text:
Đề thi thử môn tiếng Anh THPT Quốc gia năm 2016 - Đề số 10_du học trong tầm tay sưu tầm
Thứ Fri,
SECTION A (8 points):
Mark the letter A, B, C, or D on your answer sheet to indicate the words whose pronunciation is different from the others in each of the following questions.
Question 1: A. works B. stops C. washes D. speaks
Question 2: A. amount B. astound C. mouthful D. shoulder
Mark the letter A, B, C, or D on your answer sheet to indicate the word that differs from the rest in the position of the main stress in each of the following questions.
Question 3. A. apply B. visit C. appear D. attend
Question 4. A. curriculum B. kindergarten C. contaminate D.conventional
Question 5. A. suspicious B. marvelous C. physical D. argument
Mark the letter A, B, C or D on your answer sheet to indicate the correct answer that best completes each unfinished sentence.
Question 6. This is a valuable ................ chair which dates back to the eighteenth century.
A. traditional B. old-fashioned C. antique D. ancient
Question 7. Look! The yard is wet. It______ last night.
A. must have rained B. couldn't have rained
C. must rain D. should have rained
Question 8. Last year she earned _______ her brother.
A. twice as much as B. twice more than
C. twice as many as D. twice as more as
Question 9. The girls and flowers ______ he painted were vivid.
A. whose B. that C. which D. who
Question 10. When his alarm went off, he shut it off and slept for ________15 minutes.
A. other B. others C. another D. the others
Question 11. Is it necessary that I _____ here tomorrow?
A. am being B. were C. be D. would be
Question 12. I saw him hiding something in a_______ bag.
A. small plastic black B. black small plastic
C. small black plastic D. plastic small black
Question 13.This is the __________spot on which the first pilgrims landed in America.
A. historic B. historical C. history D. historicity
Question 14. She earned a degree in Physics and went on _________ another degree in Mathematics.
A. taking B. to take C. taken D. being taken
Question 15. Those trousers are far too big. Why don't you have them___?
A. taken on B. taken in C. taken over D. taken out
Question 16. So little_______about mathematics that the lecture was completely beyond me. A. I have known B. I knew C. do I know D. did I know
Question 17. I gave the waiter a $50 note and waited for my _________.
A. change B. supply C. cash D. cost
Question 18. I read the contract again and again _________ avoiding making spelling mistakes.
A. in terms of B. by means of C. with a view to D. in view of
Question 19. Van Gogh suffered from depression ______ by overwork and ill-health.
A. brought on B. coming about C. taken up D. pull through
Question 20. He had spent ____________ time writing an essay on his childhood.
A. a few B. a large number of C. a great deal of D. many
Question 21. ________he study harder, he will do better in the exam.
A. Should B. If C. When D. Unless
Question 22. When there are small children around, it is better to put breakable ornaments out of _________.
A. reach B. hand C. place D. hold
Question 23. Harry:" ______________" Kate: "Yes. I'd like to buy a computer."
A. Do you look for something? B. Good morning. Can I help you?
C. Excuse me. Do you want to buy it? D. Can you help me buy something?
Question 24. Tom: " Sorry, I forgot to phone you last night." -Mary: "________"
A. I have nothing to tell you. B. Oh. Poor me!
C. Never mind! D. You was absent - minded.
Mark the letter A, B, C, or D on your answer sheet to indicate the word(s) CLOSEST in meaning to the underlined word(s) in each of the following questions.
Question 25. Whenever Susan invites us to dinner, she customarily prepares a lot of delicious food.
A. naturally B. necessarily C. usually D. certainly
Question 26. It is hoped that illiteracy will soon be eradicated in our country as more and more people are struggling against it.
A. evolving B. plunging C. starting D. striving
Question 27. His new yatch is certainly an ostentatious display of his wealth.
A. beautiful B. showy C. large D. expensive
Mark the letter A, B, C, or D on your answer sheet to indicate the underlined part that needs correction in each of the following questions.
Question 28. After writing it, the essay must be duplicated by the student himself and handed
in to the department secretary before the end of the month.
Question 29. All of the cities in Texas, San Antonio is probably the most picturesque.
Question 30. My cousin composes not only the music, but also sings the songs for the maior
Broadway musicals.
Question 31. The Indians of the southwestern United States are famous for their beautiful art
work, especially handmade jewelry cast from silver, carved from stones, or decorations with
B C D
beads and feathers.
Question 32. Although no country has exactly the same folk music as those of any other, it is
significant that similar songs exist among widely separated people.
Read the following passage and mark the letter A, B, C, or D on your answer sheet to indicate the correct word or phrase that best fits each of the numbered blanks from 33 to 42.
Earth is the only place we know of in the universe that can support human life. (33) ______ human activities are making the planet less fit to live on. As the western world carries on consuming (34)_______ of the world's resources while half of the world's population do so just to stay (35)_______ we are rapidly destroying the only resource we have (36)________ which all people can survive and prosper. Everywhere fertile soil is (37)______ built on or washed into the sea. Renewable resources are exploited so much that they will never be able to recover (38)______. We discharge pollutants into the atmosphere without any thought of the consequences. As a (39)______, the planet's ability to support people is being reduced at the very time when rising human numbers and consumption are making increasingly heavy demands on it.
The Earth's (40)______ resources are there for us to use. We need food, water, air, energy, medicines, warmth, shelter and minerals to (41)______ us fed, comfortable, healthy and active. If we are sensible in how we use the resources they will last indefinitely. But if we use them wastefully and excessively they will soon (42)_______ and everyone will suffer.
Question 33: A. Still B. Despite C. Yet D. Although
Question 34: A. two-thirds B. two- third C. two-three D. two-threes
Question 35: A. survival B. alive C. existent D. live
Question 36: A. on B. at C. in D. by
Question 37: A. sooner B. rather C. either D. neither
Question 38: A. completely B. quite C. greatly D. utterly
Question 39: A. result B. product C. development D. reaction
Question 40: A. natural B. real C. living D. genuine
Question 41: A. stay B. keep C. maintain D. hold
Question 42: A. run out B. run away C. run up D. run off
Question 43: The aircraft carrier is indispensable in naval operations against sea or shore based enemies.
A. unique B. novel C. unnecessary D. exotic
Question 44. This shouldn't be too taxing for you.
A. comfortable B. demanding C. easy D. relaxing
Read the following passage and mark the letter A, B, C, or D on your answer sheet to indicate the correct answer to each of the questions from 45 to 54.
Basic to any understanding of Canada in the 20 years after the Second World War is the country's impressive population growth. For every three Canadians in 1945, there were over five in 1966. In September 1966 Canada's population passed the 20 million mark. Most of his surging growth came from natural increase. The depression of the 1930's and the war had held back marriages, and the catching-up process began after 1945. The baby boom continued through the decade of the 1950's, producing a population increase of nearly fifteen percent in the five years from 1951 to 1956. This rate of increase had been exceeded only once before settle. Undoubtedly, the good economic conditions of the 1950's supported a growth in the population, but the expansion also derived from a trend toward earlier marriages and an increase in the average size of families. In 1957 the Canadian birth rate stood at 28 per thousand, one of the highest in the world.
After the peak year of 1957, the birth rate in Canada began to decline. It continued falling until in 1966 it stood at the lowest level in 25 years. Partly this decline reflected the low level of births during the depression and the war, but it was also caused by changes in Canadian society. Young people were staying at school longer; more women were working; young married couples were buying automobiles or houses before starting families; rising living standards were cutting down the size of families.It appeared that Canada was once more falling in step with the trend toward smaller families that had occurred all through the Western world since the time of the Industrial Revolution.
Although the growth in Canada's population had slowed down by 1966 (the increase in the first half of the 1960's was only nine percent), another large population wave was coming over the horizon. It would be composed of the children who were born during the period of the high birth rate prior to 1957.
Question 45. What does the passage mainly discuss?
A. Educational changes in Canadian society.
B. Canada during the Second World War.
C. Population trends in postwar Canada.
D. Standards of living in Canada.
Question 46. According to the passage, when did Canada's baby boom begin?
A. In the decade after 1911 B. After 1945
C. During the depression of the 1930's D. In 1966
Question 47. The word "five" in line 2 refers to__________.
A. Canadians B. years C. decades D. marriages
Question 48. The word "surging" in line 3 is closest in meaning to__________.
A. new B. extra C. accelerating D. surprising
Question 49. The author suggests that in Canada during the 1950's____.
A. the urban population decreased rapidly B. fewer people married
C. economic conditions were poor D. the birth rate was very high
Question 50. The word "trend" in line 8 is closest in meaning to__________.
A. tendency B. aim C. growth D. directive
Question 51. The word "peak" in line 11 is closest in meaning to___________.
A. pointed B. dismal C. mountain D. maximum
Question 52. When was the birth rate in Canada at its lowest postwar level?
A. 1966 B. 1957 C. 1956 D. 1951
Question 53. The author mentions all of the following as causes of declines in population growth after 1957 EXCEPT__________.
A. people being better educated B. people getting married earlier
C. better standards of living D. couples buying houses
Question 54. It can be inferred from the passage that before the Industrial Revolution _________.
A. families were larger B. population statistics were unreliable
C. the population grew steadily. D. economic conditions were bad
Ethology is concerned with the study of adaptive, or survival, value of behavior and its Evolutionary history. Ethological theory began to be applied to research on children in the1960's but has become even more influential today. The origins of ethology can be traced to the work of Darwin. Its modern foundations were laid by two European zoologists, Konrad Lorenz and Niko Tinbergen.
Watching the behaviors diverse animal species in their natural habitats, Lorenz, and Tinbergen observed behavior patterns that promote survival. The most well-known of these is imprinting, the early following behavior of certain baby birds that ensures that the young will stay close to their mother and be fed and protected from danger. Imprinting takes place during an early, restricted time period of development. If the mother goose is not present during this time, but an object resembling her in important features is, young goslings may imprint on it instead . Observations of imprinting led to major concept that has been applied in child development" the critical period . It refers to a limited times span during which the child is biologically prepared to acquire certain adaptive behaviors but needs the support of suitably stimulating environment. Many researchers have conducted studies to find out whether complex cognitive and social behaviors must be learned during restricted time periods. for example, if children are deprived of adequate food or physical and social stimulation during the early years of life, will their intelligence be permanently impaired? If language is not mastered during the preschool years, is the child's capacity to acquire it reduced?
Inspired by observations of imprinting, in 1969 the British psychoanalyst John Bowlby applied ethological theory to the understanding of the relationship between an infant and its parents. He argued that attachment behaviors of babies, such as smiling, babbling, grasping, and crying, are built-in social signals that encourage the parents to approach, care for, and interact with the baby. By keeping a parent near, these behaviors help ensure that the baby will be fed, protected from danger, and provided with the stimulation and affection necessary for healthy growth. The development of attachment in human infants is a lengthy process involving changes in psychological structures that lead to a deep affectional tie between parent and baby.
Question 55: What was Darwin's contribution to ethology?
A. Darwin was the first person to apply ethological theory to children.
B. Darwin's work provided the basis for ethology.
C. Darwin was the professor who taught Lorenz and Tinbergen.
D. Darwin improved on the original principles of ethology.
Question 56: The word "foundations" in line 4 is closest in meaning to______.
A. institutions B. inventions C. discoveries D. researches
Question 57: The word "ensures" in line 7 is closest in meaning to __________.
A. assumes B. guarantees C. proves D. teaches
Question 58: According to the passage, if a mother goose is not present during the time period when imprinting takes place, which of the following will most likely occur?
A. The mother will later imprint on the gosling.
B. The gosling may not find a mate when it matures.
C. The gosling will not imprint on any object.
D. The gosling may imprint on another object.
Question 59: The word "He" in line 19 refers to __________.
A. an infant B. a psychoanalyst C. parent D. a baby
Question 60: The word "acquire" in line 12 is closest in meaning to __________.
A. gain B. reach C. have D. work
Question 61: The author mentions all of the following as attachment behaviors of human infants EXCEPT__________.
A. smiling B. crying C. eating D. grasping
Question 62: According to the passage, attachment behaviors of infants are intended to___________.
A. prepare the infant to cope with separation
B. provide the infant with a means of self-stimulation
C. allow the infant to become imprinted on objects that resemble the parent
D. get the physical, emotional and social needs of the infant met
Question 63: The phrase "affectional tie" in line 24 is closest in meaning to _________.
A. behavioral change B. psychological need
C. cognitive development D. emotional attachment
Question 64: It can be inferred from the passage that ethological theory assumes that __________.
A. failure to imprint has no influence on intelligence
B. to learn about human behavior only human subjects should be studied
C. there are similarities between animal and human behavior
D. the notion of critical periods applies only to animals
SECTION B (2 points):
I.Finish each of the following sentences in such a way that it means the same as the sentence printed before it.Write your answers on your answer sheet.(0.5points)
Question 1: It is said that the Great Pyramid of Giza was built over a 20-year period.
The Great Pyramid of Giza ………………………………………………………
Question 2: Mary started learning English 3 years ago.
Mary has .............................................................................. …..
Question 3: Mr. Smith knew little about the Internet, so he didn't invest into any computer companies.
Had ……………………………………………………………………..................
Question 4: We can't have breakfast in the garden because it is very cold.
It is so……………………………………………………………………………………..
Question 5: Immediately after his arrival, things went wrong. Hardly……………………………………………………………………….
II. In about 140 words, write a paragraph about the benefits of playing sports. Write your paragraph on your answer sheet.(1,5 points).
The following prompts might be helpful to you.
- Keeping fit and healthy
- Entertaining and relaxing
- Earning money
----------- THE END ----------
1.C 2.D 3.B 4.B 5.A 6.C 7.A 8.A 9.B 10.C
11.C 12.C 13.A 14.B 15.B 16.D 17.A 18.C 19.A 20.C
21.A 22.A 23.B 24.C 25.C 26.D 27.B 28.A 29.A 30.A
31.D 32.B 33.C 34.A 35.B 36.D 37.C 38.A 39.A 40.A
41.B 42.A 43.C 44.C 45.C 46.B 47.A 48.C 49.D 50.A
51.D 52.A 53.B 54.A 55.B 56.A 57.B 58.D 59.B 60.A
61.C 62.D 63.D 64.C
Nguồn:sưu tầm trên internet
Tags: bí quyết du học, chuyện du học, du học, du học Anh, đề thi thử tiếng Anh, đề thi tiếng Anh, đề thi tốt nghiệp năm 2016


