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526202

PCOGR: Phylogenetic COG ranking as an online tool to judge the specificity of COGs with respect to freely definable groups of organisms

Background The rapidly increasing number of completely sequenced genomes led to the establishment of the COG-database which, based on sequence homologies, assigns similar proteins from different organisms to clusters of orthologous groups (COGs). There are several bioinformatic studies that made use of this database to determine (hyper)thermophile-specific proteins by searching for COGs containing (almost) exclusively proteins from (hyper)thermophilic genomes. However, public software to perform individually definable group-specific searches is not available. Results The tool described here exactly fills this gap. The software is accessible at and is linked to the COG-database. The user can freely define two groups of organisms by selecting for each of the (current) 66 organisms to belong either to groupA, to the reference groupB or to be ignored by the algorithm. Then, for all COGs a specificity index is calculated with respect to the specificity to groupA, i. e. high scoring COGs contain proteins from the most of groupA organisms while proteins from the most organisms assigned to groupB are absent. In addition to ranking all COGs according to the user defined specificity criteria, a graphical visualization shows the distribution of all COGs by displaying their abundance as a function of their specificity indexes. Conclusions This software allows detecting COGs specific to a predefined group of organisms. All COGs are ranked in the order of their specificity and a graphical visualization allows recognizing (i) the presence and abundance of such COGs and (ii) the phylogenetic relationship between groupA- and groupB-organisms. The software also allows detecting putative protein-protein interactions, novel enzymes involved in only partially known biochemical pathways, and alternate enzymes originated by convergent evolution.

Background The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups (COGs) [ 1 , 2 ]. Each of the COGs consists of individual proteins or groups of orthologs from at least 3 lineages and thus corresponds to a conserved domain. The COG collection currently consists of 138,458 proteins, which form 4,873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms [ 3 ]. In addition, the database now includes KOGs containing the clusters of seven eukaryotic genomes. The COG database is an ideal source to search for proteins specific to a certain group of organisms. Several such surveys aimed at finding (hyper)thermophile-specific proteins that made use of the COG-database are published. For instance, Forterre detected reverse gyrase as the only hyperthermophile-specific protein [ 4 ]. In addition, a survey to find specific genes important for hyperthermophily [ 5 ] and a study identifying thermophile-specific proteins [ 6 ] are published. However, those studies used rather nonflexible tools designed for other purposes [ 7 ] or software especially written and not accessible for the public. To overcome these issues, a more flexible software-tool is needed that allows defining the group of organisms individually for which specific COGs can be searched. Here we describe phylogenetic COG ranking (PCOGR), a platform independent software tool capable to rank all COGs with respect to a freely definable group of organisms versus a group of reference organisms. Implementation PCOGR is written in PHP (v.4.3.3) including the domxml (v.20020815) plugin and runs on an openBSD (v.3.4) operating system at dmz.uni-wh.de in an apache (v.1.3.28) web-server environment. In addition, at the clients-side, HTML, javascript, and CSS are used. Phylogenetic COG ranking (PCOGR) is an online-tool to analyze the microbial COG, or after clicking "Switch to PKOGR", to analyze the eukaryotic KOG database. PCOGR provides a means for determining the specificity of each COG with respect to the presence of sequences from organisms belonging to a predefined group (groupA) versus the absence of sequences from organisms belonging to a second predefined reference group (groupB). For that purpose, each of the organisms can be assigned to one of the two groups or defined to be ignored by the analysis. The software then calculates a specificity index S for every individual COG. The highest ranking COGs (large S) contain sequences from the most groupA-organisms whereas the most sequences from groupB-organisms are absent. To process S for each individual COG, the algorithm starts at S = 0, adds a constant A for each groupA-organism and subtracts a constant B for each groupB-organism being present in the COG under analysis with A = A tot /B tot and B = B tot /A tot where A tot is the total number of organisms belonging to groupA and B tot is the total number of organisms belonging to groupB. After all COGs have been processed in this way, all S-values are scaled to values between 0 and 1. Then, all COGs are output in the order of their specificity indexes S. In addition, a graphical representation shows the number of COGs as a function of their S-values in discrete intervals. The total number of intervals to be displayed can be specified by the user (default = 40 for PCOGR and 7 for PKOGR). A Javascript-mouseover info box intuitively explains all functions of the graphical user interface of PCOGR. Furthermore, additional information about both, organisms and output COGs, are available by the implementation of links to Figure 1 , 2 , and 3 show screenshots of the parameter input and output sections, respectively. Results and discussion PCOGR allows detecting group-specific proteins by both ranking all COGs and graphically showing their distribution over their specificity indexes. The graphical representations can be interpreted as follows: If the two predefined groups are rather related, one expects a single peak in the middle of the graph, i. e. there are little or no proteins specific to one of the groups resulting in a specificity value of around 0.5 for most COGs. In contrast, if the two groups are rather distant, further maxima, either on the left, the right or on both sides become visible, i. e. there are group-specific proteins with S-values around 1 and/or S-values around 0. Even two single organisms can be compared by assigning the first to groupA, the second to groupB and ignoring all other organisms. For instance comparing the closely related Escherichia coli strains O157:H7 EDL933 and O157:H7 results in a prominent single peak in the middle of the graph whereas two further peaks on the edges become visible if two more distant organisms e. g. Aquifex aeolicus and Saccharomyces cerevisiae are compared. Distance and relationship may be interpreted either in phylogenetic or in physiologic terms. To demonstrate that physiologic relevant differences in protein distributions indeed can be detected by PCOGR, two parameter-presets are selectable: (i) a specificity ranking of hyperthermophile-specific versus non-thermophile-specific proteins as published by Makarova et al. [ 5 ] and of thermophile-specific versus non-thermophile-specific proteins as described by Klinger et al. [ 6 ]. For the ranking according to Makarova et al., optimum growth temperatures of corresponding organisms belonging to groupA are all above 80°C and all other organisms are assigned to groupB. For the specificity ranking according to Klinger et al., the optimum growth temperature needed for an organism to be assigned to groupA is above 55°C instead of 80°C. The user will notice that for the two presets, there are two additional peaks, the first corresponding to COGs containing (hyper)thermophile-specific proteins, and the second peak corresponding to COGs containing mesophile-specific proteins. A further attractive potential of PCOGR lies in the easy way to detect novel protein-protein interactions since physically interacting proteins should phylogenetically similarly be distributed [ 8 ]. Thus, if the phylogenetic pattern for a putative interacting protein target is known, a ranking with this pattern as the input will result in a ranking of potentially interacting candidates. To simplify such a procedure, the phylogenetic pattern of a certain COG defined by the user can automatically be assigned as the preset of a subsequent ranking. As an example, we performed a ranking choosing the phylogenetic pattern of COG2025 (electron transfer flavoprotein, alpha subunit). This ranking resulted in only two high-scoring outputs (specificity value S = 1): COG2025 (the target) and COG2086 (electron transfer flavoprotein, beta subunit) which is shown by x-ray crystallography to build a complex with the alpha subunit [ 9 ]. All following proteins have specificity values below 0.9 indicating the suitability of such a search for protein-protein interactions. Not only protein-protein interactions can be detected but also enzymes involved in the same biochemical pathway as a certain target enzyme [ 8 ]. This possibility may be useful to find the biochemical function of yet uncharacterized proteins given that one or more catalysts of the same pathway are already characterized. For example, a search performed with the phylogenetic pattern of COG0135 (phosphoribosylanthranilate isomerase), an enzyme involved in the biosynthesis of L-tryptophan, results in four (COG0135, COG0159, COG0547, and COG0134) of the five enzymes involved in tryptophan biosynthesis at the top four places of the ranking. The beta subunit of tryptophan synthase is the only missing enzyme also involved in this pathway. A closer look reveals that this protein is assigned to two instead of one COGs (COG0133: rank 29 and COG1350: rank 1770). The latter COG is annotated as "predicted alternative tryptophan synthase beta-subunit (paralog of TrpB)". This double assignment may explain the absence of the beta subunit of tryptophan synthase from high-scoring proteins of the ranking. Another attractive use of PCOGR can be to look for an alternative enzyme form catalyzing the same reaction but originated by non orthologous gene displacement (NOGD). Occurrence of NOGD in essential functions can be explored systematically by detecting complementary, rather than identical or similar, phylogenetic patterns [ 10 ]. A ranking performed with COG0588 (phosphoglycerate mutase 1) indeed resulted in COG3635 (predicted phosphoglycerate mutase, AP superfamily) at the seventh last rank (rank 4867 out of 4873) demonstrating that PCOGR is also well suited for such a purpose. Conclusions With the online availability of PCOGR researchers can perform their own individual searches for group-specific proteins. This will not only allow a deeper insight into phylogenetic relationships of organisms or groups of organisms but also help to detect new highly group-specific proteins worth for isolation and further biochemical characterization. In addition, novel protein-protein interactions could be detected in silico, and this tool is also suitable to assign proteins of unknown function to partially known biochemical pathways. A further application lies in the search of alternate enzymes originated by convergent evolution. Availability and requirements Project name: Phylogenetic COG ranking (PCOGR) Project home page: Operating system(s): Platform independent Programming language: PHP, javascript, CSS and HTML Other requirements: Web-browser capable to execute javascript License: GNU General Public License Any restrictions to use by non-academics: Contact authors Authors' contributions FM carried out the software development and programming work. MK conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526202.xml

524373

The fallacy of enrolling only high-risk subjects in cancer prevention trials: Is there a "free lunch"?

