Given the randomized design, unbiased estimates of channeling impacts could be obtained simply by comparing mean values of outcomes for the treatment and control groups. The approach actually used in the evaluation, however, was ordinary least squares regression. The regression models used in the evaluation, the types of statistical tests and significance levels employed, and the interpretation of estimates and tests are the topics of this section.

A. The Regression Model

Regression, or equivalently, analysis of covariance, offers three advantages over simple differences in means as a way of estimating program impacts. First, although the two experimental groups should have very similar average characteristics initially, there may in fact be differences between them, either by chance or because of different patterns of sample attrition for treatment and control groups. If these differences are fully reflected in the observed initial characteristics of the sample, the regression model can control for such differences between the groups. Second, the ratio of treatments to controls differs across sites, ranging from 1:1 to 2:1. If sample members differ across sites, the treatment/control differences in mean outcomes will reflect not only effects of channeling but the different distributions as well. Again, regression will control for these differences.^{12} Finally, to the extent that outcomes are related to baseline or screen characteristics, regression can explain some of the variation between individuals, leading to more precise estimates of channeling impacts than are obtained from differences in means.^{13}
The regression model used was
(1) Y = a_{o} + a_{B}T_{B} + a_{F}T_{F} + a_{s}S + a_{x}X + e, where Y is the outcome variable that is hypothesized to be affected by channeling; T_{B} and T_{F} are binary variables equal to one for sample members in the basic (B) and financial control (F) sites; S is a set of binary site variables; X is a set of explanatory variables taken from the screen or baseline interviews; e is a disturbance term, and the a's are coefficients to be estimated. Under this model the coefficients a_{B} and a_{F} measure the treatment/control differences in mean outcomes, controlling for any differences which exist between the two groups on baseline explanatory variables. Hence, a_{B} and a_{F} are our estimates of channeling impacts.^{14}
The same regression model was used to estimate the impacts of channeling. on all outcome measures examined in the evaluation. Although it may seem unlikely that the factors which affect wellbeing (for example) are exactly the same as the factors which affect nursing home use and other outcomes, there exists a strong justification for this approach. The outcome variables are highly interrelated and depend on each other as well as on many of the same exogenous variables. However, the interrelationship of the outcome variables is very complex, and trying to model it could lead to biased estimates, since some of the explanatory variables would be endogenous (i.e., correlated with the disturbance term e in the regression).^{15} Furthermore, we are interested first and foremost in the total effect of channeling on outcome variables, and are not particularly interested in how much of the impact on wellbeing (in our example) was indirectly due to channeling's effect on nursing home use and how much was due directly to the case management services provided by channeling. Therefore, we estimate the "reduced form" equation for the outcome variables. In the reduced form all explanatory variables must be exogenous (baseline/screen) variables and any exogenous variable that affects any of the interrelated outcome variables of interest is included. Thus, the explanatory variables in the reduced form include any baseline or screen variables that directly or indirectly affect the particular outcome variable being examined.^{16}
The advantage of this approach is that we need not make arbitrary exclusions of explanatory variables from some outcome equations but not others. Including in the regression explanatory variables that do not really affect a given outcome variable, directly or indirectly, does not bias the estimates of channeling impact, and given the large number of observations available, has no discernible effect on the standard errors of the estimates. On the other hand, excluding from the regression equation explanatory variables that do affect the outcome of interest can lead to biased estimates. Thus, the reduced form approach is more likely to yield unbiased estimates of channeling impacts than would specifications in which the set of control variables assumed to affect a particular outcome variable is arbitrarily restricted. This approach has the added benefit of providing consistency across the many analyses of channeling impacts that were conducted by different individuals at different points in time, and associated economies in estimation through standardizing estimation programs.
The explanatory variables that were used in the regression model fell into six categories:
 Sample member's absolute level of need for assistance due to physical or mental disabilities
 The availability of informal caregivers to provide this assistance
 The amount of formal care received by sample members at baseline
 The sample member's ability to pay for additional services or nursing home care
 The availability of nursing home beds and other areaspecific factors
 The sample member's outlook on life and demographic characteristics.
