Anxiety and orienting of gaze to angry and fearful faces

Neuroscience research indicates that individual differences in anxiety may be attributable to a neural system for threat-processing, involving the amygdala, which modulates attentional vigilance, and which is more sensitive to fearful than angry faces. Complementary cognitive studies indicate that high-anxious individuals show enhanced visuospatial orienting towards angry faces, but it is unclear whether fearful faces elicit a similar attentional bias. This study compared biases in initial orienting of gaze to fearful and angry faces, which varied in emotional intensity, in high- and low-anxious individuals. Gaze was monitored whilst participants viewed a series of face-pairs. Results showed that fearful and angry faces elicited similar attentional biases. High-anxious individuals were more likely to direct gaze at intense negative facial expressions, than low-anxious individuals, whereas the groups did not differ in orienting to mild negative expressions. Implications of the findings for research into the neural and cognitive bases of emotion processing are discussed.
1
Introduction
Research into the cognitive and neural mechanisms underlying emotion processing indicates neural circuitry, involving subcortical and cortical structures (including the amygdala and prefrontal cortex), which is responsible for detecting threat-related cues in the environment and triggering a variety of cognitive, behavioural and physiological responses, in particular, attentional vigilance (LeDoux, 1996; Davis and Whalen, 2001). Neuroimaging studies show that the amygdala, which plays a central role in this circuitry, is reactive to threat-related cues, such as fearful faces (e.g., Whalen et al., 1998). Individual differences in the operation of this threat-processing system may underlie individual differences in vulnerability to anxiety and anxiety disorders (Davis and Whalen, 2001).
From a clinical perspective, cognitive models of anxiety also assume the existence of a threat-processing system and propose that anxiety is characterized by cognitive biases (in particular, in stimulus evaluation and selective attention) which favour the processing of threat cues. These biases are proposed to be responsible for individual differences in anxiety vulnerability (e.g., Beck and Emery, 1985; Williams et al., 1997; Mogg and Bradley, 1998). Cognitive models, such as Beck and Emery's (1985), are important because they provide the basis for cognitive-behaviour therapy (CBT), which aims to remove these threat-processing biases and is an effective treatment for many anxiety disorders (e.g., Ballenger, 1999). Research into cognitive models of anxiety indicates that anxious individuals have an enhanced attentional bias for threat cues, compared with non-anxious individuals, and that this bias operates in early aspects of processing (e.g., review by Mogg and Bradley, 1998).
In order to understand the functional properties of this postulated threat-processing system, it is necessary to clarify the type of stimuli to which it is sensitive. Neuroimaging research typically indicates that the amygdala is more reactive to fearful than angry faces (e.g., Whalen et al., 2001; Blair et al., 1999; Davis and Whalen, 2001; but see Yang et al., 2002). Davis and Whalen (2001) suggested that fearful faces elicit more amygdala activity because they are ambiguous (i.e. they signify the presence of danger, but do not provide information about its source) and that the threat-processing system is more reactive to ambiguous (or indirect) threat cues because it is designed to promote attention to stimuli which require more detailed processing in order to determine appropriate responding (e.g., escape or approach). Neuroimaging evidence has been reported supporting the hypothesis of enhanced processing of ambiguous threat cues, as indexed by amygdala responses to angry and fearful faces (Adams et al., 2003). Thus, according to Davis and Whalen's (2001) theoretical view, fearful faces should be particularly effective in capturing attention and in eliciting vigilance.
Biases in visuospatial orienting to threat faces have been investigated using visual-search, visual-probe and eye-movement paradigms. Visual-search studies require participants to search arrays of faces for discrepant emotional expressions and results have indicated faster detection of schematic angry faces, relative to positive faces (e.g., Öhman et al., 2001; Fox et al., 2000). In the visual-probe task, pairs of faces (e.g., angry face and neutral face of the same individual) are briefly presented, followed by a probe stimulus (e.g., small dot) to which the participant responds. Response times (RTs) to probes reflect the allocation of attention to the faces, as RTs are typically faster to probes which appear in attended, rather than unattended, locations. Visual-probe studies provide evidence of enhanced attentional biases for angry faces in individuals with high non-clinical anxiety, and in anxious patients (e.g., Bradley et al., 1998, 1999; Mogg et al., 2004). They also indicate that the attentional bias increases as the intensity of the angry expression increases (Wilson and MacLeod, 2003). Eye-movement methodology has extended this research, as it provides a direct and ecologically-valid measure of visual orienting, and further indicates that anxious individuals have a greater bias to direct their gaze initially towards angry faces (relative to neutral or happy faces), compared with non-anxious individuals (Bradley et al., 2000; Mogg et al., 2000), which suggests that the attentional bias for threat operates at a relatively early stage of visual processing and guides eye-movements. However, there has been little research using these paradigms to assess biases in visual orienting towards fearful faces. For example, one study using a visual-search task suggested that angry, but not fearful, faces preferentially attract attention (Williams et al., 2005), whereas another study using a visual-probe task indicated an attentional bias for fearful faces that had been perceptually degraded (i.e. low spatial frequency; Holmes et al., 2005). Neither study examined the influence of anxiety.
Consequently, the main aim of the present study was to compare biases in initial orienting of gaze towards fearful versus angry faces in high- and low-anxious individuals. A second aim was to examine the effect of manipulating the emotional intensity of the faces (cf. Blair et al., 1999; Wilson and MacLeod, 2003) on attentional responses. Neuroimaging research (Davis and Whalen, 2001) predicts that fearful faces should elicit stronger vigilance responses than angry faces. In addition, cognitive models of anxiety predict that high-anxious individuals should show greater biases in initial orienting of gaze towards both angry and fearful faces, than low-anxious individuals. Furthermore, if biases in initial orienting to threat-related cues are primarily a function of the affective salience of the stimuli (Mogg and Bradley, 1998), then these biases in orienting should increase as the intensity of the fearful and angry expressions increases.
2
Method
2.1
Participants
Participants were undergraduates who were screened on the trait version of the State-Trait Anxiety Inventory (STAI; Spielberger, 1983); those with trait anxiety scores of 40 or less were allocated to the low anxiety group, and those scoring 50 or more to the high anxiety group. Additional selection criteria were fluency in spoken English and visual acuity within normal limits. Nine volunteers did not complete the study due to equipment calibration problems and six participants had excessive missing eye-movement data (described later). The final sample comprised 28 participants (3 M, 25 F) in the low-anxious group, and 21 (3 M, 18 F) in the high-anxious group.
2.2
Materials and apparatus
Twenty-four face stimuli were selected from the NimStim Set of Facial Expressions (http://www.macbrain.org/faces/).1 They consisted of angry, fearful and neutral prototypical expressions posed by eight models (four female and four male numbered: 01f, 03f, 07f, 08f, 21m, 23m, 27m, 34m). For each model and type of facial expression (fearful or angry), the emotional face was blended with the neutral face to produce a continuum comprising five exemplars of increasing emotional intensity: i.e. neutral prototype (0%), three morphed expressions (25%, 50% and 75%) and the emotional prototype (100%); see Fig. 1. The preparation of the morphed faces used Gryphon Morph v2.5 software (Gryphon Software Corporation, 1994). Each emotional face (i.e. 25%, 50%, 75% or 100% exemplar of a fearful or angry face) was paired with the neutral expression (0%) of the same model to create 64 face pairs for use in the attentional task.
The task was administered using MEL2 software (Schneider, 1995), Pentium 450 MHz PC, 15 in. VGA monitor and MEL2 response box. Eye-movements were monitored with 120 Hz infrared pan/tilt eye tracking system (Model 504, Applied Scientific Laboratories, Bedford, Massachusetts) and E5000 software (Applied Science Group, 2000) which was run on a Pentium 333 MHz PC. Testing was conducted in a dimly lit room.
2.3
Procedure
Participants completed a visual acuity check and were seated 1 m from the monitor, with the eye tracking camera positioned 50 cm in front of them, below the right eye. The equipment was calibrated by displaying the numbers 1 to 9 on the screen in a 3 × 3 array and recording the direction of gaze whilst participants looked at each number in turn.
Each trial of the attentional task started with a central fixation-cross shown for 1000 ms, followed by a pair of pictures presented side by side for 500 ms. The pictures measured 90 mm × 110 mm, with their inner edges 45 mm apart. Immediately after the offset of the picture-pair, a probe (pair of dots : or ..) was presented in the position of one of the preceding pictures (visual angle of 7.7° between two probe positions) until a manual response. Participants were asked to press one of two keys as quickly as possible to indicate the type of probe. Inter-trial interval varied randomly between 750 and 1250 ms. Participants were instructed to keep their head still throughout the task and to look at the fixation-cross at the start of each trial. Eye-movement data were recorded from the onset of the fixation-cross until the manual response. There were eight practice trials followed by two blocks, each consisting of two buffer trials and 256 experimental trials, with a short rest-break between the blocks. Across the whole task, the 64 face pairs were presented eight times, balanced for emotional face location and probe location (left vs. right). Trials were presented in a new random order for each participant. If eye calibration quality deteriorated during the task, the task was briefly interrupted to repeat the calibration procedure.
After the attentional task, participants completed three face rating tasks: forced-choice discrimination, anger and fear ratings, which were included to check the effectiveness of the manipulation of emotional intensity. Each task presented the faces from the attentional task, one at a time, in random order. Each trial consisted of a central fixation-cross (500 ms), followed by a face and rating scale, displayed until a keyboard response. In the forced-choice discrimination task, participants indicated whether each face was angry or fearful. In the anger and fear rating tasks, they rated each face for how angry or fearful it appeared on a scale ranging from 0 (not at all) to 8 (extremely). Finally, they completed questionnaires including the state and trait versions of the STAI, and short-form Social Desirability Scale (SDS; Strahan and Gerbasi, 1972; the latter was included because defensiveness can have a confounding effect on measures of anxiety and attentional bias, e.g., Eysenck, 1997).