Background There is a common belief that most cancer prevention trials should be restricted to high-risk subjects in order to increase statistical power. This strategy is appropriate if the ultimate target population is subjects at the same high-risk. However if the target population is the general population, three assumptions may underlie the decision to enroll high-risk subject instead of average-risk subjects from the general population: higher statistical power for the same sample size, lower costs for the same power and type I error, and a correct ratio of benefits to harms. We critically investigate the plausibility of these assumptions. Methods We considered each assumption in the context of a simple example. We investigated statistical power for fixed sample size when the investigators assume that relative risk is invariant over risk group, but when, in reality, risk difference is invariant over risk groups. We investigated possible costs when a trial of high-risk subjects has the same power and type I error as a larger trial of average-risk subjects from the general population. We investigated the ratios of benefit to harms when extrapolating from high-risk to average-risk subjects. Results Appearances here are misleading. First, the increase in statistical power with a trial of high-risk subjects rather than the same number of average-risk subjects from the general population assumes that the relative risk is the same for high-risk and average-risk subjects. However, if the absolute risk difference rather than the relative risk were the same, the power can be less with the high-risk subjects. In the analysis of data from a cancer prevention trial, we found that invariance of absolute risk difference over risk groups was nearly as plausible as invariance of relative risk over risk groups. Therefore a priori assumptions of constant relative risk across risk groups are not robust, limiting extrapolation of estimates of benefit to the general population. Second, a trial of high-risk subjects may cost more than a larger trial of average risk subjects with the same power and type I error because of additional recruitment and diagnostic testing to identify high-risk subjects. Third, the ratio of benefits to harms may be more favorable in high-risk persons than in average-risk persons in the general population, which means that extrapolating this ratio to the general population would be misleading. Thus there is no free lunch when using a trial of high-risk subjects to extrapolate results to the general population. Conclusion Unless the intervention is targeted to only high-risk subjects, cancer prevention trials should be implemented in the general population.

Background Some prevention trials are restricted to high-risk subjects. If the investigators are only interested in the effects of the intervention on subjects at increased risk [ 1 ] or if the study is designed to be a preliminary investigation in preparation for a definitive study in the general population, we think this restriction is reasonable. However some investigators who are interested in studying the effect of the intervention in the general population may be tempted to design a "definitive" study to estimate the effect of the intervention in a high-risk group. Some investigators may believe that a trial of high-risk subjects would have greater power than a trial of the same size among average-risk subjects. Some examples of this type of thinking can be found in papers on risk prediction models [ 2 , 3 ]. Some investigators may believe that a trial of high-risk subjects with the same power as a trial of average-risk subjects would have lower costs than a trial of average-risk subjects. Some investigators may believe the ratio of benefits to harms can be correctly extrapolated from high-risk to average-risk subjects. Although the rationales for these various beliefs are related, they involve some distinct underlying assumptions that are important to critically examine. Methods and results Possibly lower statistical power To crystallize our thinking about statistical power, we consider the following simple hypothetical and realistic example. Investigators want to estimate the effect of intervention in the general population, so they first consider designing a randomized trial among the general at-risk population. Suppose they anticipate that the cumulative probability of incident cancer over the course of the study is p C = .02 in the control arm and p I = .01 in the study arm, and they believe that the difference in probabilities is clinically significant. Also suppose that due to the limited availability of the intervention, they can enroll at most n = 2000 study participants in each arm. The investigators compute power using the following standard formula [ 1 ] setting the two-sided type I error at .05, where NormalCDF is the cumulative distribution function for a normal distribution with mean 0 and variance 1, Δ is the anticipated difference one wants to detect, n is the sample size per arm, se Null is the standard error under the null hypothesis, and se Alt is the standard error under the alternative hypothesis. Let p = ( p C + p I )/2. As discussed in [ 1 ], for a study designed to estimate the absolute risk difference, the statistic of interest is , so For a study designed to estimate the relative risk, the statistic of interest is , so Applying these formulas to the above example and substituting either (2) or (3) into (1), the investigators obtain a power of .74 based on the absolute risk difference statistic and a power .76 based on a relative risk statistic [see Additional file 1 ]. Suppose the investigators think this power is too low. To increase power they propose to restrict the study to a high-risk group in which the probability of cancer is .04. Also suppose the investigators make the typical assumption that if the intervention yields a relative risk of .5 in the general population, it would also yield a relative risk of .5 in the high-risk group. Applying (1–3) with high risk subjects for whom p C = .04 and p I = .02 with n = 2000, the investigators compute a power of .96 using either the absolute risk difference or relative risk. Because the power is higher using high-risk subjects, the investigators plan the study for a high-risk population and will generalize the results to the general population. Is there a free lunch? An underlying assumption in this example is that the relative risk is invariant between the general population and the high-risk group. There is no free lunch because the impact of violating this assumption could be substantial. For example, suppose instead that the absolute risk difference is invariant between the general population and the high risk group. Under this scenario the absolute risk difference in the general population is .01, so the absolute risk difference in the high-risk group is also .01. In this case for p C = .04, p I = .03, and n = 2000, the power (computed using either absolute risk difference or relative risk statistics) for the trial of high-risk subjects is only .41. The decreased power in a high risk group under a constant risk difference model is not surprising: if the risk difference p C - p I is the same, but p I is increasing, the variances, p C (1 - p C )/ n and p I (1 - p I )/ n , will increase as p C increases up to .5, which will reduce the power. A crucial issue is whether or not the absolute risk difference or the relative risk is likely invariant between average-risk subjects in the general population and high-risk subjects. The answer depends on the cancer, the interventions, and the biology. To gain some appreciation of this issue, we analyzed published data (summarized in Table 1 ) from a prevention trial of particular interest to us, a study of tamoxifen for the prevention of breast cancer [ 5 ]. Rather than limit the analysis to one particular high-risk group, we investigated subjects at various levels of risk defined separately by three variables: age, predicted risk, (the five-year risk of cancer based on the Gail model [ 3 ]), and family risk. We fit four models separately to each variable: Table 1 Data from a cancer prevention trial for investigating assumptions of constant risk difference and relative risk when risk groups change. Placebo group Tamoxifen group Variable risk group cancer at risk cancer at risk age at entry 1 ≤ 49 68 10149 38 10045 2 50–59 50 7912 25 8040 3 >60 57 7719 26 7782 predicted risk 1 ≤ 2.00% 35 6318 13 6311 2 2.01–3.01% 42 8108 29 8262 3 3.01–5.00% 43 7313 27 6959 4 ≤ 5.01% 55 4142 20 4425 family risk 1 0 38 5891 17 5724 2 1 90 15000 46 15182 3 2 37 4263 20 4211 4 3 10 729 6 855 Cancer is invasive breast cancer. Predicted risk is the 5-year predicted risk. Family risk is number of first degree relatives with breast cancer. Data are from Table 5 of [5] with number at risk computed by dividing number of breast cancers by reported breast cancer rate. constant risk difference, where δ is the risk difference that is constant over groups; varying risk difference, where δ i is the risk difference that varies over groups; constant relative risk, where β is the relative risk that is constant over groups; varying relative risk, where β is the relative risk that varies over groups. We obtained maximum likelihood estimates of δ , δ i , β , and β i using a Newton-Raphson procedure [see Additional file 2 ]. To investigate the plausibility of the constant relative risk and constant risk difference models in this example, we plotted the estimates of δ , δ i , β , and β i along with confidence intervals (Figure 1 ). In the top row of Figure 1 we plotted points corresponding to with (100 - 5/ k ) % confidence intervals and horizontal lines for with 95% confidence intervals. We also presented the p-values corresponding to twice the difference in log-likelihoods for Varying RD versus Constant RD . Similarly, in the bottom row of Figure 1 , we plotted points corresponding to with (100 - 5/ k )% confidence intervals and horizontal lines for with 95% confidence intervals. We also presented the p-value corresponding to twice the difference in log-likelihoods for Varying RR versus Constant RR . Out of 6 p-values (3 risk factors × 2 statistics) only one, for absolute risk difference under the risk factor of predicted risk had a small p-value (and the p-value of .01 would not be significant at the .05 level under a Bonferroni adjustment of .05/6). Based on these p-values and inspection of Figure 1 , the models Constant RD and Constant RR are both plausible, especially for age and family risk. Figure 1 Data from the tamoxifen prevention trial. See text for a description of groups. Horizontal lines are estimates and 95% confidence intervals for model for constant absolute risk difference per 1000 (RD) or relative risk (RR). P-values correspond to likelihood ratio tests comparing the models with varying and constant risk difference or relative risks. The trial designer does not know the true state of nature. If Constant RD is the true state of nature, the power will be lower in the high-risk group than the general population. However if Constant RR is the true state of nature, the power will be greater in the high-risk group than the general population. Thus there is high probability that the power could be reduced when studying high-risk subjects than when studying the general population. Therefore, there is no free lunch in terms of lowering statistical power. Possibly increased costs Even if the model is correct (namely p C and p I are correctly chosen), the smaller trial of high-risk subjects may be more expensive than the larger trial of average-risk subjects from the general population. Consider the following two trials with a power of .90 and a one-sided type I error of .05. In the trial of high-risk subjects p C = .04 and p I = .02, and in the trial of average-risk subjects, p C = .02 and p I = .01. Suppose the statistic of interest is the absolute risk difference. To obtain sample size for each randomization group we use the standard sample size formula [ 4 ], where p = ( p C + p I )/2, 1.644485 is the z-statistics corresponding to the 95th percentile of the normal distribution (for a one-sided type I error of .05) and 1.28155 is the z-statistics corresponding to the 90th percentile (for a power of .90). Based on (4), the sample size for a trial using average-risk subjects from the general population study is 2529 per group and the sample size for a trial of high-risk subjects is 1244 per group. Let C R denote the cost of recruitment per subject and C I denote the cost of intervention and follow-up per subject averaged over the two randomization groups . Suppose high risk subjects comprise a fraction f of the general population. The total cost of the trial for average-risk subjects from the general populations is C general = 2( C R 2529 + C I 2529),    (5) and the total cost of the trial for high-risk subjects is C high-risk = 2( C R 1244/ f + C I 1244).    (6) where the factor of 2 is for the two randomization groups. The condition for the trial of high-risk subjects to cost more than the trial of average-risk subjects (namely C high-risk > C general ) is when 1244/ f - 2529 > 0. If f = .20, the trial of high-risk subjects will cost more than the trial of average-risk subjects if C R / C I > .34. If f = .10, the trial of high-risk subjects will cost more than the trial of average-risk subjects if C R / C I > .13. In many cancer prevention trials the above values of C R / C I are likely. For example, diagnostic testing to identify high-risk smokers can include expensive airway pulmonary function tests or bronchoscopy. In the future, more trials will likely involve expensive genetic testing of subjects [ 5 ] with costs ranging from $350 to almost $3,000 per test according to recent information from Myriad Genetic Laboratories. As part of a sensitivity analysis related to genetic testing of subjects prior to enrollment in a trial, Baker and Freedman [ 5 ] considered values of .1, .5, and 1 for ratios similar to C R / C I . Even without diagnostic testing, the costs of obtaining high-risk subjects can be substantial. If f = .10, the initial recruitment will require ten times the number of people as for a trial of average-risk subjects from the general population. This increased recruitment would likely require higher advertising costs and increased overhead costs from the inclusion of additional institutions. One additional consideration is how noncompliance and contamination affect the intent-to-treat analysis. If noncompliance and contamination can be anticipated, the investigator can correspondingly adjust the sample size and costs. Mathematically the effect of noncompliance and contamination is to change the values of p C and p I in (4), which would then affect (5) and (6). In some settings, investigators may anticipate that high-risk subjects are more likely to comply with the intervention than average-risk subjects. To compensate for the anticipated increased compliance, study designers could reduce the sample size which would lower costs. However, in other situations, investigators may anticipate that subjects found to be at high-risk on a diagnostic test would likely seek the best therapy outside of the trial rather than chance randomization to standard or experimental therapy. To compensate for the anticipated dilution in treatment effect, investigators would need to increase the sample size which would increase the costs. For the above reasons even if the probabilities under the alternative hypothesis are correctly specified, some trials of high-risk subjects may be more expensive than larger trials of average-risk subjects with the same power and type I error. Possibly misleading ratio of benefits to harms When there is strong evidence prior to the trial of a high probability of harmful side effects due to the intervention, one would want to restrict the intervention to high-risk subjects. Otherwise, some investigators may be tempted to estimate the ratio of benefit to harms in the trial of high-risk subjects and extrapolate the ratio to average risk subjects. Unfortunately, even if the assumption of constant relative risk over risk categories were true, extrapolating the benefit-harm ratio from a high risk group to the general population could be misleading. Suppose that in a randomized trial involving average-risk subjects from the general population the probability of cancer is .02 in the control arm and .01 in the study arm. Also suppose that relative risk is same in the general population as in the high-risk group, so that in a randomized trial involving a high-risk group, the probability of cancer is .04 in the control arm and .02 in the study arm. Furthermore, suppose that the probability of harmful side effects is the same for high-risk subjects as for average-risk subjects in the general population, namely .015 in the control arm and .025 in the study arm. Based on these results, for every 1000 high-risk persons who receive the intervention, (.04 - .02) 1000 = 20 will benefit from the intervention and (.025 - .015) 1000 = 10 will be harmed by side effects, yielding a benefit-harm ratio of 20:10 = 2:1. Similarly for every 1000 average-risk person who receive the intervention, (.02 - .01) 1000 = 10 will benefit from the intervention and (.025 - .015) 1000 = 10 will be harmed by side effects yielding a benefit-harm ratio of 10:10 = 1:1. In this example it would be incorrect to extrapolate the high benefit-harm ratio estimated from the high-risk group to the general population for whom the benefit-harm ratio is much lower. For many cancer prevention interventions, the ratio of life-threatening disease avoided to life threatening harms would be favorable in the high-risk group but not favorable when extrapolated to the general population. Conclusion There is no "free lunch" when using high-risk subjects in prevention trials design to make inference about the general population. Using high risk subjects instead of average-risk subjects from the general population may lower statistical power, increase costs, and yield a misleading ratio of benefit to harms than actually the case. Given the substantial costs of definitive randomized trials in cancer prevention, and the importance of accurately assessing the balance of benefit and harm when treating healthy and asymptomatic people, it is therefore important to conduct trials in the actual target population rather than try to conduct them in high-risk populations with the plan to extrapolate results to the general population. Competing Interests The authors declare that they have no competing interests. Authors' contributions SGB wrote the initial draft, and BSK and DC made valuable improvements. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 Appendix A, worked-out calculations of power. Click here for file Additional File 2 Appendix B, likelihood formulations Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524373.xml