These six categories of characteristics were represented in the regression model by variables obtained from the baseline and screen interviews. Need for assistance was reflected by sample members' impairment on activities of daily living (ADL) tasks (eating, dressing, toileting, mobility, bathing), continence, whether they had a recent change in health condition, whether they were cognitively impaired (i.e., whether they had behavioral problems or were disoriented), the number of unmet needs for assistance that they had and expected to continue for 6 months or more, the number of physician visits in the two months prior to baseline, and whether the individual was referred to channeling by a hospital or nursing home or home health agency.^{17} We also included variables indicating whether the sample member completed the baseline without help from a proxy, required some help from a proxy, or required a proxy to complete the entire baseline.
The availability of informal care was captured by two variables: sample members' living arrangement and the number of hours of care they were receiving care from visiting informal caregivers during a typical week at the time of the baseline. Living arrangement was defined by whether sample members lived alone but were receiving informal care at baseline, lived alone without such care, lived with one of their children, or lived with someone but not with their child.
The receipt of formal care is also represented by two variables: whether such care was received from visiting caregivers, and the number of hours of inhome care received from visiting formal caregivers during a "typical" week at the time of the baseline.
Sample members' outcomes also depended on the availability of hospital and nursing home beds and on other area characteristics such as the availability of formal services and case management, population density, what services the state Medicaid program covered, and any other city, state or regional differences that could affect outcomes. Since the area characteristics faced were the same for all sample members residing in a given site, binary variables indicating in which site the sample member resided were sufficient to capture the effects of any such differences across sites.^{18} Hence, we included 9 binary site variables in the regression.^{19}
In addition to the amount of services received or available at baseline, another important factor affecting outcomes was the ability to pay for additional services, either in the community or in an institution. To capture these effects we included variables for whether sample members were eligible for Medicaid at baseline or would be eligible within a short period of time after entering a nursing home, based on their current income and assets.^{20} Whether sample members were homeowners was also included as a measure of their wealth.
The attitudes of elderly individuals are also important in explaining outcomes; hence, we included the baseline measure of sample members' overall satisfaction with life. Variables indicating whether the sample members had already applied for admission to a nursing home or were in a nursing home at the screen were included, because they indicate individuals' predisposition towards institutionalization. Also included was a binary variable indicating whether the sample member had lost a close friend or relative to death within the two months prior to baseline, since major losses are felt by many to have serious effects on elderly individuals' health.
Gender, age, and ethnic background are demographic variables included in virtually every study of the impaired elderly. There may be differences between elderly men and women in ability to care for themselves, in the difficulty caregivers face in caring for them, and in the likelihood that they will have a surviving spouse to care for them. Age is included because individuals' health deteriorates with age. Furthermore, the older a sample member is, the older his or her children and friends are likely to be and the less able to provide informal care. Ethnicity was included to capture any cultural differences in the intergenerational dependency, informal support systems, or attitudes toward nursing homes of the aged.
TABLE III.1. Mean Values of Explanatory Variables Used in Regression Model Variable Mean Variable Mean Need for Assistance Availability of Informal Care ADL Impairment (S) Living Arrangement (B) Extremely Severe 0.233 Lives Alone, No Informal Support 0.073 Severe 0.348 Lives Alone, Informal Support 0.282 Moderate Impairment 0.223 Lives with Child 0.251 Mild or No Impairment 0.196 (Lives with Someone Other than Child) 0.377 Incontinence (S) Missing Information 0.016 Incontinent 0.472 Hours of Care Received per Week from Visiting Informal Caregiver (B) 12.0 Needs Help with Colostomy Bag or Other Device 0.102 Demographic Characteristics and Attitudes (Continent) 0.426 Whether Male (B) 0.285 Cognitive Impairment (S) Age (B) 79.6 Severe 0.153 Ethnicity (S) (Moderate Impairment) 0.318 Black 0.223 Mild or No Impairment 0.471 Hispanic 0.037 Missing Data 0.058 (White or Other) 0.740 Unmet Needs (S) Whether Currently Married (B) 0.318 High Unmet Needs 0.303 Overall Satisfaction with Life (B) (Moderate Unmet Needs) 0.340 Completely 0.117 Low Unmet Needs 0.302 (somewhat) 0.248 Missing Data 0.054 Not Very 0.288 Whether Experienced Recent Change in Health (S) 0.818 Missing Data 0.348 Whether Death of Close Friend or Relative Other Than Spouse (S) Ability to Pay for Care Death of Close Person 0.244 Whether Home Owner (B) 0.421 (No Death) 