2.4
Data preparation
2.4.1
Eye-movement data
These were prepared using the Eyenal Data Analysis Program (Applied Science Group, 2000). Direction of gaze was measured in degrees approximately once every 8 ms. If eye-movements were stable within 1° of visual angle for 100 ms or more, this was classified as a fixation to that position. Initial fixations to the pictures were calculated if the following criteria were met: (i) gaze was fixated in the central region before picture onset, (ii) fixations occurred at least 100 ms after picture onset, and (iii) fixations were directed at either the left or right picture (>1.3° wide of central fixation position on the horizontal plane). Six participants had excessive missing data (85% or more of trials lacked identifiable fixations), so were excluded from all analyses. The amount of missing data did not vary as a function of anxiety group, F(1, 47) < 1.7, emotional face type, emotional face intensity, or their interaction, F(3, 141) <1.5, all p's > .2. Gaze-direction bias scores were obtained for each participant and each level of intensity of fearful and angry faces by calculating the number of trials in which the first shift of gaze was towards the emotional face, as a proportion of the number of trials in which the initial shift in gaze was made to either the emotional or neutral face (bias scores > .5 reflect an orienting bias towards the emotional face; .5 = no bias).
2.4.2
RT data
Data from error trials (2%) and RTs <200 ms or >1250 ms (1%) were excluded. The groups did not differ in missing data or overall mean RT, ts < 1. An inverse transformation was applied to the RT data to reduce the influence of skewness and outliers (Ratcliff, 1993). Attentional bias scores were calculated using a standard formula (e.g., Bradley et al., 1998), for each participant and each level of intensity of fearful and angry faces, by subtracting the mean RT when the emotional face and probe were in the same location from the mean RT when the emotional face and probe were in different locations. RT bias scores were corrected (reverse sign and multiply by 106) so that they would be easier to comprehend, i.e. positive values of bias scores reflect an attentional bias for emotional faces, relative to neutral faces (0 = no bias).
Kolmogorov–Smirnov tests showed that the distributions of RT bias scores and gaze-direction scores were within normal limits.
3
Results
3.1
Group characteristics
The high- and low-anxious groups differed significantly in trait and state anxiety (see Table 1), but not in social desirability scores, age or gender ratio.
3.2
Eye-movement data
Gaze-direction bias scores were entered into a 2 × 2 × 4 mixed design ANOVA with anxiety group (high, low) as the between-subjects independent variable (IV), and emotional face type (angry, fearful) and intensity of emotional face (25%, 50%, 75%, 100%) as within-subject IVs (see Fig. 2 for means). There were significant main effects of anxiety group, F(1, 47) = 4.68, p < .01, , and emotion intensity, F(3, 141) = 10.49, p < .01, , which were qualified by a significant anxiety group × emotion intensity interaction, F(3, 141) = 3.02, p < .05, . There were no other significant results, e.g., anxiety group × emotion intensity × type of emotional face: F < 1. Separate ANOVAs of bias scores from each group showed a significant main effect of emotional intensity on gaze direction in the high-anxious group, F(3, 60) = 9.54, p < .001, , but not in the low-anxious group, F(3, 81) = 2.04, p = .12, . To clarify further the anxiety group × emotion intensity interaction, the groups were compared on their bias scores at each level of intensity (averaged across angry and fearful faces).
There was no significant difference between the high- and low-anxious groups in bias scores for 25% negative faces, (Ms = .508 vs. .500, t < 1, NS, d = .11), 50% negative faces (.533 vs. .533, t < 1, NS, d = .003), or 75% negative faces (.561 vs. .528, t(47) = 1.34, NS, d = .39). A one-sample t-test was used to contrast the RT bias scores against a value of .5 (which indicates no bias); results showed that, irrespective of group, participants showed no attentional bias for 25% negative faces (.504, t < 1, NS, d = .05), but a significant bias for 50% negative faces (.533, t(48) = 2.99, p < .01, d = .43) and also for 75% negative faces (.542, t(48) = 3.39, p < .01, d = .48).
For 100% negative faces, the high-anxious group showed a significantly greater bias in orienting towards them, compared with the low-anxious group (.629 vs. .547, t(47) = 3.21, p < .01, d = .91). One-sample t-tests showed a significant bias in gaze towards 100% negative faces, relative to neutral faces, in both the high-anxious (.629, t(20) = 5.66, p < .001, d = 1.24) and low-anxious (.547, t(27) = 3.26, p < .01, d = .62) groups. Thus, the bias for participants to direct their gaze initially at prototypical (100%) negative faces, was significantly stronger in the high-anxious group.
3.3
Manual RT data
RT bias scores were entered into a 2 × 2 × 4 mixed design ANOVA with anxiety group, type of emotional face and intensity of emotional face as IVs. There was a significant group × emotion intensity interaction, F(3, 141) = 2.83, p < .05, , and no other significant results (e.g., group × intensity × face type: F < 1). The pattern of significant findings corresponded to that in the eye movement data. The high- and low-anxious groups did not significantly differ in RT bias scores for 25% negative faces (−7.4 vs. 16.4, t(47) = 1.40, NS, d = .40), 50% negative faces (13.1 vs. 19.0, t < 1, NS, d = .11), or 75% negative faces (4.5 vs. 24.9, t(47) = 1.17, NS, d = .33). Contrasts of RT bias scores against zero showed that, irrespective of group, participants showed no attentional bias for 25% negative faces (6.2 vs. zero, t < 1, d = .10), a significant bias for 50% negative faces (16.4, t(48) = 2.12, p < .05, d = .30), and a near-significant bias for 75% negative faces (16.2, t(48) = 1.87, p < .07, d = .27).
For 100% negative faces, there was a significant group difference in attentional bias (t(47) = 2.04, p < .05, d = .60). Contrasts of the bias scores against zero showed a significant attentional bias for 100% negative faces in the high-anxious group (37.4, t(48) = 3.48, p < .01, d = .76), but not in the low-anxious group (4.2, t < 1, d = .07).
The RT bias and gaze-direction bias scores for 100% negative faces significantly correlated with each other (r = .35, p < .05). There were no significant associations between these two measures of attentional bias for negative faces at lower levels of emotion intensity (r = .11, .10 and .13 for 25%, 50% and 75% negative faces, respectively, all NS).2
3.4
Rating tasks
3.4.1
Forced choice discrimination task
A 2 (anxiety group) × 2 (emotional face type) × 4 (intensity of emotional face3) ANOVA of the proportion of faces correctly classified as angry or fearful showed a significant main effect of intensity, F(3, 141) = 173, p < .001, , as, unsurprisingly, more intense emotional expressions were classified more accurately. Mean proportion of correctly classified negative faces with 25%, 50%, 75% and 100% intensity was .70, .94, .97 and .96, respectively. There were no other significant results (e.g., group × intensity, F(4, 188) = 1.26, p > .25; group × intensity × face type: F < 1).
3.4.2
Anger ratings
A 2 (anxiety group) × 5 (emotion intensity) ANOVA of ratings of faces from the anger continuum showed only a significant main effect of intensity, F(4, 188) = 594, p < .001, , with no effects involving group, Fs < 1. Mean anger ratings of 0%, 25%, 50%, 75% and 100% angry faces were 1.6, 2.8, 4.8, 6.2 and 7.0, respectively. Bonferroni-corrected contrasts showed that each mean differed significantly from each other, p < .005.
A 2 × 5 ANOVA of anger ratings of faces from the fear continuum showed only a significant effect of intensity, F(4, 188) = 11.63, p < .001, , with no effects involving group, Fs < 1. Mean anger ratings of 0%, 25%, 50%, 75% and 100% fearful faces were 1.6, 1.1, 0.8, 0.8 and 0.7, respectively. Bonferroni-corrected contrasts showed that faces with fearful content (i.e. 25–100%) were rated as less angry than neutral faces, p < .005.
3.4.3
Fear ratings
A 2 × 5 ANOVA of fear ratings of faces from the fear continuum showed a significant main effect of intensity, F(4, 188) = 515, p < .001, , with no effects involving group (group: F < 1; group × intensity: F(4, 188) = 1.20, NS). Mean fear ratings of 0%, 25%, 50%, 75% and 100% fearful faces were 1.3, 2.3, 4.4, 5.9, and 6.6, respectively. Bonferroni-corrected contrasts showed that each mean differed significantly from each other, p < .005.
A 2 × 5 ANOVA of fear ratings of faces from the anger continuum showed a significant main effect of emotion intensity, F(4, 188) = 2.72, p < .05, , and no effects involving group, Fs < 1. Mean fear ratings of the 0%, 25%, 50%, 75% and 100% angry faces were 1.3, 1.5, 1.2, 1.0 and 0.9, respectively. Bonferroni-corrected contrasts did not reveal significant differences between these ratings.
4
Discussion
The present findings indicate that fearful and angry faces elicited similar biases in visuospatial orienting. There was a greater tendency for participants to direct gaze initially towards faces with moderate or intense threat-related facial expressions (50–100% intensity), relative to neutral faces, whereas mild threat-related facial expressions (25% intensity) did not elicit a bias in initial orienting. Moreover, visuospatial orienting to both fearful and angry faces was significantly influenced by individual differences in anxiety: high-anxious individuals showed a greater tendency to direct gaze at prototypical (100%) threat-related faces, irrespective of whether the faces depicted fear or anger. The secondary measure of attentional bias, which was obtained from manual RTs, showed a similar pattern of results to that obtained in the eye-movement data.
The enhanced attentional bias in high-anxious individuals, which was found for prototypical angry faces, is compatible with previous findings, e.g., from visual-probe or eye-movement studies (e.g., Mogg and Bradley, 1998; Mogg et al., 2000). The present results further indicate that high- and low-anxious individuals did not differ significantly in visual orienting to threat-related faces which had weaker emotional expressions. This may explain why some studies might fail to find evidence of an effect of individual differences in anxiety on attentional bias, if they use less salient exemplars of emotional facial expressions than the prototypical faces used here.
The finding that fearful and angry faces elicited a similar pattern of attentional bias is consistent with cognitive and neural models which posit the existence of a threat-processing system which modulates vigilance for potential sources of danger. Previous research using prototypical expressions suggests that this bias in visual orienting of gaze is specific to threat-related faces, as, for example, anxious patients have a greater bias to shift their gaze initially towards angry faces, but not happy or sad faces, compared with non-anxious controls (Mogg et al., 2000). Given this previous finding, the present study did not include these additional face types; moreover, these would have made the task too long and fatiguing for participants.
