Comments on Health Status Transitions Byron G Spencer McMaster University

WPIII & WPIV Andrew Bebbington and Judith Shapiro, “Incidence of Poor Health and Long-Term Care: Health Transitions in Europe – Results from the European Community Household Panel Survey and Institutional Data”, December 2006 Maria Hofmarcher, Monika Riedel, Alexander Schnabl, and Gerald Sirlinger, “Ageing, Health Status, and Determinants of Health Expenditure (AHEAD): Health Status Transitions”, January 2007

The concern: the estimation of first order transitions -- only two periods of information required. To be done using a panel survey from 1994 to 2001, initially involved 130,000 individuals living in 12 EU countries; Austria, Finland and Sweden added later.

Aside from sample attrition, more than 130,000 observations each year for a period of up to eight years, and hence up to seven transitions However the information challenges proved to substantial

The problems survey instruments not standardized key questions differed from one country to another –that was true even of SAH; the ‘variation was substantial’ (B&S, p 4) even so, the results were usable; among those who remained in the sample, less than 10% of transitions could not be calculated a major problem was sample attrition the concern here (as always) is that the attribution is not random –it seems likely that attrition would be disproportionately among those with relatively poor health, and thus would be importantly associated with (unobserved) transitions to institutions or to death –however, it appears that attrition rates differ little when classified by initial state of health and the second-year state includes death

but the biggest problems arise from very considerable under reporting of (or failures to report) transitions to residence in long-term care facilities and to death furthermore the exercise requires that the count of deaths distinguish between those who died when normally residents of private dwellings or of LTC facilities data problems were so severe that “it was not possible to draw comparative conclusions on mortality or institutionalization from any of these records” (B&S, p 8) – even though that was a central purpose of the exercise! the problems of measuring the institutional population, and specifically the flows into and out of that population are well documented in B&S

WPIII – what is done? They consider two measures: -SAH: “VG, G, F, B, VB” -combined B and VB because of small numbers -HHC: “Hampering Health Condition” combined “No chronic condition” with “chronic condition but not hampered”; compared to “chronic condition and hampered” -(WP IV focussed only on SAH) in both studies the intention is to allow also transitions from each of these health states to LTC and to death, but not back

within each country observations on annual health transitions were pooled poststratification adjustments to the weights of those who died, based on vital statistics records falling outside the sample however, similar adjustments are not possible for institutionalization partially ordered-probit functions estimated for persons living in the community (separately for those under 65 and those over); age and gender were entered as covariates and separate estimates were obtained for each initial health state allowance is made for possible transitions to each of the initially designated health states and also to death as an absorbing state, but not to residential care for those 65 and older, probit functions were estimated for admission to residency in a health care institution, using age-group admission rates which, under the assumptions, are the same as mortality rates for those institutions; estimates are by gender; age and age-squared are the covariates

Comments for those living in the community, why not pool those under 65 and 65+ – using a more flexible functional form if necessary it would be helpful to display illustrative results graphically – for example, plots showing the probabilities of transition from each initial health state as a function of age authors note that the preferred functional form provided a ‘suitable fit for all countries’ and that there are plausible regularities in the estimated results that are similar across countries –that suggests that some cross-country pooling of observations might be possible with appropriate adaptation of the error specification – something that could be explored further

their finding that gender rarely has significant explanatory power in the transition equations is consistent with our results for Canada, once we controlled for income and education ignorance of the population in long-term care facilities remains a major problem –that is true also in the Canadian context, where each province defines the term –differences in definition rather than practice appear to account for much of the differences in reported rates of institutionalization across provinces

WP IV The purpose of this work is, again, “to build a picture of the movements in health status of the whole population of each country by age” (p 1), but to do so building on WP III, since not all the relevant transitions were considered there

the major data problems are, of course, the same –transitions into both LTC and death are greatly under reported –not possible using the ECHP to estimate mortality rates separately for those in LTC and those not the solution adopted is to produce a set of demographic accounts using the best available information about population stocks and annual flows the general idea is to make the best use of all relevant information in order to complete the demographic accounting matrix

much effort was put into obtaining data on residential care –in the end – remarkably – very few countries were able to provide satisfactory (or even minimal) information –only 8 of 14 countries could provide estimates of the population in residential care by age –of those, only six could provide estimates also by sex –only the Netherlands and Finland could provide estimates by a/s of the number of deaths among those in residential care; even here, serious concerns about the quality of the numbers information is limited, but suggests that the prevalence of LTC varies enormously across the countries surveyed –in consequence, it was concluded that transitions into (and out of) LTC would have to be country specific

given the available information, it was decided to limit the development of the complete tables to three countries – Belgium, Germany, and the UK –Dutch mortality rates for those in LTC assumed to apply –no correction is made for the under reporting of either deaths of number of residents, thereby implicitly assuming that the ratio of the two values is independent of such a correction

Comments why is the analysis is not based entirely on the proportions (or probabilities) rather than on the number or level of transitions? ‘data smoothing’ could apply to the proportions

the Stone algorithm was applied to levels rather than proportions, and major problems were encountered with implied negative transition probabilities, even after data smoothing –perhaps such problems would have been reduced if proportions had been used throughout as an aside, the use of Sprague multipliers to adjust the age-specific numbers in LTC (fig 2) produces odd results – why does the series not increase with age? because of measurement problems the sum of the rows and columns in the matrix are uncertain; hence they are allowed to vary; I note that working with proportions would avoid that problem and simplify the analysis

the estimated transition probabilities appear generally plausible summarizing those probabilities through panel regression analysis is helpful, and allows us to see how patterns vary with age and sex, and across countries indeed, one might place greater confidence in the estimated probabilities based on the regressions; it is entirely plausible that they would progress smoothly (though not linearly) with age, as required by imposing such a functional form

calculations relating to life expectancy conditioned on LTC also help with the interpretation –the results here are surprising; for men aged 65 it varies from 6 weeks (in Germany) to 13 weeks (Belgium); the average is somewhat more than twice as long for women, except in Belgium, where it is estimated to be more than three times as long at most ages –one would like to get behind these estimates, to assess their reliability I note that they are not consistent with other evidence: B&S who state that the median time from admission to mortality in England is 20 months Inconsistent also with the inverse of the turnover rate, which suggests about 30 months the experiments designed to see what it would take to reduce weeks lived in residential care are interesting –not surprisingly, one conclusion is that a lesser reduction in the transition probabilities would be required if people were in better initial health

some four pages are spent discussing the possible of an EU benchmarking model based on the transitions, apparently in order to identify the most efficient health care provide however, given the huge data problems, such an application would seem premature at this time

Canadian SLID This is a repeated six-year panel. –The first in 1994, with new ones starting in 1997, 2000, and 2003 –We now have three complete panels, each with about 35,000 adults followed for six years. –Information about health is limited, respondents are asked the standard SAH questions –But Statistics Canada has generally been able to follow respondents who were institutionalized or died, partly by matching with vital statistics. Attrition rates were extremely low – e.g., only 0.8% of the 1996 respondents were not accounted for in 1998.