0.406 Medicaid Coverage (B) Missing Data 0.350 Currently Eligible 0.226 Referral Source (S) Eligible Within 3 Months 0.304 Hospital or Nursing Home 0.297 (Not Eligible in 3 Months) 0.401 Home Health Agency 0.173 Missing Information 0.069 (Other) 0.531 Site Number of Physician Visits in Previous Two Months (B) 1.7 Basic Whether Waitlisted or Applied to Nursing Home, or in Nursing Home at Screen (B) 0.097 Baltimore 0.108 Type of Respondent at Baseline Eastern Kentucky 0.079 Self Respondent 0.417 Houston 0.111 (Mixed Proxy/Self Respondent) 0.298 Middlesex County 0.112 All Proxy Respondent 0.285 (Southern Maine) 0.079 Receipt of Formal Services Financial Control Whether Received Formal InHome Care (B) 0.600 Cleveland 0.093 Hours of Formal IHome Care Received per Week (B) 7.3 Greater Lynn 0.96 Model Miami 0.118 (Basic) 0.488 Philadelphia 0.140 Financial Control 0.512 (Rensselaer County) 0.065 NOTE: Means were computed for the Medicare sample, the largest analysis sample (N = 5,554) employing these standard control variables (see test for description of this sample). Letters in parentheses following variable names indicate whether data used were from the baseline (B) or screen (S) interviews. For variables represented by a set of binary indicators (e.g., ADL) one of the categories must be excluded from the regression to avoid perfect colinearity. Parentheses indicate which category was excluded, although this choice has no bearing on the estimates of treatment/control differences. The means of the variables included in the model are given in Table III.1. All variables were obtained from the screen or baseline interviews, as designated in the table. Most of the variables are binary and selfexplanatory. However, a few require some explanation. Impairment on activities of daily living (ADL) was defined according to sample members' most serious impairment, using the following hierarchy: eating, transfer or toileting, dressing, bathing. Thus, sample members impaired on eating were classified as extremely severe, those whose most serious impairment was transfer or toileting were severely impaired, those whose most serious impairment was dressing were moderately impaired, and others were classified as mildly impaired. Cognitive impairment was defined by whether sample members at screen exhibited behavioral problems or disorientation that required constant supervision (severe cognitive impairment), had behavioral problems that did not require daily supervision (moderate impairment), or had only mild or occassional problems with disorientation (mild or no cognitive impairment). Unmet needs was simply a count of the number of areas (0 to 5) in which the sample member needed more help and expected this need to continue for six months or more. "Change in health status" is a binary variable indicating whether the sample member reported experiencing the onset or worsening of any of several health conditions or illnesses.
Observations lacking data on one or more of the control variables were retained in the analysis by imputing values for missing variables. Data on some of the control variables were available from both the screen and baseline interviews; if data from the primary source for these variables was missing, 'values were imputed from the other source. Sample means were imputed in instances where no data were present on the desired variable from either the screen or the baseline, provided that less than 3 percent of the sample required imputation on that variable.^{21} If more than 3 percent of the sample were missing data on a particular variable, zero values were imputed and a separate binary variable was created, indicating for which observations the data were missing on the control variable. This missing data indicator was included in the regression equation to capture differences in outcomes between those with and without available data on a particular control variable.


B. Testing Strategy

The regression procedure described above provides estimates of treatment/control differences in outcomes, controlling for any initial differences between the two groups. These are our best estimates of channeling impacts. However, even if channeling had no impact the treatment and control groups may have somewhat different outcomes strictly by chance. Hence, we relied on statistical tests to determine whether the estimated differences were sufficiently large that they were unlikely to have occurred by chance.
Three types of tests were used in the analysis: ttests on the estimates of a_{B} and a_{F}, the estimated treatment/control differences, to determine whether they were significantly different from zero; Ftests to test whether the estimated impacts in the basic and financial control models differed from each other by more than might have been expected to occur by chance; and multivariate Ftests to test whether estimates of channeling impacts on sets of related outcome measures were equal to zero. Each of these tests is described below.
1. Tests of Whether Channeling Impacts Existed
The widely used ttest simply tests whether an estimated regression coefficient differs from zero by more than might reasonably be expected to occur because of sample variation. In our application, the regression coefficients a_{B} and a_{F} estimate the treatment/control differences. If the true effect of channeling on some outcome is zero, the estimates of a_{B} and a_{F} should be relatively small. The test enables us to determine, with some known probability of error, whether channeling had some impact on the outcome examined.