The finding of an equivalent pattern of attentional bias for fearful and angry faces is not consistent with expectation from some recent neuroimaging research findings (discussed earlier) which indicated that the amygdala, which is proposed to modulate attention to threat-related cues, is more sensitive to fearful than angry faces (Davis and Whalen, 2001). However, this might be resolved by considering the different component processes of attention, namely, shifting versus maintenance (LaBerge, 1995; Serences et al., 2005). Thus, the amygdala may indeed modulate attention to threat, but its level of activation may be a function of both initial orienting and maintained attention. Fearful and angry faces may have a similar capacity to attract attention initially (as indicated by the present findings), but may differ in the extent to which they hold attention. After initial orienting to angry faces, attention may be subsequently directed away from them, e.g., due to emotion-regulation processes (Mogg and Bradley, 1998; Rohner, 2002; Garner et al., 2006); whereas attention may be maintained longer on fearful faces if they require more detailed processing to determine appropriate responding, as suggested by Davis and Whalen (2001). This hypothesis could be addressed by both cognitive and neuroimaging studies: for example, eye-movement studies, which employ longer display times to assess the time-course of attentional responses (e.g., >1 s), could compare biases in both initial orienting and gaze dwell time for fearful versus angry faces.4 Neuroimaging studies could examine whether amygdala response to angry faces is positively associated with initial orienting to threat, but inversely associated with subsequent attentional avoidance; as suggested by research indicating that amygdala activity is suppressed by inhibitory influences of the prefrontal cortex, which mediates emotion-regulatory processes including attention control (Nomura et al., 2004; Ochsner and Gross, 2005). Thus, although many neuroimaging studies have focused largely on the role of the amygdala in emotion processing, it is important to consider it within the context of a broader network involving other structures (e.g., prefrontal cortex, thalamus, hippocampus, periaqueductal gray, locus coeruleus), with which it has reciprocal connections and which subserve a variety of functions, including attention, startle, escape and avoidance (Davis and Whalen, 2001; McNaughton and Corr, 2004).
The results from the face rating tasks confirmed the content validity of the morphed face stimuli, which were well-differentiated in emotion intensity. The groups did not differ in their explicit ratings of the faces, which is compatible with previous research that has also failed to reveal anxiety-related differences in ratings of emotional faces, and which might be due to demand or social desirability effects obscuring anxiety-related effects on self-report data (e.g., Wilson and MacLeod, 2003). These null results contrast with those from the attentional task which indicated that individual differences in anxiety played an important role in determining the processing of emotional stimuli, namely, in visual orienting. Thus, to clarify the neurocognitive mechanisms underlying emotion processing, it would seem helpful for neuroimaging studies to make greater use of tasks that are sensitive to individual differences in anxiety, such as attentional paradigms (e.g., Monk et al., 2006), and also to routinely assess individual differences in anxiety, given that this is an important determinant of attentional responses to threat.

anxiety
fearful faces
angry faces
attentional bias
eye movements


Specialized palliative care services are associated with improved short- and long-term caregiver outcomes

Goals of work The goal of this study was to evaluate, at a population level, the association between specialized palliative care services (SPCS) and short- and long-term caregiver outcomes.
Introduction
In the more than 7,300 specialized palliative-care services (SPCS) internationally [6], a variety of models of service delivery are in place [42]. A common model of SPCS relies on trained specialist providers, whose work is largely in palliative care; in this model, coordination of care occurs wherever the patient is located.
To date, evaluation of SPCS has been difficult [1, 28]. A recent meta-analysis demonstrated that SPCS had a positive impact on patients’ pain, other symptoms, and caregiver satisfaction. Evidence is lacking for other caregiver outcomes, or for economic benefit to justify the community’s healthcare investment in these services [31].
Palliative care is person-centered care that focuses on optimizing function and comfort in the setting of life-limiting illness. The patient, family member(s), and informal caregiver(s) comprise the “unit of care.”
SPCS must be judged by their net health impact not only on the patient but also on caregivers including short-term (while the individual performs the functions of caregiver) and long-term (once the role has ceased) outcomes [14]. There are extensive demands on informal caregivers serving patients with life-limiting illness; the caregiver burden encompasses physical, emotional, financial, existential, and social responsibilities [2, 20, 53]. Many caregivers perform a variety of roles, and those with a larger number of roles exhibit greater caregiver strain [33]. Caregiving may compromise health and is associated with premature mortality long after the death of the patient [9, 47, 50, 56, 58]. Depression and restriction of activities are common among caregivers [5, 22, 66]. Caregiver outcomes depend upon a host of variables including gender, age, socioeconomic status, living situation, and the type and quality of relationship between the caregiver and the recipient of care [43].
In the short term, SPCS coordinate support and services in an attempt to minimize caregiver distress and respond to unmet needs [40, 59]. “Unmet needs”, defined as needs not being addressed plus needs receiving insufficient attention, can be psychological, social, informational, physical (activities of daily living and household management), existential, legal, or financial [40]. The experience of caregiving has been described in five domains, each of which carries support needs and outcome repercussions; these domains are (1) disrupted schedule, (2) financial problems, (3) lack of family support, (4) loss of physical strength, and (5) caregiver self-esteem [24, 43]. Inadequate support of caregivers may predict financial burden and poorer caregiver health [59]. A summary measure for differences in caregiver outcomes can be the total number of unmet needs [40, 59].
In the intermediate to long term, SPCS programs are designed to facilitate families’ and caregivers’ adjustment to the loss that they have experienced [34]. High-level long-term caregiver outcomes, with which to evaluate these programs, are poorly developed [27, 28, 68]. One such potential caregiver outcome is “moving on.” Inability to “move on” has been identified as a marker of a complicated course for caregivers after the death [50, 51]. Conversely, caregivers often reflect that they are starting to move on with their life at some point after death. Psychiatric and social-work literature defines moving on as establishing a new caregiver-defined equilibrium after having experienced a period of disequilibrium and integrating the potentially life-changing impact of caregiving into one’s new self-perception and life roles in areas including relationships, intimacy, work, and finances [7, 13, 17, 19, 55]. Moving on is not simply “taking up where one left off” [7]; rather, it implies some degree of re-adjustment and integration of the caregiving and bereavement experiences. It may be characterized as returning to a sense of well-being, “reframing,” or “adapting” [53, 54, 65]. Despite the widespread use of the concept, a validated instrument for measuring moving on has not yet been developed. Nonetheless, the concept of enabling a caregiver to move on can be a first approximation of the long-term impact of SPCS on caregivers.
Historically, efforts to assess SPCS’ influence on short- and long-term caregiver outcomes have been limited because of difficulties in identifying a population of caregivers for people with life-limiting illnesses who do not interact with SPCS [2, 41, 63]. For a population-based assessment of SPCS outcomes, the appropriate denominator must include all caregivers for people with life-limiting illnesses, not only those who are referred to SPCS [15, 48]. We have previously described a population-based survey methodology, the South Australian Health Omnibus Survey, for identifying caregivers of people with life-limiting illnesses where SPCS were and were not involved; we used this methodology again in the current study [16].
Figure 1 depicts the conceptual model being tested in this study. Its aims are to determine whether:
SPCS meet the short-term needs of caregivers at the time they are providing support for someone with a life-limiting illness; andAn association exists between SPCS use and differences in caregiver-defined long-term outcomes in the years after they have completed their role.Fig. 1Conceptual model of the relationship between caregiving, ‘moving on’, specialized palliative care services (SPCS), and unmet needs
The null hypothesis was that SPCS were associated neither with differences in unmet needs nor with caregivers’ ability to move on after bereavement.
Methods
Setting and subjects South Australia has a population of 1.47 million people [4]. The South Australian government provides funding for SPCS to support general practitioners and community nurses. The programs offered by SPCS in South Australia reflect service structures in the United Kingdom (UK), United States (US), and elsewhere [26, 28, 31, 44]. Services delivered include care for inpatients, hospital consultation, outpatient clinics, and community visits. Patients access SPCS through referral from any source. The primary eligibility criterion is a life-limiting illness; approximately 85% of people referred to SPCS have cancer.
Survey methodology The data for this survey were collected in the South Australian Health Omnibus Survey, a state government-associated face-to-face health survey conducted annually since 1991 with approximately 3,000 randomly selected respondents each year. The full survey methodology has been detailed elsewhere [16, 67]. Previously, the content and construct validity in palliative care service planning has been validated [16]. The survey has Ethics Committee (aka, Internal Review Board; IRB) approval.
The survey is revised and piloted annually with 50 people using the planned methods for the main survey (Fig. 2). Anonymous face-to-face interviews were conducted by trained interviewers in the respondents’ homes annually during September to November in 2001, 2002, and 2003. A total of 13,200 households across these 3 years were selected for the survey. Metropolitan households were selected in a skip pattern from a randomly selected starting point within 340 Australian Bureau of Statistics collectors’ districts. In nonmetropolitan areas, households were selected using 100 starting points; all towns with a population greater than 10,000 were included, and towns with a population above 1,000 were randomly selected with probability proportional to size. One interview was conducted per household with the person aged 15 or older who most recently had a birthday. Data were double-punched; missing responses were followed up by telephone. Supervisors recontacted 5% of the study population to verify accuracy of the data.
Fig. 2All palliative care survey questions embedded in the South Australian Health Omnibus Survey, 2001–2003
Caregiving was defined for the respondent as follows: “‘Care’ includes attention to any of the needs of the person, including hands-on care, overnight care, respite, shopping, collection of medications, taking to appointments, emotional support, bathing, etc.” To incorporate differing levels of caregiver burden into the analysis, respondents were asked if they provided: “day-to-day hands-on care” (care 5–7 days per week); “intermittent hands-on care” (care 2–4 days per week); or, “rare hands-on care” (care 1 or less days per week). While this method does not encompass information on caregivers’ multiple roles (e.g., parent, employee, spouse, and caregiver), it does stratify respondents along one axis of caregiver burden—the intensity of care provided.
In the 2003 Health Omnibus Survey, a list of response categories was provided to patients to elicit their perceptions of specific unmet needs (Fig. 2). “Unmet needs” were categorized into 14 types of extra support which the respondent felt would have helped him/her as a caregiver; these were selected based on our clinical experience with patients and caregivers in palliative care, the published literature, and pilot testing of the Health Omnibus Survey [40]. These categories map to the five domains of caregiver experience described by Given et al. [24] in the following way: disrupted schedule (better out-of-hours care, respite care); financial problems (financial support/financial planning, legal planning); lack of family support (emotional support for me, emotional support for the person who died, other emotional support, spiritual support, bereavement support); loss of physical strength (support with the physical care of the person who died); and caregiver self-esteem (emotional support for me). Additionally, the study response categories included informational and medical management needs (information about what would happen as the illness progressed, information about services available as the illness progressed, assistance with medications, assistance with physical care and control of symptoms).
