The effect of stress on the expression of the amyloid precursor protein in rat brain

The abnormal processing of the amyloid precursor protein (APP) is a pivotal event in the development of the unique pathology that defines Alzheimer's disease (AD). Stress, and the associated increase in corticosteroids, appear to accelerate brain ageing and may increase vulnerability to Alzheimer's disease via altered APP processing. In this study, rats were repeatedly exposed to an unavoidable stressor, an open elevated platform. Previous studies in this laboratory have shown that a single exposure produces a marked increase in plasma corticosterone levels but animals develop tolerance to this effect between 10 and 20 daily sessions. Twenty-four hours after stress, there was an increase in the ratio of the deglycosylated form of APP in the particulate fraction of the brain, which subsequently habituated after 20 days. The levels of soluble APP (APPs) tended to be lower in the stress groups compared to controls except for a significant increase in the hippocampus after 20 days of platform exposure. Since APPs is reported to have neurotrophic properties, this increased release may represent a neuroprotective response to repeated stress. It is possible that the ability to mount this response decreases with age thus increasing the vulnerability to stress-induced AD-related pathology.
The role of amyloid precursor protein (APP), or its processed fragments, in normal brain function is not well understood. There is a significant body of evidence to suggest that the membrane-bound protein plays a role in cell–cell interaction, neuronal plasticity and the formation and the consolidation of synaptic connections [9]. Soluble APPα (APPsα) generated following cleavage by the α-secretase enzyme is thought to promote cell survival, neurite outgrowth and synaptogenesis [15]. The administration of APPsα in vivo can reduce the level of neuronal damage following traumatic brain injury [23] and increase the number of EGF-responsive progenitor cells [6]. Lower levels of APPs in rats were associated with poor performance in the watermaze [1] whereas intracerebroventricular administration of sAPP enhanced the performance of mice in various learning tasks [17]. However if membrane-bound APP is cleaved by the β- and γ-secretase protease enzymes it generates the Aβ peptide that aggregates to form the neuritic plaques which define Alzheimer's disease pathology [9].
For a majority of AD cases it is not known what causes the shift to the amyloidogenic pathway although there is some preclinical and clinical evidence to suggest that stress may exacerbate the progression of AD and may be associated with the pathological hallmarks of the condition. In animals, exposure to isolation stress accelerated the age-dependent deposition of amyloid plaques in the Tg2576 mouse model of AD [11]. Chronic immobilisation stress had a similar effect on both memory impairment and amyloid deposition within the hippocampus and cortex of the APPV7171-CT100 mouse model [13]. These effects may be a direct consequence of the corticosteroids released during the response to stress as prolonged exposure to high levels of these hormones appears to accelerate age-related decrements in neuronal morphology and function [19]. Studies on patients with AD have shown that hypercortisolism is associated with greater cognitive deficits [25] and that increased plasma cortisol is associated with greater decline in both clinical and cognitive measures of dementia severity [10].
The aim of this study was to determine whether exposure to acute and repeated stress affected the levels of the two primary forms of APP in the normal rat brain and whether this was regionally selective.
Male Sprague Dawley rats (Harlan, UK) weighing between 250 and 300 g were maintained on a 12:12 h light:dark cycle with lights on at 6.00 a.m. and access to food and water ad libitum. After 1 week they were divided into 4 treatment groups (n = 5 rats per group): a 20 days stress group, a 10 days stress group, an acute stress group (1 day) and a control group. Stress exposure involved placing the rats on an open elevated platform for 60 min. Acutely this procedure reliably increases the levels of corticosteroid hormones in the blood [2] but this response habituates after between 10 and 20 days exposure [18,22]. Control animals were not exposed to the stressor but were taken to the same laboratory and handled in an equivalent manner. All experimental procedures were subjected to local ethical review and covered by a U.K. Home Office project licence (PPL 60/2845).
Rats were killed 24 h after the last stress exposure or handling session to measure the effect of stress-induced genomic changes on APP. Brains were removed and the frontal cortex, parietal cortex, striatum and hippocampus were dissected on ice and stored at −80 °C prior to analysis. The pathological changes that characterise Alzheimer's disease first appear in the cortical regions of the brain and the hippocampus. The striatum was included as an area also rich in cholinergic neurones that remains relatively free of pathology until advanced stages of the disease. The brain samples were then homogenised (10% w/v) in homogenising buffer (1 mM EDTA, 1 mM EGTA, 0.32 M sucrose, 1 mM Tris, pH 8.0 containing protease inhibitor cocktail) at 4 °C. Homogenates were centrifuged at 100,000 × g for 60 min at 4 °C and the resulting supernatant, containing the soluble brain fraction, was aliquoted into 1 ml fractions and stored at −20 °C. The pellet was resuspended in 1% (v/v) Triton X-100 and re-centrifuged at 100,000 × g for 60 min at 4 °C. The supernatant, containing the membrane, mitochondrial and nuclear fractions, was stored at −20 °C in 1.0 ml aliquots while the triton-insoluble pellet was discarded.
The total protein concentration of each sample was determined spectrophotometrically using the method of Bradford [4] and APP expression determined in 25 ug protein samples by Western blot analysis using a monoclonal primary antibody (MAB348 clone 22C11 Calibochem 1:1500) [16]. This recognises an N-terminal epitope (AA66-81) that is common to both APP and the amyloid precursor-like protein 2 (APLP2). The autoradiographs were scanned and band densities calculated using the SCION image (v3) software package. In order to determine whether there were any regional differences in APP expression between the groups the data were analysed by repeated measures analysis of variance using stress exposure as the between subject factor (GROUP) and brain region as the within subject factor (REGION). Post-hoc analysis of significant effects was carried out using one-way ANOVA or Dunnett t-tests where appropriate.
In the soluble brain fractions APPs was detected as a single band with a molecular weight of approximately 128 kDa (Fig. 1). The global analysis revealed that there was a significant effect of GROUP (F[3, 16] = 3.79, p < 0.05) and a significant interaction between GROUP and REGION (F[9, 48] = 3.58, p < 0.005) suggesting that stress had a regionally selective effect on the level of APPs. Subsequent analysis of this interaction revealed that this was due to the significant increase in APPs in the hippocampal region only following 20 days of stress (p < 0.05).
In the particulate brain fractions, APP was detected as a doublet with molecular weights of approximately 128 and 121 kDa (Fig. 2A). Global analysis of total APP revealed a significant effect of GROUP (F[3, 16] = 8.02, p < 0.005) but no interaction between GROUP and REGION suggesting that stress had influenced the level of APP but that this was not a regionally selective effect. Post-hoc testing confirmed that all stress groups expressed a significantly greater level of APP protein in the particulate brain fractions compared to controls (p < 0.05). Therefore, although the pattern appeared to be different in the parietal cortex compared to other regions, this effect was not large enough to influence the overall statistical analysis.
The 121 kDa molecular weight protein band has previously been demonstrated to represent the deglycosylated form of the protein [16]. When this band was expressed as a percentage of total APP (Fig. 2B) global analysis confirmed a significant effect of GROUP (F[3, 16] = 11.6, p < 0.001) but again no interaction between GROUP and REGION suggesting that stress had affected the APP band ratio but not in a regionally selective way. Post-hoc testing confirmed that, overall, only the acute and 10 days stress group had a significantly increased proportion of deglycosylated APP relative to control (p < 0.05). Close inspection of the data (Fig. 2B) suggests that the APP band ratio was still increased above control levels in the frontal cortex after 20 days stress but this effect did not significantly influence the global analysis.