Mr. Dale Mann is a certified Forensic Chemist with over 30 years of experience performing a wide variety of forensic analyses in support of criminal and civil investigations.
He has extensive experience in analytical chemistry, general forensic evaluations, and forensic chemistry. He has performed investigative chemical and physical analyses in thousands of investigations involving fires, bombings, homicides, assaults, break-ins, drugs and clandestine drug manufacturing, hazardous material spills, and product tampering.
Mr. Mann has worked on hundreds of construction defect issues involving coatings, flooring systems, plastic components, and degradation of installed materials. He regularly assists criminal defense councils in reviewing prosecutorial investigations and trials. His areas of expertise include scene documentation, laboratory analyses and scenario testing.
Mr. Mann has been the principal researcher and author in several topic areas involving criminalistics and forensic chemistry. He has taught at the Federal Bureau of Investigation (FBI), Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF), and the National Forensic Science Training Center (NFSTC). Mr. Mann has presented numerous papers and lectures at a variety of scientific society meetings, and has provided testimony in over 350 trials, depositions, mediations, and arbitrations.


text:
'use strict';

describe('Directive: addStuff', function () {

  // load the directive's module
  beforeEach(module('findTheMovingBoxApp'));

  var element,
    scope;

  beforeEach(inject(function ($rootScope) {
    scope = $rootScope.$new();
  }));

  it('should make hidden element visible', inject(function ($compile) {
    element = angular.element('<add-stuff></add-stuff>');
    element = $compile(element)(scope);
    expect(element.text()).toBe('this is the addStuff directive');
  }));
});