545955

Antitumor effects of two bisdioxopiperazines against two experimental lung cancer models in vivo

Background Probimane (Pro), an anti-cancer agent originating in China, was derived from razoxane (ICRF-159, Raz), a drug created in Britain, specifically targeting at cancer metastasis and as a cardioprotectant of anthrocyclines. Pro and Raz are bisdioxopiperazine compounds. In this work, we evaluated the anti-tumor and anti-metastatic effects of Pro and Raz in vivo against two lung tumor models, one of murine origin (Lewis lung carcinoma, LLC) and one of human origin (LAX-83). Results After determining the lethal dosage of Pro and Raz, we assessed and compared the inhibitory effects of Pro and Raz against primary tumor growth and metastatic occurrences of LLC at the dosage of LD 5 . Pro and Raz were active against primary tumor growth and significantly inhibited pulmonary metastasis of LLC at same dose-ranges (inhibitory rates > 90 %). Both Raz and Pro were effective in 1, 5, and 9 day administration schedules. Three different schedules of Raz and Pro were effective against the primary tumor growth of LLC (35–50 %). The synergistic anticancer effect of Raz with bleomycin (Ble) (from 41.3 % to 73.3 %) was more obvious than those with daunorubicin (Dau) (from 33.1 % to 56.3 %) in the LLC tumor model. Pro was also seen to have synergistic anti-cancer effects with Ble in the LLC model. Both Raz and Pro inhibited the growth of LAX 83 in a statistically significant manner. Conclusions These data suggest that both Raz and Pro may have anti-tumor potentiality and Raz and Pro have combinative effects with Ble or Dau. The potential targets of bisdioxopiperazines may include lung cancers, especially on tumor metastasis. The anti-cancer effects of Raz and Pro can be increased with the help of other anticancer drugs.