Although stress exposure appears to have altered the processing of APP with changes in the glycosylation patterns of the particulate form and the levels of APPs, it is not clear whether this is due to changes in the release of corticosterone over this time period [18,22] or if it is due to the action of other neurotransmitters, such as acetylcholine, that are altered following stress [14]. Other studies have shown that directly treating rats with the glucocorticoid agonist dexamethasone can also increase particulate APP levels in the rat cortex, cerebellum and brain stem without significant changes in APPs [5]. However, hippocampal APP expression was not specifically reported in that study. In this study, stress significantly elevated the proportion of deglycosylated APP in particulate brain samples for up to 10 days but after 20 days there was no significant difference from control. This follows the previously published pattern of habituation of the plasma corticosterone response in rats treated with exactly the same protocol [18,22]. Corticosteroids have also been shown to control the process of protein glycosylation and in the hippocampus sialyltranferase activity appears to be highly regulated by aldosterone [8]. The expression of the mineralocorticoid receptor, which has a relatively high affinity for aldosterone, is dynamically altered in the dorsal hippocampus during 20 days exposure to the elevated platform [18]. The stress-induced increase in the particulate deglycosylated form of the protein may therefore be a consequence of corticosteroid receptor changes. Although statistically, there was no significant difference between the ratio of deglycosylated to total APP in the hippocampus compared to other brain regions, this was the area that demonstrated the greatest change following acute exposure and was the only one where the ratio fell below control levels after 20 days of stress (Fig. 2B).
The hippocampus was also the only area where the amount of APPs increased following repeated stress. The hippocampus is involved in terminating the corticosteroid response to stress [20] and may also be important in habituating to repeated stress as previous studies have shown that 20 days exposure to the elevated platform produces a selective increase in GR immunoreactivity in the dorsal hippocampus [18]. The increase in APPs most likely represents an increase in soluble APPsα that has been shown to exhibit neurotrophic properties. Since the hippocampus it also an area of the brain thought to be crucial for the formation of certain types of memory, the changes in APPs may have consequences for cognitive function. Previous studies have shown that stress can impair memory [21] or under certain circumstances can enhance it [3]. In particular, repeated exposure to the elevated platform has been shown to improve spatial memory using a watermaze task in young rats but only if they have been previously exposed to the apparatus [26].
The increase in deglycosylated APP seen following one or 10 days stress exposure may indicate an increased risk of Aβ formation as the deglycosylated form of the protein is less likely to be trafficked to the cell membrane [16] for cleavage by α-secretase but rather may be retained in the endoplasmic reticulum/golgi. Since β-secretase appears to be localised to the golgi and endosomal compartments of the cell [24] the deglycosylated APP is more likely to undergo amyloidogenic processing by β- and γ-secretase pathway. Recent studies have shown that glucocorticoid treatment of either mouse neuronal cells or the 3XTg-AD mouse increases the formation of Aβ [12]. This appears to be due to an increase in the expression levels of both APP and β-secretase. The formation of Aβ peptide oligomers may also disrupt cognitive function, even prior to nerve cell death [7]. The increase in APPs within the hippocampus over the same period as habituation to a repeated stressor could therefore represent a compensatory response in relatively young adult rats. Further studies will be required to determine the exact mechanisms responsible for the changes in APP processing and whether an age-induced impairment in this neuroadaptation increases the risk of the stimulation of the AD-associated protein processing pathways.

amyloid
alzheimer's disease
elevated platform
glycosylation
habituation
hippocampus


Response shift due to diagnosis and primary treatment of localized prostate cancer: a then-test and a vignette study

Aim Whether a prostate cancer diagnosis induces response shift has not been established so far. Therefore, we assessed response shift in men who were diagnosed with localized prostate cancer.
Introduction
Response shift, defined as an adaptation to changing health [1], is a beneficial process for patients because it can help in adapting to a new situation. However, it may complicate the correct interpretation of change in health-related quality of life (QoL) scores over time in intervention studies, and therefore needs to be understood. Response shift refers to a change in the meaning of QoL over time [2] and can result from a change in one’s internal standards of measurement (i.e. recalibration), a change in the importance attributed to the domains constituting QoL (i.e. change in values or reprioritization), or a change in the definition of the concept of QoL (i.e. reconceptualization) [3, 4]. These three forms of response shift are illustrated in the following example. Imagine a woman X. When asked to rate her QoL she thought of her (50-h a week) job, her partner and playing volleyball with friends, and rated her QoL as very good. Unfortunately she fell ill. For some months she was not able to work. Her partner and relatives supported her a great deal, which she appreciated enormously. Gradually she recovered and started working again for 20 h a week, but was no longer able to play volleyball. She rated her new QoL again as very good, but this time the main aspects of her QoL consisted of her partner, her family and work. Her ratings did not change, i.e. twice ‘very good’, but in fact three forms of response shift had occurred. ‘Work’ changed from a 50-h a week job to 20 h a week (recalibration), her partner became more important (reprioritization), and her concept of QoL has changed: no more sports, but relatives instead (reconceptualization).
This paper focuses on the assessment of response shift induced by a prostate cancer diagnosis. Schwartz and colleagues systematically addressed the state-of-the-art in the assessment and interpretation of response shift [5]. In a meta-analysis, following Cochrane guidelines, the magnitude and clinical relevance of response shifts across 19 longitudinal studies were evaluated. Most studies addressed global QoL and specific QoL domains such as fatigue, well-being, and pain, usually by conducting the then-test. Effect sizes, defined as the mean difference between tests divided by the standard deviation (SD) of the first assessment, were computed by the authors. These were generally small according to Cohen’s criteria [6], with the largest effect sizes found for fatigue, followed by global QoL, physical role limitation, physical well-being, and pain. Effect sizes varied in direction, which complicated their interpretation. Schwartz et al. concluded with recommendations for future response shift publications, such as explaining the meaning of the study results in terms of recalibration, reprioritization, and reconceptualization [5].
Response shift is more likely to occur when an intense and pervasive change in health is experienced [7]. A cancer diagnosis may have a large impact on a person’s experienced health. Our group previously described the process of being diagnosed with prostate cancer through a screening process consisting of a Prostate Specific Antigen (PSA) test and, if indicated, a biopsy. Typically, localized prostate cancer diagnosed through screening is not associated with any physical symptoms. Men’s mental health and the valuation of their own health decreased significantly after they received their diagnosis, and we concluded that being diagnosed with prostate cancer was a deeply felt change in health [8]. We started the present study because we expected that a prostate cancer diagnosis induces response shift. We hypothesized that the pre-diagnosis health state would be rated more positively in retrospect (i.e. if assessed after diagnosis) than at the reference point itself (i.e. pre-diagnosis).
Collecting data on QoL before a cancer diagnosis is usually not feasible, since it is unknown who will develop cancer and when, so that the inclusion of a very large cohort would be required. However, the context of the European Randomized study for Screening on Prostate Cancer (ERSPC) [9] enabled us to include a cohort of men shortly before they were screened and subsequently diagnosed. We aimed at assessing the magnitude and direction of response shift effects after diagnosis and again after primary treatment. We employed two methods: the common then-test and a novel approach including rating of vignettes related to side effects of prostate cancer treatment (urinary, bowel, and erectile dysfunction).
Patients and methods
Ethics approval and informed consent
The Ethics Committee of the Erasmus MC approved the research protocol. All participants gave additional written informed consent to be interviewed for the study.
Parent study
Inclusion of the ERSPC participants was initiated in 1994 among all male inhabitants of the Rotterdam region aged between 55 and 74 years. The only exclusion criterion was a previous prostate cancer diagnosis. Details on study recruitment for the ERSPC have been reported earlier [9].
Respondents
Randomly selected participants from the parent study were approached. All men who were due for the second (n = 2,798) or third screening round (n = 2,024) between January 2003 and May 2004 were sent a short questionnaire on health (see below) by mail. Men who were diagnosed through the screening process were interviewed twice by one of the authors (IK); one month post-diagnosis (but before treatment) and again 7 months post-diagnosis.