redpajama_set_name: RedPajamaGithub


text:
Introduction & Background
Data and methods
Expansion or compression of long-term care in Germany between 2001 and 2009? A small-area decomposition study based on administrative health data
Daniel Kreft1Email author and
Gabriele Doblhammer2
Population Health MetricsAdvancing innovation in health measurement201614:24
Received: 23 December 2015
Accepted: 30 June 2016
Studies state profound cross-country differences in healthy life years and its time trends, suggesting either the health scenario of expansion or compression of morbidity. A much-discussed question in public health research is whether the health scenarios are heterogeneous or homogeneous on the subnational level as well. Furthermore, the question arises whether the morbidity trends or the mortality trends are the decisive drivers of the care need-free life years (CFLY), the life years with care need (CLY), and, ultimately, the health scenarios.
This study uses administrative census data of all beneficiaries in Germany from the Statutory Long-Term Care Insurance 2001–2009. We compute the CFLY and CLY at age 65+ for 412 counties. The CFLY and CLY gains are decomposed into the effects of survival and of the prevalence of care need, and we investigate their linkages with the health scenarios by applying multinomial regression models.
We show an overall increase in CFLY, which is higher for men than for women and higher for severe than for any care need. However, spatial variation in CFLY and in CLY has increased. In terms of the health scenarios, a majority of counties show an expansion of any care need but a compression of severe care need. There is high spatial heterogeneity, with expansion-counties surrounding compression-counties and vice versa, which is mainly caused by divergent trends in the prevalence of care need. We show that mortality is responsible for the absolute changes in CFLY and CLY, while morbidity is the decisive driver that determines the health scenario of a county.
Combining regionalized administrative data and advanced statistical methods permits a deeper insight into the complex relationship between health and mortality. Our findings demonstrate a compression of life years with severe care need, which however, depends on the region of residence. To attenuate regional inequalities, more efforts are needed that improve health by medical and infrastructural interventions and by the exchange of insights in the efficiency of small- and large-area policy measures between the vanguard and the rearguard counties. In future research, the underlying latent mechanisms should be investigated in more detail.
Dynamic equilibrium
Healthy life expectancy
Time trends
Health inequality
The health scenarios
Three hypothetical scenarios with contrasting assumptions about future developments of morbidity in populations with decreasing mortality were established and repeatedly examined. In Ernest Gruenberg's [1] and Morton Kramer's [2] theory of "Expansion of Morbidity," the general survival progress and the later-active (or even missing) improvements in health prevention and in recovery lead to an increasing duration of morbidity and a higher prevalence of health limitations. The contrary hypothesis is the "Compression of Morbidity" scenario by James Fries [3, 4], in which a general decrease in the incidence of morbidity is expected due to, for example, a healthier life style of the individuals, technological and medical advancements, and interventions in primary and secondary prevention of diseases. The morbidity shrinkage – combined with steadily improved survival rates – causes a postponement of unhealthy life years into the very last ages of life and results in a decline of the population's prevalence of chronic diseases in total. Fries [5, 6] later developed a modified and differentiated scenario: the absolute and the relative compression of morbidity. Absolute compression describes a situation in which the total number of unhealthy life years decreases, while there is a relative compression when the proportion of unhealthy life time to total remaining life time declines. Furthermore, relative compression is defined as a special case of absolute compression - differing in the development of the disabled life years. If the number of disabled life years is stable or shrinking, there is an absolute compression, and if there is a slight increase in the number of disabled life years (but lesser than the gain in non-disabled life years), then the situation is defined as a relative compression.
In conjunction with the compression scenarios, there are two expansion scenarios: the absolute and the relative expansion of morbidity. The total number of unhealthy life years increases in the absolute expansion scenario, while the proportion of unhealthy life time to total remaining life time gains in the relative compression scenario.
The idea of looking at relative more than at absolute changes in morbidity prevalence rates is one of the basics of the theory of "dynamic equilibrium" [7, 8]. This scenario integrates the frameworks of compression and expansion of morbidity. Manton [7] assumes that gains in life expectancy go together with increasing years in ill-health; however, the share of unhealthy to total remaining life years remains relatively constant. Furthermore, while the total number of persons with chronic diseases is growing, the prognosis according to the theory expects a shift from more to less and moderate severe diseases and disability states. Behavioral, technological, and medical progression are the causes of this redistribution and will lead to a general improvement in survival as well [9].
To evaluate these frameworks of the future trends in population's health status, summary measures were developed that combine information about morbidity and mortality data. One appropriate concept is the above-mentioned care need-free life years (CFLY). By combing the CFLY with the indicators life years with care need (CLY) and the health ratio (HR), five theoretical health scenarios can be identified (Table 1).
Scheme of combinations of care need-free life years, life years with care need and health ratio by scenario of future health development (given that life expectancy increase continues)
Disability-free life years
Disabled life years
Health ratio
Absolute Compression
▼=
Relative Compression
Dynamic Equilibriuma
Relative Expansion
Absolute Expansion
Note: ▼: decrease; ▲: increase; =: stable
aWith considering the shift in the severity of morbidity, special case of "stability" that is defined by the same scenario but without considering the shift in the severity of care need
Until now, the CFLY indicator has been predominantly used for cross-country comparisons of time trends (e.g., [10, 11]). However – as mentioned above – the CFLY can equally be applied for regional comparisons within a country (e.g., [12, 13]).
A methodological problem occurs when time trends are studied based on changes in prevalence, because they can be the result of changes in the incidence, in the mortality of the prevalent population, and in the mortality of the non-prevalent population. Because longitudinal data on these three influencing factors are rare, Nusselder and Looman [14] introduced a decomposition method that allows for the retrospective separation of changes in prevalence (morbidity effect), changes in the survival of the population with morbidity (mortality effect on CLY, MortΔCLY), and changes in the survival of the population without morbidity (mortality effect on CFLY, MortΔCFLY). The two morbidity effects on CFLY and on CLY, are – by definition of a two-state decrement life table – the same in numbers but with opposite signs. A positive morbidity effect is defined as a decrease in prevalence. A positive MortΔCFLY implies a decrease of mortality rates in the population without care need, and a positive MortΔCLY is a decrease of mortality in the population with care need.
Factors of and time trends in care need
Factors of care need
In our study care need is a complex, multidimensional concept of morbidity in contrast to the widely used health outcomes such as limitations in (instrumental) activities of daily living (ADL and IADL), self-rated health, or mobility limitations. Our measure of care need is based not only on an objective medical assessment of health problems due to IADL and ADL limitations according to the German Statutory Long-Term Care (SLTC) insurance, but also on the willingness to apply for benefits (see data section below).
In general, the risk of care need is affected by various determinants that can be differentiated into micro- and macro-level factors. Kibele [15], by adapting [16, 17], defines four subgroups of micro-level determinants: the socioeconomic status, lifestyle, living conditions, and human biology/genetic factors. Even if these factors are situated on the individual level, there is a spatial variation in the concentration of persons with specific health promoting or jeopardizing attributes in Germany (e.g., see review by [15]). In addition, three macro-level determinants can be identified: socioeconomic conditions, medical care provision, and environmental conditions (e.g., see review by [15]). Both micro- and macro-level factors influence – in a complex, interfering, and interplaying way - the disease burden situation in the particular German counties. Profound county differences were reported for a series of diseases and conditions all related to care need, such as dementia and hearing impairments [18], multiple sclerosis [19], smoking and obesity [20], depression [21], hypertension, diabetes mellitus, hypercholesterolemia [22], and acute stroke admission [23]. County differences also exist for all leading causes of death [15].
In addition, the decision to apply for SLTC- benefits depends on the individual and his or her family. In 2013, 71 % of the SLTC-beneficiaries received outpatient and informal care in their private homes; among these 66 % were only cared for by a family member [24]. Thus, the family (primarily the spouse and/or the children) plays an important role as care givers in the SLTC-system. One may assume that in most cases the decision of whether to apply for SLTC-benefits is discussed with and supported by the partner and the family. Potential factors influencing this decision may be the availability of a (distant) living partner/children, the amount of financial (individual or family) resources, the severity of the limitation or disease, the time consumption and psychological burden of care, the infrastructural situation in the residential area, and the normative, cultural, and other individual beliefs and concerns of all involved persons. Up to now, there has been a lack of studies that investigate these factors on the county level; however, Kreft [13] concluded that the risk of long-term care is higher in counties where there are more deprived households.
Time trends in care need
We identified six studies published since 2001 which analyze trends in care need in Germany (Table 2). The review indicates an inconsistent picture of the trends in health in the last decades. Half (three) of the selected studies found a compression of long-term care [25–27]. The results of two studies can be interpreted as evidence for the dynamic equilibrium hypothesis [28, 29]. Only one study [30] found a relative expansion.
Selection of studies investigating the health scenarios in Germany, publication year 2001 through 2015
Type of health
Regional comparison
Type of method
Long-term care (in general; incidence)
(Slight) Compression
Age standardized prevalence, incidence estimations
Long-term care (in general and severe)
North Rhine-Westfalia/Germany
Sullivan method
Long-term care (transition rates)
Berlin/Germany
Compression (but: policy influence assumed)
Transition models
Long-term care (by severity)
Dynamic equilibrium (Expansion for all types and stability for severe types of care)
Federal states and 3 regions
Sullivan method, standardized morbidity ratios
Long-term care (by severity of disability)
Dynamic Equilibrium (Expansion for all types and stability for severe types)
Long-term care (in general)
Note: Words in bold letters indicate that the results are interpreted with a direct link to the health scenarios
These findings for Germany match the findings for other European countries and the United States (see [31–33] for reviews). The three reviews give evidence for different trends by severity of a health problem and indicate a dynamic equilibrium with expansion in mild health problems and stability or compression in severe disability. However, the choice of the health indicator (e.g., incidence, prevalence, or composed measures), the characteristics of the population under study (e.g., age groups, inclusion of institutionalized persons), the choice of the time perspective (due to societal and medical changes), and the design of the data (survey or administrative) affect the comparability of the findings of the studies.
The studies of Hackmann and Moog [25] and Häcker and Hackmann [27] used different methods and data than the other studies that applied the Sullivan method. While Hackmann and Moog [25] estimated the age standardized prevalence of care need by using 2004/06 data from the German Ministry of Health and explored incidence rates based on the (arbitrary) assumption of a stable internal age structure, Häcker and Hackmann [27] computed a transition model that used individual level data for SLTC-recipients in Berlin, a highly urbanized German region. Thus, the findings are not comparable to our study. In contrast, Pinheiro and Krämer [26], Pattloch [28], Unger and colleagues [29], and Hoffmann and Nachtmann [30] used the Sullivan method and an administrative data source (the SLTC census, except Unger and colleagues [29], who used health claims data from the Gmündener Ersatzkasse/GEK). Results of these studies indicate an expansion/relative expansion of long-term care in general and stability in severe types of care (dynamic equilibrium). Only Pinheiro and Krämer [26] found a compression in general and severe care need, which may be explained by the above-average decrease in prevalence of care between 1999 and 2007 (see Pattloch [28]:153).
