DÄ internationalArchive9/2012Population Aging and Hospitalization for Chronic Disease in Germany

Original article

Population Aging and Hospitalization for Chronic Disease in Germany

Dtsch Arztebl Int 2012; 109(9): 151-7; DOI: 10.3238/arztebl.2012.0151

Nowossadeck, E

Background: The population of Germany is aging, i.e., the elderly currently make up an increasing percentage of the population from year to year. Furthermore, many common chronic diseases mainly affect the elderly. For these two reasons, the overall cost of health care in Germany is expected to increase. We studied the effect that population aging has had on the number of hospitalizations for major types of chronic disease in Germany since the year 2000.

Methods: This study is based on nationwide hospitalization statistics, classified by diagnosis, that were published by the German Federal Statistical Office. We analyzed data for three classes of diagnoses—malignant neoplasia, cardiovascular diseases, and diseases of the musculoskeletal system and connective tissue—which were further broken down into nine diagnostic subgroups. Changes in inpatient case numbers might be due either to population aging or to changing rates of hospitalization for individual diagnoses. We used index decomposition analysis to determine the relative influence of these two factors on changing case numbers.

Results: The author found that the aging of the population increased the number of hospitalizations for all of the diagnoses studied. This was particularly evident with respect to the large birth cohorts born in the 1920s (with the diagnosis of congestive heart failure) and in the period 1934–1944 (with the diagnoses ischemic heart disease, lung cancer, colorectal cancer, and osteoarthritis). On the other hand, changing rates of hospitalization for individual diagnoses increased the number of hospitalizations for some diagnoses (congestive heart failure, diseases of the spine and back) and decreased it for others (ischemic heart disease, cerebrovascular diseases, colorectal cancer, breast cancer).

Conclusion: The aging of the population and the changing rates of hospitalization for various diagnoses are exerting separate effects on the number of hospitalizations for chronic diseases in Germany. Predictions of hospital case numbers in the future must take both factors into account.

LNSLNS

The consequences of the aging of the population in Germany are a subject of frequent debate. Population aging means that older age groups comprise a growing segment of the population as a whole as time goes by. Over the past 10 years, the proportion of the general population made up by those aged 65 or more has increased from 16.6% to 20.7% (calculated from data in [1]). The average age rose from 41 to 43 years (1). One of the reasons for this phenomenon, alongside persistently low birthrates and increasing life expectancy, is variation in the number of children born per year (2, 3). Fluctuations result from historical events; for example, fewer children were born directly after each of the two world wars, but rates rose again in the following years (see [e1] for details). Population “waves” are generated. Table 1 shows the birth cohorts that form the “crests” and “troughs” with their present ages.

Demographic waves
Demographic waves
Table 1
Demographic waves

It seems plain that increasing demands may be placed on the health care system by the chronic diseases that are more common in the elderly.

Population aging is not a new phenomenon; on the contrary, it has been observed for many years. This leads to the question: What influence has population aging had on health care provision to date? The consequences for inpatient care can be analyzed in detail on the basis of the nationwide hospitalization statistics, classified by diagnosis, supplied by the German Federal Statistical Office. These figures have been published each year since 2000, permitting analysis of a 10-year period.

In this article I set out to show the influence of population aging on the numbers of patients treated in hospital for chronic diseases in selected diagnostic classes and subgroups since the year 2000.

Method

The data analyzed were the statistics provided by the German Federal Statistical Office (4), which break hospitalizations down by 5-year age cohort, sex, and diagnosis according to the 10th edition of the International Classification of Diseases (ICD-10), for the 10-year period from 2000 to 2009. It should be noted that hospitalization statistics count admissions, not patients. For example, a person treated several times in the same hospital will be counted anew each time.

The three diagnostic classes with the most cases were analyzed: malignant neoplasia (C00–C97, without C44), cardiovascular diseases (I00–I99), and diseases of the musculoskeletal system and connective tissue (M00–M99). Within these classes I analyzed particular diagnostic subgroups of chronic diseases that are important for inpatient care: ischemic heart disease (I20–I25), heart failure (I50), cerebrovascular diseases (I60–I69), colorectal cancer (C18–C21), lung cancer (C33–C34), breast cancer (C50), prostate cancer (C61), arthrosis (M15–M19), and dorsopathies (M40–M54).

