care costs, whatever the actual reason for the care. The encompassing nature of this definition has not been explicitly noted in subsequent references to this work, although, by any measure, the costs for the care of chronic disease would still be staggering, even with a more restrictive definition.

Indirect costs, although real, are more difficult to determine and are highly dependent on definition and even philosophy. Hoffman et al (3) defined them broadly in terms of morbidity and mortality costs. Morbidity costs were defined as lost economic output from days of missed work and imputed costs for home care by family members or others not in the labor force. Mortality costs also encompassed projected economic losses, assuming that an individual would have remained gainfully employed over his or her otherwise estimated life expectancy, for work absent the cause of death.

Chronic Disease and the Structure of the U.S. Health Care System

There are a number of features of the current health care system in the United States that impede efficient high-quality care of patients with chronic disease or who are at risk for developing a chronic disease. Most important, despite the high prevalence of chronic diseases, the health care system in the United States is still fundamentally designed to deliver ad hoc episodic care to patients with acute illness or acute manifestations of chronic illness. Acute care hospitals dominate the organizational structure of the health care system and account for over 30% of health care expenditures (2).

The heavy inpatient focus of hospitals is not cost-effective for the management of chronic disease; from a hospital perspective, chronic disease is too often managed as a series of admissions for acute exacerbations. Data for diseases such as asthma and congestive heart failure (4,14,21–23) clearly indicate that cost savings and quality improvements come from what happens over the long term outside of hospitals to prevent acute episodes from occurring. The health care system, as currently structured, is at its best when an acutely ill patient presents for care of an acute illness or condition or when a patient with a diagnosed condition requires an elective procedure or other therapy.

Hospitals too often treat their outpatient activities as secondary adjuncts to their core inpatient missions or as “loss leaders” for high-revenue admissions and have not come close to redesigning their activities for the contemporary needs of patients with chronic disease through adoption of disease-management programs and comprehensive information systems. Outpatient facilities built on hospital campuses can be burdened with the high cost structure and overhead of associated inpatient facilities, which discourages robust investment in lower-margin outpatient care. Hospitals are only now investing in electronic medical records systems that encompass outpatient, as well as inpatient, care for patients. Such systems are necessary for the efficient organization and tracking of long-term care of chronic disease.

Many physicians’ practices are also still organized, in large part, in relationship to hospitals on the basis of employment or staff privileges, especially in academic and metropolitan settings. Practice patterns for these physicians will be highly influenced by hospital dominance of the delivery system for the foreseeable future. Forty percent of physicians in community practice settings are in solo practice. They are fragmented organizationally and have no structural basis through which to deliver coordinated care: no common medical records system or way to track disease progress together. Hospitals and large physician groups are the only nongovernmental provider organizations, in the aggregate, with access to the substantial amounts of capital resources required for creating new care paradigms to manage chronic disease, including investment in information technology.

A second major set of issues deals with payment systems for health care. The dominant payment mechanism, even many decades after the concepts of managed health care and capitation were introduced, continues to be fee-for-service payment. The units of service are typically defined by the Current Procedural Terminology (CPT) (24) system maintained by the American Medical Association.

The structure of fee-for-service payments for CPT-coded procedures does not come close to adequately rewarding efforts by physicians, hospitals, or other health care organizations for prevention programs, including counseling and patient education. The CPT-based fee-for-service system does not allow payment for many of the specific services known to improve quality and reduce overall costs, such as home monitoring of patients with congestive heart failure. Insurance companies will pay for treatment of pulmonary edema in a hospital but not for a phone call to see how a patient is doing at home. Gruman and Gibson (23) noted, “Insurance, for example, will pay for the amputation of a limb for diabetes related gangrene but not for the sustained diabetes self-management and monitoring that can lessen the probability of needing more costly interventions later.” The majority of reimbursable CPT codes are for services rendered in the treatment of acute illness or of acute exacerbations and complications of chronic conditions.

In the current fee-for-service reimbursement system, providers who manage chronic diseases effectively risk losing out twice: first, because the payment system typically does not compensate them for the extra costs associated with more effective management and, second, because the savings (due to more effective management) from reduced hospitalizations and reduced treatment of long-term complications remain with the insurance company and are not passed on to the provider who did the extra work to provide better care. These are powerful financial disincentives to providers and hospitals that earn their revenue service by service and admission by admission. Likewise, fee-for-service discourages the kind of teamwork between physicians that is desirable in caring for many patients with complex problems. Pay-for-performance systems are beginning to address the need for payers to share savings from more effective care with providers and to reward providers for achieving better results.