Two criteria must be specified by the researcher in conducting ttests: whether onetailed or twotailed tests are to be used and the significance level of the test. The choice of twotailed or onetailed tests depends on whether channeling is expected to affect the level of some outcome in a particular direction, or whether the impact could be in either direction. For most outcomes examined, the intention was that channeling would have a particular directional effect (e.g., reduction in nursing home use). However, for the vast majority of the outcomes, there were plausible reasons why the impact could be in the opposite direction, and for some important outcomes there was a high degree of uncertainty about the direction of channeling impacts to expect (e.g., informal caregiving and costs). Since we would clearly not ignore estimates that were of the "wrong" sign but were large and statistically significant had a twotailed test been conducted, the appropriate test to use is the twotailed test.
To avoid the appearance of arbitrariness in the selection of tests and confusion in the minds of readers as to which type of test was being employed in any given table, particularly in reports covering multiple outcomes, twotailed tests were used throughout the analysis, even for those few outcomes where the only plausible hypothesis about channeling's impact is unidirectional. The use of twotailed ttests also should result in greater consistency between these tests and the multivariate Ftests (described below), which are, by definition twotailed.
The use of twotailed tests did on occasion result in the inference that channeling's impact on a particular outcome in some time period was not significantly different from zero when a onetailed test would have led to a different conclusion. In such cases, supporting evidence from other time periods and related outcome measures was used to obtain the correct inference about whether channeling appeared to have impacts on the behavior under examination. The magnitude of the estimate also was considered in drawing these inferences. The interpretation of coefficients and test statistics is described in more detail in Section C below.
The significance level at which to conduct the ttests was the other testing decision. To make it relatively unlikely that chance differences between the two groups would be interpreted as channeling impacts, we followed customary conventions of statistical testing, conducting the ttests at the .05 (5 percent) significance level. This means that based on the sample size and observed sample variation, there was a small prior probability that treatment/control differences of the magnitude estimated would have occurred by chance, and that such differences are therefore likely to be due to the effects of channeling. Tables in final reports on channeling impacts containing estimated impacts also indicated which estimates would still have been statistically significant had the test been conducted at the .01 level, implying an even smaller likelihood that the observed difference was due to chance sample variation.
Although we believe these decisions about onetailed versus twotailed tests and significance levels are the most appropriate, throughout the final technical reports on channeling impacts we provide the tstatistics along with the estimates. Readers can therefore determine for themselves whether and how inferences would change if alternative choices had been made.
2. Tests of Equivalence of Impacts Between Basic and Financial Control Models
In addition to determining whether the basic and financial control models affected specific outcome measures, we were also interested in knowing whether the models differed from each other in thesize of the impact. It was hypothesized that the greater resources and flexibility of funding available under the financial control model would result in larger impacts for this group. However, differences between the environments into which the two models were introduced could also produce differences in the size of impacts achieved by the alternative models.^{22}
Simple Ftests of the equivalence of a_{B} and a_{F} (from the regression equation) provided the tests of this hypothesis. The tests were conducted at the .05 level, consistent with the significance level selected for the ttests. To reduce the likelihood of inconsistencies in the test results (such as the estimate for one model being significantly different from zero and the other not, but an Ftest indicating no significant difference between the two models), the Ftests were conducted in two stages. We first tested whether a_{B} and a_{F} were both equal to zero using a joint Ftest. If that hypothesis could not be rejected, no further test of equivalence was necessary. If the test did indicate rejection of the hypothesis that both were equal to zero we then tested whether they were equal to each other.
3. Multivariate Tests of Whether Channeling Impacts Existed
The individual tests described above were conducted at a significance level that made it relatively unlikely that, for any particular outcome measure, chance differences between treatment and control groups would be interpreted incorrectly as channeling impacts. However, because so many outcomes were examined (each for 2 models and 3 time periods), the probability that such errors would occur in at least a few instances was very high. To lessen the probability of making such errors, multivariate tests were employed that simultaneously tested the hypothesis that channeling impacts on a set of related outcome measures were jointly equal to zero. For example, estimates of channeling impacts on nursing home days, the probability of being admitted to a nursing home, and nursing home expenditures were tested jointly to determine whether any were significantly different from zero. The advantage of this type of test is that if (for example) only one of the 6 impact estimates (3 for each model) were significantly different from zero using the individual ttests, and the other impact estimates were all small and far from being statistically significant, it is probably unlikely that channeling really influenced nursing home use. The multivariate test in such cases would typically indicate (depending on the size and significance of the estimates) that we could not reject the hypothesis that channeling's impact on the set of nursing home outcomes was zero.