Among the 50 pilot respondents in the 2003 survey year, those who answered the respondent-defined moving on question were subsequently asked, “What does the concept of ‘moving on’ mean to you?” Responses were incorporated into the categories for the survey (Fig. 3).
Fig. 3Bereaved caregivers’ definitions of ‘moving on’ (n = 24)
Data analysis The survey respondents were standardized against the population of all South Australia for gender, 10-year age group, socioeconomic status, and region of residence per the 2001 Australian Census [4] using direct standardization [18] and macros combining multiple survey years obtained from the South Australian Department of Human Services [16, 67]. Each respondent was assigned a standardized weight and only weighted data were analyzed. Annual datasets were compared; there were no statistically significant differences, before combining the datasets, between the years in response rate, demographics, recency of death, deaths due to cancer, family/friend relationship between the respondent and the deceased, or care provided.Descriptive statistics were used to summarize respondent characteristics and responses. Relationships between categorical variables were tested using the chi-square test or chi-square test for trend, as appropriate. Relationships between continuous variables were tested using the Student’s t test or one-way analysis of variance (ANOVA), as appropriate. Two-tailed p values were reported; statistical significance was assumed if p < 0.05. After testing for multi-collinearity (all tolerances >0.9266), variables were entered into a multivariable logistic regression model (PROC LOGISTIC) to determine the factors accounting for categorical caregiver outcomes, or a linear regression model (PROC GLM) with Tukey corrections [10] to determine the factors accounting for continuous outcomes. The Statistical Analysis Software (SAS) statistical package was used for analysis (The SAS System, release 8.02, Cary, North Carolina, USA).
Sample size calculation A sample size requirement was calculated for the comparison of the mean number of unmet needs (“extra supports needed”) among caregivers where SPCS were used vs. SPCS not used. Assuming that a difference in one unmet need was clinically meaningful and a standard deviation of 2.1, then a sample size of 94 caregiver respondents was needed for an alpha 0.05, beta 0.80, and ratio of cases to controls of 0.6 [21]. A sample size requirement was also calculated for the dichotomous comparison of the proportion moving on by use of SPCS. If the baseline rate of moving on was 70% among those who did not use SPCS and an improvement of 10% with SPCS was deemed clinically meaningful (i.e., increased rate of moving on to 80% when SPCS was used), then a sample size of 385 caregiver respondents was needed for an alpha 0.05, beta 0.80, and ratio of cases to controls of 0.60 [21]. Both sample-size expectations were met.
Results
Response rate Of the 13,200 households approached, the weighted number of respondents was 9,088. The response rate was 70.7% after exclusion of 401 uninhabited houses. Of those approached, reasons given for not responding included refusal (too busy or not interested; 2,006, 15.2%), unable to contact (1,059, 8.0%), respondent unable to speak English (195, 1.5%), selected respondent away for duration of survey (176, 1.3%), illness or mental incapacity precluded participation (173, 1.3%), and terminated interview (6, 0.05%).
Population affected by a death About 37% of the population (3,340) indicated that someone close to them had “died of a terminal illness like cancer, motor neuron disease, or emphysema” in the preceding 5 years (Table 1). Respondents reporting a death of someone close to them were significantly more likely to be older, married, or widowed, in rural settings, working, and finished with school than those who did not report a death (all p ≤ 0.01).
Table 1Characteristics of respondents who reported a death of a loved one due to a terminal illness and bereaved respondents who identified themselves as caregiversCharacteristicAll respondents who reported death of a loved one due to terminal illnessRespondents who identified themselves as caregivers of the deceased individualsRespondents who were not caregivers of the deceased individualsP value between caregivers and noncaregivers of the deceased individualsaN (% of all respondents)3,340 (37)949 (10)2,391 (26)Age group (years)15–29692 (21)184 (19)507 (21)0.508730–44946 (28)274 (29)673 (28)45–59912 (27)274 (29)638 (27)60–74518 (15)147 (15)371 (15)75 and over273 (8)70 (7)203 (8)GenderMale1,558 (47)362 (38)1,196 (50)<0.0001Female1,782 (53)587 (62)1,196 (50)Marital statusMarried or de facto2,215 (66)603 (63)1,612 (67)<0.0001Separated or divorced273 (8)62 (7)210 (9)Widowed210 (6)111 (12)99 (4)Never married643 (19.173 (18)470 (20)Residential locationMetropolitan2,270 (68)645 (68)1,625 (68)0.9786Rural1,071 (32)304 (32)767 (32)Country of birthEnglish-speaking background2,992 (90)859 (90)2,133 (89)0.4447Non-English-speaking background348 (10)90 (9)258 (11)Missing1 (0)1 (0)Highest level of educational attainmentDid not complete school, high school or still studying1,660 (50)479 (50)1,182 (49)0.6010Trade qualification, certificate, diploma or higher1,680 (50)471 (50)1,210 (51)Work statusWork full-time or part-time1,852 (55)526 (55)1,326 (55)0.9910Not working including home duties, student, retired, unemployed and other1,488 (45)423 (44)1,066 (45)Values are numbers (percentages)aChi-square test or chi-square test for trend, as appropriate.
Caregivers affected by a death Approximately 10% (949) of respondents (28.4% of people bereaved) identified themselves as caregivers (Table 1) for someone who had died from a life-limiting illness in the past 5 years. When compared to all noncaregivers who reported death of a loved one, caregivers were significantly more likely to be female and widowed (Table 1). About 905 (95.4%) caregivers knew if a SPCS was involved in the care of the deceased individual. Caregivers who knew about SPCS usage were more likely to be close family members (p < 0.0001; chi-square test for trend) but otherwise reported similar patient and caregiver profiles to that of those who “did not know” if SPCS were involved. Subsequent analyses describe the 905 caregivers who knew about SPCS usage.
About 321 of 905 (35.5%) individuals provided day-to-day hands-on care for 5 to 7 days per week; 344 (38.0%) intermittent hands-on care for 2 to 5 days per week; and 240 (26.5%) rare hands-on care for one or less days per week (Table 2). On average, all caregivers provided care for 22.3 (SD 37.2) months, with day-to-day hands-on caregivers providing care for 24.3 (SD 39.9) months, intermittent caregivers for 23.2 (SD 38.9) months, and rare caregivers for 18.2 (SD 30.4) months (p = 0.518; one-way ANOVA).
Table 2Relationship between the caregiver and the decreased individual and characteristics of the care providedRelationship between the caregiver and the deceased individualCaregivers (n)a905Family relationshipSpouse/partner92 (10)Child (of a parent who died)209 (23)Parent (of a child who died)61 (7)Sibling37 (4)Other relative303 (33)Friend175 (19)Other27 (3)Close relation (spouse/parent/child)362 (40)Distant relation or friend543 (60)Level of care providedDay-to-day hands-on care321 (35)Intermittent hands-on care344 (38)Rare hands-on care240 (26)Duration of care providedbMonths [mean (SD)]22.3 (37)Care for <1 year134 (45)Care for ≥1 year163 (55)Values are numbers (percentages) except as indicatedaReports are from caregivers who knew if a palliative care service was used (n = 905, 95% of caregivers).bReports are from caregivers responding to the 2003 survey only (n = 314).
Use of SPCS Caregivers reported that SPCS provided support in at least 60% of deaths of terminally-ill people in South Australia (Table 3) and more frequently in people who died of cancer than of noncancer illnesses (84.3 vs. 64.7%, p < 0.0001; chi-square test).
Table 3Characteristics of the deceased individuals and use of specialized palliative care services (SPCS)Characteristics of the deceased individualSPCS involved in the care of the deceased individualSPCS not involved in the care of the deceased individualAll deceased individuals cared for by caregiver respondents in this surveyP valueaDeceased individualsb539 (60)366 (40)905 (100)Recency of deathMonths [mean (SD)]25.3 (17)26.0 (18)25.6 (17.6)0.5578Cause of deathCancer454 (84)235 (64)689 (76)<0.0001Noncancer85 (16)128 (35)213 (24)Values are numbers (percentages) except as indicatedaFrom student t test or chi-square test, as appropriate.bReports are from caregivers who knew if a palliative care service was used (n = 905, 95% of caregivers).
Unmet needs of caregivers while delivering care and the use of SPCS The relationship between SPCS involvement and additional caregiver supports needed during illness through death was evaluated using the 2003 survey dataset. Approximately 154 (48.1%) caregivers identified that some type of extra support was needed—92 (28.8%) identified physical support needs, 52 (16.3%) information needs, 78 (24.4%) emotional needs, 29 (9.1) financial needs, and 26 (8.1%) other needs—with a mean 1.3 (SD 2.1) extra supports identified per deceased individual. In a linear regression model evaluating the relationship between number of supports needed, level of caregiving, and involvement of a SPCS, involvement of the SPCS helped to predict unmet needs for hands-on caregivers (Fig. 4). For those without SPCS involvement, the number of extra supports needed increased with the level of caregiving (t tests for means; noncaregivers vs rare hands-on caregivers, 0.52 vs. 0.81, p = 0.2433; noncaregivers vs intermittent caregivers, 0.52 vs. 1.56, p < 0.0001; noncaregivers vs day-to-day caregivers, 0.52 vs. 2.35, p < 0.0001); this trend was not noted when SPCS were involved, indicating that caregiver gain, in which unmet needs were addressed, occurred in the context of SPCS. Day-to-day caregivers indicated fewer extra support needs when SPCS were involved in the care of the deceased individual (t tests for means; no SPCS vs. SPCS, 0.95 vs. 2.3, p = 0.0031). The frequency of the top five extra support needs of day-to-day caregivers, by SPCS use or not, is presented in Table 4.
Fig. 4Relationship between the number of categories of additional supports needed during the period of the illness through death, level of care provided, and specialized palliative care service (SPCS) involvement (p = 0.0014 for the interaction of level of care and SPCS). Data from 2003 survey (total of 994 respondents and 314 caregiver respondents). Linear regression model with Tukey corrections (PROC GLM); numbers are least squares means of the number of extra support needs identified by respondents. Vertical bars represent 95% confidence intervalsTable 4Five most frequently identified unmet needs of day-to-day hands-on caregivers (by percentage of respondents)Need forPercentage of respondents identifying this unmet needSPCS involved in the care of the deceased individual (n = 120)More help with the physical care of the person22%More information about what to expect as the illness progressed13%Better emotional support for the caregiver13%Better emotional support for the person who had the life-limiting illness13%More information about services that could be accessed12%SPCS not involved in the care of the deceased individual (n = 88)Better emotional support for the caregiver30%Better emotional support for the person who had the life-limiting illness23%More information about services that could be accessed22%More help with the physical care of the person21%Better symptom control19%
Impact of SPCS on long-term caregiver outcomes When SPCS were involved in care, caregivers were significantly more likely to be able to move on with their lives after the death (Table 5; 86.4 vs 77.2%, p for trend = 0.0016). More than 80% of people who used SPCS were able to move on by 1 year, whereas it took 2 years for 80% of people who did not use SPCS to move on (Fig. 5). About 3 years after the death, 10–14% of all caregivers still had not been able to move on.