In sum, this study has two objectives. First, we investigate the trends in LE, CFLY, CLY, and HR on the level of counties and classify them according to the theoretical health scenarios: expansion, compression, and stability. Second, we explore whether the changes in mortality or in morbidity are the driving factors behind experiencing a specific health scenario. We examine this by decomposing the county-specific CFLY and CLY trends into the effects of morbidity and mortality.
Hypothesis 1: Based on the findings of previous studies [12, 13, 15, 20, 34–48] we hypothesize that there are county-specific differences in the trends of the health indicators which may lead to a heterogeneous pattern of the health scenarios. Given the remarkable increase in life expectancy of East Germany since reunification, it is not obvious whether the distribution of county-specific health scenarios is similar to West Germany. In addition, there are large subnational differences in the patterns of selected chronic diseases, their direct (e.g., smoking and obesity) and indirect (e.g., socioeconomic deprivation) risk factors, the medical infrastructure, and the major causes of death [13, 15, 18–23] These health(−relevant) regional characteristics combined with the multiple factors that affect the decision to apply for SLTC may result in different health scenarios.
Hypothesis 2: However, based on the previous hypothesis and on earlier research that points towards a compression or equilibrium scenario [30, 49], we expect that this is also true in most – but not all – counties.
Hypothesis 3: Turning to the contributions of the mortality and morbidity effects to the health scenarios, we do not have a specific hypothesis. A priori it is not obvious whether the same factor drives both the absolute changes in years of life with and without care need, and the resulting health scenarios. The reason for this is that the health scenarios are the result of interfering developments in the three distinct indicators CFLY, CLY, and LE. However, a decomposition analysis of trends in ADL among the French population aged 65 and above concluded that the compression found from 2004 to 2008 was predominantly caused by the change in the disability component rather than in the mortality component [50]. Whether this is also true for Germany is not clear.
This study is based on the German Statutory Long-Term Care (SLTC) Censuses for the years 2001, 2003, 2005, 2007, and 2009. The SLTC Census is an official mandatory register of all long-term informal and formal care and care allowance receivers living in private households and institutions in Germany. The register is updated every two years and covers more than 2 million recipients of long-term care benefits as defined by the German Social Code Book XI. The register includes individual level information about sex, age, year of observation, care level (level 1 to 3/case of hardship), and the official ID of the residential county (NUTS 3 level) on December 31st of each year; no additional socioeconomic or demographic information is available. We aggregated the individual micro data by 5-year age groups (65–69, 70–74, 75–79,80–84, 85+), by sex, by year, by county, and by care level (level 1+ versus level 2+).
As participation is mandatory, the SLTC Census is not biased by non-response. Another advantage is the adequately high number of persons in need of care at the county level (Additional file 1: Table S1). To ensure data privacy, we use the total sample via remote access by the Research Data Centres of the Statistical Offices of the Federation and the Länder.
We combine the aggregated SLTC Census data with the vital data (population and death counts) of the official regional database of the National Statistical Office. Two problems with the data occur in the data management process.
First, the highest age group in the county-specific population statistics in 2001 is 75+, while in the other years there is a disaggregation in 5-year age groups until age 85 + .1 Thus, we estimated the population for the 5-year age groups by using available data for 2003–2009 and by assuming a constant change of the population shares within persons at age 75+ by sexes and counties from 2001 to 2009. We use an extrapolation method to estimate the population at the age groups 75–79, 80–84, and 85 and older in 2001.2
Second, in the observation period, two large – Saxony-Anhalt in 2007 and Saxony in 2008 – and two small reforms – Hanover in 2001 and Aachen in 2009 – of the counties were carried out. Most of these reforms were fusions of counties, which are unproblematic in terms of data management. For these counties, the data of the affiliated counties are pooled. For six counties in Saxony-Anhalt3 the reform of the counties fundamentally changed the geographical entities, which requires a more complex data management strategy. We choose an allocation of death counts and of the number of care receivers by using overall population based weights.4 The underlying assumption of this strategy is that the deaths and the persons in need of care are equally distributed in area of the counties and are not clustered in specific parts within a county.
The care levels represent the intensity of restrictions in basic and instrumental activities of daily living (ADL and IADL) over a longer period. They are separated by the frequency and the time consumption of care assistance by non-professionals: persons with care level 1 need assistance at least once a day that takes more than 45 min for essential personal care and at least 90 min in total for general help; persons with care level 2 and higher need assistance for at least three times a day that takes 120 min or longer for essential personal care and at least 180 min in total for general help. The intensity of care is specified during a substantial home examination by members of the German medical service of health insurance [51].
As only official registered care need is used as the health outcome, there may be undercoverage of care need in general due to a lack of knowledge or high barriers of entry – for example, for persons with a migration background. However, there could also be differences (illegal, therefore hidden) in the evaluation process of the care level, as lobbyism towards the medical services and the financial resources of the insurance agencies may vary within Germany. In addition, it can be assumed that there is also a continuing (perhaps policy driven) change of assessment of the potential beneficiaries by the medical services in the observation period [27]. Further limitations are potential county-specific differences between East and West German counties in terms of individual acceptance of social benefits, as well as socioeconomic differences in terms of private financial resources to compensate public benefits.
We calculated care need-free life years (CFLY) and defined care need in terms of receiving financial and/or personnel support from the German SLTC insurance. Hereafter, the words long-term care, disability, and care need are used synonymously. The CFLY estimation is based on the Sullivan method [52] and on the Chiang method [53] for life expectancy (LE). We computed prevalence rates of care need separated by sex, age group ('under 60', '60–69', '70–74', '75–79', '80–84', '85+'), year of observation (2001, 2003, 2005, 2007, 2009), county, and care level.
We use two definitions of care need: all types of care (levels 1–3) versus severe type of care (level 2 and 3/case of hardship). The life years with care need (CLY) are calculated as the remaining total LE minus CFLY. The health ratio (HR) is the proportion of CFLY in total remaining LE. We estimate yearly LE, CFLY, CLY, and HR for both sexes and care levels, and for all 412 German counties within the borders of 2009. To reduce random fluctuations in the county's death rates, we use pooled 3-year death counts for the estimation of the abridged life tables.
In the first stage, we separately examine the temporal changes in the general level of the seven indicators (LE, CFLYany, CFLYsevere, CLYany, CLYsevere, HRany, HRsevere). We combine the information of the indicators to classify the counties into the five established health scenarios plus regions with decreasing life expectancy (Table 1). To minimize random fluctuations in the indicators, we used pooled data for the two starting years (2001/2003) and the two final years (2007/2009). We define the trends as the estimated value in the last two years subtracting the estimated value in the first two years. An increase (a decrease) in an indicator is defined as a positive (negative) change, while, since continuous variables are used, stability is defined as an indicator change between −0.1 and +0.1.
In the second stage, we decompose county-specific CFLYany, CFLYsevere, CLYany, and CLYsevere into the effects of morbidity and mortality, which measures the life years lost or gained due to changes in mortality or morbidity rates. We use the decomposition method by Nusselder and Looman [14], which is an extension of the Arriaga method [54]. We compare sex-specific CFLY and CLY in 2001/03 (t1) versus 2007/09 (t2). The change in the number of person-years with care need (CLY) for a particular county, sex, and care level is measured by
$$ {}_iCL{Y}_x{=}_{\mathrm{i}}{\mathrm{Mort}}_{\Delta \mathrm{CLY},\ \mathrm{x}}{+}_{\mathrm{i}}{\mathrm{Morb}}_{\Delta \mathrm{CLY},\ \mathrm{x}}=\left(\frac{{}_ipre{v}_{x_{t1}}{+}_ipre{v}_{x_{t2}}}{2}\right)\times {\varDelta}_i{L}_x+\left(\frac{{}_i{L}_{x_{t1}}{+}_i{L}_{x_{t2}}}{2}\right)\times {\varDelta}_ipre{v}_x, $$
where x depicts age, i the length of the age interval, iLx the product of person-years lived, and iprevx the prevalence of care need. The number of person-years without care need (CFLY) is decomposed in the same manner.
Multinomial logistic regression
In the third stage, we estimated multinomial logistic regression models to analyze the association of the morbidity and mortality effects with the health scenarios. We used the three theoretical health scenarios (expansion, stability, compression) rather than the five categories presented in Table 1 due to the low number of counties in some of the categories. The explanatory variables are the mean centred morbidity (Morb) and mortality effects in CFLY (MortΔCFLY) and in CLY (MortΔCLY), which are measured in life days. To account for county-specific uncertainty of CFLYany and CFLYsevere estimation, we use weighted regression models.5
The regression model for persons aged 65+ and of a particular sex and care level is defined by
$$ Logi{t}_{i,j}= log\frac{Pr\left({Y}_i=j\right)}{Pr\left({Y}_i=j\hbox{'}\right)}={\alpha}_j+{\beta}_{1,j}{\mathrm{Morb}}_i+{\beta}_{2,j}{\mathrm{Mort}}_{\Delta \mathrm{CFLY},i}+{\beta}_{3,j}{\mathrm{Mort}}_{\Delta \mathrm{CLY},i}, $$
where i depicts the county, j is the particular health scenario (stability or compression), j' is the reference health scenario (expansion), α is the intercept, and the βs are the estimated coefficients.
All calculations are performed using Stata 12.1 and a decomposition tool programmed in R by WJ Nusselder and CWN Looman.6 The results are given as relative risk ratios (RRR) on the chance of being a "stability" or a "compression" county versus being an "expansion" county (reference) for both sexes aged 65+, and for any/severe care level.
In the period from 2001 to 2009, the number of persons in care need has increased from 2.04 to 2.34 million. Thus, the raw care need prevalence is about 2.5 % in 2001 and 2.9 % in 2009. Of these, nearly 50 % have care level 1 (2001: 0.89 million; 2009: 1.25 million persons). The majority are female (2001: 1.40 million; 2009: 1.57 million); however, the increase between 2001 and 2009 is higher for males (+20 %) than for females (+12 %). About 81 % (2001) respectively 83 % (2009) are 65 years and older and the total increase is solely due to these ages (+18 %). On the contrary, the absolute number of persons younger than 65 is nearly stable (+0.09 %) over time.
Trends according to the five health scenarios
Taking the unweighted mean over all counties, remaining LE, CFLYany, and CFLYsevere have been continuously increasing for both sexes (Table 3). CLYany also increased, while there was no significant time trend for CLYsevere. An analysis of the time trends in HR – separated by men and women and by severity of care need – confirms the findings. The proportion of life years free from any care level (HRany) decreased, while the proportion of life years free from severe care level (HRsevere) remained stable or even increased slightly.
Level (measured by the county-level mean) and spatial dispersion (measured by interquartile range; IQR) of life expectancy total, with and without any care level and with and without severe care level and the health ratios, men and women at age 65+, 2001–2009
[15.90–16.03]
IQR
CFLEany
CLYany
[1.56–1.60]
HRany
CFLEsevere
CLYsevere
HRsevere
Source: Statistical Offices of the Federation and the Länder, Statutory Long-Term Care Censuses 2001–2009 & Regional database (2013); author's calculation