Assessing response shift
To assess the magnitude and direction of response shift effects two methods were used: the then-test, and vignettes (a novel method in response shift research). For the resulting study scheme, see Fig. 1.The then-test is a retrospective evaluation of an earlier assessment (retrospective pre-test-post-test design). At post-test respondents are asked to remember how they were doing at the reference point and to retrospectively rate their level of functioning or QoL at that time. The then-test was originally developed to measure recalibration. The method assumes that respondents will use their post-test internal standards when providing a re-evaluation or ‘then-test’ rating of their health at the reference point [5]. The comparison between the then-test and the post-test is thus assumed not to be confounded by recalibration and can be considered as an indication of true change [2]. The comparison of the mean pre-test, which is the assessment that was completed at the reference point, and then-test scores would reflect an estimate of the magnitude and direction of response shift [2]. Because respondents in our study were included before diagnosis, they could provide then-test scores relating to their health before diagnosis and to their health between diagnosis and treatment. Before completing the then-test, respondents were explicitly reminded about the period the then-test was referring to: e.g. the time when the respondents had not yet been diagnosed with prostate cancer and were unaware of having prostate cancer. Respondents were then asked to re-assess their health at that time. Three then-tests were conducted: two referencing pre-diagnosis health (measured at 1 month post-diagnosis and 7 months post-diagnosis) and one referencing 1-month post diagnosis health (measured at 7-months post-diagnosis), see Figs. 2, 3, 4. The respondents completed generic QoL measures, i.e. the Short-Form 36 (SF-36) mental health and vitality, and the EQ-5D VAS for self-rated health, as a pre-test, post-test and then-test. The SF-36 consists of eight scales on physical and mental domains of health. We used the scales on mental health (five items on being nervous, down, peaceful, depressed and happy) and vitality (four items on being full of life, having a lot of energy, being worn out and tired). Higher scores (0–100) indicate better mental health and vitality [10]. The EuroQol (EQ) 5D valuation of own health is a visual analog scale on current overall health, anchored at the lower end (0) by ‘worst imaginable health state’ and at the upper end (100) by ‘best imaginable health state’ [11].As a novel method to assess response shift we used vignettes that each described a health state relating to side effects of therapy for localized prostate cancer, i.e. urinary, bowel or erectile dysfunction. The vignettes contained items of the EQ-5D self-classifier complemented with items on dysfunction, for instance, ‘Mr. A has no problems in walking about, has no problems washing or dressing himself, experiences urinary leakage daily, has no pain or discomfort, is not anxious or depressed’. Respondents were asked to indicate how good or bad they evaluated these health states on visual analog scales anchored at the lower end (0) by ‘very bad’ and at the upper end (10) by ‘very good’. We used the vignettes to explore reprioritization. We hypothesized that men would value the health states as less detrimental after diagnosis than before. After diagnosis they knew they might experience these dysfunctions themselves in the context of prostate cancer treatment.Additionally, information on respondents’ age and on the Gleason score (a clinical criterion for histological grading of the aggressiveness of the tumour) were obtained through the screening office.
Fig. 1Study schemeFig. 2Original and then-test scores of the EuroQol valuation of own health by prostate cancer patients (n = 52). If we measure only EQ-VAS preceding diagnosis and at 1-month post-diagnosis, the difference between these scores is regarded the ‘observed change’. However, if the retrospective pre-diagnosis assessment provides a more valid comparison with the post-diagnosis assessment, the ‘true change’ is reflected by the difference between the retrospective pre-diagnosis assessment and the post-diagnosis assessment. The difference between the pre-diagnosis assessment and the retrospective pre-diagnosis assessment provides an indication of the size and direction of the ‘response shift’ induced by the diagnosis. Similar explanations are valid for the other data points in the figureFig. 3Original and then-test scores of the SF-36 mental health by prostate cancer patients (n = 52). ‘Observed change’, ‘True change’ and ‘Response shift’ refer to the differences in SF-36 mental health scores between the assessment at 2 months before diagnosis and post- and then-test at 1 month after diagnosis (for further explanation, see caption at Fig. 2.)Fig. 4Original and then-test scores of the SF-36 vitality by prostate cancer patients (n = 52). ‘Observed change’, ‘True change’ and ‘Response shift’ refer to the differences in SF-36 vitality scores between the assessment at 2 months before diagnosis and post- and thentestthen-test at 1 month after diagnosis (for further explanation, see caption at Fig. 2.)
Statistical analysis
Procedures concerning imputation of missing responses in the SF-36 items were conducted according to the guidelines of the SF-36 Health Survey Manual [12]. Differences between assessments were tested with paired-samples t-tests. P-values ≤ 0.05 were considered statistically significant. The type I error rate, i.e. the ratio of significant findings to the number of comparisons, was calculated. To assess the magnitude of the differences between the assessments we used Cohen’s effect sizes, defined as the mean difference between tests divided by the SD of the first assessment, and interpreted as follows: 0.2 < d < 0.5 indicates a small, 0.5 ≤ d < 0.8 a moderate, and d ≥ 0.8 a large effect size [6].
The minimal important difference (MID), which is the smallest change in a patient-reported outcome that is perceived by patients as beneficial or that would result in a change of treatment, was operationalized as a difference of at least half a SD [13].
Non-response bias was analysed by testing differences between the respondents and the non-respondents with unpaired t-tests.
Results
Out of the 3,892 men who completed the initial questionnaire before screening on prostate cancer, 82 were subsequently diagnosed. Of these, 52 (response 63%) consented to participate in two additional telephone interviews at 1 and at 7 months post-diagnosis. All 52 respondents participated in the first interview, which took place before treatment had been initiated. Due to personal circumstances one respondent later refused the second telephone interview. Average age at screening was 67.3 years (SD 4.4), ranging from 60 to 74 years. The Gleason score was favourable in 42 of the 52 patients, i.e. below seven (Table 1). In all respondents but one, treatment had been initiated at 7 months post-diagnosis, i.e. radical prostatectomy (n = 25), brachytherapy (n = 12), active surveillance (n = 10), external radiotherapy (n = 3), or hormonal treatment (n = 1), see Table 1.
Table 1Gleason scores and treatment modality of the respondents (n = 52)Gleason score < 7 n = 42Gleason score ≥ 7 n = 10Total n = 52Radical prostatectomy18725External radiotherapy123Brachytherapy1313Active surveillance99Hormonal treatment 11No treatment choice yet11
Original scores, i.e. scores relating to the respondents’ health at the time of the assessment and interviews, and then-test scores relating to the two reference points are given in Table 2. For example, ‘85.2’ in the upper right corner of Table 2 reflects the ‘EQ valuation of own health’ score of the then-test measured at 7 months post-diagnosis referencing 2 months pre-diagnosis. Mental and self-rated health scores worsened significantly from 2 months preceding diagnosis to 1 month post-diagnosis. The average mental health score, for instance, was 83.2 at 2 months pre-diagnosis, and 75.8 at 1-month post-diagnosis; a decrease of 7.4 that exceeds the MID. At 7 months post-diagnosis mental and own health scores had increased again, but not to their original level.
Table 2Mean health scores (standard deviation) of the respondents (n = 52) before and after diagnosis, original and thentests scoresReferring toOriginal scores (n = 52)P-value*Thentests1 month post-diagnosis (n = 52)P-value: thentest vs. originalEffect size: thentest vs original 7 months post-diagnosis (n = 51)P-value: thentest vs. originalEffect size: thentest vs. original2 months pre-diagnosisEuroQol own health80.2 (11.7)83.2 (9.0)0.058−0.2685.2 (7.6)0.002a−0.43SF-36 mental health83.2 (11.6)84.5 (11.0)0.304−0.1083.2 (10.9)1.000.01SF-36 vitality75.3 (15.6)79.6 (12.0)0.008−0.2879.4 (12.4)0.046−0.261 month post-diagnosis, before initiation of treatmentEuroQol own health74.5 (15.1)0.01074.1 (14.5)0.6180.03SF-36 mental health75.8 (16.8)0.001a72.8 (18.3)0.0420.17SF-36 vitality74.7 (14.2)0.77172.6 (13.7)0.1110.157 months post-diagnosis, after intiation of treatment in 80% of respondentsEuroQol own health77.6 (13.7)0.196SF-36 mental health80.4 (13.8)0.066SF-36 vitality73.1 (17.7)0.213*P-value of difference with previous original score (P ≤ 0.05 are considered significant)aChange exceeds the minimal important difference (operationalised as ½ standard deviation)
Original scores of pre-diagnosis health were lower, indicating worse health than on the then-test scores. For example, the original pre-diagnosis mental health score was 83.2 on average, but the then-test score measured at 1 month post-diagnosis was 84.5, indicating a more positive judgement of pre-diagnosis mental health in retrospect. Original scores of health between diagnosis and treatment, on the other hand, were higher, indicating better health than on the then-test scores. The original vitality score, for instance, was 74.7 at 1 month post-diagnosis, but the then-test score measured at 7 months post-diagnosis was 72.6. This means that vitality between diagnosis and treatment was judged worse when measured in retrospect than when measured at the reference point itself. Original and then-test scores are presented in Figs. 2–4, including estimates of the response shift effects, i.e. the difference between mean pre-test and then-test scores, and estimates of ‘true’ change, i.e. the difference between the mean post-test and then-test scores.