Tests that impacts on the set of outcomes being considered jointly were all zero were conducted for the basic model, the financial control model, and for both models together. We also used multivariate tests to determine whether impacts on given sets of outcomes in the basic model were equal to those in the financial control model. In each case the tests were conducted on related outcome measures, such as alternative measures of wellbeing or informal care, for a given time period. Because the tests require that the same observations be used in all of the equations for which the coefficients are being tested jointly, outcomes in different time periods were tested separately.
The lower likelihood of erroneously concluding that channeling affected outcomes when the treatment/control differences were actually due to chance makes the use of multivariate tests attractive. Furthermore, it suggests that they should be used hierarchically, that is, that ttests should only be examined if the multivariate tests indicate that not all channeling impacts in a given substantive area are zero. In this instance ttests would indicate which of the outcomes channeling did appear to affect. However, strict adherence to test results in this fashion would increase the probability of making the opposite type of errorconcluding that channeling had no impacts when in fact it had. The method of assessing and interpreting the many estimates and test statistics produced in the analysis, described in the section below, was designed to strike a balance between these two types of errors.


C. Interpretation of Estimates

Performing statistical tests at the .05 significance level ensures a low probability of erroneously concluding that channeling affected a given outcome when the observed treatment/control difference is actually due to chance ("type I" errors). The use of multivariate tests further decreases the probability of such errors. The discussion of the power of the statistical tests presented earlier suggested that the sample sizes were sufficiently large that with ttests performed at the .05 significance level we could be quite confident that large channeling impacts (i.e., those of policyrelevant magnitude) would not be misclassified as due to chance. However, strict adherence to the more stringent multivariate test to reduce further the probability of type I errors means that it is more likely that we will make the opposite errorconcluding that channeling had no impact when the program was truly effective (type II errors).^{23}
Because of the desire to avoid both types of error, we do not rely solely on the hierarchical testing structure raised in the previous section, nor on any single statistic to ascertain whether channeling affected outcomes of interest. The sheer number of outcomes examined and test performed means that strict reliance on test statistics would result in a number of both type I and type II errors.
To decrease the number of such errors, throughout the analysis we looked not only at the statistical significance of the estimates but also their magnitudes and patterns. Specifically, to assess whether channeling affected a given outcome, we looked for consistency in the direction, size, and statistical significance of estimated impacts on: (1) related outcome measures, (2) the same outcome in other time periods, and (3) the same outcome in the other channeling model.
We also examined the estimated impact at the site level, to see if the model level estimate was essentially due to one or two particular sites rather than being widespread. Dependence on patterns across model, time period, and site to verify whether impacts exist cannot be rigid, since there are reasons why effects may differ across these dimensions. Nevertheless, if patterns exist they provide evidence that the observed differences were due to channeling rather than to chance.
Finally, we also drew on theory and results from the process analysis (Carcagno et al., 1986) to assess the likelihood that the estimates obtained represented real impacts rather than chance differences. It is clearly inappropriate to conclude that only those estimates with the expected sign were due to real effects of channeling and all others were due to chance. However, awareness of how outcomes interrelate and the process by which channeling was likely to bring about effects on individuals, coupled with knowledge about how the local programs operated, were useful inputs to the assessment of whether impacts existed when the statistical evidence was mixed.
The following example demonstrates this strategy of evaluating the impact estimates. Suppose that estimated channeling impacts on nursing home days in the basic model at 6 months were statistically significant, and admissions were not significant and that the multivariate test was not significant. However, suppose that the tstatistics on these other impact estimates and the multivariate test statistics were quite near the critical values necessary for the estimates to be considered significant, and the estimates were all of the expected sign. Suppose further that the estimates for the 7 to 12 month period were also of the same sign and roughly comparable in magnitude, but not significant at the .05 level. In such cases, it would seem likely that channeling did have an impact on nursing home use, although perhaps not a strong effect or perhaps an effect that was concentrated in a few sites or in clients of a certain type. The magnitude of the estimates and effects on subgroups of sample members were examined to address these possibilities. Finally, since channelingInduced reductions in nursing home use were expected to be obtained by increases in formal communitybased services, we would examine service impact estimates for confirmation. Thus, we used the collective evidence of several estimates and test statistics to determine whether channeling influenced outcomes in a given area.