Background Razoxane (ICRF-159) ( Raz ), first developed in UK, was the earliest agent against spontaneous metastasis for the murine model (Lewis lung carcinoma) in 1969 [ 1 ]. A large volume of papers and projects have been published in the utilities and mechanisms of Raz for anticancer actions, like assisting radiotherapy, [ 2 ] overcoming multi-drug resistance (MDR) of daunorubicin and doxorubicin [ 3 ], inhibiting topoisomerase II [ 4 ] and so on. More importantly, Raz , as a cardioprotectant of anthrocyclines, has been licensed in 28 countries in 4 continents. Since morpholine groups in some structures were reported to be responsible for cytotoxic or modulative actions on tumors, an anticancer agent, probimane [1,2-bis (N 4 -morpholine-3, 5-dioxopeprazine-1-yl) propane; AT-2153, Pro] was synthesized by introducing two morpholine groups into Raz in China.[ 5 ]. Raz and Pro belong to bisdiopiperazines . Like Raz , Pro also exhibits anti-tumor activity both in vivo and in vitro against experimental tumor models in a small scale investigation [ 6 , 7 ] and limited clinical data showed that Pro could inhibit human malignant lymphoma even for those resistant to other anticancer drugs [ 8 ]. Pro exhibits the same pharmacological effects as Raz , like detoxication of Adriamycin ( ADR ) induced cardiotoxicities, and synergism with ADR against tumors [ 9 , 10 ]. We have found some novel biological effects of Pro , like inhibiting the activity of calmodulin ( CaM ), a cell-signal regulator, which can explain anticancer actions and the combined cytotoxic effect of Pro and ADR [ 11 ]. Pro was also shown to inhibit lipoperoxidation ( LPO ) of erythrocytes [ 12 ], influence tumor sialic acid synthesis [ 13 ] and inhibit the binding of fibrinogen to leukemia cells [ 14 ]. Lung cancer is the No 1 killer among all categories of cancers in urban areas in China and many Western countries. The high mortality rate of lung cancer can easily be caused by inducing multi-drug resistance ( MDR ) and by high metastatic occurrence in clinics [ 15 ]. Since we assume that Pro , like Raz may possess useful therapeutic potentialities, we evaluated in vivo the chemotherapeutical parameters of Pro and Raz for lung cancer of both murine and human origins. Results Lethal toxicity of Pro and Raz in mice The lethal dosage of Pro and Raz is tabulated in Table 1 . Since the toxicity of Pro and Raz seemed to lack sex specificity in mice, we were able to combine their numbers for LD 50 and LD 5 calculations. We used the approximate dosage of LD 5 of Pro (60 mg/kg ip × 7) and Raz (20 mg/kg ip × 7) as equitoxic dosages for further treatment studies. Table 1 The subacute toxicity of Pro and Raz in mice: Mouse survival was observed for 1 month. The numbers of mice in each group were 20 for each of the 5 dosages of a single agent. Drugs Protocols LD 5 mg/kg LD 50 mg/kg Probimane ip × 10 66 121 Razoxane ip × 10 23 53 Antitumor and antimetastatic effects of Pro and Raz on LLC Antitumor and antimetastatic effects of Pro and Raz on LLC are tabulated in Table 2 and Table 3 . Pro and Raz at equitoxic dosages (LD 5 ) showed a noticeable anticancer effect on primary tumor growth (inhibitory rates, approximately 30–45 %), and significantly inhibited the formation of tumor metastases (inhibitory rates on pulmonary metastasis > 90 %, P < 0.001). Primary tumor growth of LLC was inhibited more by Pro (48 %) than by Raz (40.3%) in a 20 day trial, whereas the inhibition of Pro (35.7%) was slightly less than that of Raz (40 %) on an 11 day trial. Pro seems to be more persistent than Raz in inhibiting primary tumor growth of LLC . Antitumor effects of bisdioxopiperazines for different schedules and in combination with other anticancer drugs Antitumor effects of Raz and Pro on LLC are included in Table 4 , 5 , 6 . We evaluated 1, 5 and 9 day administration schedules in our study. We found that Raz and Pro were effective in a statistically significant manner with the 3 injection schedule of the 1, 5 and 9 day administrations on LLC . If we administered Raz to tumor-bearing mice once on day 1, 5 and 9, there was no difference between treatment and vehicle control. Antitumor effects of Raz in combination with Ble on LLC (73.3 %) were better than those in combination with Dau (56.3 %) (Table 5 and Table 6 ). Pro also showed synergistic effects in combination with Ble (Table 7 ). Table 2 The influence of Pro and Raz on primary tumor of LLC (using Student T-test): Route: ip × 7 daily. Experiment term was 11 days. * P < 0.05 (treatment vs vehicle control). The numbers of mice were 30 for the control group and 20 for each treatment group. 100 % survival was observed in each group. Compounds Dosage mg/kg/d Body weight (g) Tumor weight (g) Tumor inhibition% Control -- 23.3/24.4 2.80 ± 0.04 -- Razoxane 20 23.3/23.4 1.61 ± 0.03* 40.0 Probimane 30 23.4/21.6 1.91 ± 0.03* 32.1 Probimane 60 23.3/23.8 1.80 ± 0.03* 35.7 Table 3 The influence of Pro and Raz on primary and metastatic tumor of LLC: PTI (%) – Primary tumor inhibition. MFCPM – metastatic foci count per mouse. Route: ip × 7 every 2 days. Experiment term was 20 days, * P < 0.001(treatment vs vehicle control). The numbers of mice were 30 for both control group and each treatment group. 100 % survival was observed in each group. Compounds Dosage mg/kg/d Body weigh (g) PTI(%) MFCPM Control --- 22.8/21.4 -- 30.9 ± 7.3 Razoxane 20 22.7/21.5 40.3 1.2 ± 0.5* Probimane 30 23.3/22.5 42.0 1.5 ± 0.5* Probimane 60 23.3/20.3 48.0 1.0 ± 0.2* Table 4 Antitumor effects of bisdioxopiperazines of different schedules on Lewis lung carcinoma: *Administration every 3 hours, 16 mice were included in each testing group. **p < 0.05 (treatment vs control), Experimental term was 11 days Compounds Dosage Schedule Tumor weight Tumor inhibition mg/kg 1, 5, 9 administrations (g) % Control -- -- 2.36 ± 0.05 Razoxane 80 1 time a day 2.49 ± 0.05 -5.5 Razoxane 40 1 time a day 2.32 ± 0.07 1.7 Razoxane 20 1 time a day 2.80 ± 0.06 -18.6 Razoxane 10 3 times a day* 1.51 ± 0.04** 36.0 Probimane 20 3 time a day* 1.19 ± 0.05** 49.6 Table 5 Antitumor effects of Raz on Lewis lung carcinoma in combination with daunorubicin: *Administration every 3 hours. Experimental term was 11 days Compounds Dosage Schedule Tumor weight (g) Tumor inhibitions mg/kg 1, 5, and 9 administrations % Control 2.34 ± 0.05 Razoxane (Raz) 10 3 times a day* 1.57 ± 0.05 32.9 Daunorubicin (Dau) 2 1 time a day 1.10 ± 0.04 53.0 Raz + Dau 10 + 2 3 times/1 time a day 1.02 ± 0.04 56.4 Table 6 Antitumor effects of Raz on Lewis lung carcinoma in combination with bleomycin: * Administrate every 3 hours in one day. ** p < 0.01 (treatment vs vehicle control). Experimental term was 11 days Compounds Dosage Schedule Tumor weight Tumor Inhibition mg/kg 1, 5, and 9 administration (g) % Control -- -- 2.46 ± 0.06 Razoxane (Raz) 10 3 times a day* 1.44 ± 0.07 41.5 Bleomycin (Ble) 15 1 time a day 1.50 ± 0.06 39.0 Raz + Ble 10 + 15 3 times + 1 time a day 0.66 ± 0.05** 73.2** Table 7 Antitumor effects of Pro on Lewis lung carcinoma in combination with daunorubicin or bleomycin: *Administration every 3 hours. Experimental term was 11 days Compounds Dosage Schedule Body weight Tumor weight (g) Tumor inhibitions mg/Kg 1, 5, and 9 administration g % Control -- -- 20.6/21.6 2.62 ± 0.08 Pro 20 3 times a day 20.6/20.8 1.45 ± 0.07 44.6 Dau 2 1 time a day 20.6/20.0 1.14 ± 0.08 56.5 Ble 15 1 time a day 20.7/21.2 1.36 ± 0.08 48.1 Pro + Dau 20 + 2 3 times/1 time a day 20.6/20.9 1.07 ± 0.05 59.2 Pro + Ble 20 + 15 3 times/1 time a day 20.7/19.8 0.59 ± 0.04 77.5 Antitumor activity of Pro and Raz on LAX-83 The experiments showed that LAX-83 was sensitive to Raz (40–60 mgKg -1 , ip × 5) and Pro (80–100 mgKg -1 ip × 5) with inhibitory rates of 25–32 % and 55–60 % respectively (P < 0.01 vs control). CTX , as a positive anticancer drug (40 mgKg -1 ip × 5), exhibited antitumor activities against the growth of LAX-83 with an inhibitory rate of 84 %. Obvious necrosis in tumor tissues was observed by histological evaluation of CTX and Pro treatment groups, but Pro showed larger vacuoles than CTX . Drug inhibition on tumor volumes were calculated and outlined in Table 8 . We have tested the 5 most commonly used anticancer drugs – cyclophosphamide (CTX), 5-fluoruoracil (5-Fu), methotrexate (MTX), cisplatin (DDP) and vincristine (VCR) (Table 9 ). In the LAX-83 model, CTX has been shown to be the most effective one. The anticancer effect of Pro was the same or better than those of MTX, DDP and as well as 5-Fu against LAX-83 tumor growth. Table 8 