Effect sizes of the differences between then-test and original scores were small (Table 2).
The vignettes describing urinary, bowel and erectile dysfunction states were rated significantly higher (i.e. better) by respondents at 1 month post-diagnosis than at 2 months pre-diagnosis (P-values 0.038, 0.011, and <0.001, respectively). The valuation of erectile dysfunction showed the largest increase; i.e. from 5.3 to 6.7 on a 0–10 scale, with a moderate effect size of 0.57 (Table 3). This implies that respondents considered especially erectile dysfunction less detrimental after diagnosis with prostate cancer than before diagnosis. The differences between pre- and post-diagnosis valuations of the vignettes exceeded the MID in 4 out of 6 cases (Table 3).
Table 3Average valuation by VAS (SD) of prostate cancer specific vignettes, scale 0-10, P-values ≤ 0.05 were considered significantHealth state descriptionPre-diagnosis (n = 52)1 month post-diagnosis (n = 52)P-value pre-diagnosis vs. 1 month post-diagnosisEffect size pre-diagnosis vs. 1 month post-diagnosis7 months post diagnosis (n = 51)P-value pre-diagnosis vs. 7 months post-diagnosisEffect size pre-diagnosis vs. 7 months post-diagnosisDaily urinary leakage5.6 (2.1)6.3 (1.5)a0.038−0.325.9 (1.5)0.348−0.14Daily bowel cramps5.3 (2.0)6.0 (1.6) 0.011−0.416.2 (1.4)a0.012−0.39Serious erectile dysfunction5.3 (2.2)6.7 (1.8)a<0.001−0.576.5 (1.8)a0.005−0.47achange compared to previous score exceeds minimal important difference
The results of the then-test were significant in 4 out of 9 comparisons, the results of the vignettes in 5 out of 6. The overall type I error rate, which is the ratio of significant findings to the number of comparisons, was 0.6 (9 out of 15).
Non-response analysis
The baseline average age in men who were diagnosed with prostate cancer but did not respond to the questionnaire (n = 30) was 66.7 (SD 4.3, range 59–73) years. Respondents and non-respondents did not differ significantly in age or other health measures (data not shown).
Discussion
Men diagnosed with prostate cancer evaluated their pre-diagnosis health in retrospect as better than at the reference point itself. Post-diagnosis–pre-treatment health was rated worse in retrospect than at the reference point. This suggests that ‘true’ changes in health between the first assessment before diagnosis and the second one at 1 month post-diagnosis were larger than the original scores disclosed, and that  response shifts were induced by first, the diagnosis, and second, subsequent treatment. The sizes of the response shifts induced by the diagnosis were larger than those induced by the treatment. The negligible to small effect sizes indicated that only some recalibration occurred. The directions of the effect sizes were interpretable and consistent with our hypotheses.
Additionally, men evaluated vignettes relating to side effects of prostate cancer treatment as less detrimental after they were diagnosed than before diagnosis. We interpreted this change as a reprioritization of respondents who became aware after being diagnosed with prostate cancer that they were at risk of experiencing these health states themselves as a consequence of being treated for prostate cancer. In this new context dysfunctional health states were evaluated as less bad than before. The effect sizes were moderate for erectile dysfunction and small in the two other ones, indicating that reprioritization also occurred. The directions of the effect sizes were interpretable and consistent with our hypotheses.
The overall type I error rate was 0.6, which indicated that the statistical significance is very unlikely to be caused by chance. This is an additional indication that our findings reflect real differences. We conclude that the results of the then-tests and the ratings of the vignettes both indicate the presence of a response shift and adaptation of the patients to their new situation.
In the meta-analysis of Schwartz et al., the largest effect sizes on response shift (although still small) were found for the dimensions global QoL and fatigue [5]. These dimensions, represented in our study by EQ-5D on own health and the SF-36 vitality scale respectively, also resulted in small effect sizes.
An important criticism of the then-test approach is its susceptibility to recall bias. Respondents are supposed to be able to remember their previous health at the reference point, which is extremely difficult in case of a chronic disease with no obvious trend towards better or worse health [14]. However, in a study on response shift in cancer patients undergoing various forms of treatment, there was evidence that recall bias was absent [2]. We assume that in the case of a deeply felt change in health (such as being diagnosed with cancer or the initiation of cancer therapy) recall will not cause memory difficulties for most respondents. Therefore, in our study we expect that recall bias did not have a major influence on the results.
The then-test results in a retrospective judgement that subsequently is used to construct ‘real change’ since the reference point. This approach assumes that the information that was acquired after the original judgment was made leads to more accurate estimates of QoL than the original judgment itself. This assumption is, however, not always true; for example in the case that the newly acquired information is not correct [14].
The valuation of disease-specific vignettes (the second method used to assess response shift) has to our knowledge not been described before. It resulted in a moderate effect size considering the vignette on erectile dysfunction. The directions of the effect sizes were consistent with our hypotheses. Our results showed that response shift can be studied by using vignettes. We consider the valuation of vignettes as a useful addition to the already available collection of tools to assess response shift. Apart from this theoretical value, the results of the vignettes may also have implications for clinical practice. In case of a diagnosis of localized prostate cancer several treatment options are available. Since there is no consensus about which of these treatments has the best outcome in terms of survival and QoL, considerations of patient preferences regarding mode of treatment and side effects are an essential element in shared decision making on the choice of therapy. To elicit a patient’s preferences and his individual trade-offs between benefits and side effects of various modes of treatment, vignettes can be useful [15].
However, our study showed that patient preferences may change in the course of the diagnostic and treatment process, which illustrates how difficult it is for a patient to imagine the consequences of an intervention in advance. This finding confirms the point made by Cowen et al. to recommend the use of individual utilities (“actually prefer”) instead of population’s utilities (“should prefer”) to optimise the choice of treatment for patients with prostate cancer [16].
We recommend further investigation of the vignettes method. The fact that being diagnosed with prostate cancer was found to induce response shift may be seen as an indication that men regard a prostate cancer diagnosis as a major life event, and is additional evidence for earlier findings [8].
In another study, men with metastic or locally advanced prostate cancer completed assessments on prostate symptoms shortly after diagnosis, and 3 and 6 months thereafter. The second and third assessments included then-tests. The presence of a response shift was suggested in patients and their spouses [17, 18]. The authors remarked that retrospective and prospective assessments cannot be used interchangeably.
Lepore and Eton tested two conceptual models of response shift among men newly diagnosed with prostate cancer to explain the frequently observed lack of association between health problems and QoL in cancer patients. No support was found for the suppressor model, according to which health change leads to response shift, which in turn leads to a change in QoL. Some evidence was found for the buffering model, according to which response shift effects moderate the negative association between health problems and QoL. Two aspects of response shift, recalibration and reprioritization, were assessed by then-tests and a measure of primary life goal changes, respectively. They were found to moderate the relation between negative changes in physical health and changes in QoL [19].
Indications of response shift were also found in an earlier study on men treated for localized prostate cancer. Men stated, for instance, that they accepted the side effects of treatment because ‘If they hadn’t intervened, that operation, maybe I wouldn’t be here anymore’ [20].
The present study has several strengths and limitations. The study design is one of its strengths; the unique context of the ERSPC enabled the inclusion of respondents before they (or anyone else) were aware that they had prostate cancer, which is usually unfeasible. To our knowledge this is the first study to measure response shift in men who were diagnosed with cancer. An additional strength is the compliance of the respondents; 51 of the 52 respondents completed the 7-month assessment.
