To estimate incidence and prevalence of NIDDM, sufficiently large empirical studies with adequate follow-up are preferred. To conduct such a study is difficult and expensive, and consequently most studies are too small to yield reliable estimates. This limitation is true in particular for incidence estimates, of which there are only a few, and they are of limited use because of small numbers when age-specific rates are required. As a result, the estimates vary widely.
As an alternative to direct observation, this paper describes an indirect method to estimate the incidence of NIDDM. It is based on the prevalence estimate from a previous study. Added to that is information about the excess mortality of diabetics as compared with nondiabetics, 16 and theoretical knowledge about the relation between incidence, prevalence, and mortality.
The relation between epidemiologic variables is not simple. The oft-cited relation that prevalence equals incidence times duration is an oversimplification. 19 With rising age, true duration is lower because of increasing general mortality and thus declining life expectancy, whereas duration estimated by age-specific prevalence divided by incidence is increasing because of the accumulation of prevalence cases.
The most convenient way to model a disease process is to assume independence from all other causes of death. Under that assumption, the mortality rate of the disease can be obtained from the disease-specific mortality as reported by the national statistics bureau, and an “all-other-causes” mortality rate can easily be calculated. 20 An independence assumption, although convenient, is not appropriate in the case of diabetes mellitus; in addition to being coded as a cause of death itself [International Classification of Diseases, 9th revision (ICD-9) 250], diabetes also acts as a risk factor for a range of causes of death, such as cardiovascular diseases. We therefore constructed a diabetes-related mortality rate with relative risks for mortality of diabetics, as well as the prevalence of diabetes. Because the diabetes-related mortality rate includes all excess mortality for diabetics, it is independent from all other mortality. Therefore, it can be used, unlike the specific mortality restricted to diabetes mellitus (ICD-9 250), in an IPM model that assumes independence from the non-diabetes-related mortality.
The incidence is jointly determined by the shape of the prevalence curve and the rate of mortality selection. From Figures 2 and 3, it can be deduced that the former is by far the more important influence; the 95% confi-dence intervals of the relative risk for total mortality, which determine the rate of mortality selection, result in a very narrow range. The rate of mortality selection is determined by the excess absolute risk, which, for most of the age range involved, remains small. Only for the higher ages among men does this absolute risk become big enough to have a sizable impact. Therefore, the incidence estimate is most sensitive to the prevalence used.
The incidence estimates comply well with the available empirical incidence estimates from four Dutch incidence studies, but have upper and lower bounds that are considerably more narrow than the observed range of values. Three of the four empirical studies reported a (much) lower incidence for the open-ended (oldest) age groups as compared with our estimates. This difference may be due in part to the calculation of the average age for the oldest age groups using a life table. Institutionalized people, who, on average, will be older than the noninstitutionalized, were not included in the empirical studies, but are in the life table. The calculated average age will therefore tend to be higher than the actual one in the studies, causing the data points to be plotted too far to the right.
An assumption implicit in using cross-sectional data is that there is no time trend in incidence and survival. With strong recent time trends in incidence or survival, currently observed cross-sectional prevalence will not be at their equilibrium values. For example, if incidence has recently increased, it will take several years for prevalence to reach the new, higher-equilibrium value. Because the incidence estimate is based on prevalence, it will reflect conditions prevailing in the past, so our estimates may diverge from current incidence.
The IPM model used in this study ignores remission; an extra parameter would be required, of which the value is uncertain. One study evaluated the diabetic status of patients after 8 years of follow-up and reported a remission of 14%. 23 There are reasons to suspect misclassification at baseline to be responsible for at least some of these cases. We assumed remission to be mostly temporary and too small to affect the incidence estimates substantially.
Our resulting incidence estimates are internally consistent with the prevalence and mortality estimates, which is not necessarily a property of empirical estimates, even when done in the same population. For example, the huge increases in prevalence (+40%) simultaneous with large decreases in incidence (–20%), as reported for Manitoba between 1986 and 1991, are difficult to explain. 24,25
The estimate is the incidence necessary to explain observed prevalence and estimated diabetes-related mortality. This calculation allows us to assess the impact on diabetes prevalence and excess mortality of interventions or trends that influence diabetes incidence, even if diabetes incidence itself is difficult to observe. The method is general enough to be used for other diseases for which incidence assessment is a problem, such as dementia. It can also be applied for specific ethnic groups with high prevalences of NIDDM, for example, to assess the potential effectiveness of interventions in such groups. It is in this field, of intervention impact assessment, where the method may prove most useful.
We thank Paul J. van der Maas (Department of Public Health, Erasmus University Rotterdam) and Edith J. Feskens (National Institute for Public Health and the Environment, Bilthoven) for their helpful comments on an earlier draft. This research was funded by The Netherlands Heart Foundation and The Netherlands Institute of Health Sciences.
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