Antitumor activities of Pro and Raz on human tumor LAX-83 using subrenal capsule assay: Route: ip × 5 daily from the day after surgery. * P < 0.05, ** P < 0.001 (treatment vs vehicle control). Experiment was completed within 7 days. Tumor volume = 1/2 × width 2 × length (using T-test) Compounds Dosage mg/kg/d No mice Body weight (g) Tumor volume (mm 3 ) Inhibition% Control --- 16 19.2/21.0 39.8 ± 3.2 -- Razoxane 40 12 20.8/21.5 29.7 ± 3.0* 25 Razoxane 60 12 19.8/18.8 27.2 ± 2.8* 32 Probimane 80 12 20.0/19.6 18.0 ± 2.6** 55 Probimane 100 12 20.0/20.0 15.8 ± 2.6** 60 Cyclophosphamide 40 12 21.0/20.9 6.4 ± 2.0** 84 Table 9 Antitumor activities of anticancer drugs on human tumor LAX-83 using subrenal capsule assay: Route: ip × 5 daily from the day after surgery. * P < 0.05, ** P < 0.001 (treatment vs vehicle control). Experiment was completed within 7 days. Tumor volume = 1/2 × width 2 × length (using T-test) Compounds Dosage mg/kg/d No mice Body weight (g) Tumor volume (mm 3 ) Inhibition% Control --- 16 20.9/22.5 29.7 ± 3.2 -- Methotrexate 1.5 12 21.2/21.9 27.4 ± 3.0 7.7 Cis-platin 1.5 12 22.8/21.7 16.6 ± 2.6** 44.1 5-fluoruoracil 37.5 12 21.7/21.4 12.8 ± 2.6** 57.5 Cyclophosphamide 30.0 12 21.0/20.9 5.8 ± 2.3** 80.5 Vincristine 0.3 12 20.8/20.8 7.6 ± 2.2** 74.4 Discussion Explanations of anticancer and antimetastatic mechanisms of bisdioxopiperazines are now inconclusive. The present explanation for the anticancer mechanisms of Raz has been attributed to antiangiogenesis and topoisomerase II inhibition.[ 16 ] Since the antimetastatic activities of Raz and Pro were much stronger than those actions against primary tumor growth, this special targeting on metastasis ought to be more useful in clinical cancer treatment. Raz and Pro show typical characteristics of antiangiogenesis agents, which target small nodule of tumors. Meanwhile, recent reports on drugs targeting angiogenesis indicate that most anti-vascular drugs have low or even no effects on most cancers when they are used alone in clinics, but they show synergistic effects in combination with other anticancer drugs. [ 17 , 18 ] Our study shows synergistic anticancer actions of Raz and Pro with Ble or Dau basing on this theory. Previous work showed that Pro and Raz could reduce the cardiotoxicity of anthrocycline ,[ 1 , 9 , 10 ] so we may reasonably deduce that they can also reduce the cytotoxicity of anthrocyclines . The data in our study suggests that the synergistic effects of Raz with anthrocyclines are present, but not as potent as those with Ble . Since we have tested the antitumor activity of clinically available anticancer drugs (CTX, 5-Fu, MTX, DDP and VCR) against LAX-83, CTX being the best one, two bisdioxopiperazines studied on this work show overall similar anticancer effective as commonly used drugs. Although the anticancer effects of CTX and VCR are better than those of Pro, for other commonly used drugs, such as DDP, MTX and 5-Fu, the antitumor effects are no better than those of Pro. Since the antitumor effects of MTX and DDP are even less effective than those of Pro and Raz , we suggest that anticancer effects of Pro and Raz are within the effective anticancer ranges of commonly available anticancer drugs. The other useful property of Pro is that it is the most water-soluble among the bisdioxopiperazines . Most bisdioxopiperazines are less water-soluble and given orally in clinics. Although oral administration is easy for patients, bioavailability varies from patient to patient. For some patients who have a poor absorption of bisdioxopiperazines in oral administration, Pro can be injected iv to maintain stable drug levels. Our previous work showed that Pro could strongly accumulate in tumor tissue while Pro levels in other tissues decrease rapidly [ 19 ]. Presently, a stereo-isomer of Raz , (dexrazoxane, ICRF-187 ), a water-soluble Raz, is being reinvestigated and has aroused the interests of clinical oncologists. Phase III clinical studies are currently underway in the US. More importantly, ICRF-187 was licensed in 28 countries in 4 continents. This work shows a noticeable inhibition of Pro and Raz on lung cancers and suggests possible usage of Raz and Pro on lung cancer in clinics. Conclusions The advantages of bisdioxopiperazines in clinical treatment of lung cancers are as follows: (i) Pro and Raz can inhibit the growth of lung cancers, with and without the help of other anticancer drugs, like Dau and Ble ; (ii) like Raz , Pro strongly inhibits spontaneous pulmonary metastasis of LLC ; (iii) since Pro can inhibit CaM [ 11 ], a calcium activated protein that's associated with MDR and metastatic phenotypes, synergistic anticancer effects of Pro and Raz can be expected in combination with other anti-cancer drugs, like Dau or Ble . Now, new concepts of the relationship between tumor metastasis and MDR in cancers have been stated,[ 20 ] whereas bisdioxopiperazines can inhibit both tumor metastasis and MDR . As a counterpart of Raz , Pro might be of interest and have chemotherapeutic potential in clinics. Methods Drugs and animals Cyclophosphomide ( CTX ), daunorubicin ( Dau ) and bleomycin ( Ble ), 5-fluororacil (5-Fu), vincristine (VCR), cisplatin (DDP), methotrexate (MTX) were purchased from Shanghai Pharmaceutical Company. Pro and Raz were prepared by Department of Medicinal Chemistry, Shanghai Institute of Materia Medica, Chinese Academy of Sciences. C57BL/6J and Kun-Min strain mice were purchased from Shanghai Center of Laboratory Animal Breeding, Chinese Academy of Sciences. Nude mice (Swiss-DF), taken from Roswell Park Memorial Institute, USA, were bred in Shanghai Institute of Materia Medica, Chinese Academy of Sciences under a specific pathogen free condition. Human pulmonary adenocarcinoma xenograft ( LAX-83 )[ 21 ] and Lewis lung carcinoma ( LLC ) were serially transplanted in this laboratory. All animal experiments were conducted in compliance with the Guidelines for the Care and Use of Research Animals, NIH, established by Washington University's Animal Studies Committee. Bouin's solution consists of water saturated with picric acid: formaldehyde: glacial acetic acid (75: 20: 5, v/v/v). Lethal dosage determination in mice Mice of Kun-Min strain (equal amount of male and female) were ip injected with Pro and Raz daily for 10 successive days. The deaths of mice were counted after 1 month. Lethal dosage of agents was calculated by Random Probity tests . Antitumor and antimetastatic studies of LLC C57BL/6J mice were implanted sc with LLC (2 × 10 6 cells) from donor mice. The mice were injected intraperitoneally with drugs daily or every two days for 7 injections. On day 11 or day 20, mice were sacrificed, and locally growing tumors were separated from skin and muscles and weighed, and lungs of host mice were placed into a Bouin's solution for 24 h, and then the lung samples were submerged into a solution of 95 % alcohol for 24 h. Finally, the numbers of extruding metastatic foci in lungs were counted. Antitumor actions of different schedules and in combinations with different drugs C57BL/6J mice were implanted sc with LLC (2 × 10 6 cells) from donor mice. Mice were injected intraperitoneally with drugs on day 1, 5, 9. Single injection or 3 injections every 3 hours were used. Tumors were separated and weighed on day 11. Antitumor activity study of human tumors Nude mice were inoculated with LAX-83 under the renal capsule (SRC method).[ 22 ] Nude mice were injected intraperitoneally with drugs daily during next five days after inoculation of LAX-83 . Then nude mice were sacrificed, and their kidneys were taken out for measurement of tumor sizes using a stereomicroscope a week after transplantation. Tumor volume was calculated as 1/2(ab 2 ) where a and b are their major and minor axes of the lump. Kidneys with tumors were paraffin-embedded, sliced and hematoxylin dyed. The tumor tissues were then observed from a light microscope. Statistical analysis Student's t-test was used to assess the differences between control and drug treatment groups of above methods. List of abbreviation used are Pro, probimane; Raz, razoxane; CaM, calmodulin; LPO, lipoperoxidation; Dau, daunorubicin; Ble, bleomycin; LLC, Lewis lung carcinoma, LAX-83; a lung adenocarcinoma xenograft; ADR, adriamycin; Author's contribution The experimental design was made by Bin Xu and Da-Yong Lu. Experiments were performed by Da-Yong Lu (anticancer activity tests) The manuscript was written by Da-Yong Lu, and Jian Ding. Figure 1 Structural formulas of razoxane and probimane