In addition to ascertaining whether channeling affected outcomes, we also examined the size of the estimated impact. In general, it makes little difference whether channeling impacts in a given area were zero or just very modest in size. To obtain some indication of the proportionate magnitude of impacts, mean values of the outcome variables for the control group were presented alongside the estimated impacts on these outcomes in tables displaying the results. Impacts exceeding 20 percent of the control group mean were generally felt to be large, although this could vary with the absolute magnitude of the control group mean (20 percent of a very small mean is still a very small impact). The dollar value of the impact also provided a useful way of assessing the importance of a given estimate for some outcome measures.


D. Estimated Impacts for Subgroups

In addition to determining whether channeling impacts differed by model, we also tested whether impacts on key channeling outcomes differed across sites and for various subsets of the sample defined by characteristics of the sample members. The baseline and screen characteristics used to form these subsets, described more fully n Grannemann et al. (1986), include:
 Impairment on activities of daily living (extremely severe, severe, moderate, mild/none)
 Continence (incontinent, need help with device to be continent, continent)
 Unmet needs (high, medium, low)
 Living arrangement (alone without current informal support, alone with current support, with own child(ren), with someone but not with child)
 Health system contact (in nursing home at randomization, on nursing home wait list, in a hospital or referred to channeling by a hospital or nursing home, referred by a homehealth agency, referred by family or other source or self)
 Medicaid eligibility (eligible at baseline, not eligible but would be within 3 months after entering nursing home, would not be eligible)
 Cognitive impairment (severe, moderate, mild/none)
 Site
All of these characteristics were also explanatory variables in the standard regression model given in equation 1. (See Table III.1 for sample means of these variables.)
To obtain estimated impacts for the 3 to 5 subgroups formed by each of the classifying variables, the standard regression was modified as follows:
(2) Y = a_{0} + a_{T}T + a_{1}X_{1} + a_{2}X_{2} + a_{T1}T*X_{1} + a_{S}S + a_{TS}T*S + e, where X_{1} is a vector that contains the binary variables representing the characteristics defining the subgroups and X_{2} contains the other explanatory variables used in the standard regression model.^{24} This equation was estimated separately for the basic and financial control models, to reduce the number of parameters and simplify the calculation of impacts and standard errors.
The estimate of channeling's impact obtained from this model is
Impact = a_{T} + a_{T1}X_{1} + a_{TS}S,
which depends on the set of 8 characteristics defining the subgroups. Estimated impacts for a particular subgroup were calculated by setting the variables in X_{1} representing the classifying characteristic of interest at 1 for the category for which impact estimates were desired and 0 for the other categories of this characteristic, and setting all of the other characteristics in X_{1} at the sample mean. Impacts were estimated in this way for each subgroup defined by each of the classifying variables. Standard errors of these estimated impacts were computed and used to form tstatistics to test whether impacts were significantly different from zero.
The primary tests conducted, however, were of whether the estimated impacts differed from each other across the subgroups defined by each of the classifying variables.^{25} The hypothesis that no such difference occurred was tested by performing for each classifying characteristic an Ftest of whether the coefficients in a_{T1} (or a_{TS} for tests of equivalence across sites) on the binary variables representing that characteristic were equal to zero. Given the large number of such tests, however, we first jointly tested all of the coefficients in a_{T1} to determine whether they were equal to zero. Rejection of this hypothesis indicated that channeling impacts on a given outcome did vary with at least one of the classifying characteristics. In such cases, the Ftests for each characteristic were then examined to determine with which of the characteristics channeling impacts varied. More details on the computation and interpretation of these test statistics is given in Grannemann et al. (1986).

View full report
"methodes.pdf" (pdf, 2.16Mb)
Note: Documents in PDF format require the Adobe Acrobat Reader®. If you experience problems with PDF documents, please download the latest version of the Reader®