The ability to use insurance-premium dollars wisely to maintain health through preventive services and the ability to reduce long-term health care costs by reducing the likelihood of future illness would appear to be a reasonable strategy for insurance companies and is one of the fundamental assumptions underlying the concept of health maintenance organizations (HMOs). In capitation arrangements with HMOs, providers receive contracted payments and accept risk for the costs of delivering needed care. In theory, more money spent up front on prevention should be cost-effective and reduce downstream costs. However, in the report of the National Committee for Quality Assurance, The State of Health Care Quality: 2004 (22), major gaps between best practices and health plan performance continued to be observed, including gaps in indicators for chronic disease. For control of high blood pressure, the average performance among the health care plans surveyed was only 62%. At the 90th percentile, control was achieved in 71% of patients. The National Committee for Quality Assurance asserts (22) that if all Americans with hypertension received care at even the 90th percentile of performance, 15 000–26 000 deaths annually could be prevented and sick days could be reduced by more than 21 million.

Why, then, has there not been more interest by insurance companies in providing and even insisting on more preventive services for patients with chronic disease and implementation of comprehensive disease-management programs by providers, and why have HMOs not scored better on the National Committee for Quality Assurance surveys? While the complete answer is complex, one obvious point is that the high turnover of clients from year to year is a disincentive for insurance companies, as well as for HMOs, to invest in preventive care. Simply put, if a client changes insurance coverage, some organization downstream is more likely to benefit from the salutary effects of the investment in prevention, so why spend the money? The Kaiser Family Foundation survey (25) of employer health benefits for 2004 reported that 56% of firms that offer health care benefits shopped for a new plan and that 31% of those changed insurance carriers. This is hardly a prescription for long-term investment by an insurance company, but it raises the interesting question of why employers are not pushing harder from their side to realize long-term benefits of better preventive care. New employer-initiated pay-for-performance plans such as Bridges to Excellence are beginning to address this point.

Radiology and Chronic Disease

Imaging services are obviously of direct importance in the diagnosis and long-term management of many chronic diseases and conditions. Cancer care is heavily structured to involve imaging, and multi–detector row computed tomography (CT) is opening new doors in many areas, including the heart and vascular system. Imaging is literally the guiding hand for diagnosis of musculoskeletal disease.

At the same time, relatively little investment has been made in the study of the optimum use of imaging or how to integrate imaging into evidence-based disease-management programs of the kind highlighted by the Institute of Medicine in its landmark publication, Crossing the Quality Chasm: A New Health System for the 21st Century (4). Radiologists will need to address these issues in a much more robust way than in the past because of increasing pressures to reduce overutilization of all medical services, especially rapidly growing ones such as imaging. How often should imaging be applied? which method should be used? and how much radiation is acceptable? are all questions germane to the care of people with chronic disease who are likely to need imaging services over a period of time.

The establishment of the American College of Radiology Imaging Network (ACRIN) (26) under the direction of Bruce Hillman, MD, is an important step in the direction of strengthening technology-assessment research in imaging. ACRIN has initiated clinical trials aimed at establishing the efficacy and, therefore, the role of emerging imaging methods. Current trials address questions of major interest, such as the role of digital mammography versus screen-film mammography and that of CT colonography versus conventional colonoscopy. A major trial is underway to assess the costs and benefits of radiography versus those of lung CT imaging for lung cancer. Broad participation by radiologists benefits ACRIN by increasing patient recruitment into trials and is highly encouraged.

Restructuring the Health System for Care of Chronic Disease

The issue of chronic disease is not going to go away; quite the opposite, the number of affected people will increase, as will costs. Chronic diseases are especially hard on the elderly because they result in disability and diminished quality of life (6). Much of the knowledge is in hand to achieve better outcomes and reduce costs in the care of people with chronic diseases: Better prevention, more patient education, involvement in self-help and empowerment, systematic use of evidence-based disease-management programs, closer adherence to best practices, information systems with patient-focused electronic records to track disease progress and therapy, and a team approach by physicians and other care givers are all proven winners (14,16,21–23). The health care system in the United States, now dominated by inpatient facilities, needs to recast its basic mission as that of keeping people out of hospitals through life-long health care programs and the prevention of complications of chronic diseases.

Paying for the care required in unnecessary episodes of acute illness because of gaps in preventive care will reward failure and increase overall health care costs and may create disincentives for individual providers to optimize long-term care of people with chronic conditions. Adherence to proven best practices must be a commitment made by every physician and provider organization. The payment system must be rebuilt to reward providers for keeping people as healthy as possible and out of hospitals as much as possible, whether these are accomplished through pay-for-performance plans or other approaches. Until the financial incentives are aligned to compensate providers for doing that, it will not happen enough. The reimbursement system must recognize and reward the extra work and infrastructure investments necessary to achieve improved quality in the care of patients with chronic disease. All stakeholders in the health care system—including patients, providers, payers, the public, and the government—should now recognize the growing imperative of caring for people with chronic diseases and should come together to design better structures for lifelong continuity of care, with emphasis on evidence-based practice and disease prevention.

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Footnotes

• Author stated no financial relationship to disclose.

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