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Perceived personal, social and environmental barriers to weight maintenance among young women: A community survey

Background Young women are a group at high risk of weight gain. This study examined a range of perceived personal, social and environmental barriers to physical activity and healthy eating for weight maintenance among young women, and how these varied by socioeconomic status (SES), overweight status and domestic situation. Methods In October-December 2001, a total of 445 women aged 18–32 years, selected randomly from the Australian electoral roll, completed a mailed self-report survey that included questions on 11 barriers to physical activity and 11 barriers to healthy eating (relating to personal, social and environmental factors). Height, weight and socio-demographic details were also obtained. Statistical analyses were conducted mid-2003. Results The most common perceived barriers to physical activity and healthy eating encountered by young women were related to motivation, time and cost. Women with children were particularly likely to report a lack of social support as an important barrier to physical activity, and lack of social support and time as important barriers to healthy eating. Perceived barriers did not differ by SES or overweight status. Conclusions Health promotion strategies aimed at preventing weight gain should take into account the specific perceived barriers to physical activity and healthy eating faced by women in this age group, particularly lack of motivation, lack of time, and cost. Strategies targeting perceived lack of time and lack of social support are particularly required for young women with children.

Introduction In many developed countries, overweight and obesity have reached epidemic proportions [ 1 - 8 ]. One group at particular risk of weight gain and the development of obesity is young women[ 2 , 9 , 10 ]. In the US, for example, one study that tracked weight in a large population sample over a 10-year period found that major weight gain (increased body mass index (BMI) > 5 kg/m 2 ) was twice as common in women (5.3%) as in men (2.3%) [ 2 ]. A recent study of almost 9,000 women aged 18–23 years in Australia showed that 41% of the sample gained more than 5% of their BMI baseline over a four-year period (1996–2000) [ 9 ]. This risk of weight gain and the development of obesity places young women at increased risk of a range of chronic medical conditions and diseases, such as hypertension, type-2 diabetes, cardiovascular disease, and certain cancers [ 11 ]. In an effort to reverse the current global epidemic of overweight and obesity, strategies to promote increased physical activity and to encourage healthy eating have been promoted in many countries [ 12 - 15 ]. In Australia, for instance, individuals are encouraged to consume diets that are low in fat, high in fibre and rich in fruits and vegetables[ 13 ], and to participate in at least 30-minutes of moderate-intensity activities at least five days/week [ 12 ]. Despite such efforts, many young women do not meet the current physical activity recommendations [ 16 ] and their diets are less than optimal. For example, mean daily intakes of fruits and vegetables fall well below recommended levels [ 17 ] and 50% of young Australian women are consuming at least one takeaway meal per week, which is likely to be high in energy density [ 9 ]. Poor compliance with dietary and physical activity guidelines is not unique to Australia [ 18 - 20 ]. In addition, recent work we have conducted suggests that many young women do not consider the kinds of lifestyle changes that are being recommended as feasible for them in the context of their daily lives [ 21 ]. An understanding of the perceived barriers faced by young women in achieving healthy lifestyle changes is therefore important. Most existing studies examining perceived barriers to physical activity and healthy eating have focused on the general population,[ 18 , 22 - 25 ]. with few specifically considering the perceived barriers experienced by those at particular risk of weight gain, such as young women. However, the perceived barriers faced by young women are likely to differ from those faced by other groups, such as by men or older women. For example, a study in the USA showed that women more frequently report 'tiredness' and 'time' as significant perceived barriers to healthy habits than do men, and that this may be partly attributable to their domestic situation [ 25 ]. In addition, young women are more likely than older women to experience particular life events (e.g. leaving family homes, starting work, entering a marital or de facto relationship, and becoming mothers) that may influence their physical activity and dietary habits [ 26 , 27 ]. As well as perceiving different barriers to those faced by other groups in the population, the perceived barriers to increasing physical activity and improving diet that young women face may vary according to their social and personal circumstances. For example, having children is likely to impact on a women's ability to adopt healthy habits [ 21 , 28 , 29 ]. In addition, persons of lower socioeconomic status (SES) may have poorer access to parks, walking or jogging trails, and gym equipment than those of higher SES [ 25 ]. Access to good quality, inexpensive healthy foods has also been reported to be more limited among persons of low SES; for instance, the cost of healthy foods has been reported to be greater for those living in deprived areas. [ 30 , 31 ]. A number of studies have suggested that a lack of knowledge is a greater barrier to eating a healthy diet among those of lower education level [ 22 , 23 ]. Being overweight can also be perceived as a significant barrier to physical activity [ 32 ]. However, whether or not these factors are perceived as barriers to physical activity and healthy eating among young women is unknown. In order to develop appropriate and effective obesity prevention strategies for young women it is important to understand the barriers they perceive in attempting to control their weight. The aim of this study was to examine perceptions of a range of personal, social and environmental barriers to physical activity and healthy eating, specifically related to weight maintenance, among young women, and how these vary by domestic situation, SES and overweight status. Methods Participants A total of 445 women provided data for this study. Initially, a sample of 1200 women aged 18–32 years was selected from the Australian Electoral Roll using a stratified random sampling procedure, with strata based on the number of eligible cases in each of the eight States/Territories of Australia. As voting is compulsory for Australian adults, the electoral roll provides a complete record of population data on Australian residents aged 18 years and over. Excluding those who had moved and left no forwarding address, the study achieved a response rate of 41% (462 women participated), which is comparable to response rates reported in similar postal surveys with this age group [ 33 , 34 ]. Data from 17 women who were pregnant were excluded. The socio-demographic characteristics of the sample are reported in full elsewhere [ 21 ]. Briefly, 42% of the respondents were tertiary-educated. Half of the women were married and one in three had at least one child. One in three respondents was classified as overweight or obese. The socio-demographic profile of the sample was comparable to that of women of similar age (18–44 y) who participated in the most recent (2001) Australian National Health Survey [ 35 ]. Procedures A questionnaire was developed and pilot-tested with a convenience sample of 10 women in the same age group as participants. The questionnaire, a study description, an invitation to participate, a consent form and a reply-paid envelope for returns were mailed to the study sample of women in October 2001. Non-responders were sent a reminder postcard two weeks later and a second reminder with replacement questionnaire a further three weeks later. Measures The participants completed the following questions. Socio-demographic background The socio-demographic questions included domestic situation (household composition) and education. Domestic situation was assessed by asking 'Who lives with you?' with response options: No-one , I live alone ; Partner / spouse ; Own children ; someone else's children ; parents ; brothers / sisters ; Other adult relatives ; and Other adults who are not family members . This was subsequently re-categorized as living with parental family; living alone/share 'flatting'; living with partner (no children); or living with children (including those living with partner and child/ren, and single mothers). Education level (highest level of schooling: still at school , primary school , some high school , completed high school , technical / trade school certificate / apprenticeship , or University / tertiary qualification ) was subsequently categorized as tertiary educated or not tertiary educated and used as an indicator of SES. Body weight Women were asked to self-report their height and weight and this information was used to calculate body mass index (BMI = weight (kg)/height (m 2 )). Self-reported height and weight have been shown to provide a reasonably valid measure of actual height and weight for the purpose of investigating relationships in epidemiological studies [ 36 ]. Women were categorised as overweight (BMI ≥ 25) or not overweight (BMI < 25) [ 11 ]. Perceived barriers to weight maintenance Young women's perceptions of barriers to weight maintenance were assessed using 22 items. Participants were asked 'How important are the following as barriers to you keeping your weight at the level you want?' The complete list of barrier items is included in Tables 1 and 2 . These items were based on a review of the literature investigating barriers to weight maintenance behaviours in other population groups [ 22 - 25 ]. There were two sets of perceived barriers assessed, those related to physical activity and those to healthy eating. For each set of questions, participants were asked about access to information; motivation; enjoyment; skills; partner support and children's support (where relevant); friends' support; access; cost; time due to job demands; and time due to family commitments as possible barriers. Response options for all barrier items were: Not a barrier ; A somewhat important barrier ; A very important barrier ; Not applicable . For analyses, responses Not applicable and Not a barrier were combined. Table 1 Perceived barriers to physical activity (N = 445) Barriers to physical activity Factor loadings Not a barrier (%) A somewhat important barrier (%) A very important barrier (%) Factor 1: Personal barriers to physical activity (Eigenvalue = 4.21, 38% variance, Cronbach's alpha = 0.76) Do not have the motivation to do physical activity, exercise or sport 0.58 26 34 40 Not enjoying physical activity, exercise or sport 0.80 57 25 18 Do not have the skills to do physical activity, exercise or sport 0.70 81 14 5 Factor 2: Social support barriers to physical activity (Eigenvalue = 1.13, 10% variance, Cronbach's alpha = 0.68) No partner's support to be physically active 0.80 78 13 9 No children's support to be physically active 0.82 94 4 2 No friends' support to be physically active 0.57 84 11 5 Factor 3: Environmental barriers to physical activity (Eigenvalue = 1.22, 11% variance, Cronbach's alpha = 0.71) Do not have enough information about how to increase physical activity 0.75 83 12 5 Not having access to places to do physical activity, exercise or sport 0.57 66 23 11 Not being able to find physical activity facilities that are inexpensive 0.70 49 29 22 Not having the time to be physically active because of job 0.76 42 29 29 Not having the time to be physically active because of family commitments 0.68 63 22 15 Table 2 Perceived barriers to healthy eating (N = 445) Barriers to healthy eating Factor loadings Not a barrier (%) A somewhat important barrier (%) A very important barrier (%) Factor 4: Personal and environmental barriers to healthy eating (Eigenvalue = 4.61, 42% variance, Cronbach's alpha = 0.83) Do not have enough information about a healthy diet 0.70 72 17 11 Do not have the motivation to eat a healthy diet 0.70 34 41 25 Do not enjoy eating healthy foods 0.80 64 26 10 Do not have the skills to plan, shop for, prepare or cook healthy foods 0.70 73 19 8 Do not have access to healthy foods 0.65 80 16 4 Not able to buy healthy foods that are inexpensive 0.60 60 27 13 Factor 5: Social and environmental barriers to healthy eating (Eigenvalue = 1.23, 11% variance, Cronbach's alpha = 0.72) No partner's support to eat a healthy diet 0.76 79 13 8 No children's support to eat a healthy diet 0.80 97 2 1 No friends' support to eat a healthy diet 0.57 83 12 5 Not having time to prepare or eat healthy foods because of job 0.47 57 23 20 Not having time to prepare or eat healthy foods because of family commitment 0.55 77 15 8 Most important perceived barriers In order to ascertain women's perceptions of the single most important barrier to physical activity and healthy eating (which may not have been included in the list of barriers developed by the researchers), participants were asked the following two open-ended questions: 'What is the one thing that makes it