For the then-test we selected measures that are considered subjective (i.e. SF-36 mental health and vitality, and EQ-5D of own health) but no objective items, which can be considered a drawback of the study. Furthermore, we acknowledge that offering questionnaires in two different modes (self-administered questionnaires before diagnosis vs. telephone interviews afterwards) may have been less than optimal. This design was chosen based on practical considerations, because assessments by telephone in 3,892 screen participants was not feasible, and self-administered questionnaires at 1 month after diagnosis undesirable since we wanted these assessments to be completed before the initiation of treatment. The unavailability of information on marital status and education is also a drawback. Another potential limitation of our study is that the interval between the initiation of treatment and the assessment at 7-months post-diagnosis was not the same for all respondents; it is possible that response shift may vary according to the length of time that elapsed since treatment. However, information on this interval had been of limited use. The most common therapies for localized prostate cancer nowadays are surgery, radiotherapy, and active surveillance. These therapies differ greatly (by nature) in duration, the onset of side effects and their course over time.
It may be that particular groups may be more prone to response shift than others, e.g. depending on age or prognosis. We plan to address this issue further, preferably in a larger sample than used in the current study.
Conclusions
Using two complementary techniques we found that a diagnosis of prostate cancer induces response shift. From a methodology point of view, the vignette-method needs to be explored further.

response shift
prostate cancer
quality of life
patient-reported outcome


Evaluation of Harmful Algal Bloom Outreach Activities

With an apparent increase of harmful algal blooms (HABs) worldwide, healthcare providers, public health personnel and coastal managers are struggling to provide scientifically-based appropriately-targeted HAB outreach and education. Since 1998, the Florida Poison Information Center-Miami, with its 24 hour/365 day/year free Aquatic Toxins Hotline (1–888–232–8635) available in several languages, has received over 25,000 HAB-related calls. As part of HAB surveillance, all possible cases of HAB-related illness among callers are reported to the Florida Health Department. This pilot study evaluated an automated call processing menu system that allows callers to access bilingual HAB information, and to speak directly with a trained Poison Information Specialist. The majority (68%) of callers reported satisfaction with the information, and many provided specific suggestions for improvement. This pilot study, the first known evaluation of use and satisfaction with HAB educational outreach materials, demonstrated that the automated system provided useful HAB-related information for the majority of callers, and decreased the routine informational call workload for the Poison Information Specialists, allowing them to focus on callers needing immediate assistance and their healthcare providers. These results will lead to improvement of this valuable HAB outreach, education and surveillance tool. Formal evaluation is recommended for future HAB outreach and educational materials.
1. Introduction
Harmful algal blooms (HABs) are caused by blooms of algae known as phytoplankton; these include organisms such as dinoflagellates, diatoms and cyanobacteria [1–6]. HABs can occur in all aquatic environments. In marine environments, they are also known as “red tides” because some of these organisms can change the color of the water to red or brown. HABs may cause harm to the environment and other organisms in two ways. First, through the severe overgrowth of the HAB organisms that depletes oxygen in the local environment, and second when the HAB organisms produce extremely potent natural toxins.
Phytoplankton are the base of the marine food web; thus, the toxins they produce can bioconcentrate in organisms higher up in the food chain. Several human illnesses are caused by the ingestion of seafood contaminated with the natural toxins produced by the HAB organisms [1–6]. In addition to exposure through seafood ingestion, environmental exposures can occur through skin contact with contaminated water or by inhalation when the HAB organisms are broken up by waves and their toxins become aerosolized. For example, human exposure to aerosols containing brevetoxins from the Florida red tide dinoflagellate, Karenia brevis, has been associated with reports of respiratory distress, particularly but not exclusively in persons with asthma, as well as increased emergency room admissions for pneumonia, bronchitis and asthma among coastal residents during active Florida red tides [7–12].
With the increasing number of persons interacting with the coastal areas (both freshwater and marine) and with the apparent increase of HABs around the world, healthcare providers, public health personnel, and coastal managers are struggling to provide scientifically-based information [13–17]. There is a paucity of appropriately targeted outreach and educational materials for healthcare providers and persons with possible exposure to the marine and freshwater toxins, as well as coastal resource managers, the media, and the general public. A variety of educational outreach materials and services have been created, but there has been almost no formal evaluation to determine whether these materials are reaching their target audiences and meeting these audiences’ expectations.
1.1. Florida Poison Information Centers
The Florida Poison Information Center/Miami (FPIC/Miami) is one of three poison centers created in the State of Florida in 1989 by an act of the Florida Legislature (FS 395.038), and it has rapidly grown into a cost-effective model of a poison center system. The FPIC/Miami is located at the University of Miami Miller School of Medicine and Jackson Health Systems, and directly serves the residents of Southeastern Florida (i.e. Miami-Dade, Broward, Monroe, Palm Beach, Lee and Collier counties) and beyond with toll-free 24 hour/day services in several languages. For example, in 2006 the FPIC/Miami responded to over 57,340 calls; of these calls, 35,741 (62%) were for known or suspected exposures to toxic substances. Approximately 80% of the patients calling from home were managed in the home, without the need for an emergency department visit. The FPIC/Miami placed 18,833 (33%) follow-up calls to continually assess this home management. The FPIC/Miami toxicologists provided over 1,705 consultations with Florida’s physicians to assist in the care of hospitalized patients. In an attempt to prevent poisonings, the FPIC/Miami educators coordinated 669 outreach and education programs for the lay public, health professional, and the media. They also distributed over 198,213 informational brochures, handouts and telephone stickers, reaching over 72,000 persons.
Since 1998, with funding from the Florida Department of Health, the National Center for Environmental Health (NCEH) of the Centers for Disease Control and Prevention (CDC), and the University of Miami National Institute of Environmental Health Sciences (NIEHS) Marine and Freshwater Biomedical Sciences Center and the National Science Foundation (NSF)- NIEHS Oceans and Human Health Center, the FPIC-Miami established a toll-free hotline (1–888–232–8635) dedicated to providing information concerning aquatic health issues. All calls are answered by Poison Information Specialists, who are highly trained physicians and nurses. All calls are reported as a form of passive surveillance of HAB-related illness and information requests to the Aquatic Toxins Program (http://www.doh.state.fl.us/ENVIRONMENT/community/aquatic/) and to the Foodborne Illness Program of the Florida Department of Health.
Recently, the Hotline was expanded to include additional options for callers. Using an automated call processing menu system, the “Aquatic Toxins Hotline,” now allows callers to access information in English or Spanish about the possible health effects and locations of the Florida red tide (including the National Atmospheric and Oceanographic Administration (NOAA) Harmful Algae Bloom (HAB) Bulletin http://coastwatch.noaa.gov/hab/bulletins_ns.htm), information on ciguatera fish poisoning, blue green algae (cyanobacteria) and resources for learning about general marine toxin issues, as well as still giving the callers the opportunity to select to speak directly to a Poison Information Specialist if they wish. These materials were reviewed and developed with cooperation between the FPIC-Miami, the University of Miami, the Florida Department of Health, the CDC, Mote Marine Laboratory, and the Florida Fish and Wildlife Commission.
Concurrent with this initiative, a number of academic, public health and community partners (including Solutions to Avoid Red Tide or START; http://www.start1.com/) collaborated to create a series of informational signs advertising the Aquatic Toxins Hotline as a source of information for Florida red tide. After focus groups with public health personnel, beach managers, hotel and restaurant owners, and tourist boards, these signs were designed to be posted on and near beaches, and targeted at beach users including tourists (see Figure 1).
This Pilot Study evaluated whether the new automated system was used by callers and whether the callers considered the information to be useful to them, as well as to collect caller recommendations for the improvement of this service. The goal of this pilot study was to evaluate whether the Florida Aquatic Toxins Hotline was serving the self-identified needs of current callers.