In detail, mean male LE increased from 15.97 to 17.43 years and mean female LE rose from 19.26 to 20.55 years. Thus, the gain was higher for men (0.18 life years per annum) than for women (0.16 life years per annum). While the spatial variation in LE increased for men (from interquartile range IQR = 0.898 to 1.014), that of women decreased (from 0.900 to 0.808) in this period. CFLY shows an increase in both, CFLYany and CFLYsevere. Mean CFLYany rose from 14.39 years (IQR = 1.053) to 15.60 (IQR = 1.157) in men and from 16.22 (IQR = 1.049) to 17.17 years (IQR = 1.231) in women. CFLYsevere has increased from 15.14 (IQR = 0.956) to 16.58 years (IQR = 1.082) in men and from 17.67 (IQR = 0.968) to 18.97 (IQR = 0.907) in women. Thus, the increase in CFLEsevere is higher than in CFLEany. Mean CLYany of males increased from 1.58 to 1.83 years and those of females from 3.05 to 3.38 years. In contrast, male CLYsevere stagnated at around 0.85 and female CLYsevere at around 1.61 years.
The trends are weakly correlated with the starting level in 2001/2003. While in the case of male LE, there is no association of the level with the trend component (Pearson correlation = -0.07, p > 0.1), the increase in female LE is lower in counties with a high LE starting level (-0.33, p < 0.001). For CFLY, there are inconsistent associations. There is a weak positive correlation in case of CFLYany in men (0.17, p < 0.001), but no correlations in male CFLYsevere and in female CFLYany (both 0.06, p > 0.1). However, we did find a weak negative correlation in female CFLYsevere (-0.18, p < 0.001). In CLY, there are no correlations in CLYany (men: -0.07; women: 0.03, both p > 0.1) and weak negative correlations in CLYsevere (men: -0.20, p < 0.001; women: -0.11, p = 0.03).
We spatially plot selected variants by the starting level in 2001/2003 and by the trends up to 2007/2009, and detect notable clusters of counties with very favorable and very unfavorable combinations. In case of LE, CFLY, and HR, unfavorable combinations are defined as a low starting level and the lowest (more than one standard deviation below the county-level mean) change over the period. In the case of CLY, in contrast, unfavorable combinations are defined as a high starting level and the highest (more than one standard deviation above the county-level mean) change over the period. For the trends in LE, CFLY, CLY, and HR, there is a slight but consistent gradient between the most disadvantaged counties in the North, Middle and East of Germany – including eastern Bavaria – and the most advantaged counties in the South and West (Additional file 1: Table S2–S5).
By combining the trends in the various indictors into the health scenarios for all of Germany, we find a relative expansion for any care level for both sexes, but a stable trend in severe care level of males and a relative compression in severe care level of females.
In contrast to the picture of a nationwide consistent trend, the health scenario classification on level of counties reveals a high subnational heterogeneity (Fig. 1). Obviously, there is no clear east–west or north–south gap, but a high divergence within the particular federal states. Nevertheless, in case of any care level, the majority of the counties have experienced a relative expansion. Almost every county in the federal states Lower Saxony, Hesse, northern Rhineland-Palatinate, northern and eastern Bavaria, and the majority of the East German counties are in the relative expansion cluster. The highest spatial heterogeneity can be stated for Schleswig-Holstein, North Rhine-Westphalia, Baden-Württemberg, and Saxony. The general spatial pattern of the health scenarios is consistent for men and women (Spearmans rho = 0.60); however there are some exceptions (some counties in Schleswig-Holstein, Saxony-Anhalt, North Rhine-Westphalia, and Bavaria).
Clusters by health scenarios for any care level and for severe care level, men and women, age 65+, 2001/03–2007/09. Source: Statistical Offices of the Federation and the Länder, Statutory Long-Term Care Censuses 2001-2009 & Regional database (2013); author's calculations and mapping
In terms of trends in severe care levels, the number of counties experiencing an expansion is lower than in case of any care level. As a consequence, there are comparatively more counties classified as counties with relative and absolute compression. However, there is a higher level of bipolarization with counties experiencing a relative expansion and counties experiencing a compression for males than for females. This is the explanation for the stable trend for males on the national level.
Looking at any and severe care level simultaneously, the majority of counties show either an expansion in both care levels or a dynamic equilibrium, including the shift from more to less severe levels as defined by Manton [7]. In case of men, we classify 161 out of 412 counties into these two groups and, in case of women, 137 out of 412 counties. In contrast, 93 counties (men) and 108 counties (women), respectively, experienced a compression in both levels. An expansion/equilibrium in any care level combined with a compression/equilibrium in severe care level is detected in 154 counties (men) respectively 161 counties (women).7
Decomposition of the trends - the role of morbidity and mortality effects
Over all counties and for both sexes, the mortality trends have the highest effect on CFLY and CLY in absolute values. On average, from 81 up to 92 % of the increases in CFLY are caused by mortality reductions in CFLY and only 8 to 19 % by morbidity changes (Table 4). Mean MortΔCLY is low, but the overall mean morbidity effect is even lower. The proportion of MortΔCLY ranges between 135 and 656 %. Thus, survival improvements are of higher impact on CLY trend than on the trends in CFLY, especially in case of trends in CLYsevere. The spatial mapping of the trends of the mortality and morbidity effects shows high heterogeneity and no clear clusters (Additional file 1: Table S6–S8).
Mean absolute and relative change in life expectancy, care need-free life years and life years with care need at age 65+ by sex and care level, 2001/03–2007/09
Mean change in
CFLY change due to
CLY change due to
CFLY
Any Care Level
−0.075
Severe Care Level
−253 %
Note: All means are weighted by 1/∑ i = 2001/03 2007/09 (σ2(CFLYi))
Source: Statistical Offices of the Federation and the Länder, Statutory Long-Term Care Censuses 2001–2009 & Regional database (2013); author's calculations
The results of the decomposition reveal a high variability in terms of combinations of the morbidity and the two mortality effects. We define the categories "low" ("high") as values less (more) than one standard deviation below (above) the mean, and "medium" as values close to the mean. By definition, most counties have medium morbidity and mortality effects. These counties are mostly expansion counties in case of any care level and mostly compression counties in case of severe care level.
Some combinations do not exist. These are the combinations of a low mortality effect in CLY trend (MortΔCLY) and a high mortality effect in CFLY trend (MortΔCFLY) – the most favorable trend – and vice versa – the most unfavorable trend.
The two counties Greifswald and Barnim in northeast Germany show the most unfavorable trends and are both experiencing an expansion in any and severe care need. Almost every county with a high morbidity effect is a compression county, while nearly all counties with low morbidity effects are expansion counties. The counties with the second most unfavorable trend ("low morbidity – high MortΔCLY – medium MortΔCFLY") are counties in East Germany, in Lower Saxony, and Eastern Bavaria and for females (any care level), these are central Germany (Fig. 2). The counties with the most favorable trends are located in the South German regions and, for females, in the very north of Schleswig-Holstein. These counties are merely compression counties.
Groups of effect combinations by sex and severity of care level. Source: Statistical Offices of the Federation and the Länder, Statutory Long-Term Care Censuses 2001-2009 & Regional database (2013); author's calculations and mapping
More insight can be gained from the association of the morbidity effects with each of the two mortality effects. We estimated bivariate linear regressions for each combination of the three effects differentiated by compression and expansion counties (Additional file 1: Table S9–S11). In terms of CFLY, both the morbidity and the mortality effect add up to additional healthy life years. Counties where morbidity improvements lead to large gains in CFLY are merely compression counties. This association is weakly dependent on MortΔCFLY, as indicated by the weak positive slope of the regression line (slopes = [0.040; 0.284]). The slope is similar in compression and expansion counties. The weak positive association is true for both sexes as well as for any and severe care level. In terms of CLY, the morbidity effect must be larger than the mortality effect in terms of compression countries. Thus, the correlation of MortΔCLY and morbidity effects is higher in the compression counties (slopes = [0.537; 0.632]) than in the expansion counties (slopes = [0.176; 0.391]).
Turning to the multinomial regression, we find that the morbidity effect has the highest impact on the health scenarios (Table 5). An increase of CFLY due to reductions in prevalence leads to a massively higher chance of being a dynamic equilibrium county (RRR = [1.271;2.679]) and a compression county (RRR = [1.640;9.893]). Additionally, a gain in MortΔCFLY also results in a negligibly higher chance of experiencing stability (RRR = [1.012;1.052]) or a compression (RRR = [1.011;1.098]). The influence of MortΔCFLY is statistically significant for males only. On the contrary, an increase in MortΔCLY leads to a significant decrease in the chance of a county to experience stability (RRR = [0.494;0.851]) or a compression (RRR = [0.205;0.722]).
Results of the four multinomial regression models for males and females at age 65+ by care level, mean centred morbidity and mortality effects are measured in change in life days
Covariates (Health scenarios)
Cases (Counties)
effect in CLY
effect in CFLE
Miss-
sings
Ref: Expansion
<0.001
Note: counties are weighted by 1/∑ i = 2001/03 2007/09 (σ2(CFLYi))
To our knowledge, this is one of the first studies that explores trends in life years with and without care need and in the resulting health scenarios on a small-area level. Our study confirms that there is high county-level heterogeneity in the trends of the health indicators and in the health scenarios.
Turning to our first research question, the stratified investigation of the trends by care level shows that there are different care need trends in any and in severe care level. While the majority of counties experience a relative expansion of any care level, the mean remaining life span with a severe care level shows stability or compression. For both sexes, the majority of the counties experience a similar health scenario as the whole country. One exception is males with severe care level. For those, the aggregation of the expansion and compression counties to the total country level leads to the wrong conclusion of a stable trend. By combining these trends, our findings confirm the extended theory of dynamic equilibrium that assumes an expansion of morbidity with a shift from severe to moderate types of morbidity [7]. Thus, our conclusions are consistent with previous findings [28, 29, 56, 57].
The diversity in the trends in the health indicators and the notable subnational heterogeneity in terms of the health scenarios cause a disparity in the level of current and future challenges in public health and in social policy according to financial, infrastructural, sociohumanitarian, and welfare state aspects. In the most disadvantaged situation are those counties where the population shows an absolute expansion of care need. The most favorable position is found in counties experiencing an absolute compression. In contrast to the spatial pattern of LE [15], there was no indication for a clear northeast versus southwest gap in both, the health scenarios and the sole trends in the particular indicators.
These findings are strong evidence that there are profound differences between quantity (life expectancy) and quality (care-free life years, health ratio) of life time in the longitudinal trend of the indicators. The classification of the counties by starting level and by trend of the health indicators observed over time unfolds the expected spatial pattern showing counties with unfavorable levels and trends in the North, East and Middle of Germany versus counties with favorable levels and trends in the South and West. Hence, the vanguard counties increased their lead over the rearguard counties in 2001–2009. Furthermore, our study shows that through all counties the higher the level of female LE, of female CFLYsevere, and of CLYsevere for both sexes in 2001/2003, the lower the changes until 2007/2009. This is an indication for an upper level of these indicators. Only for male CFLY does there seem to be an accelerating process of increase which indicates a much higher potential of gains in life years without care need in future.
We explain these findings by a complex interference of different epidemiological processes. On the one hand, regional disparities are expected to be the result of divergent historical regional developments and current regional conditions that have joint interfering, mediating, and suppressing regional specific effects. Those can be period and/or cohort effects on the behavior, the psychosocial capacity, and the material situation over the life course (timing and duration) of the individuals [58] that in turn have an indirect effect on the total population's composition. On the other hand, the disparities are the direct result of different compositions of the county's population due to the continuous processes of selectivity because of regional specific trends in mortality and migration [59–61].