hardest for you to be physically active?' and 'What is the one thing that makes it hardest for you to eat a healthy diet?' Statistical Analyses Analyses were conducted mid-2003, using SPSS version 11.0.0 statistical software. [ 37 ]. Initially, descriptive analyses were performed to describe the proportion of women rating each of the items as not a barrier, a somewhat important barrier or a very important barrier. Content analyses of the open-ended questions were undertaken to identify main recurring themes. Two separate exploratory factor analyses using SPSS FACTOR were performed with the 11 barriers to physical activity and the 11 barriers to healthy eating, to identify underlying patterns of relationships among individual items, and to reduce and simplify the items in order to facilitate subsequent analyses. Principal components analysis with varimax rotation (since factors were not correlated) was used. For any cross-loading items (i.e. items that had loadings of greater than 0.4 on more than one factor), only the higher loading was taken into account when calculating final factor scores. Inter-item reliability for each factor was assessed by Cronbach's α coefficients. Kaiser's measure of sampling adequacy was used to confirm the appropriateness of factor analysis [ 38 ]. Standardized factor scores were computed for each factor, with a large positive score representing more important barriers and a large negative score, less important barriers. Analysis of variance or t-tests were performed separately for each of the standardized factor scores to investigate differences in perceived barriers to physical activity and healthy eating with regard to domestic situation, SES and overweight status. Results Perceived barriers to physical activity Table 1 presents the proportions of women reporting each of the perceived barriers to physical activity. The main barriers reported by young women related to motivation, time and cost. Combining the response categories 'somewhat important' and 'very important', 74% of the sample reported lack of motivation – 'not having the motivation to do physical activity, exercise or sport', time (58%) – 'not having time to be physically active because of my job,' and cost (51%) – 'not being able to find physical activity facilities that are inexpensive' – as common barriers to physical activity. Lack of time due to work commitments (reported by 58%) was more commonly reported than lack of time due to family commitments (37%), perhaps due to the relatively small proportion (30%) of young women in this study with at least one child. Less common perceived barriers to physical activity included lack of information, skills, partners' and children's support, and friends' support. Perceived barriers to healthy eating Table 2 presents perceived barriers to healthy eating. As with physical activity, lack of motivation (66%), lack of time due to job commitments (43%), and cost (inability to buy healthy foods that are inexpensive: 40%) were common perceived barriers. Less commonly reported barriers included lack of information, skills and friends', partners' and children's support, and access. As with physical activity, lack of time related to job demands (reported by 43%) was more common than lack of time due to family commitment (23%). The most important perceived barriers to physical activity and healthy eating Consistent with women's responses to the closed-ended questions, the most important perceived barriers to physical activity reported in response to the open-ended questions were lack of time due to work, study or family commitments (78%), lack of motivation (37%) and childcare issues (25%). The most important perceived barriers to healthy eating related to taste (24%); lack of time (21%); lack of motivation (13%); and the perception that healthy foods are inconvenient or expensive (13%). Factor analysis of perceived barriers to weight maintenance The factor analysis of the perceived barriers to physical activity revealed three interpretable factors (Table 1 ) with eigenvalues greater than one. These factors together explained 60% of the total variance. Two items – 'not having access to places to do physical activity, exercise or sport' and 'not having friends' support to be physically active' – cross-loaded on two factors and these items were included only on factors on which each item showed the largest loading. The Cronbach's α coefficients for the three factors ranged from 0.68 to 0.76, indicating moderate internal reliability. Provisional names were assigned for these three factors: 'personal barriers', 'social support barriers' and 'environmental barriers'. The items included as personal barriers to physical activity were related to motivation, enjoyment, and skill. Social support barriers encompassed lack of support from family and friends; and environmental barriers related to information, access, cost, and time. The principal components analysis of the 11 barriers to healthy eating resulted in two distinct interpretable factors with eigenvalues greater than one (Table 2 ). The Cronbach's α coefficients for the two factors were 0.72 and 0.83, indicating moderate to good internal reliability. Together the two factors explained 53% of the total variance. Provisional names were assigned to these factors: 'personal and environmental barriers' and 'social and environmental barriers'. Personal and environmental barriers to healthy eating included motivation, enjoyment, skills, information, cost, and access. Social and environmental barriers were related to lack of support from family and friends and time constraints. Associations of domestic situation, education and overweight status with perceived barriers Mean factor scores did not vary according to women's overweight status or SES. Mean factor scores did differ significantly by domestic situation for two factors: social support barriers to physical activity and social and environmental barriers to healthy eating (see Table 3 ). Compared with women living in other domestic situations, women with children had the lowest score on the social support for physical activity factor, suggesting that lack of support from partners, children and friends was a more important perceived barrier to physical activity for these women. This group also had the lowest score on social and environmental barriers to healthy eating factor, suggesting that lack of social support and insufficient time were more important perceived barriers to healthy eating among women with children than among other women. Conversely, young women who lived with their parents had the highest scores on these factors, indicating the relative lack of importance of social support for physical activity, and social and environmental barriers to healthy eating, for this group. Table 3 Mean standardized factor scores on weight maintenance by domestic situation a Factor Domestic situation Parents Alone/ Share Partner Children p Personal barriers to physical activity 0.08 0.22 -0.09 -0.07 0.12 Social support for physical activity -0.37 -0.14 -0.06 0.55 .000 Environmental barriers to physical activity 0.13 0.12 0.03 -0.18 0.11 Personal and environmental barriers to healthy eating -0.04 0.23 0.02 -0.04 0.25 Social and environmental barriers to healthy eating -0.30 -0.16 -0.06 0.49 .000 a . A large positive score represents more important barriers; a large negative score, less important barriers. Discussion This study suggests that a lack of motivation, time constraints due to work, and cost issues are the key perceived barriers to maintaining weight faced by young women. Overall these findings support other research that has examined barriers to physical activity and healthy eating [ 18 , 22 , 25 , 39 ]. However, the present study is unique in providing an insight into the relative importance of a range of personal, social and environmental factors as perceived barriers to weight maintenance among young women, a high risk group for weight gain. Findings showed that young women tended to rate personal factors as key perceived barriers to physical activity and healthy eating, followed by environmental factors, with social factors rated as less important. While the environment is likely to be an important source of influence on obesity-related behaviours [ 40 ], these findings highlight that efforts to prevent obesity should not ignore the central role of cognitive factors. Given the striking similarities in the types of barriers reported to impede physical activity, and the perceived barriers to healthy eating, findings also suggest that there may be potential economies of scale in health promotion programs aimed at preventing weight gain among young women. For example, strategies aimed at boosting motivation for healthy behaviour may help to promote both increased physical activity and healthy eating simultaneously. While motivating young healthy women to adopt healthy eating and physical activity behaviors is likely to be challenging, recent intervention research suggests that motivationally-tailored interventions may be more successful that other approaches (e.g. based on social-cognitive theory) in promoting physical activity and healthy eating [ 41 , 42 ]. It is noteworthy that perceived barriers to weight maintenance did not vary by socio-economic status or overweight status in this sample of women. In contrast, previous research has shown that overweight men and women face a number of perceived physical activity barriers [ 32 ]. Similarly, given that diet varies by socio-economic status [ 43 , 44 ] we expected that women of lower socio-economic status would be more likely to experience barriers to eating a healthy diet. Previous studies also suggest that persons of low SES often live in areas where the cost of food is greater, and access to healthy foods is poorer [ 30 , 31 ]. The reasons for the difference between the present results and earlier findings are unclear. It may be, however, that in this sample of relatively young women, many were still acquiring their education, and hence any SES differences in perceived barriers to healthy behaviours were not yet established. Compared to other young women, those living with children were the most likely to report lack of social support for physical activity, and lack of support and time for healthy eating, as key perceived barriers to maintaining their weight. Young women who lived with their parents were the least likely to perceive these to be barriers to weight maintenance. These findings are consistent with those of previous studies showing that getting married and having children are associated with decreased physical activity and greater weight gain [ 21 , 26 ]. Any weight gain prevention program targeting women with children should incorporate a focus on enlisting social support for both physical activity, and shopping for and preparing healthy foods. In a previous study with the same sample, we reported that while the majority of the women were in a healthy weight range (51%) or overweight/obese (31%), 18% of the women were underweight [ 21 ]. It should be acknowledged that some women in this sample, particularly those who were underweight, may have been trying to gain weight. One limitation of the present study was that the questions assessing perceived barriers to weight maintenance did not distinguish women trying to keep their weight down, from those trying to keep their weight up, and interpretation of the questions on perceived barriers may have been slightly different between these groups. However, attempts to gain weight are relatively uncommon among young women [ 45 ], and hence this is likely to have affected only a small proportion of the sample. A second limitation of this study is that the barriers were not assessed objectively, but rather through self-reports (ie perceived barriers). Nonetheless, it is important to consider women's perceptions of factors hindering their efforts to engage in healthy behaviours, since objective barriers may be perceived differently by different women (e.g., poor access to a gym may be viewed as less of a barrier to physical activity among a woman who walks for exercise than one who prefers aerobics). Finally, although the study achieved a somewhat modest response rate, the sample was selected from a nationally representative sampling frame and the socio-demographic profile of women was comparable to that of similarly-aged women in the wider population [ 35 ]. Conclusions The findings of this study highlight the need for health promotion strategies that provide increased motivation, support and skills to enable young women to shop and prepare healthy, quick and inexpensive meals. Similarly, the findings suggest a need to promote more time-efficient physical activity alternatives. Additional strategies that recognize the perceived barriers to physical activity and healthy eating faced by young women with children are particularly required. Competing interests The authors declare that they have no competing interests. Authors' contributions SA conducted the literature review, final statistical analyses and early drafts of the results and conclusions sections. KB and DC conceived the study, design and measures, collected the data, coordinated the analyses and participated in the write-up of all sections. NW conducted preliminary analyses and drafting of early results. VI contributed to drafting the final manuscript.