The method of multiplicative index decomposition analysis was used to analyze the influence of aging. First, the number of hospital treatments in 2009 was divided by that in 2000. The resulting index was then multiplicatively split into two factors. The first factor quantified the change in case numbers as a consequence of change in the rate of hospitalization. The hospitalization rate depends not only on rates of incidence or prevalence, but also on other factors, such as altered “admission risks,” introduction of new diagnostic or therapeutic procedures, establishment of screening programs, and changes in coding behavior and practice, e.g., as a consequence of the introduction of the diagnosis-related groups (DRG) system for remuneration. This factor is comparable with the customary age standardization in epidemiology, in which differences related to age structure are filtered out. The second factor quantifies the influence of population aging (see eBox for details).

Methods
Methods
eBox
Methods

Results

The results of index decomposition analysis are shown in Table 2, while Figure 1 presents the age structure of all hospital cases in the years 2000 and 2009.

Number of hospital treatments (ICD-10 codes A00 to T98) by age group in 2000 and 2009: men
Number of hospital treatments (ICD-10 codes A00 to T98) by age group in 2000 and 2009: men
Figure 1
Number of hospital treatments (ICD-10 codes A00 to T98) by age group in 2000 and 2009: men
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009 (both sexes)
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009 (both sexes)
Table 2
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009 (both sexes)

The hospitalization rate for the diagnoses A00 to T98 increased by 5% from 2000 to 2009. Aging alone would have brought about an increase of 6.1%. In contrast, if only the risk had changed (with age structure constant as in 2009) the number of hospital treatments would have sunk by 1%. In the diagnostic subgroups there were particular noticeable decreases in treatments for ischemic heart disease (–26%) and colorectal cancer (–30%), while treatments for heart failure (+52%), dorsopathies (+60%), and arthrosis (+41%) showed an increasing tendency.

Division of the data by sex shows the same underlying trends for males and females, but with quantitative differences (eTable). For example, the increase in the number of treatments for heart failure was far greater in men than in women.

Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009
eTable
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009

In contrast, the two sexes show distinctly different trends for lung cancer. In men there was a slight decrease in treatments, in women a sharp increase.

The index partition showed that in the period analyzed, population aging increased the case numbers in all selected diagnostic subgroups. Particularly strong effects were observed for the diagnoses heart failure, cerebrovascular diseases, and prostate cancer. Basically, the aging effects were (sometimes much) greater in men than in women.

Equally, it could be seen that the effects of change in the hospitalization rate varied according to diagnostic subgroup. The number of treatments for ischemic heart disease went down by 36%—and for breast cancer by as much as 45%—because of the change in hospitalization rate alone. In other diagnostic subgroups, however, change in hospitalization rate increased the case numbers. This was observed particularly for dorsopathies (+51%), arthrosis (+26%), and heart failure (+24%).

Various combinations of the two factors were seen in different diagnostic subgroups:

  • Cumulative effects of the two factors were observed for arthrosis, dorsopathies, and heart failure, where the separate influences exerted by population aging and change in risk added up to an increase in case numbers of 40% or more.
  • For lung cancer and prostate cancer, the effects of aging more than compensated for the decrease in case numbers brought about by lowering of the hospitalization rate, resulting in a slight increase of around 5% in the number of cases.
  • In ischemic heart disease, cerebrovascular diseases, colorectal cancer, and breast cancer, the effects of the decreased rate of hospitalization more than compensated for those of aging, resulting in distinct decreases in case numbers of up to 41%.

Discussion

The wavelike changes in age structure brought about by population aging affect case numbers. Figure 1 shows how particular birth cohorts produce peaks and troughs that progress through the age structure of hospital cases. For example, the large number of births in the years following 1933 resulted in the peak of 60- to 65-year-olds in 2000 and the peak of 70- to 75-year-olds in 2009.

By means of index decomposition analysis, the changes in case numbers in the study period were split into two factors: an age structure-related factor, and a factor that identified the effects of the “risk” of receiving inpatient treatment, i.e., the hospitalization rate, for a given diagnosis. Each of these factors can both elevate and depress numbers of cases.

Sizable increases in case numbers were observed for the diagnostic subgroups heart failure and dorsopathies. These increases may have been the result of increasing prevalence. For example, the growth in the frequency of heart failure in the USA has been described as an epidemic (5, 6); the numbers of cases with heart failure as primary or secondary diagnosis tripled between 1979 and 2004 (7).