Turning to our second research question, where we did not have a specific a priori hypothesis, we find that in absolute terms, by far the majority of the absolute increase in disability-free life years and disabled life years is caused by the increase in the survival of the non-disabled and disabled. In other words, the decrease of mortality rates is decisive for the number of additional years with and without care need. In terms of the health scenarios, however, the morbidity effects, respectively the trends in the prevalence of care need, are the decisive drivers of the chance to experience a compression or an expansion. The mortality effects on the change in disabled life years and on the change in disability-free life years are of much lower importance. This can be stated for both sexes and for any and severe care levels. Thus, slight absolute changes in the prevalence rates of care need have a very high impact on a county's health scenario. These findings confirm the results of Cambois and colleagues [50].
One explanation for the differences between any and severe care level is that the findings are evidence for the dynamic equilibrium theory assuming a shift from severe to moderate care need. Improvements in health services, a higher awareness of health problems, increased medical knowledge, earlier diagnostics, and better and less risky surgical and medical interventions lead to an enlargement of life time with (severe) physical and mental limitations [33, 62]. Another explanation for the expansion is that the increase is a result of a changed behavior of the elderly in terms of acceptance of social benefits, which can be described as a shift from a "gratitude" generation to a "demand" generation. One indication for this argument is the disproportional increase in the initial health evaluations by the medical services of the STLC insurance. Between 2001 and 2009, there was a gain of 23 % [63], while the population at age 65+ increased only by 9 % [64]. The different trends of the two care level groups may be only the result of a higher restriction in legal acceptance assuming that the higher the care level, the more intensive the medical evaluations and the higher the legal and individual barriers. Indirect evidence for the higher restrictions are the decisions of the re-evaluations of more than 40 % of the care receivers conducted annually by the medical services. For example, in 2006, 45.8 % (outpatient) and 69.7 % (inpatient) of the re-evaluated persons in care level 1 were upgraded to a higher care level, while it was only 36.6 and 56.3 % respectively of the persons in care level 2 [65].
Our study has profound strengths. One advantage is the large number of persons included in the STLC censuses, allowing us to investigate trends on subnational level. Because the census is mandatory for all private and public STLC beneficiaries, from an administrative and health care planning point of view, the data are not biased by missing records or problems of loss due to follow-up. The health outcome itself is another advantage, because it is an objective, nationally standardized evaluation by medical experts of the health insurance companies. A third strength is that we assume only a marginal bias due to cultural differences in the definition of care need, as all SLTC regulations are harmonized and binding for all counties. We used the established healthy life years measure that allows comparisons of the health situation even for small populations and only if cross-sectional data for the individuals is available. The use of the advanced method of decomposition by Nusselder and Looman [14] provides deeper insights in the complex interactions of changes in the subnational mortality and morbidity patterns and how these affect health scenarios in Germany. The longitudinal design of the data of the counties is an advantage in many ways; e.g., to investigate the stepwise changes and to compare baseline level with time trends.
However, there are also limitations. First, because only aggregated data was accessible, we are not able to identify whether the disparities are the result of changes in the population's composition due to 1) (health-related) selective migration and selective mortality or are 2) causally related to the life time accumulation or coping mechanisms on the residential hazardous conditions of the individuals. Second, there is also the restriction that the design of the study did not allow us to reveal whether specific cohort or period effects in care need cause a higher magnitude and a higher pace of the county-specific changes. Third, a limitation caused by the design of the study may be the definition and the restrictions in the temporal and cross-county comparability of the health indicator. Fourth, another potential bias may be the quality of the data for the sex- and age-specific population in the counties. Because the population information (unlike birth and death statistics) is not based on registers or a census, but rather on extrapolation estimations, unregistered in- and out-migration may lead to a bias that is expected to be higher at the oldest age groups [66].8 Post-analyses, however, show that the bias is marginal. Fifth, registration problems of the SLTC census for the years prior to 2009 may affect the results. Until 2008, an unknown number of persons with semi-inpatient care were double-counted, leading to an overcoverage of persons with care need [67]. Because the share of persons in semi-inpatient care to all persons with care need is very low – in Germany in total about 2 % [67] – this bias is also expected to be marginal. Sixth, yet another limitation is that we do not have county-specific population data by age groups beyond age 85; thus, we are not able to analyze the trends in the internal composition and in the regional disparities at the highest age groups. The prevalence of care need at these ages, however, is very high and therefore regional disparities in CFLY and CLY may be underestimated. Seventh, methodological problems may be caused by using prevalence data with the Sullivan's method instead of individual-level panel data about specific transitions in a multi-state model. Prevalence data overlook the duration of care need or the complexity of possible transitions, which leads to a large bias when the transition rates are highly fluctuable ([68]:86). One study ([28]:101), however, concluded that the limitations of the Sullivan method are acceptable for SLTC data, as the transition rates are assumed to be very stable (incidence/mortality) or very low (rehabilitation). Another problem with the Sullivan method is the dichotomization of the health outcome (with/without care need) which is a simplification of a complex morbidity continuum. We use the strategy of Pattloch [28] to face this problem by analyzing the trends in care need by different levels of severity.
Our study shows a high diversity in care need challenges on level of counties in Germany. While some counties show very positive trends in terms of a compression of care need, others are confronted with a growing proportion of persons in care need living longer with disability. Overall, the shift from severe to moderate care need is a favorable development considering the financial and emotional burden for individuals and society.
Furthermore, our study detects that the place of residence is another important influence factor of the trends in care need. The study demonstrates that there is a complex interaction between trends in care need prevalence and mortality rates. Since we found that the prevalence is the main driver of the health scenarios, higher efforts are required to reduce the prevalence rates. This is of particular importance in counties in the north and the east of Germany that already have the highest share of persons in care need. In future research it will be important to investigate the trends in the new9 care level 0 and the causes of the diversity in the mortality and morbidity effects. Thus, one of the emerging questions is whether the specific living conditions in the counties and their changes over time are associated with the trends in care need and mortality. Previous studies [13, 44, 69–71] have found associations of regional characteristics with small-area health conditions, but studies about health trends are rare. Further investigations are needed to uncover the underlying mechanisms of health aging to understand and to deal with the challenges of an increasingly more heterogeneous aging society.
An additional disaggregation of the population data at the ages 85–89, 90–95, and so on is not available on a county level due to data privacy laws.
For instance: If there is a %PMen,85+,county 1,2003(proportion of men at age 85+ to all men at age 75+ in county 1 in 2003) of 15 % and a %PMen,85+,county 1,2005 of 16 %, the estimation of %PMen,85+,county 1,2001 is 14 %.
The counties are Harz, Salzlandkreis, Jerichower Land, Anhalt-Bitterfeld, Wittenberg, and Dessau-Roßlau.
The overall population weights are based on the total population counts for those years 2001–2006 in which we have information for the old and the new regional entities. For each single year, the (positive or negative) difference between the population of the new and of the old (Pold) entities equals the population that experienced an administrative change (Pchange). In the last step, the population weights are computed by Pchange divided by Pold.
The county- and sex-specific precision weights are computed by 1 dividing by the variance of CFLYany, respectively CFLYsevere at age 65 + . For further information for the calculation of the variance and the standard errors of CFLY, see [55].
The decomposition tool and the user guide are available on request (contact: w.nusselder@erasmusmc.nl)
The remaining 4 (men) or 6 (women) counties show inconsistent combinations for both levels or a LE decrease.
By comparing the extrapolation results and the results of the official population census in 2011, we show that the cumulated overestimation of the population 65+ in 2011 is only about 1.89 % in Germany as total and only 1.42 % in the unweighted median of all counties. Further, estimated partial CFLY, CLY, HR and LE for the ages 65–84 are highly correlated with those at age 65+ (correlations range from r(LE, females, 2007/09) = 0.86 to r(HR, any care, females, 2007/09) = 0.97)
Care level 0 was introduced in 2008 and covers persons with permanent significant limitations in everyday life competence, who have a need for assistance in the field of basic nursing and household assistance, but who do not yet fulfill the requirements for the qualification into the nursing care level 1.
The authors gratefully acknowledge Wilma Nusselder, Viviana Egidi, Renée Lüskow and the colleagues of the Statistical Offices of the Federation and the Länder.
DK designed the study, acquired the data, carried out the analysis, and wrote the first version of the manuscript. GD helped in the design of the study and revised the manuscript. Both authors read and approved the final manuscript.
To ensure reproducibility and transparency of the study results, the computed summary health measures, the decomposition results, and the different health scenario classifications can be found in an additional tabular data [see Additional file 2].
Additional file 1: Table S1. Mid-year population, deaths, and number of persons with any and severe care level by sex at age 65+ in 2001, 2003, 2005, 2007, and 2009, Germany. Table S2. LE trend by county for men and women at age 65, 2001/03-2007/09. Table S3. CFLY trend by county and severity of care need for men and women at age 65, 2001/03-2007/09. Table S4. CLY trend by county and severity of care need for men and women at age 65, 2001/03-2007/09. Table S5. HR trend by county and severity of care need for men and women at age 65, 2001/03-2007/09 (PP=percentage points). Table S6. Decomposition results: Effects of mortality on the CFLY trend by county, sex and severity of disability for persons at age 65+, 2001/03-2007/09. Table S7. Decomposition results: Effects of mortality on the CLY trend by county, sex and severity of care need for persons at age 65+, 2001/03-2007/09. Table S8. Decomposition results: Effects of morbidity by county, sex and severity of care need for persons at age 65+, 2001/03-2007/09. Table S9. Scatterplots of morbidity effects and mortality effects on the CFLY trend at age 65+ by care level and sex (only counties with expansion or compression included), higher symbol size indicates a higher estimation precision/a lower uncertainty. Table S10. Scatterplots of morbidity effects and mortality effects on the CLY trend at age 65+ by care level and sex (only counties with expansion or compression included), higher symbol size indicates a higher estimation precision/a lower uncertainty. Table S11. Scatterplots of mortality effects on CFLY trend and mortality effects on CLY trend at age 65+ by care level and sex (only counties with expansion or compression included), higher symbol size indicates a higher estimation precision/a lower uncertainty. (DOCX 5.91 MB)
Additional file 2: County-specific study results. (XLS 398 kb)
Institute for Sociology and Demography, University of Rostock and Rostock Center for the Study of Demographic Change, Ulmenstraße 69, D-18055 Rostock, Germany
Institute for Sociology and Demography, University of Rostock, German Center for Neurodegenerative Diseases, and Rostock Center for the Study of Demographic Change, Ulmenstraße 69, D-18055 Rostock, Germany
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Ralph LaLama (West Aliquippa, 30 januari 1951) is een Amerikaanse jazzsaxofonist en componist van de modernjazz en hoogleraar.