Fig. 5Relationship between time and the ability to “move on” with life for those with and without involvement from a specialized palliative-care service (SPCS). Chi-square tests were used at each timepoint (likelihood of moving on by use of SPCS)Table 5Impact of specialized palliative care services (SPCS) on caregiver outcomesOutcomeSPCS involved in the care of the deceased individualSPCS not involved in the care of the deceased individualP value for trendaN537364Ability to “move on” with life “I have been able to move on with my life”464 (86)281 (77)0.0016 “I am starting to move on with my life”60 (11)67 (18) “I have not been able to move on with my life”13 (2)16 (4)Experience vs. expectations Better than expected97 (18)55 (15)0.6310 As expected172 (32)121 (33) Worse than expected240 (45)163 (45) I didn’t know what to expect27 (5)22 (6)Values are numbers (percentages)aChi-square test for trend.
Predictors of caregivers’ ability to move on In a multivariable logistic regression model, use of SPCS was positively associated with an ability for caregivers to move on [odds ratio (OR) 0.54, CI 0.36–0.80; Table 6]. This translates to a 46% improvement on the ability to move on when a SPCS is involved. No other factors in the model positively influenced ability to move on.
Table 6Multivariable logistic regression model (PROC LOGISTIC) predicting caregivers unlikely to “move on” with lifeVariablesOdds-ratios (95% confidence interval)Age (years)0.99 (0.98, 1.01)Gender (reference: female)Male0.87 (0.58, 1.32)Marital status (reference: married)Separated or divorced1.25 (0.58, 2.79)Widowed1.75 (0.93, 3.29)Never married1.25 (0.67, 2.32)Residential location (reference: metropolitan)Rural0.87 (0.57, 1.33)Country of birth (reference English-speaking background)Non-English-speaking background1.23 (0.65, 2.31)Highest level of educational attainment (reference: trade qualification, certificate, diploma, or higher)Didn’t complete school or still studying0.96 (0.63, 1.45)Work status (reference: Work full or part time)Not working, home duties or other1.43 (0.90, 2.25)Recency of death (reference: death >2 years ago)Death in past 2 years2.58 (1.68, 3.97)Cause of death (reference: cancer)Noncancer0.77 (0.47, 1.25)Family relationship (reference: distant relation or friend)Close relation (spouse/parent/child)3.34 (2.13, 5.23)Level of care provided (reference: rare hands-on care)Day to day hands-on care3.72 (1.93, 7.16)Intermittent hands-on care2.42 (1.25, 4.69)Expectations vs experience (reference: Better than expected)As expected1.33 (0.63, 2.78)Worse than expected/I didn’t know what to expect3.43 (1.76, 6.68)Specialized palliative care service (SPCS) involvement (reference: No SPCS involvement)SPCS involved in the care of the deceased individual0.54 (0.36, 0.80)Unlikely to “move on” with life is defined as “I am starting to move on or I have not moved on with my life” since the death of the individual
Predictors of caregivers inability to move on In a multivariable logistic regression model, caregivers who were unlikely to have “moved on” were significantly more likely to have: had a worse-than-expected experience between diagnosis and death (OR 3.43, CI 1.76–6.68; Table 5); provided day-to-day or intermittent hands-on care (OR 3.72, CI 1.93–7.16, and OR 2.42, CI 1.25–4.69); been in a close family relationship with the deceased (OR 3.34, CI 2.13–5.23); and been bereaved in the preceding 2 years (OR 2.58, CI 1.68–3.97).
Discussion
SPCS are a significant health-system investment. For 30 years, researchers have been working to define the improved outcomes associated with SPCS use [2, 12, 25, 30, 31, 45, 46, 48, 49, 61, 64]. The Health Omnibus data support that SPCS are associated with better meeting of needs for day-to-day hands-on caregivers while in the caregiving role and that SPCS have a subsequent long-term impact in improving a caregiver-defined outcome, moving on.
Finding the whole caregiver population (rather than only those referred to services) has been the dominant challenge in establishing caregiver benefits from SPCS involvement [41]. By using a population-level method [16], this study avoided the bias introduced in studies which only access caregivers through clinicians, case note audits, or registries (clinical or death).
Impact of SPCS on caregivers while in the role Our study builds on evidence that caregivers providing constant care who accessed SPCS had fewer unmet needs than did an otherwise identical population who did not access SPCS [31]. Considering the five domains of the caregiving experience described by Given et al. [24], family support was the main domain identified as a source of unmet need; the proportion of respondents who identified unmet needs in this domain decreased when SPCS were involved. An additional category of unmet needs important to the caregivers in this study pertained to information needs and help with physical and medical aspects of caring. The largest proportion of identified unmet needs reported were in this category; fewer needs in this category were reported by caregivers of deceased individuals who had SPCS involvement.
Impact of SPCS on caregiver long-term outcomes SPCS need proactively to minimize the health risks associated with caregiving [23]. Other population approaches have shown associations of benefit with SPCS use in caregiver morbidity and mortality [63]. Christakis and Iwashyna [15] analyzed 31,000 spousal survivors of someone who died from 1 of 13 frequent causes of expected death vs. propensity-matched to controls from the same US Medicare data set. Mortality rates for the surviving spouses were compared 18 months after the death. There was decreased mortality in the group who used SPCS for 24,721 female caregivers (5.4 vs 4.9%, OR 0.92, CI 0.84–0.99) and 6,117 male caregivers (13.7 vs 13.2%, OR 0.95, CI 0.84–1.06). The Omnibus study adds to the work of Christakis by suggesting not only that caregivers who interact with SPCS might have an association with less post-role mortality, but that they may adjust more rapidly to their new life after having been a caregiver.
Like Christakis’ work, this current study cannot attribute a cause-and-effect relationship, although it can demonstrate strength of association. Those willing to access services may have had a better outcome because of problem-focused coping strategies [53].
McCorkle et al. [37] conducted a randomized controlled trial (RCT) evaluating a home nursing intervention to support palliative caregivers. Despite initial improvement in psychological morbidity of bereaved caregivers in the intervention group, differences that were clinically and statistically significant at 6 and 13 months were no longer apparent by 25 months. Zisook and Shuchter [69] demonstrated that caregivers’ self-rated “adjustment” on a categorical scale showed progressive reductions in the first year. Such patterns are mirrored by our data.
In the McCorkle, Zisook, and Omnibus studies, there is a sizeable group of caregivers who still could not move on after several years. The fact that caregiver distress can last for so long after the death of the person for whom they have cared means that the measurement of service impact demands longitudinal approaches [32, 48, 60].
An RCT from Norway randomized access to SPCS by whole populations in a setting comparable to the current study [54]. The primary outcome for “close family members” (whose level of caring was not clear) was the intensity of grief reactions as measured in the second part of the Texas Revised Inventory of Grief. There was no difference in grief reactions at 13 months in the 183 family members of the 434 patients who were originally enrolled in the study. By contrast, the higher level question, “Have you been able to ‘move on’?” in our study elicited a substantial difference over time between people who did and did not access SPCS. Although moving on constitutes only part of the definition for complicated grief [51] (or prolonged grief reaction), the measured rates of complicated grief in similar populations in the literature appear to be of the same order of magnitude, and at the same time after the death of the care recipient, as the findings from the Omnibus study [11, 39, 52].
Other predictors of caregiver outcomes Our study supports other broad population-based observations that the intensity of the level of care provided directly correlates with longer-term caregiver outcomes including burden, health, and mortality [57, 59]. The level of caregiving helped to predict both unmet needs and the ability to move on.
One of the strongest predictors of an inability to move on was if the experience from diagnosis to death was “worse than expected;” this finding is consistent with other work in the area [8, 29]. The discrepancy between expectations and what actually happens can amplify feelings of lack of control over a situation. Conversely, a perceived sense of control is known to be related to overall wellbeing [36], and may contribute to an ability to move on.
Limitations to the study Analysis was limited to the information collected by the South Australian Health Omnibus Survey, a retrospective population-based approach which is dependent upon recollections of bereaved respondents. Methodologically, a prospective study would have been preferable in order to establish potential causal relationships between SPCS and short- and long-term caregiver outcomes; however, it would not be practically feasible to conduct such a prospective controlled research study among caregivers at a population level.
Because the area of caregiving is a relative newcomer to the realm of clinical research, a paucity of robust assessment measures is, as yet, a major limitation of studies in this population. In assessment of short-term caregiver outcomes, we developed a variable to indicate level of intensity of caregiving, which we could link to caregiver strain. We could not encompass in this variable other factors not provided by the 2001–2003 Omnibus dataset, such as roles that caregivers might have been simultaneously performing.Thus, within our measure of caregiver strain defined by intensity of caregiving, we were unable to differentiate those caregivers who performed multiple roles from those who were solely caregivers.
With respect to long-term caregiver outcomes, as noted above, a validated instrument to measure moving on has not yet been developed; in the absence of such a measure, we acknowledge the respondent-defined nature of the concept as utilized in this study. As a first step toward defining this construct, we asked the 50 respondents for the 2003 annual Health Omnibus Survey pilot for input regarding what the concept of moving on meant to them. Results appear in Fig. 3 and provide some initial parameters for development of a measure of this construct.
People who live in remote South Australia and those without caregivers were not represented, and people from some cultural backgrounds may not be seen in these data. All results were based upon the recall of the respondent, which is a validated approach [35, 38, 62]. Caregiver-derived information about the uptake of SPCS is directly derived from first-hand knowledge. Other limitations of the approach have been outlined previously [16].
The programs offered through SPCS and their funding varies widely across the world, making global assessments of the impact of SPCS difficult [3, 15, 42]. This study reflects a variety of palliative care service types ranging from single-nurse-led rural services to large regional metropolitan interdisciplinary programs. These results can be generalized to similar health settings internationally.
Future directions
The relationship between fewer unmet needs and SPCS suggests that SPCS do provide substantial support in helping to plan care and identify contingencies in future care. An understanding of the specific attributes of SPCS that make the most difference in meeting caregivers’ needs will be pursued through future work using similar methods.