2. Methods
The FPIC-Miami receives the telephone numbers of all callers to the Aquatic Toxins Hotline, whether the caller speaks with a Poison Information Specialist or just the automated system. In this evaluation, telephone numbers from the calls made to the Aquatic Toxins Hotline from September 2006 through January 2007 were re-contacted. Of note, throughout this study time period, there was an active Florida red tide on the West Coast of Florida.
This evaluation aimed to determine:
Basic Caller demographics
What information/services callers were seeking
Which menu selections were the most commonly chosen
Which menu options, in the caller’s opinion, were the most helpful
If callers were satisfied with the service provided by the Aquatic Toxins Hotline
If callers would call again
What would, in the callers’ opinion, make the Aquatic Toxins Hotline service more useful.
Trained interviewers contacted the Aquatic Toxins Hotline callers by phone in English and Spanish to explain the study, obtained verbal consent for participation and conducted a short scripted interview (available on request from the corresponding author). Specifically, the interviewers asked a brief series of questions about using the Aquatic Toxins Hotline and about the caller’s perception of the system’s usefulness. Study participants also had an opportunity to make suggestions to improve the system. No personal identifying information was collected. To maximize success in contacting callers, the interviewers called each telephone number three times (once during a week day, once during a week night, and once during the weekend) in an attempt to re-contact the specific person who had called the Aquatic Toxins Hotline.
This study was approved by both the University of Miami and Florida Department of Health Human Subject Committees. The data were collected into a Study Access Database. The data were analyzed using SAS version 9.1. The data were analyzed by frequencies as a descriptive process.
3. Results
The Aquatic Toxins Hotline has experienced increasing use over the past few years, particularly during periods of active Florida red tides. Since its inception in 1998, there have been 25,000 calls to the Hotline, with 7,000 occurring between 2001 and 2006; during 2006 alone, 2,415 calls were made to the Aquatic Toxins Hotline. During the study period from Sept 2/06 through Jan 31/07, there were 1,163 calls made to the hotline, with 315 (27%) answered by a Poison Information Specialist; of these calls, 53% were for Florida red tide, 6% for ciguatera, 17% for jellyfish, 2% each for PSP/pufferfish, scombroid, and stingrays, and 0.6% blue green algae. This means that only 27% of the callers selected to speak with a Poison Information Specialist from the automated HAB Menu in English or Spanish, while the vast majority (73%) only accessed the automated HAB menu.
The Interviewers attempted to contact each of the callers during the study period as described above. Even though the follow-up calls were made within two months of the original contact, only a small percentage (10%) of the callers was able to be re-contacted. Inability to re-contact these individuals was due to a variety of factors such as: no answer despite repeated calls, answering machines, and no real contact number (i.e. original call made from hotel, hospital, etc). Nevertheless, once contacted, all callers (100%) agreed to participate.
A total of 118 callers were successfully re-contacted and agreed to participate. Of these, only 89 callers reported that they recalled making a call to the Florida Aquatic Toxins Hotline, and thus these 89 callers were considered the participants in this Pilot Study. Of note, both the actual number of respondents and the percentages are reported below because not all subjects answered all the questions.
The majority of the 89 participants were women (41 [61%]), self-described as white (82 [100%]) and non Hispanic (66 [94%]) whose primary language was English (80 [96%]) and with a mean age and standard deviation of 55.5 +/− 12.2 years (see Table 1). The participants were predominantly from Florida (50 [77%]), with the majority from the West coast of Florida (46 [60%]) particularly in Sarasota, the area of recurrent Florida red tide. There were 18 (23%) callers from other parts of the US, including 1 from the US Virgin Islands (data not shown).
The majority of the participants had heard about the Aquatic Toxins Hotline from a website (35 [39%]), newspaper or magazine article (16 [18%]), a friend or personal recommendation (13 [15%]), or a sign (7 [8%]). Many of the participants (38 [48%]) called with general information questions; of note, none of the participants reported calling because they or someone they cared for was sick or believed they had been exposed to a toxin.
Overall, the majority of participants (59 [68%]) reported that they received an answer to the question that prompted their call to the Florida Aquatic Toxins Hotline (see Table 2). Furthermore, 59 (68%) reported that the Florida Aquatic Toxins Hotline was easy to use, with only 17 (20%) reporting otherwise. Finally, 69 (80%) of the participants reported that they would be likely to use the Florida Aquatic Toxins Hotline again, and 68 (79%) stated that they would recommend the Hotline to others.
With regards to the specific information supplied by the automated system, 34 (39%) of participants reported speaking with a Poison Information Specialist; among these participants, 23 (74%) were satisfied and only two (7%) were dissatisfied (see Table 3). Among those participants who used the automated HAB menu, the largest number (46 [54%]) reported listening to the NOAA HAB Bulletin which gives current and forecasted information on the location of the Florida red tides; among these participants, 30 (70%) were satisfied and seven (17%) were dissatisfied. The second largest group (37 [44%]) reported listening to general information about Florida red tides; among these participants, 26 (70%) expressed satisfaction, and 8 (22%) were dissatisfied. Participants also reported listening to information about other marine toxin issues (28 [33%]) with 20 (71%) expressing satisfaction; Ciguatera fish poisoning (11 [13%]) with 10 (90%) expressing satisfaction; and blue green algae (cyanobacteria) (10 [12%] with 9 (90%) expressing satisfaction.
At the end of the questionnaire, the participants were asked if there was anything in particular they thought should be done to improve the usefulness of the Florida Freshwater and Marine Health Hotline as an open ended question. The majority of the comments can be summarized as: a) wanting more up-to-date information (particularly geographic location of the Florida red tides) on even a daily basis; and b) wanting to speak with a person directly or listening to an automated system. Specifically, the participants recommended that the Hotline provide the geographic location of the Florida red tides on a map; provide more information to tourists in hotels; and make sure that the Poison Information Specialists were up-to-date with HAB and Florida red tide information.
4. Discussion
This study was the first known systematic evaluation of the use of and satisfaction with educational outreach materials for HABs, specifically evaluating callers to an automated Florida Aquatic Toxins Hotline of the South Florida Poison Information Center. The majority (68%) of participants reported that they were satisfied with the information provided by the Aquatic Toxins Hotline; 80% that they would use the Hotline again; and 79% that they would recommend the Hotline to others. Furthermore, even when evaluating the specific services offered by the Aquatic Toxins Hotline automated system (including speaking directly with a Poison Information Specialist), the high level of satisfaction persisted. Of interest, only 8% of participants reporting hearing about the Aquatic Toxins Hotline from signs, with the rest learning about the hotline from a website (39%), newspaper or magazine article (18%), or friend (15%). Our evaluation demonstrated that the primary advantage of the new automated system is that it provided useful HAB information for the large majority of the callers, without requiring contact with a Poison Information Specialist. By decreasing the workload of purely informational calls for the Poison Information Specialists, the Hotline allows the Specialists to focus on those persons who need immediate assistance.
4.1. Limitations
As seen in previous studies using the Poison Information data [18,19], only a small percentage (10%) of the callers were able to be re-contacted. This lack of participation in telephone surveys is part of an unfortunate and growing nationwide trend recognized by the evaluation research community [20,21]. Furthermore, the majority of the study participants (77%) gave zip codes of addresses within Florida; since the follow-up calls were made two months from the original call, it would appear that the majority of the participants were either part-time seasonal (i.e., “snow-birds”) or permanent residents. Based on the review of the zip codes and area codes of all Hotline callers, many were likely tourists, and thus were “lost to follow-up” when they could not be reached two months after their original call to the Hotline [20,21].
In addition, these participants were not entirely representative of the overall Hotline caller population since a greater proportion (39%) reported speaking with a Poison Information Specialist than was reported (27%) for the entire population of callers to the Florida Aquatic Toxins Hotline during the study period. However, based on the limited demographic information, the participants interviewed were similar to those contacted in other studies which have used the overall South Florida Poison Information Center Caller Database, i.e., predominantly white non Hispanic older females [18,19]. We did not identify many Hispanic callers in this study. Of note, most of the relevant marine HAB activity is concentrated on the Gulf Coast, while the majority of Florida’s Hispanic population is concentrated on the east coast of Florida. Finally, there were missing data (e.g. 22 out of 89 persons did not report their gender).