The strong aging effect seen in heart failure, particularly in men, was accompanied by a high average age. This corresponded to findings from analyses of hospital patients in other countries (710). The reason for the aging effect lies in the age cohort born between 1919 and 1930. This population had crossed the threshold age of 60 years, after which the risk of suffering heart failure doubles with every additional decade (11) and the hospitalization rate for this diagnosis begins to increase steeply (12). In Germany, a considerable proportion of men born in that period died in World War II (Table 1). The proportion of the population made up by this birth cohort is steadily decreasing, and they are being replaced by later-born cohorts not affected by war losses.

Altogether, population aging and the growing hospitalization rate had cumulative effects on the frequency of the diagnosis heart failure.

Only a small part of the marked increase in the numbers of cases with the diagnosis dorsopathies was attributable to aging. A far greater role was played by the increased rate of hospitalization for this diagnosis. A study from the USA shows growing prevalence rates in the population (13). Increasing prevalence could also go at least some way towards explaining the rising hospitalization rate in Germany. The diagnostic subgroup arthrosis comprised mainly arthrosis of the knee and hip, and the increase in cases was plainly an expression of the higher rates of implantation of prostheses in these joints (Table 3).

Operations for implantation of a knee- or hip-joint prosthesis
Operations for implantation of a knee- or hip-joint prosthesis
Table 3
Operations for implantation of a knee- or hip-joint prosthesis

The apparent stability of the number of cases with the diagnosis prostate cancer was illusory; a glance at the results of index decomposition analysis shows that the hospitalization rate went down, while the effects of aging increased case numbers. These effects were exerted by different age groups than was the case for heart failure; the risk of prostate cancer increases considerably from the 55- to 59-year age group onwards (14). During the study period this age was reached by those in the birth cohort 1934 to 1944, which was larger than those from the previous and following years, thus increasing the number of cases.

The number of cases with the diagnosis ischemic heart disease was dominated by opposing trends: An aging-associated increase was more than compensated by the decrease in hospital admissions. In the USA, researchers have noted a decline in the incidence of acute myocardial infarction (15), with a total fall of 24% between 1998 and 2008. Decreasing incidence has also been reported from the UK (e2). This decrease—together with the observed fall in mortality (e3)—is due to improvements in cardiovascular risk factors (16). The rates of hospitalization for acute myocardial infarction in the USA have been sinking since the mid-1990s (17). The diagnostic subgroup ischemic heart disease includes more than just myocardial infarction; however, the other diagnoses in the subgroup are all manifestations of the same underlying disease, namely arteriosclerosis of the coronary vessels. The number of patients discharged after a short hospital stay with the primary diagnosis of ischemic heart disease fell by 26% in the USA between 1997 and 2007 (18).

It can be affirmed that the mortality, incidence, and hospitalization rates have been decreasing for well over 10 years in the USA and the UK. With the exception of one analysis of mortality (e4), no studies of this nature have been carried out in Germany. It can be assumed, however, that the underlying trends are similar in this country. In this respect, the decrease in the number of cases with the diagnosis ischemic heart disease would (at least partially) reflect a decrease in incidence.

The situation is similar for cerebrovascular diseases. Falling rates of incidence and mortality have been reported from several countries (1922), together with decreasing rates of hospital treatment (18, 19, 23, 24).

As outlined above, the rate of hospitalization for a given diagnosis may depend not only on the incidence but also on other factors, as shown for cerebrovascular diseases. One such factor is the introduction of the DRG system for remuneration and the associated changes in coding behavior.

The most important disease in the diagnostic subgroup cerebrovascular diseases is stroke, for which there are several ICD codes. Of the 391 000 inpatient cases with the diagnosis cerebrovascular diseases in the year 2000 in Germany, 136 000 were coded as “cerebral infarction” (I63) and 112 000 as “stroke, not specified as hemorrhage or infarction” (I64). Together, these two codes made up 63% of all diagnoses in the code group I60 to I69. The DRG coding instructions specify that I64 is to be assigned only in cases where the codes I60 to I63 do not apply (e5). This ruling was put into practice gradually, as shown by the development of the age-standardized rates of treatment for the two principal diagnoses I63 and I64 (Figure 2). The treatment rates of cases coded with I64 are decreasing sharply, while those coded with I63 are on the increase. There has thus been a shift from one ICD code to another.