Biografie
Ralph LaLama is de zoon van een drummer en een zangeres. Hij studeerde aan de Dana School of Music of Youngstown State University in Ohio en begon zijn muzikantencarrière in de band van Thad Jones, die hem naar New York haalde. LaLama werkte daarna in de bigbands van Woody Herman, Buddy Rich, Carla Bley en het Thad Jones/Mel Lewis Orchestra, dat nu als het Vanguard Jazz Orchestra verder bestaat en waarin LaLama solist is. Bovendien speelde hij met o.a. Barry Harris, Harold Danko, Mel Tormé, Carmen McRae, Tom Harrell en Joe Morello, werkte mee aan plaatopnamen met Gary Smulyan, Danny D'Imperio, het Mel Lewis Jazz Orchestra, het Vanguard Jazz Orchestra en de Carnegie Hall Jazz Band o.l.v. Jon Faddis. In 2000 werkte hij mee aan twee producties van het Joe Lovano Nonet voor Blue Note Records, 52nd Street Themes en het in 2003 verschenen album On This Day...At The Vanguard.

LaLama nam sinds 1990 een reeks albums op onder zijn eigen naam voor het Nederlandse Criss Cross Jazz, waaraan o.a. Kenny Barron, George Cables, Richard Wyands, Peter Washington en Kenny Washington meewerkten.