Moving on was defined by respondents of a single year of the Health Omnibus Survey; broader validation is planned. Subsequent information will enable evaluation of whether moving on, as a measurable outcome, can be improved. An interesting line of inquiry, which would be made possible by inclusion of a baseline question in a future Health Omnibus Survey, is whether respondents who accessed SPCS were further along in the trajectory of psychological acceptance than were respondents who had not accessed SPCS. A positive correlation might indicate a self-selection bias in which respondents who had used SPCS and who had succeeded in moving on had a predisposition to move on, in comparison to respondents who had not chosen to access SPCS. While of academic interest, this information would not impact the utility of the finding that SPCS facilitated moving on among those individuals who accessed services.
The methodological strength of the approach used in this study, in which we probed data collected via the Health Omnibus Survey to answer a health services question on the population level, could be improved by favorable comparison of data from the Omnibus on caregiver assessment to data collected using a validated caregiver assessment instrument. This step represents a possible future avenue of study but is not yet possible due to the lack of such an instrument.
The relationship between caregivers’ expectations (diagnosis through death) and outcomes will be explored with two subsequent years of data. All results need to be confirmed in other health-delivery systems.

palliative care
caregivers
outcome assessment (health care)
health services needs and demands


Bone Degeneration and Recovery after Early and Late Bisphosphonate Treatment of Ovariectomized Wistar Rats Assessed by In Vivo Micro-Computed Tomography

Bisphosphonates are antiresorptive drugs commonly used to treat osteoporosis. It is not clear, however, what the influence of the time point of treatment is. Recently developed in vivo micro-computed tomographic (CT) scanners offer the possibility to study such effects on bone microstructure in rats. The aim of this study was to determine the influence of early and late zoledronic acid treatment on bone in ovariectomized rats, using in vivo micro-CT. Twenty-nine female Wistar rats were divided into the following groups: ovariectomy (OVX, n = 5), OVX and zoledronic acid (ZOL) at week 0 (n = 8), OVX and ZOL at week 8 (n = 7), and sham (n = 9). CT scans were made of the proximal tibia at weeks 0, 2, 4, 8, 12, and 16; and bone structural parameters were determined in the metaphysis. Two fluorescent labels were administered to calculate dynamic histomorphometric parameters. At week 16, all groups were significantly different from each other in bone volume fraction (BV/TV), connectivity density, and trabecular number (Tb.N), except for the early ZOL and control groups which were not significantly different for any structural parameter. After ZOL treatment at week 8, BV/TV, structure model index, Tb.N, and trabecular thickness significantly improved in the late ZOL group. The OVX and ZOL groups showed, respectively, higher and lower bone formation rates than the control group. Early ZOL treatment inhibited all bone microstructural changes seen after OVX. Late ZOL treatment significantly improved bone microstructure, although the structure did not recover to original levels. Early ZOL treatment resulted in a significantly better microstructure than late treatment. However, late treatment was still significantly better than no treatment.
Postmenopausal osteoporosis affects millions of women worldwide and results in loss of bone mass and bone microstructural changes, which lead to reduced bone strength. It remains unknown how exactly the microstructure is affected over the time course of osteoporosis development and to what extent this structure can be recovered by drug treatment. Since the assessment of bone structure in humans is still limited to a few peripheral sites, the ovariectomized rat is used in many studies as an animal model for osteoporosis in cross-sectional evaluations. Numerous cross-sectional studies have determined the loss of trabecular bone in the proximal tibia in rats following ovariectomy (OVX) at different time points by measuring bone structural parameters [1–3]. OVX has been shown to decrease bone volume fraction, connectivity, and trabecular number and to increase trabecular separation and structure model index, which indicates that trabecular bone changes from plate-like to more rod-like in the proximal tibial metaphysis of the female rat. However, conflicting results were found for the effects of OVX on trabecular thickness, showing increases, decreases, or no response at all [4–6]. These cross-sectional studies are limited to comparing averages per group only, which provides a reduced sensitivity for detection of differences. In addition, no information is obtained regarding the specific locations of bone changes.
Recently, however, in vivo micro-computed tomographic (CT) scanners became available, which can monitor the process of bone loss and microstructural changes in small, living animals. So far, two studies have been reported in which this equipment was used to monitor changes in rat bones after OVX. Interestingly, these studies reported, respectively, significant increases [5] and decreases [7] in trabecular thickness after OVX, indicating that the response may vary. While the process of bone degeneration after OVX is not fully understood, even less information is available for the process of recovery after drug treatment. The most widely used drugs for treatment of diseases associated with increased bone resorption, such as postmenopausal osteoporosis, are now bisphoshonates.
Many cross-sectional studies have determined the effects of bisphosphonates in rats either as a preventive treatment, before development of OVX-induced bone loss, or as a recovering treatment after development of bone loss. Although the results are difficult to compare directly due to differences in experimental design, preventive treatment in animals may lead to the same amount of final bone mass as recovering treatment. The associated microstructure in terms of number and thickness of trabeculae, however, can be different. While the influence of the time point of treatment is highly relevant clinically, no studies were found that directly compared the effects of a preventive and a recovering treatment on final bone mass and associated microstructure. In addition, the fact that only results of cross-sectional studies have been reported complicates comparison of the final and original bone structure and provides little or no information on the changes in structure over time. Here also we expect that longitudinal in vivo micro-CT imaging will potentially provide more information about the specific changes in the bone, both spatially and temporally.
For the present study, we had two goals: (1) to determine the process of bone loss and bone microstructural changes in ovariectomized rats and (2) to determine the effects of early and late treatment of osteoporotic rats with a bisphosphonate. We used an in vivo high-resolution micro-CT scanner to image the proximal tibia of female adult Wistar rats. For the treatment, zoledronic acid (ZOL) was used, since this is a third-generation bisphosphonate that proved a potent osteoclast inhibitor. It has shown to be effective in cancer patients with bone metastases and is currently being tested in clinical trials as a promising therapeutic agent for osteoporotic patients [8, 9].
Methods
Animals
Thirty-six female, 30-week-old, retired breeding Wistar rats were obtained from Harlan Laboratories (Horst, The Netherlands) and allowed to acclimate for 7 days before the start of the experiment. The rats were maintained with a cycle of 12 hours light and 12 hours darkness and allowed to eat and drink ad libitum. The experiment was approved by the Animals Ethics Committee of the University of Maastricht, The Netherlands. The rats were divided into four groups based on weight: control (n = 9), OVX (n = 9), OVX and early ZOL (n = 9), OVX and late ZOL (n = 9). All rats were ovariectomized at week 0, and the control group underwent sham OVX. Success of OVX was confirmed at necropsy by determining atrophy of the uterine horns. ZOL (kindly donated by Novartis Pharmaceutical, Basel, Switzerland) was dissolved in a saline vehicle. The early ZOL group was administered ZOL at a single dose of 20 μg/kg body weight s.c. at OVX (comparable to the phase III clinical trial dose used in osteoporotic patients). The late ZOL group was administered the same single dose 8 weeks after OVX. Eleven and 4 days before necropsy, the fluorescent labels calcein (Fluka, Buchs, Switzerland) and alizarin (Merck, Dietikon, Switzerland) were administered s.c. in a saline vehicle to all rats at doses of 30 mg/kg and 20 mg/kg body weight, respectively, to evaluate bone formation dynamics in the proximal tibial metaphysis. Rats were killed at 16 weeks by exsanguination.
Micro-CT Scanning
Directly after the operation, a 6-mm micro-CT scan (70 kV, 85 μA, 1,000 projections per 180 degrees, 350 ms integration time) with an isotropic resolution of 15 μm was made of the proximal tibia using an in vivo micro-CT scanner (vivaCT 40; Scanco Medical, Bruettisellen, Switzerland). The CT scanner was calibrated, and a beam-hardening correction algorithm was applied to all scans. Follow-up in vivo CT scans were made after 2, 4, 8, 12, and 16 weeks to monitor bone structure. One CT scan took 35 minutes, during which the animal was anesthetized with isoflurane and the scanned leg was placed in a custom-made leg-fixating device. The design of the rat holder was such that the left leg was not exposed to radiation while scanning the right leg. Radiation damage to the scanned bone was not expected to occur, based on a previous study in which 8-weekly CT scans with the same radiation dose caused no detected bone damage [10]. In that study, we also showed that the reproducibility of all structural parameters was high, with a coefficient of variation of about 1%.
Image processing included gaussian filtering and segmentation and was described elsewhere in detail [10]. In brief, the same filtering and segmentation values were used for every measurement of each animal (sigma = 0.7, support = 1, threshold density = 0.504 g hydroxyapatite/cc, equivalent to 22% of maximal gray-scale value). From every baseline and follow-up CT scan, the metaphyseal trabecular bone was manually selected and bone structural parameters (bone volume fraction [BV/TV], connectivity density [Conn.D], structure model index [SMI], trabecular number [Tb.N], trabecular thickness [Tb.Th], and trabecular separation [Tb.Sp]) were automatically determined. The average attenuation coefficient of the trabecular bone tissue was determined for all measurements using a protocol provided by the manufacturer of the micro-CT scanner. With this protocol, the gray levels of voxels near the trabecular surfaces are not included, to ensure that the measurements are not affected by partial volume effects. Also, cortical thickness of the metaphysis was analyzed. The cortical bone starting from the tibia–fibula junction was manually selected for each measurement, resulting in a region approximately 1.8 mm long. The same filtering and segmentation values were used as for the trabecular analysis. Cortical thickness was automatically determined from the selected region.
When two CT scans of the same animal are made at different time points, the position of the animal in the scanner will not be exactly the same and, therefore, the CT scans will also differ. In order to detect bone structural changes on a micro level, one CT scan needs to be translated and rotated to match the other. We developed image registration software that registers two scans based on minimizing the correlation coefficient [11]. Every follow-up scan is registered with the first scan, thereby revealing any possible changes in the microstructure and minimizing errors in the region of interest for the structural parameters.
Mechanical Testing
After sacrifice, all left tibiae were dissected and used for two different mechanical tests. First, a standard three-point bending test was applied. The tibiae were placed on the lateral surface on two rounded supporting bars with a distance of 2.4 mm. A preload of 1 N was applied (Z020; Zwick, Ulm, Germany) at the medial surface of the diaphysis by lowering a third rounded bar. A constant displacement rate of 6 mm/minute was applied until failure. Displacement was measured from the actuator displacement transducer of the testing machine. Stiffness, ultimate force, and ultimate displacement were calculated for each sample.