Age-standardized treatment rates for the diagnoses ICD-10 I63 and I64 in 2000 and 2009 (cases per 100 000 inhabitants, standardized by age, old European standard population)
Age-standardized treatment rates for the diagnoses ICD-10 I63 and I64 in 2000 and 2009 (cases per 100 000 inhabitants, standardized by age, old European standard population)
Figure 2
Age-standardized treatment rates for the diagnoses ICD-10 I63 and I64 in 2000 and 2009 (cases per 100 000 inhabitants, standardized by age, old European standard population)

An interesting constellation can be observed on sex-specific analysis of the treatments for the diagnosis lung cancer. In both sexes the case numbers were increased by aging effects. The large birth cohort 1934 to 1944 reached the age of 65 to 80 years, at which the rate of hospitalization for lung cancer is greatest, during the study period. The hospitalization rate changed differently in men and women. In men it decreased considerably, while in women it increased. This finding corresponds with falling incidence rates for lung cancer in men and rising incidence in women (25). This contrasting development can be attributed to changes in smoking behavior in previous decades (e6, e7).

The reduction in the number of cases with the diagnosis colorectal cancer resulted from the decreasing rate of hospitalization. The latter parallels the (age-standardized) incidence of colorectal cancer, which rose in the final years of the 20th century but has been falling since (14). This decrease was partially compensated by the effects of aging.

Breast cancer showed a similar development. The reduction in hospitalization rate—which has also been described elsewhere, e.g., in Switzerland (e8)—was partially compensated by aging.

Limitations

When interpreting the data it is important to realize that changes in case numbers do not necessarily reflect changes in incidence or prevalence at the population level. Patients may be admitted for hospital treatment several times during a calendar year. However, the present analysis of data from various hospital entities shows that the nationwide hospitalization statistics, classified by diagnosis, can be used to identify trends in inpatient care that correspond to trends at population level.

Conclusions

The aging-related changes in case numbers are determined particularly by the large birth cohorts in the years 1934 to 1944 (diagnostic subgroups ischemic heart disease, lung cancer, colorectal cancer, and arthrosis) and 1948 to 1958, with the low-birthrate years of 1945 to 1947 (diagnostic subgroups cerebrovascular diseases, prostate cancer, and breast cancer) sandwiched in between. Those born in the years after World War I have strongly affected the development with regard to heart failure.

The strong aging effects in men arise above all from the fact that surviving members of the generation with a sex imbalance owing to the deaths in World War II are steadily becoming fewer. The following cohorts have a more balanced sex distribution and thus a higher proportion of men than the war generation. Another reason is a more pronounced increase in male compared to female life expectancy in recent years (e9).

The changes in hospital case numbers caused by aging can therefore be attributed to particular age groups and thus to particular birth cohorts. The wavelike evolution of age structure—the succession of “crests” and “troughs”—affects inpatient care in different ways at different times. It can be assumed that population aging will continue to have this kind of undulating effect on inpatient treatments of various chronic diseases in the future. Moreover, the hospitalization rate according to diagnostic subgroup has changed noticeably.

Predictions of future developments in hospital treatment must therefore take account of both population aging and possible changes in risk. Many prognoses in the field of health care are based exclusively on population aging (see [e10] for a selection). Needing to consider possible changes in risk places more strenuous demands on prognoses and makes their preparation more complex. The present research article is intended as a contribution to enabling such predictions.

Conflict of interest statement
The author declares that no conflict of interest exists.

Manuscript received on 27 June 2011, revised version accepted on 25 October 2011.

Translated from the original German by David Roseveare.

Corresponding author
Dipl. oec. Enno Nowossadeck
Abt. für Epidemiologie und Gesundheitsberichterstattung
Robert Koch-Institut
General-Pape-Str. 62–66
13302 Berlin, Germany
NowossadeckE@rki.de