5. Discussion and Recommendations
This Pilot Evaluation Study demonstrated that the additional outreach provided by the Hotline was successful in getting information to people who wanted it. It also suggested that there needs to be increased outreach and education efforts using a range of media to inform additional target populations about the Aquatic Toxins Hotline, such as residents and tourists, and their healthcare providers. For example, it has been shown in the case of ciguatera fish poisoning, that even in an endemic area such as Miami (Florida), the majority of healthcare providers do not recognize, nor (more important from the point of view of HAB surveillance) do they report cases of ciguatera to the Health Department even though it is a reportable illness throughout the US [22]. Therefore, additional outreach and education not only to the possible victims of HAB-related illness, but also for their healthcare providers is needed.
Recommendations for improving the Aquatic Toxins Hotline automated HAB system include the following: 1) Existing automated materials need to be revised to incorporate an ongoing evaluation component, with a focus on tourist not just resident callers, and in coordination with ongoing efforts in providing geographic location [23]; 2) Existing materials will need constant updating; and 3) Ongoing training should be provided for the Poison Information Specialists (as well as other healthcare providers) concerning the possible exposures and health effects from the aquatic toxins as new HAB research data and resources are made available [13, 23–29]. In addition, as different HAB educational outreach materials and services are created, these materials and services need to be evaluated before and after implementation in the target populations to improve their quality and utility. Based on the participation rate in this study, rapid follow up is necessary to ensure an effective evaluation. This will require the incorporation of additional resources and collaboration with investigators with expertise in outreach, education and evaluation in future HAB activities.

harmful algal bloom (hab)
outreach and education
poison information centers
cyanobacteria
brevetoxins
florida red tide
karenia brevis
ciguatera fish poisoning
blue green algae
ciguatoxins
human health effects
neurotoxic shellfish poisoning (nsp)
paralytic shellfish poisoning (psp)
solutions to avoid red tide (start)


Adherence to HAART therapy measured by electronic monitoring in newly diagnosed HIV patients in Botswana

Aims This pilot study was designed to evaluate the feasibility and benefits of electronic adherence monitoring of antiretroviral medications in HIV patients who recently started Highly Active Anti Retroviral Therapy (HAART) in Francistown, Botswana and to compare this with self-reporting.
Introduction
Botswana is one of the countries worst affected by the HIV pandemic with a prevalence of approximately 17% of the entire population [1]. An estimated 38.5% of those aged 15–49 years are HIV-positive, and it is estimated that one in every eight children is born with HIV [2].
Since 2002 the nation has embarked on the provision of antiretroviral drugs for all its eligible citizens by implementing the MASA program.(The national antiretroviral therapy program was given the name MASA, the Setswana world for “dawn”).
The use of potent antiretroviral combinations has provided unprecedented opportunities for effectively treating HIV disease by suppressing viral replication and has led to dramatic decline in HIV mortality [3, 4].
Adherence to antiviral regimens in HIV infected patients is essential for adequate suppression of viral replication. When adherence falls below a certain level, intermittent viremia will occur, and this may increase the chance of the development of resistant strains possibly followed by therapy failure [5–7].
In contrast, nonadherence to the prescribed antiretroviral regimens is associated with a rapid selection of resistant HIV strains resulting in treatment failure [8, 9].
The required high level of antiretroviral drug adherence in a poor resource setting remains therefore a serious concern. Assessment of adherence in HIV patients such as in this pilot-study may also provide tools to allow feedback and education on an individual health care provider–patient base.
For this reason patients in the Botswana Infectious Disease Care Centers (IDCC) treatment program are urged at each visit to the IDCC facility to comply with the prescribed ART regimen. This occurs in three stages: (1) in group instruction sessions together with other HIV patients and individual counselling by a trained nurse or pharmacist, (2) by their individual health care provider (physician), and (3) by the pharmacist. In addition, patients are usually accompanied by a close family member who is asked to assist or remind patients of the pill intake (adherence partner).
Reliable information about the actual tablet intake is a prerequisite for any form of management or modification of the adherence to therapy. It has been recognised that many of the traditional methods of assessing adherence such as pill counts, diaries, or self-reports are unreliable. Electronic monitoring enables the recording of the time points of pill bottle openings. This method also has drawbacks and may underestimate adherence [10] and, of course, it does not provide evidence of actual ingestion of the drug [11]. Despite these drawbacks it has been so far the closest to a gold standard for adherence measuring [12], although other methods remain of value.
A new method to measure adherence to prescribed medication regimen is the use of electronic monitoring [10, 13, 14]. Such systems commonly rely upon a microprocessor located in the cap of the medication container, where time and date of each opening are recorded. Each cap opening and closing is assumed to reflect a single medication-taking event. The data stored in the microprocessor are transferred to a computer database and uploaded for analysis [11]. Other methods like self-reporting, pharmacy records, and pill counts tend to overestimate patient adherence by anywhere from 20–30% [15–21].
This study was designed as a pilot study to evaluate electronic adherence monitoring in an HIV infected patient group that was put on antiretroviral medication for the first time. A secondary objective was to compare the adherence measured by electronic monitoring with that of self-reporting by means of a medication diary.
Methods
Patients
Thirty consecutive patients were recruited into the study during the period 13–30 October 2005. All were patients with an AIDS-defining illnesses and/or CD4 cell counts< 200 cells/mm3 (uL) who were offered HAART according to the Botswana Guidelines on Antiretroviral Treatment [2]. All patients were ARV-naïve.
Design
This was a trial in which treatment-naïve patients were monitored with regard to adherence to prescribed anti-HIV medication. Patients were not informed about the use, blinded of the electronic monitoring system, and were only asked to return their pill-bottles at each visit to the pharmacy when they returned for a refill and a consultation. Patients were also supplied with a self-reporting form. The study did not involve study related interventions and the subjects were not required to change behaviour in any way. The subjects were informed that the medication was supplied in special containers that had to be returned to the clinic but not about the monitoring system to prevent bias. The study was approved by the hospital management and the chief physician of the department of internal medicine.
Treatment regimens
The treatment regimen used consisted of 2-nucleoside reverse transcriptase inhibitors (NRTIs) plus 1 nonnucleoside reverse transcriptase inhibitor (NNRTI). Patients started the recommended first-line treatment with three different agents. These were zidovudine and lamivudine in a combination tablet Combivir (CBV) plus efavirenz (EFV) or nevirapine (NVP).
Male and female patients who were not anaemic were prescribed Combivir medication whereas anaemic patients (Hb < 7.5 mmol) were given stavudine (d4T/Zerit) and lamivudine medication. The stavudine dose was adjusted according to bodyweight (30 or 40 mg).
All patients except females in the reproductive age category were given once daily EFV. All females of the former category were prescribed NVP at a 200 mg once daily dose for 14 days which, after assessing the liver function parameters, was increased to 200 mg twice daily.
The continuation of NVP or EFV was dependent on the absence of significant rise in hepatic enzymes (AST and ALT). Approximately 90% of those who started the NVP or EFV treatment are able to continue this medication. Patients returned to the IDCC after a month for a medical check and refill of their prescription unless clinical events dictated earlier visits to the clinic.
The treatment starters were booked to see the doctor after the first 2 weeks of therapy. After seeing the doctor, the self recorded medication card and the electronic monitors were collected, and the data stored in the microprocessor were transferred to a database in the computer. Following this, each bottle was refilled and provided with a new label with medication instructions. Most patients received a refill for a period of 1 month, some however for a shorter period. The potential side effects were discussed with each patient. The results of the first analysis of the electronic monitors were not used in any counselling.
The electronic monitors were MEMS IV Track Cap devices (Aardex, Zug, Switzerland) with a MEMS IV Communicator for read-out of the results.
Study endpoints
The primary study endpoint was the adherence level measured (over a minimal period of 6 weeks) by the percentage of days on which the patients took a correct dosing over the monitored period. Adherence was also expressed as the number of pill-intakes recorded on a self-reporting forms designed to reflect the intended schedule and timing of treatment.