Lalama werkt naast zijn werk als muzikant als professor aan de New York University en de SUNY Purchase. Verder leidt hij workshops aan de Manhattan School of Music.

Discografie
 1991: Feelin' and Dealin'  met Tom Harrell, Barry Harris
 1991: Momentum met Kenny Barron
 1995: You Know What I Mean met George Cables, Dennis Irwin
 1997: Circle Line
 1989: Music for Grown-Ups met Richard Wyands

Literatuur
 Richard Cook, Brian Morton: The Penguin Guide to Jazz on CD. 6e editie. Penguin, London 2002, ISBN 0-14-051521-6.

Amerikaans jazzsaxofonist
Amerikaans componist
Amerikaans hoogleraar

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CW Fall Premiere News & More
TV Source Magazine Staff @http://twitter.com/tvsource August 13, 2010 / 1:25 AM May 14, 2013
Crossovers, returns, sneak peeks and more as The CW gears up for the new primetime season!
CW News: 'One Tree Hill'/'Life Unexpected' Crossover Planned; 'Smallville' Returns; Premiere Dates
"One Tree Hill" fans should get used to watching the series on a new night. The CW has moved the long running series to Tuesday nights, pairing it with sophomore series "Life Unexpected." As OTH begins its eighth season, look for the series to pick up with the fallout from last season's jaw dropping cliff hanger!
"One Tree Hill" and "Life Unexpected" will crossover this October when OTH characters Haley (Bethany Joy Galeotti) and Mia (Kate Voegele) guest star on the October 12th episode of "Life Unexpected." "Haley and Cate are surprised to learn that they share a similar back story as mothers," teased an insider to Entertainment Weekly's Michael Ausiello. The CW is hoping the crossover will generate a bigger audience for Life Unexpected, which will make its second season debut on Tuesday, September 14.
In addition to Bethany Joy Galeotti, returning stars include James Lafferty (Nathan), Sophia Bush (Brooke), Robert Buckley (Clay), Shantel VanSanten (Quinn), Austin Nicholas (Julian) and Jackson Brundage (Jamie).
If you can't wait for the second season premiere of "The Vampire Diaries" you're not alone. Executive producer Kevin Williamson told TV Guide Magazine, "The premiere is all about introducing Katherine (Nina Dobrev). She's a badass who stirs things up, and the trio will immediately have to deal with her and what she has come back for!"
Co-executive producer Julie Plec previewed that this season will address the secret that Tyler (hottie Michael Travino) is desperate to find out.
This season is looking to be one of shifting alliances, betrayal, scandal and secrets! It's certainly "TV to talk about."
The season premiere of "The Vampire Diaries" airs Thursday, September 9.
Look for Lois Lane's family to make another visit to Smallville! Michael Ironside ("Terminator: Salvation") and Peyton List ("As the World Turns") will return as General Sam Lane & Lucy Lane in October. Ironside and List last appeared during Season 4.
James Marsters will appear as Brianiac 5, a version of Brainiac from the future. Marsters previously played Brianiac/Milton. Laura Vandervoort ("V") is set to reprise her role as Kara (aka Supergirl). Vandervoort was a series regular in season seven and made an appearance in season eight.
Keeping up with the returns for "Smallville's" final season is Clark's father Jonathan. That's right, former "Dukes of Hazzard" star John Schneider is returning as Jonathan Kent. Jonathan was killed off in Season 5, but has made sporadic appearances (mainly through flashbacks). Is Jonathan really back this time? He is for at least three episodes….
CW Entertainment President Dawn Ostroff confirmed reports that The CW is looking into bringing other comic book series to the network. "We have started to talk about if there are other DC comic book adaptations," Ostroff said to Digital Spy during the TCA press tour. "We haven't picked anything else up to develop yet, but we would like to see something on the air, so we're going to work on it."
"Smallville's" season premiere will air September 24.
THE CW PREMIERE DATES & PROGRAMMING SCHEDULE
As The CW will launch its fifth season slate of shows with ten hours of original programming including two new series.
8:00-9:00 p.m. – America's Next Top Model | 9:00-10:00 p.m. – Hellcats (new series)
8:00-9:00 p.m. – The Vampire Diaries | 9:00-10:00 p.m. – Nikita (new series)
8:00-9:00 p.m. – 90210 | 9:00-10:00 p.m. – Gossip Girl
8:00-9:00 p.m. – One Tree Hill | 9:00-10:00 p.m. – Life Unexpected
8:00-9:00 p.m. – Smallville | 9:00-10:00 p.m. – Supernatural
Tagged: 90210 The CW	Share
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