@For eReferences please refer to:
www.aerzteblatt-international.de/ref0912

eBox and eTable:
www.aerzteblatt-international.de/12m0151

1.
Statistisches Bundesamt: Bevölkerung und Erwerbstätigkeit. Bevölkerungsfortschreibung. Wiesbaden: Fachserie 1, Reihe 1.3; 2009a.
2.
Dinkel RH: Was ist demographische Alterung? Der Beitrag der demographischen Parameter zur demographischen Alterung in den alten Bundesländern seit 1950. In: Häfner H, Staudinger UM (eds.): Was ist Alter(n)? Neue Antworten auf eine scheinbar einfache Frage. Berlin: Springer 2008; 97–117.
3.
Schwarz K: Bestimmungsgründe der Alterung einer Bevölkerung – Das deutsche Beispiel. Z Bevolkerungswiss 1997; 22: 347–59.
4.
Statistisches Bundesamt: Diagnosedaten der Krankenhäuser ab 2000. (Thematische Recherche: Krankheiten/ Gesundheitsprobleme, Krankheiten allgemein; Dokumentart Tabellen) (http://www.gbe-bund.de/). (last accessed on 19.10.2011).
5.
Giamouzis G, Kalogeropoulos A, Georgiopoulou V, et al.: Hospitalization epidemic in patients with heart failure: Risk factors, risk prediction, knowledge gaps, and future directions. Journal of Cardiac Failure 2011; 17: 54–75. CrossRef MEDLINE
6.
Gheorghiade M, Sopko G, De Luca L, et al.: Navigating the cross-roads of coronary artery disease and heart failure. Circulation 2006; 114: 1202–13. CrossRef MEDLINE
7.
Fang J, Mensah GA, Croft JB, et al.: Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol 2008; 52: 428–34. CrossRef MEDLINE
8.
Nicol ED, Fittal B, Roughton M, et al.: NHS heart failure survey: a survey of acute heart failure admissions in England, Wales und Northern Ireland. Heart 2008; 94: 172–7. CrossRef MEDLINE
9.
Najafi F, Dobson AJ, Jamrozik K: Recent changes in heart failure hospitalisations in Australia. European Journal of Heart Failure 2007; 9: 228–33. CrossRef MEDLINE
10.
McMurray J, McDonagh T, Morrison CE, et al.: Trends in hospitalization for heart failure in Scotland 1980–1990. European Heart
Journal 1993; 14: 1158–62. CrossRef MEDLINE
11.
Mohacsi P, Moschovitis G, Tanner H, et al.: Prevalence, increase, and costs of heart failure. Heart and Metabolism 2001; 14 : 9–16.
12.
Cleland JGF, Gemmell I, Khand A, et al.: Is the prognosis of heart failure improving? European Journal of Heart Failure 1999; 1: 229–41. CrossRef MEDLINE
13.
Freburger JK, Holmes GM, Agans RP, et al.: The rising prevalence of chronic low back pain. Arch Intern Med 2009; 169: 251–8. CrossRef MEDLINE
14.
Robert Koch-Institut (eds.): Verbreitung von Krebserkrankungen in Deutschland. Entwicklung der Prävalenzen zwischen 1990 und 2010. Berlin: RKI; 2010.
15.
Yeh RW, Sidney S, Chandra M, et al.: Population trends in the incidence and outcomes of acute myocardial infarction. New England Journal of Medicine 2010; 362: 2155–65. CrossRef MEDLINE
16.
Hardoon SL, Whincup PH, Lennon LT, et al.: How much of the recent decline in the incidence of myocardial infarction in british men can be explained by changes in cardiovascular risk factors? Evidence from a prospective population-based study. Circulation 2008; 117: 598–604. CrossRef MEDLINE PubMed Central
17.
Fang J, Alderman MH, Keenan NL, et al.: Acute myocardial
infarction hospitalization in the United States, 1979 to 2005. The American Journal of Medicine 2010; 123: 259–66. CrossRef MEDLINE
18.
Roger VL, Go AS, Lloyd-Jones DM, et al.: Heart disease and stroke statistics–2011 update: A report from the American Heart Association. Circulation 2011; 123: e18–e209. CrossRef MEDLINE
19.
Lewsey JD, Jhund PS, Gillies M, et al.: Age- and sex-specific trends in fatal incidence and hospitalized incidence of stroke in Scotland, 1986 to 2005. Circulation: Cardiovascular Quality and Outcomes 2009; 2: 475–83. CrossRef MEDLINE
20.
Carandang R, Seshadri S, Beiser A, et al.: Trends in incidence, lifetime risk, severity, and 30-day mortality of stroke over the past 50 years. JAMA 2006; 296: 2939–46. CrossRef MEDLINE