To assess the mechanical properties of the proximal tibia, a proximal tibia compression test was performed similar to the method of Hogan et al. [12]. A 4-mm slice of the proximal metaphysis including both cortical and trabecular bone was sawed below the growth plate using a diamond saw (Accutom-5; Struers, Ballerup, Denmark). This slice was axially compressed (Z020) during five preconditioning cycles between 1 and 70 N. After the last preconditioning cycle, axial compressive load was applied at 0.5 mm/minute until failure, while axial displacement was measured using an extensometer. Stiffness, ultimate force, and ultimate displacement were determined for all samples.
Histomorphometry
At necropsy, all right tibiae were fixed in 70% ethanol and embedded in methylmethacrylate (Technovit 9100; Heraeus Kulzer, Wehrheim, Germany). Thin sections (5 μm) were cut to determine fluorochrome-based dynamic histomorphometric parameters of bone formation using an Axiophot photomicroscope (Zeiss, Oberkochen, Germany) linked to a camera (CF 15/4 MC; Kappa, Gleichen, Germany), and a QUANTIMET 600 image analysis system was used to calculate the amount of mineralized surface per bone surface (MS/BS, percentage), corrected mineral apposition rate (MAR) (μm/day), and bone formation rate (BFR) per bone surface (μm/day). Calculations of the dynamic parameters were performed as recommended previously [13].
Statistics
Since this experiment concerned repeated measures in multiple groups, several statistical tests were performed to reveal changes in parameter values over time and between groups. For all structural parameters, the percentage change at different time intervals was calculated for each animal individually. A one-way analyses of variance (ANOVA) with repeated measures was performed on the percentage change for all structural parameters and all groups. Furthermore, a one-way ANOVA with a Bonferroni post-hoc test was used to determine differences between the groups at all time points for each structural parameter. Also, a one-way ANOVA with a Bonferroni post-hoc test was used to determine differences between the groups in cortical thickness, histomorphometric results, and mechanical results. For each group, a paired Student’s t-test was performed on the absolute values of the structural parameters of each time point compared to baseline values, to determine the earliest point at which a detectable change was found. A paired Student’s t-test was used to determine late ZOL treatment effects within this group. Finally, a paired Student’s t-test was performed on the absolute values between each measurement for each group and structural parameter. P < 0.05 was considered significant.
Results
Ovariectomy
Based on the uterine weight, we determined seven unsuccessful OVXs. The rats concerned were removed from further analyses, which left nine rats in the control group, five in the OVX group, seven in the OVX and late ZOL group, and eight in the OVX and early ZOL group. For each animal, we determined the percentage change in structural parameters compared to the values at week 0. Figure 1 shows the average percentage change and upper standard deviation for all groups for BV/TV, Conn.D, SMI, Tb.N, Tb.Th, and Tb.Sp. The OVX group without treatment showed large changes in structural parameters, indicating the development of OVX-induced bone loss (Figs. 1 and 2). Paired Student’s t-test indicated significant changes within 2 weeks after OVX for all structural parameters. BV/TV, Conn.D, and Tb.N decreased during the experiment and SMI and Tb.Sp increased. Both Tb.Th and Tb.N decreased within 2 weeks, indicating that both thinning and complete resorption of trabeculae started directly after OVX. The initial rapid loss of bone and connectivity was largely accompanied by trabecular thinning, while secondary, slower loss of bone was concomitant with a decrease in Tb.N. While BV/TV, Conn.D, SMI, Tb.N, and Tb.Sp showed continuous changes in the same direction throughout the experiment, Tb.Th initially decreased significantly until 4 weeks after OVX and then increased again until after 16 weeks Tb.Th was significantly higher than in the control group.
Fig. 1Average percentage change in structural parameters in the metaphyseal proximal tibia and upper standard deviation for all groups at all time points. Brackets indicate P < 0.05 at week 16Fig. 2Same slice of an unprocessed CT scan of the same rat in the OVX group taken at weeks 0 (a), 2 (b), 4 (c), 8 (d), 12 (e), and 16 (f). Images show typical trabecular bone loss due to OVX in the metaphysis. Green line shows the analyzed metaphyseal bone
The absolute average values and standard deviations for all structural parameters of all measurements are shown in Table 1. At the starting point, some differences in the values between the groups were present. This was due to the fact that some animals, which mostly had a relatively low BV/TV, were removed from the study because they did not respond to the OVX. However, since we compared the relative changes in each animal, the results were most likely not affected by the difference in absolute values.
Table 1Mean values and standard deviation of all structural parameters of all groups at the start of the experiment GroupBV/TV (1) Conn.D (1/mm)SMI (1)Tb.N (1/mm) Tb.Th (μm) Tb.Sp (μm) Control0.17 (0.056)41 (47)1.52 (0.53)1.89 (1.38)105 (49)613 (368)OVX0.26 (0.055)141 (76)1.22 (0.39)3.77 (1.66)84 (10)335 (174)OVX + late ZOL0.23 (0.062)86 (66)1.23 (0.34)2.77 (1.55)89 (13)482 (227)OVX + early ZOL0.17 (0.056)38 (21)1.70 (0.39)1.86 (0.80)95 (7)647 (207)
ZOL Treatment
The early ZOL treatment and control groups were not significantly different at all time points for all structural parameters.
Up until 8 weeks after OVX, when ZOL was administered to rats in the late treatment group, the OVX group and the late treatment group were not significantly different for all structural parameters. BV/TV, SMI, Tb.Th, and Tb.N were significantly improved in the late ZOL group 4 weeks after the single ZOL injection (Table 2). In addition, Tb.Sp significantly improved within 8 weeks after injection, while Conn.D showed no significant response. Between weeks 12 and 16, all structural parameters stayed constant.
Table 2P values of paired Student’s t-test in the late ZOL group after ZOL treatment Structural parameterP for weeks 8–12P for weeks 8–16BV/TV0.001*0.004*Conn.D0.2490.181SMI0.003*0.004*Tb.N0.041*0.005*Tb.Th0.000*0.000*Tb.Sp0.3440.008**Significant (P < 0.05)
At week 16, BV/TV, Conn.D, and Tb.N were significantly different between all groups except for the early treatment and control groups (Table 3). Tb.Th was only significantly different between the control and OVX groups. Although substantial differences in Tb.Sp and SMI were seen between all groups, except for the early treatment and control groups, significant differences were only found between the OVX and control groups and between the OVX and early treatment groups.
Table 3P values of ANOVA plus Bonferroni post-hoc test comparing all groups at all time points for all structural parametersGroup ΔBV/TVΔConn.DΔSMI2481216248121624812161 vs. 20.010*0.000*0.000*0.000*0.000*0.025*0.001*0.000*0.000*0.000*0.027*0.008*0.004*0.001*0.003*1 vs. 30.005*0.000*0.000*0.000*0.000*0.3410.021*0.001*0.000*0.005*0.9290.4730.1410.3050.241 vs. 40.9990.9990.9990.9990.3220.9990.9990.9990.9990.9990.9990.9990.9990.9990.9992 vs. 30.9990.9990.5790.000*0.000*0.9990.9060.8110.042*0.027*0.5980.4430.6670.1480.372 vs. 40.000*0.000*0.000*0.000*0.000*0.0790.001*0.000*0.000*0.000*0.011*0.005*0.003*0.001*0.003*3 vs. 40.000*0.000*0.000*0.000*0.000*0.90.023*0.001*0.000*0.000*0.4210.290.1040.3310.206Group ΔTb.NΔTb.ThΔTb.Sp2481216248121624812161 vs. 20.0880.001*0.000*0.000*0.000*0.003*0.002*0.6730.9990.013*0.210.006*0.001*0.000*0.000*1 vs. 30.1080.019*0.005*0.007*0.018*0.008*0.002*0.3250.9990.9990.0990.0720.20.8020.9991 vs. 40.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9992 vs. 30.9990.9990.3530.001*0.000*0.9990.9990.9990.9990.1520.9990.9990.1280.001*0.000*2 vs. 40.450.001*0.000*0.000*0.000*0.001*0.001*0.2650.9990.06*0.4920.007*0.001*0.000*0.000*3 vs. 40.6230.027*0.004*0.003*0.002*0.003*0.001*0.1090.9990.9990.2790.0840.1870.6670.999Groups: 1, control; 2, OVX; 3, late ZOL; 4, early ZOL*Significant (P < 0.05)
CT-derived mineralization values in all groups did not change over time and did not differ between groups at all time points.
Aging
An ANOVA with repeated measures on all structural parameters of the control group was performed to determine if there was a significant effect of aging. BV/TV and Tb.N were found to significantly decrease and Tb.Sp was found to significantly increase due to aging. All other structural parameters did not change significantly in 16 weeks.
An ANOVA with repeated measures was performed on the percentage change in structural parameters comparing the control and early treatment groups to determine any significant differences. BV/TV and Tb.N showed a significant effect of age, treatment, and age * treatment and Tb.Sp showed a significant effect of age and age * treatment. While BV/TV decreased significantly over time in both the early treatment and control groups, it was found to decrease at a higher rate in the latter. Tb.N significantly decreased over time in the control group; however, it did not significantly change in the early treatment group as a net result of OVX, aging, and ZOL treatment. Tb.Sp significantly increased in the control group due to aging, while it did not in the early treatment group.
Cortical Thickness
OVX initially led to a decrease in metaphyseal cortical thickness, and at 4 weeks both the OVX and the late ZOL groups were significantly lower than the early ZOL group (Fig. 3). However, after 4 weeks it increased again in the OVX group, and at 16 weeks cortical thickness was significantly higher than in the control group. Early ZOL treatment inhibited the changes in cortical thickness. The late ZOL group, just like the OVX group, showed an increase in cortical thickness between 4 and 16 weeks, although this was less pronounced.
Fig. 3Average percentage change in cortical thickness in the metaphyseal proximal tibia and upper standard deviation for all groups at all time points. At week 4 the OVX and the late ZOL groups were significantly lower than the early ZOL group, and at week 16 the OVX group was significantly higher than the control group
Mechanical Testing
Experimental test results were obtained for seven (control), five (OVX), six (OVX and late ZOL), and seven (OVX and early ZOL) tibiae. No significant differences were found between all parameters determined in the three-point bending test (Fig. 4a).