In the event a patient opened his/her bottle more than was prescribed (surplus opening), it was assumed that the patient correctly took the prescribed pills. However, occasions on which a patient opened less than the prescribed dose frequency were considered as adherence failures.
Sequence and duration of trial period
Each patient was immediately given counselling and made familiar with the ART treatment. During the counselling session emphasis was given with regard to the need of strict adherence of the prescribed medication and to methods to prevent disease transmission. They were also informed about the self-reporting form and given a pen to mark taking a treatment with a cross.
ART medication was started thereafter and the adherence to the pill intake schedule was monitored by means of using electronic monitors and self-recording for a period of 6 weeks. At the start of the treatment, electronic monitors containing medication for a period of 2 weeks were provided. After an evaluation by the doctor at day 14 of treatment the electronic monitors and self-reporting form were collected and the data in the microprocessor were entered in the database. The electronic monitors were subsequently refilled with medication for the next period of 1 month. A new self-reporting form was also given.
Patients were given 2 (in case of Lamivudine/Zidovudine + NVP or EVF) or 3 electronic monitors (in case D4T, 3TC, and NVP or EFV).
Patients recruited in the study were asked to return the electronic caps and the self-reporting form on each occasion of a visit to the IDCC. A self-reporting form was issued at the start of the study. This form contains rows where the patient had to mark with x each time they took the pill at the correct time. As some people in Botswana could not read or write, this form was kept very basic. A pencil was given to every patient who participated in the study.
Results
A total of 30 HIV infected adults were enrolled in the study. In five patients full data could not be obtained because of various reasons. This leaves an evaluable group of 25 (9 male, 16 female; average age 35.6 years, range 22–55 years). Twenty patients completed the 6-week monitoring period and the mean follow-up period was 49 days (range 42–72 days).
The reasons for lack of follow-up in the five patients varied, but in three patients it was due to mortality. In these three cases, as the relatives or nursing staff did not know about the value of the medication bottle (due to the blinding of the patients), the bottles were not returned. One patient was admitted to hospital where the nursing staff discarded the pill bottles. One patient failed to return for follow-up. Full follow-up was not obtained in another five patients for various reasons. This included technical failure of the compliance monitor in two cases and a change in the return date of the subjects who then received a refill in a normal container. Adherence assessed from the dosing histories compiled by electronic monitoring are shown in Table 1.
Table 1Adherence (%) of the patients (n = 25) by MEMS caps and self-reportingPatient numberTCFollow-up (days)MEMSSelf reportLamivudine /zidovudine (bd)EFV (od)Lamivudine (bd)NVP (od/bd)d4T (bd)Drop out/non-retrievel11111001001001003227095100911003214100No data10010034151981009810054−−−−−−−−1614471100719573457370928196814410010010010092449810010098102141001001001003111448810088981224393100939313144988998100141−−−−−−−−2152449310098931621562100100623172−−−−−−−−2–1182448410084861924598100981002019100No data1001003211−−−−−−−−122172751007594232442110021292424293100939525266971001009726143100100100100271−−−−−−−−42814994No data969629156100100301472010020100avg4285988998928796Treatment codes (TC) are: 1 Lamivudine/zidovudine, Efavirenz (EFV); 2 Lamivudine/Zidovudine, Nevirapine (NVP); 3 Lamivudine, Nevirapine and stavudine (d4T); and 4 Lamivudine and Efavirenz. MEMS indicates the data combined for all different treatments. Patient numbers are not consecutive because patients who died have been omitted. Reasons for drop out and nonretrieval: 1 death; 2 MEMS thrown away; 3 missed by investigator (patients showed up on another date than the investigator expected); and 4 lost to follow-up
Assuming that surplus opening of the bottles was associated with correct medication intake, the mean adherence level was 85% (SD = 23%, range 20–100%). When surplus openings were calculated as incorrect, the adherence decreased to 70% (SD=23%, range 14–100%) (data not shown). Seven patients (23%) had an adherence under 90%, a level at which virological failure increases substantially [10]. Examples of the medication records obtained from the electronic monitoring device are shown in Fig. 1 for a patient with good adherence and a patient with low adherence to the regimen.
Fig. 1Examples of data of from patients with good and respectively poor adherence
Adherence assessed by means of self-reporting
The mean adherence assessed by means of self-reporting of medication intake was 98% with two (6%) patients recording adherence level lower than 90%. Adherence by self-reporting differed significantly from the adherence measured by the MEMS monitors method (p < 0.05, paired t-test). Three patients did not hand in their diary.
Discussion
In this pilot study we assessed the use of MEMS monitors to study the adherence to antiretroviral medication prescribed for HIV patients living in a low resource health care system. We demonstrated that assessment of adherence with this technique is feasible and may provide useful results. There was an approximately 30% drop-out rate of the recruited patients due to inability to recover data or early mortality. This may seem unacceptable in a well-resourced health care system. It is a reality in many countries where patients present with much more advanced disease, when there are sometimes great difficulties coming to the hospital, and patients can often not be reached by telephone or mail as they do not regularly have a postal address. It is likely that some association exists between failure to return to the hospital and adherence to the drug regimen and the current patient set therefore may reflect an overestimation of adherence.
Self-reporting of medication intake has been shown to be less reliable than the MEMS monitors [22, 23]. Therefore, it is not surprising that patients recorded adherence levels which were considerably higher compared with those assessed by the presumably more objective electronic monitors. Clearly, none of the methods used to measure adherence record actual intake of medication, and there are even indications that at least in some instances self-reporting is a more accurate record of adherence [24]. Despite these findings we consider the MEMS monitors more appropriate for a developing country with a larger potential for illiteracy. Patients knowledge about the monitoring of compliance will likely affect the absolute level of compliance [25], but there are no reasons why the relative ranking of compliance amongst patient groups is affected, and the device can still be used to improve adherence. In this study we chose not to inform the patients about the use of the monitoring device.
One of the critics often cited about the electronic monitor is that the opening of the bottle does not prove ingestion. However, it has been shown recently that projected plasma concentrations based on electronically compiled dosing histories correlates very well with directly measured concentrations [26]. It has also been shown that t in HIV patients electronically compiled dosing histories strongly correlates with viral suppression or the occurrence of virological failure [27]. It is therefore assumed that this method is a fair reflection of medication intake [28]. In this small study performed in HIV patients, some subjects opened their bottles more often than needed (surplus opening). Such surplus opening was not considered as nonadherence (in that case our reported adherence level would be lower). If surplus opening had led to extra intake of medication, it would not have contributed the endpoints of virological failure or the induction of resistance. We assume, therefore, that such surplus opening of the pill bottles had occurred for other reasons. Surplus openings occurred especially in the group of patients that had to take a once daily regimen together with twice daily regimens. These extra openings occurred often at the same time as when the prescribed twice-daily regimen was taken. This may have occurred because the patients were unsure about which bottle belonged to which medication. This may indicate that clearer labelling is essential, especially in an illiterate society. Under-opening most likely reflects nonadherence, although it cannot be excluded that in some cases patients removed several tablets at the same time. However, such behaviour is highly likely to increase the chance of erroneous medication intake and was for this reason registered as nonadherence.
Several studies have indicated that medication adherence lower than approximately 90% increases the chance of virological failure and the development of resistant virus strains [12, 28, 29]. The result of this pilot study indicates that even after careful counseling and guidance, a significant number of patients did not manage to adhere sufficiently to the twice-daily pill intake regimen. This observation study was too short to allow an analysis of the clinical impact of the observed low adherence in such patients on the development of virological failure. Additionally, reliable measurement of viral load was impossible in the hospital. However, our data did help to identify some patients with low adherence at the start of treatment and allowed a diversion of scarce resources for extra counseling for such patients.
The outcome of monitoring pill intake by the electronic monitors may therefore assist in timely counseling of patients with regard to their medication intake and persistence with the prescribed regimen.
Adherence measurements by means of using electronic monitors together with self and adherence partner recording of pill intake are likely to be useful in a much needed larger, long-term and more comprehensive adherence study in a low-resource health-care-system setting. Such a study can contribute to directed efforts to optimise the treatment of HIV-infected patients worldwide.