21.
Sutton CJ, Marsden J, Watkins CL, et al.: Changing stroke mortality trends in middle-aged people: an age-period-cohort analysis of
routine mortality data in persons aged 40 to 69 in England. Journal of Epidemiology and Community Health 2010; 64: 523–9. CrossRef MEDLINE
22.
Rothwell PM, Coull AJ, Giles MF, et al.: Change in stroke incidence, mortality, case-fatality, severity, and risk factors in Oxfordshire, UK from 1981 to 2004 (Oxford Vascular Study). Lancet 2004; 363: 1925–33. CrossRef MEDLINE
23.
Béjot Y, Aouba A, de Peretti C, et al.: Time trends in hospital-
referred stroke and transient ischemic attack: Results of a 7-year nationwide survey in France. Cerebrovascular Diseases 2010; 30: 346–54. CrossRef MEDLINE
24.
Fang J, Alderman MH, Keenan NL, et al.: Declining US stroke
hospitalization since 1997: National Hospital Discharge Survey, 1988–2004. Neuroepidemiology 2007; 29: 243–9. CrossRef MEDLINE
25.
Robert Koch-Institut (eds.) und die Gesellschaft der epidemiologischen Krebsregister in Deutschland e.V. (eds.): Krebs in Deutschland 2005/2006. Häufigkeiten und Trends. 7th edition. Berlin: RKI 2010.
e1.
Bundesinstitut für Bevölkerungsforschung (eds.): Bevölkerung. Daten, Fakten, Trends zum demographischen Wandel in Deutschland. Wiesbaden: 2008.
e2.
Davies AR, Grundy E, Nitsch D, et al.: Constituent country inequalities in myocardial infarction incidence and case fatality in men and women in the United Kingdom, 1996–2005. Journal of
Public Health 2011; 33: 131–8. CrossRef MEDLINE
e3.
Ford ES, Ajani UA, Croft JB, et al.: Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. New England Journal of Medicine 2007; 356: 2388–98. MEDLINE
e4.
Müller-Riemenschneider F, Andersohn F, Willich S: Trends in age-standardised and age-specific mortality from ischaemic heart disease in Germany. Clinical Research in Cardiology 2010; 99: 545–51. CrossRef MEDLINE
e5.
Zaiß AH: DRG: Verschlüsseln leicht gemacht. Deutsche Kodierrichtlinien mit Tipps, Hinweisen und Kommentierungen. Köln: Deutscher Ärzte-Verlag 2009.
e6.
Donington J, Le Q-T, Wakelee H: Lung cancer in women: Exploring sex differences in susceptibility, biology, and therapeutic response. Clinical Lung Cancer 2006; 8: 22–9. CrossRef MEDLINE
e7.
Deppermann KM: Epidemiologie des Lungenkarzinoms. Internist 2011; 52: 125–9. MEDLINE
e8.
Lippuner K, Grifone S, Schwenkglenks M, et al.: Comparative trends in hospitalizations for osteoporotic fractures and other
frequent diseases between 2000 and 2008. Osteoporos Int 2011; Epub Date: 28 May 2011. MEDLINE
e9.
Robert Koch-Institut (eds.): Sterblichkeit, Todesursachen und regionale Unterschiede. Berlin: RKI 2011.
e10.
Nowossadeck E. Morbiditätsprognosen auf Basis von Bevölkerungsprognosen. Welchen Beitrag kann ein Gesundheitsmonitoring leisten? Bundesgesundheitsblatt – Gesundheitsforschung – Gesundheitsschutz 2010; 53: 427–34. CrossRef MEDLINE
e11.
Statistisches Bundesamt. Gesundheit. Diagnosedaten der Patienten und Patientinnen in Krankenhäusern. Fachserie 12, Reihe 6. Wiesbaden; 2009.
e12.
Rinne H: Wirtschafts- und Bevölkerungsstatistik. Erläuterungen – Erhebungen – Ergebnisse. München, Wien: R. Oldenbourg Verlag; 1996.
Department of Epidemiology and Health Reporting, Robert Koch Institute, Berlin: Dipl. oec. Nowossadeck
Number of hospital treatments (ICD-10 codes A00 to T98) by age group in 2000 and 2009: men
Number of hospital treatments (ICD-10 codes A00 to T98) by age group in 2000 and 2009: men
Figure 1
Number of hospital treatments (ICD-10 codes A00 to T98) by age group in 2000 and 2009: men
Age-standardized treatment rates for the diagnoses ICD-10 I63 and I64 in 2000 and 2009 (cases per 100 000 inhabitants, standardized by age, old European standard population)
Age-standardized treatment rates for the diagnoses ICD-10 I63 and I64 in 2000 and 2009 (cases per 100 000 inhabitants, standardized by age, old European standard population)