Fig. 4Stiffness, ultimate force, and ultimate displacement determined from three-point bending tests on diaphyseal tibia (a) and axial compression tests (b) on metaphyseal bone. Groups: 1, control; 2, OVX; 3, late ZOL; 4, early ZOL. *Significant difference between groups based on ANOVA and Bonferroni post-hoc test (P < 0.05)
For the results from the proximal tibia compression test, a significant difference in ultimate displacement was found between the OVX group and the early ZOL group and between the OVX and the control groups (Fig. 4b). Although not significantly determined, a trend was observed in which OVX resulted in a lower stiffness and ultimate force, while late ZOL treatment partially recovered these values and early ZOL treatment did to a larger extent.
Histomorphometry
Histomorphometric results were obtained for five (control), five (OVX), six (OVX and late ZOL), and five (OVX and early ZOL) rats. The OVX group showed a significantly higher mineralizing surface (MS/BS) and BFR and a substantially higher mineral apposition rate (MAR) than the control group (Fig. 5). Both ZOL groups showed a significantly lower MS/BS and BFR than the OVX group and control group, while MAR was significantly lower than only the OVX group. Additionally, the late ZOL group had a significantly lower MS/BS and a nonsignificantly lower BFR than the early ZOL group.
Fig. 5Dynamic bone histomorphometric parameters for all groups in the metaphyseal proximal tibia. Groups: 1, control; 2, OVX; 3, late ZOL; 4, early ZOL. Based on ANOVA and a Bonferroni test, asignificantly different from group 1, bsignificantly different from group 2, csignificantly different from group 3, and dsignificantly different from group 4 (P < 0.05)
Discussion
In this study we assessed the effects of OVX and of early and late ZOL treatments after OVX on the microstructure of metaphyseal bone in the proximal tibia of aged, female Wistar rats over a 16-week time course, using in vivo high-resolution micro-CT.
OVX induced significant changes in all structural parameters within 2 weeks, indicating the rapid development of OVX-induced bone loss. An initial phase of rapid decreases in BV/TV and Conn.D was seen, followed by a second phase in which changes took place at lower rates. The initial rapid loss of bone and connectivity was largely caused by trabecular thinning, while the secondary, slower loss of bone was accompanied by a decrease in Tb.N. OVX induced large decreases in BV/TV, Conn.D, and Tb.N and large increases in Tb.Sp and SMI, indicating the change of more plate- to rod-like bone, which agrees with the literature on the effects of OVX in rat tibiae [2, 14–16]. A different response was, however, seen in Tb.Th, which initially decreased until 4 weeks after OVX and then increased until at the end point of the experiment it was higher than in the control group. A possible explanation for this response could be that, due to increased osteoclast activity, initially most trabeculae will become thinner, while only few are completely resorbed. After a certain period of resorption, the thinner trabeculae will be perforated and completely resorbed. Since the thicker trabeculae remain present, the average Tb.Th will increase. Another possible explanation would be that mechanical adaptation takes place. Since much bone is lost after OVX and the number and thickness of trabeculae decrease, all load bearing goes through the remaining trabeculae. Therefore, the stress increases in these trabeculae, which stimulates bone formation and would lead to increased Tb.Th. It is also possible that a combination of these mechanisms actually takes place. In the two studies published to date regarding in vivo micro-CT monitoring of the effects of OVX on the metaphyseal proximal tibia in rats, the loss of trabecular bone was similar to what was seen here [5, 7]. However, Tb.Th significantly increased in one study and decreased in the other directly after OVX. Although the same rat strain was used and the same anatomical location was studied, it could be that differences, such as the use of retired breeders vs. virgin rats, age, or type of data analysis, led to variable results.
Early ZOL injection at the time of OVX completely prevented all changes in structural parameters and, thus, the development of OVX-induced bone loss. Aging caused a significant decrease in BV/TV, Conn.D, and Tb.N and an increase in Tb.Sp in the control group, which agrees with the literature [7]. However, a significant decrease was merely seen in BV/TV in the early ZOL group and at a significantly lower rate than in the control group. This indicates that a single injection of ZOL was powerful enough to inhibit even natural deterioration of bone volume and microstructure due to aging. One year of weekly ZOL injections of a concentration as low as 0.3 μg/kg completely suppressed the effects of OVX in vertebrae of Sprague-Dawley rats, and 1.5 μg/kg was needed to retain all structural parameters and mechanical properties in the femurs [8, 17]. Similarly, 69 weeks of weekly ZOL injections in OVX rhesus monkeys reduced bone loss in a dose-dependent manner [18]. In a phase II study, both a yearly and a quarterly ZOL dose increased spinal and femoral bone mineral density in postmenopausal women [19]. In the current study, we have shown the long-term, potent inhibiting effects of ZOL on both BV/TV and the microstructure in rats.
While the preventive effects of ZOL have previously been studied in rats [8, 17], the effect of ZOL treatment after the development of OVX-induced bone loss on the microstructure, which more closely resembles the clinical situation, is new. Late ZOL injection after 8 weeks of bone loss development inhibited further bone loss. Possibly as a result of the filling of resorption cavities [20], a temporary but significant increase in BV/TV and thickness and a significant decrease in SMI were seen directly after ZOL administration. Four weeks later, however, all structural parameters stayed constant. This is in contrast with the OVX group, which still showed deterioration of bone structure after 8 weeks. Overall, bisphosphonate treatment after the onset of osteoporosis is known to stabilize or increase BV/TV and Tb.Th in both humans and other animals, which agrees with the data presented here [21–23]. It is generally thought that the underlying mechanism for an increase in BV/TV and Tb.Th is the inhibiting effect of bisphosphonates on bone resorption preventing further thinning of trabeculae and the reduction of remodeling space by filling of resorption cavities, leading to increased bone mass [20, 24].
Cortical thickness first decreased as a result of OVX, later increased again, and finally was higher than that of the control group. Both increases and decreases in metaphyseal tibial cortical thickness have been reported in cross-sectional studies, which may be due to different analytic methods or different time points of sacrifice [25, 26]. Early ZOL treatment resulted in a similar cortical thickness as seen in the control groups, which was fairly constant. Late ZOL treatment resulted in a similar pattern as in the OVX group, although less pronounced, which may be explained by the inhibiting effect of ZOL on bone remodeling rate. Cortical thickness profiles in all groups were similar to those for Tb.Th, which may be interpreted as an indication that mechanical adaptation takes place in both cortical and trabecular bone after sustained bone loss.
No significant differences were found in mechanical properties determined in the three-point bending test between the OVX and control groups, which agrees with the literature [27, 28]. Also, no significant influence of ZOL treatment was found on the mechanical properties determined in the three-point bending test, which may be expected as ZOL inhibits bone resorption and does not increase formation. In the proximal tibia compression test, ultimate displacement was found to be higher in the OVX group than in the other groups. ZOL treatment partially recovered this increase in ultimate displacement. Although not significantly, ultimate force and stiffness tended to be lower in the OVX group, while ZOL treatment inhibited this. The low number of animals per group and variability in the data unfortunately limit the interpretation of these results. It is known that while endosteal bone resorption takes place during aging and is increased as a result of OVX, periosteal apposition increases as well [29–34], which was also seen in our CT scans (data not shown), thereby compensating for the loss of endocortical and trabecular bone [35]. Therefore, loss of trabecular and endocortical bone may not necessarily mean loss of strength. While first-generation bisphosphonates, such as etidronate, have been shown to directly decrease mineralization of bone [9, 36], it is thought that ZOL may increase mineralization by inhibiting resorption and thereby allowing longer mineralization [37, 38]. Furthermore, microdamage could accumulate, and both of these effects could alter the mechanical properties of the bone tissue. Nonetheless, the trends in the mechanical properties resembled the trends in BV/TV and showed that ZOL inhibited the changes resulting from OVX.
Bone-forming activity was slightly higher in the OVX compared to the control group, reflecting an increased turnover rate. Both ZOL groups displayed lower bone-forming parameters compared to the control and OVX groups, probably due to suppression of bone formation and resorption. Additionally, the late ZOL group tended to show slightly less bone-forming activity than the early ZOL group, possibly due to the later time point of treatment. These results agree with previous reports of decreased osteoblast and osteoclast numbers after ZOL injection and decreased number of resorption lacunae [20, 39]. CT-derived estimates of mineralization of metaphyseal trabecular bone were, however, not significantly increased in ZOL-treated rats. This would indicate that ZOL treatment did not influence mineralization in this time frame.
As previously mentioned, at times, substantial differences were found between groups; however, due to a relatively high standard deviation, no significant differences unfortunately were detected. This high standard deviation was mostly seen in the OVX and late treatment group. This was most likely caused by the fact that retired breeders were used, which are known to show a variable response to OVX. Due to an unsuccessful OVX, seven animals were removed from the study. This decreased the number of animals used in the data analysis and, since these were mostly animals with a relatively low BV/TV, the average BV/TV at the start of the study was higher in the OVX and late treatment groups. Therefore, we performed statistical tests on percentage changes in structural parameters rather than on the absolute values. The trend for the percentage and absolute changes looked similar and the P values were similar for most cases. However, when ANOVA was performed on the absolute values comparing all groups at week 16, less significant differences in structural parameters were found, indicating the more sensitive approach of the in vivo experimental design.
Another limitation to this study was the fact that on average, at 8 weeks in the study, the OVX group was in a more deteriorated condition than the late treatment group, while there was no demonstrable explanation for this. Although the ANOVA showed no significant differences between the groups, the treatment group still had a more favorable starting point when ZOL was administered. However, because after ZOL injection significant improvement took place while the OVX group still deteriorated, it is shown that this will not have affected the final conclusions.
The method of in vivo CT scans combined with image registration software presented here has enabled us to study our goals in a more efficient way in terms of number of animals needed in the experiment. Follow-up micro-CT scans were made of the same animals, enabling us to use paired statistics and relative percentage changes in structural parameters. By registering all CT scans, we were able to select the same part of the metaphyseal trabecular bone for every measurement, thereby decreasing the variation in structural parameters due to a varying selected region of interest.
In conclusion, OVX induced rapid changes in trabecular bone mass and structure. A single injection of ZOL at OVX completely inhibited these changes in bone mass and structure seen in osteoporotic rats. A single ZOL injection after OVX-induced bone loss improved bone mass and structure, per group and compared to the control group. BV/TV and the associated bone microstructure at the end point of this study were, however, significantly less favorable than in the early treatment group.

bisphosphonate
rat
in vivo micro-computed tomography
osteoporosis