Figure 2
Age-standardized treatment rates for the diagnoses ICD-10 I63 and I64 in 2000 and 2009 (cases per 100 000 inhabitants, standardized by age, old European standard population)
Key messages
Demographic waves
Demographic waves
Table 1
Demographic waves
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009 (both sexes)
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009 (both sexes)
Table 2
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009 (both sexes)
Operations for implantation of a knee- or hip-joint prosthesis
Operations for implantation of a knee- or hip-joint prosthesis
Table 3
Operations for implantation of a knee- or hip-joint prosthesis
Methods
Methods
eBox
Methods
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009
eTable
Index decomposition analysis: changes in inpatient treatment of selected chronic diseases between 2000 and 2009
1. Statistisches Bundesamt: Bevölkerung und Erwerbstätigkeit. Bevölkerungsfortschreibung. Wiesbaden: Fachserie 1, Reihe 1.3; 2009a.
2. Dinkel RH: Was ist demographische Alterung? Der Beitrag der demographischen Parameter zur demographischen Alterung in den alten Bundesländern seit 1950. In: Häfner H, Staudinger UM (eds.): Was ist Alter(n)? Neue Antworten auf eine scheinbar einfache Frage. Berlin: Springer 2008; 97–117.
3. Schwarz K: Bestimmungsgründe der Alterung einer Bevölkerung – Das deutsche Beispiel. Z Bevolkerungswiss 1997; 22: 347–59.
4.Statistisches Bundesamt: Diagnosedaten der Krankenhäuser ab 2000. (Thematische Recherche: Krankheiten/ Gesundheitsprobleme, Krankheiten allgemein; Dokumentart Tabellen) (http://www.gbe-bund.de/). (last accessed on 19.10.2011).
5.Giamouzis G, Kalogeropoulos A, Georgiopoulou V, et al.: Hospitalization epidemic in patients with heart failure: Risk factors, risk prediction, knowledge gaps, and future directions. Journal of Cardiac Failure 2011; 17: 54–75. CrossRef MEDLINE
6.Gheorghiade M, Sopko G, De Luca L, et al.: Navigating the cross-roads of coronary artery disease and heart failure. Circulation 2006; 114: 1202–13. CrossRef MEDLINE
7. Fang J, Mensah GA, Croft JB, et al.: Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol 2008; 52: 428–34. CrossRef MEDLINE
8. Nicol ED, Fittal B, Roughton M, et al.: NHS heart failure survey: a survey of acute heart failure admissions in England, Wales und Northern Ireland. Heart 2008; 94: 172–7. CrossRef MEDLINE
9. Najafi F, Dobson AJ, Jamrozik K: Recent changes in heart failure hospitalisations in Australia. European Journal of Heart Failure 2007; 9: 228–33. CrossRef MEDLINE
10. McMurray J, McDonagh T, Morrison CE, et al.: Trends in hospitalization for heart failure in Scotland 1980–1990. European Heart
Journal 1993; 14: 1158–62. CrossRef MEDLINE
11. Mohacsi P, Moschovitis G, Tanner H, et al.: Prevalence, increase, and costs of heart failure. Heart and Metabolism 2001; 14 : 9–16.
12. Cleland JGF, Gemmell I, Khand A, et al.: Is the prognosis of heart failure improving? European Journal of Heart Failure 1999; 1: 229–41. CrossRef MEDLINE
13. Freburger JK, Holmes GM, Agans RP, et al.: The rising prevalence of chronic low back pain. Arch Intern Med 2009; 169: 251–8. CrossRef MEDLINE
14. Robert Koch-Institut (eds.): Verbreitung von Krebserkrankungen in Deutschland. Entwicklung der Prävalenzen zwischen 1990 und 2010. Berlin: RKI; 2010.
15. Yeh RW, Sidney S, Chandra M, et al.: Population trends in the incidence and outcomes of acute myocardial infarction. New England Journal of Medicine 2010; 362: 2155–65. CrossRef MEDLINE
16. Hardoon SL, Whincup PH, Lennon LT, et al.: How much of the recent decline in the incidence of myocardial infarction in british men can be explained by changes in cardiovascular risk factors? Evidence from a prospective population-based study. Circulation 2008; 117: 598–604. CrossRef MEDLINE PubMed Central
17. Fang J, Alderman MH, Keenan NL, et al.: Acute myocardial
infarction hospitalization in the United States, 1979 to 2005. The American Journal of Medicine 2010; 123: 259–66. CrossRef MEDLINE
18. Roger VL, Go AS, Lloyd-Jones DM, et al.: Heart disease and stroke statistics–2011 update: A report from the American Heart Association. Circulation 2011; 123: e18–e209. CrossRef MEDLINE
19. Lewsey JD, Jhund PS, Gillies M, et al.: Age- and sex-specific trends in fatal incidence and hospitalized incidence of stroke in Scotland, 1986 to 2005. Circulation: Cardiovascular Quality and Outcomes 2009; 2: 475–83. CrossRef MEDLINE
20. Carandang R, Seshadri S, Beiser A, et al.: Trends in incidence, lifetime risk, severity, and 30-day mortality of stroke over the past 50 years. JAMA 2006; 296: 2939–46. CrossRef MEDLINE
21. Sutton CJ, Marsden J, Watkins CL, et al.: Changing stroke mortality trends in middle-aged people: an age-period-cohort analysis of
routine mortality data in persons aged 40 to 69 in England. Journal of Epidemiology and Community Health 2010; 64: 523–9. CrossRef MEDLINE
22. Rothwell PM, Coull AJ, Giles MF, et al.: Change in stroke incidence, mortality, case-fatality, severity, and risk factors in Oxfordshire, UK from 1981 to 2004 (Oxford Vascular Study). Lancet 2004; 363: 1925–33. CrossRef MEDLINE
23. Béjot Y, Aouba A, de Peretti C, et al.: Time trends in hospital-
referred stroke and transient ischemic attack: Results of a 7-year nationwide survey in France. Cerebrovascular Diseases 2010; 30: 346–54. CrossRef MEDLINE
24. Fang J, Alderman MH, Keenan NL, et al.: Declining US stroke
hospitalization since 1997: National Hospital Discharge Survey, 1988–2004. Neuroepidemiology 2007; 29: 243–9. CrossRef MEDLINE
25. Robert Koch-Institut (eds.) und die Gesellschaft der epidemiologischen Krebsregister in Deutschland e.V. (eds.): Krebs in Deutschland 2005/2006. Häufigkeiten und Trends. 7th edition. Berlin: RKI 2010.
e1. Bundesinstitut für Bevölkerungsforschung (eds.): Bevölkerung. Daten, Fakten, Trends zum demographischen Wandel in Deutschland. Wiesbaden: 2008.
e2. Davies AR, Grundy E, Nitsch D, et al.: Constituent country inequalities in myocardial infarction incidence and case fatality in men and women in the United Kingdom, 1996–2005. Journal of
Public Health 2011; 33: 131–8. CrossRef MEDLINE
e3.Ford ES, Ajani UA, Croft JB, et al.: Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. New England Journal of Medicine 2007; 356: 2388–98. MEDLINE
e4.Müller-Riemenschneider F, Andersohn F, Willich S: Trends in age-standardised and age-specific mortality from ischaemic heart disease in Germany. Clinical Research in Cardiology 2010; 99: 545–51. CrossRef MEDLINE
e5. Zaiß AH: DRG: Verschlüsseln leicht gemacht. Deutsche Kodierrichtlinien mit Tipps, Hinweisen und Kommentierungen. Köln: Deutscher Ärzte-Verlag 2009.
e6. Donington J, Le Q-T, Wakelee H: Lung cancer in women: Exploring sex differences in susceptibility, biology, and therapeutic response. Clinical Lung Cancer 2006; 8: 22–9. CrossRef MEDLINE
e7. Deppermann KM: Epidemiologie des Lungenkarzinoms. Internist 2011; 52: 125–9. MEDLINE
e8.Lippuner K, Grifone S, Schwenkglenks M, et al.: Comparative trends in hospitalizations for osteoporotic fractures and other
frequent diseases between 2000 and 2008. Osteoporos Int 2011; Epub Date: 28 May 2011. MEDLINE
e9.Robert Koch-Institut (eds.): Sterblichkeit, Todesursachen und regionale Unterschiede. Berlin: RKI 2011.
e10. Nowossadeck E. Morbiditätsprognosen auf Basis von Bevölkerungsprognosen. Welchen Beitrag kann ein Gesundheitsmonitoring leisten? Bundesgesundheitsblatt – Gesundheitsforschung – Gesundheitsschutz 2010; 53: 427–34. CrossRef MEDLINE
e11.Statistisches Bundesamt. Gesundheit. Diagnosedaten der Patienten und Patientinnen in Krankenhäusern. Fachserie 12, Reihe 6. Wiesbaden; 2009.
e12. Rinne H: Wirtschafts- und Bevölkerungsstatistik. Erläuterungen – Erhebungen – Ergebnisse. München, Wien: R. Oldenbourg Verlag; 1996.