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Imbalance in the health workforce

Imbalance in the health workforce is a major concern in both developed and developing countries. It is a complex issue that encompasses a wide range of possible situations. This paper aims to contribute not only to a better understanding of the issues related to imbalance through a critical review of its definition and nature, but also to the development of an analytical framework. The framework emphasizes the number and types of factors affecting health workforce imbalances, and facilitates the development of policy tools and their assessment. Moreover, to facilitate comparisons between health workforce imbalances, a typology of imbalances is proposed that differentiates between profession/specialty imbalances, geographical imbalances, institutional and services imbalances and gender imbalances.

Introduction Imbalance in the health workforce is a major challenge for health policy-makers, since human resources – the different kinds of clinical and non-clinical staff who make each individual and public health intervention happen – are the most important of the health system's inputs [ 1 ]. Imbalance is not a new issue, as nursing shortages were reported in hospitals in the United States of America as early as 1915 [ 2 ]. It remains a major concern to this day, reported in both developed and developing countries and for most of the health care professions. Although imbalance in the health workforce is an important issue for policy-makers, various elements contribute to obscuring policy development. First, many reports of shortages are not borne out by the evidence. Rosenfeld and Moses [ 3 ] show that an overwhelming majority of newspapers, journals and newsletter articles describing the nursing situation in the United States presume the existence of a shortage. They found that even in those areas where concrete evidence of a shortage was not available, the term "nursing shortage" still appeared. Second, the notion of shortage is a relative one: what is considered a nursing shortage in Europe would probably be viewed differently from an African perspective. Finally, imbalances are of different types and their impact on the health care system varies. In consequence, there is a general need to critically review the imbalance issue. The objective of this paper is to contribute to a better understanding of the issues related to imbalance through a critical review of its definition and nature and the development of an analytical framework. Definition There are various approaches to defining imbalances [ 4 ]. From an economic perspective, a skill imbalance (shortage/surplus) occurs when the quantity of a given skill supplied by the workforce and the quantity demanded by employers diverge at the existing market conditions [ 5 ]. Labour market supplies and demands for occupational skills fluctuate continuously, so at times there will be imbalances in the labour market. In other words, a shortage/surplus is the result of a disequilibrium between the demand and supply for labour. In contrast, non-economic definitions are usually normative, i.e. there is a shortage of labour relative to defined norms [ 6 ]. In the case of health personnel, these definitions are based either on a value judgement – for instance, how much care people should receive – or on a professional determination – such as deciding what is the appropriate number of physicians for the general population. Nature One of the key questions regarding imbalances is how long these last: Is the imbalance temporary or permanent? In a competitive labour market, we should expect most imbalances to be resolved over time. Imbalances will tend to disappear faster the greater the reaction speed and also the greater the elasticity of supply (or demand) [ 7 ]. This type of imbalance (shortage or surplus) is defined as dynamic. In contrast, a static imbalance occurs because supply does not increase or decrease; market equilibrium is therefore not achieved. For instance, wage adjustments may respond slowly to shifts in demand or supply as a result of institutional and regulatory arrangements, imperfect market competition (monopoly, monopsony) and wage-control policies. Another example is physicians' education: because of the length of time required to educate physicians, changes in available supply take a long time to react significantly. Lack of information on the state of the various labour markets can also be a factor in the speed of market adjustment. To make proper labour market decisions, households and firms must be informed of the existing market conditions across markets. They must therefore know what wages are paid and the nature and location of job openings and available workers. Moreover, we should also differentiate between qualitative and quantitative imbalance. In a tight labour market, employers might not find the ideal candidate, but will still recruit someone. Under these conditions, the issue is the quality of job candidates rather than the quantity of people willing and able to do the job [ 8 ]. From the employers' perspective, a shortage of workers exists; from the job-market perspective, the existence of a shortage could be questioned because the jobs are filled. One negative hidden impact of a qualitative shortage is the number of positions that are filled with ineffective individuals [ 9 ]. A conceptual framework To better understand the role of factors affecting health workforce imbalances and to facilitate the development of policy tools, a conceptual framework is presented in this section. Introduction Factors affecting health workforce imbalances are numerous and complex, but focusing on crucial elements should permit insight into the issue of health workforce imbalances. The framework is depicted in Fig. 1 and contains six main components: the demand for health labour, the supply of health labour, the health care system, policies, resources and "global" factors. Figure 1 Framework for imbalance of human resources for health Central to this framework are the demand for and supply of health labour. Also included in the framework is the health care system, and in particular, some of its features that are likely to have an impact on health workforce imbalances. Policies constitute another crucial element of the framework. In effect, health policies but also non-health-oriented policies can have an impact on health workforce imbalances. The framework also incorporates financial, physical and knowledge resources that contribute to model the health workforce.Finally, "global" factors such as economic, sociodemographic, political, geographical and cultural factors are included. These elements contribute directly or indirectly to shaping and transforming the entire society and hence the health workforce. The demand for health personnel The first element of the framework to be examined is the demand for health personnel. The demand for health personnel can be considered as a derived demand for health services. Accordingly, we should consider factors determining the demand for health services. Personal characteristics – such as health needs, cultural and sociodemographic characteristics – and economic factors play an important role. It has often been proposed that the planning of human resources for health be based solely on estimates of health needs in the population [ 10 ]. However, relying only on the concept of need is difficult, because it can be defined either broadly or restrictedly and accordingly lead to a perception of either systematic shortage or surplus. Health needs is only one of the factors affecting the demand for health personnel. Several studies have attempted to estimate the impact of economic factors on the demand for health care. In particular in the United States, studies have attempted to estimate price and income elasticities of demand for medical services [ 11 - 13 ]. Measurements of price or income elasticities make it possible to evaluate the impact of a change in price or income on the demand for health care. Most studies reported elasticities in the range between 0.0 and -1.0, indicating that consumers tend to be responsive to price changes but that the degree of price sensitivity is not very large compared to that for many other goods and services [ 14 ]. Another element influencing the demand for health care is the value of a patient's time, such as travel time and waiting time. Acton [ 15 ] found that in the United States, elasticity of demand with respect to travel time ranged between -0.6 and -1, meaning that a 10% increase in travel time would induce a reduction of 6%-10% in the demand for health care. Other factors affect the demand for health labour. In particular, some specific features of the health care system and its features, policies, resources and environmental factors do have an impact on the demand for health labour. Their respective role will further discussed later. The supply of human resources for health After reviewing factors affecting the demand for health labour, we shall now turn to those affecting the supply of the health workforce. In particular, we shall consider the following elements: factors affecting the choice for a health professional training/education, participation in the health labour market and migration. Education/professional training choice The availability of a renewed health workforce, as well as the type of profession and specialty chosen by individuals, is a major concern for health decision-makers. These issues are of particular relevance, especially since the number of younger people, predominantly women, choosing a nursing career is declining in some countries and since in professional training/education, individuals' choices do not always match the absorptive capacity of the market. From an economic perspective, the decision to undertake professional training/education is considered an investment decision. To emphasize the essential similarities of these investments to other kinds of investments, economists refer to them as investment in human capital [ 16 ]. Since investment decisions usually deliver payoffs over time, we must consider the entire stream of costs and benefits. The expected returns on human capital investments are a higher level of earnings, greater job satisfaction over one's lifetime and a greater appreciation of non-market activities and interests. Based on the human capital approach, rate of return on education can be estimated. An average rate of return that is high and rising for a given profession will attract more individuals to that profession. On the other hand, a lower and decreasing average rate of return will discourage individuals from choosing that profession. Nowak and Preston [ 17 ], using the human capital approach, found that Australian nurses are poorly paid in comparison to other female professionals. The declining interest in nursing can be partly explained by the expansion of career opportunities in traditionally male-dominated occupations over the last three decades that entail a higher rate of return [ 18 ]. The number of young women entering the registered-nurse workforce has declined because many women who would have entered nursing in the past – particularly those with high academic ability – are now entering managerial and professional occupations that used to be traditionally male. Besides the human capital approach, the choice of a profession can also be explained by sociopsychological factors. For instance, individuals may choose a profession because it is highly valued by the society or for family tradition. In the health sector, the satisfaction afforded by caring for people and assisting them to improve their health is an important element used by nursing schools to attract new enrollees. In the light of this approach, the decline in the number of individuals choosing nursing as a career might also be explained by the fact that this profession is now less socially valued than before [ 19 , 20 ]. Participation in the labour market The economic theory of the decision to work views the decision as a choice concerning how people spend their time. Individuals face a trade-off between labour and leisure. They decide how much of their time to spend working for pay or participating in leisure activities, the latter being activities that are not work-related. An issue that has drawn a lot of attention recently is the impact of wage increases on labour participation, in particular for nurses. In the short term, higher wages can have at least two effects on the labour supply of current qualified nurses: first, qualified nurses who are working in other occupations may return to nursing activities; second, nurses now in practice may respond by working more hours. In the long run, higher wages in nursing relative to other occupations make nursing an attractive profession and will draw more people into nurse training programmes. In their literature review of wage elasticity of nursing labour supply, Antonazzo et al. [ 21 ] and Chiha and Link [ 22 ] found that most of the studies indicate a positive relationship, although not a strong one, between wages and labour supply. Accordingly, increases in nursing wages are unlikely to cause significant increases in labour participation. A literature review on the women's workforce undertaken by Killingsworth and Heckman [ 23 ] indicated that in addition to wage rate, women's participation is responsive to changes in unearned income, spouse's wage and having children (particularly of pre-school age). Another aspect of labour supply decisions that has been investigated by Philips [ 24 ] is the costs associated with entering the nursing labour market (such as costs of child care and housework). The elasticity of participation with respect to changes in working costs was evaluated at -0.67 for all nurses. This suggests that a subsidy leading to a decrease of 10% in these costs would increase the participation of nurses by 6.7%. Moreover, hospitals are also using a variety of strategies to recruit new staff. A survey of hospitals in the United States shows that richer benefits, such as health insurance and vacation time, are the most common incentives used. In addition, hospitals may offer other recruitment and retention benefits, such as tuition reimbursement, flexible hours and signing bonuses based on experience or length of commitment [ 25 ]. Many countries, but particularly developed ones, use such incentives to recruit new staff. Economic factors also play a role in physician's participation in the labour market, as demonstrated by the impact of cost-containment policies in Canada, where most provincial governments have implemented an assortment of controls of health care expenses. Threshold reductions were introduced, so that fees payable to individual physicians were reduced as billing exceeded an agreed threshold. As a consequence, physicians who had billed at the threshold level chose to take leaves of absence rather than receive a level of reimbursement they considered inadequate [ 26 ]. When health personnel choose an alternative or additional occupation, this is likely to have consequences on health labour supply. In developing countries, and particularly in Africa, attempts to reform the health care sector have frequently failed to respond to the aspirations of staff concerning remuneration and working conditions. Salaries are often inadequate and may be paid late, and health workers try to solve their financial problems in a variety of ways [ 27 ]. Private practice is only one of the many survival strategies that health personnel use to supplement their income and increase their job satisfaction. Teaching, attending training courses, supervision activities, research, trade and agriculture are some of these alternative strategies [ 28 ]. Labour market exit Parker and Rickam [ 29 ] examined the economic determinants of the labour force withdrawal of registered nurses in the United States, i.e. nurses leaving the profession to pursue a non-nursing occupation and employed nurses withdrawing from the labour force. Their results suggest that a significant number of registered nurses withdraw, at least temporarily, from the labour force. Among the significant elements influencing the withdrawal decision are the wage rate, other family income, presence of children and full-time/part-time work status. Increasing registered nurses' wages and working full-time is expected to reduce the probability of labour force withdrawal, whereas higher education levels, age and other family income increase the probability of labour force withdrawal. The relative importance of wage is also emphasized by studies investigating job satisfaction. There is support in the empirical literature for the existence of job dissatisfaction among nurses, and the link between job dissatisfaction and job exit [ 30 , 31 ]. In the United States the most important factors in nurses' resignation were, in order of importance: workload, staffing, time with patients, flexible scheduling, respect from nursing administration, increasing nursing knowledge, promotion opportunities, work stimulation, salary and decision-making. These studies suggest that salary is just one of the reasons why nurses are quitting. The relative importance of wage is confirmed by Shields and Ward [ 32 ]. Their results suggest that dissatisfaction with promotion and training opportunities has a stronger impact than workload or pay. Migration Migration of health personnel can have a serious impact on the supply of human resources in health, because it may exacerbate health personnel imbalances in "sending" countries. It is suggested that migration is an "individual, spontaneous and voluntary act" that is motivated by the perceived net gain of migrating – that is, the gain will offset the tangible and intangible costs of moving [ 33 ]. Decisions to migrate are often a family strategy to produce a better income and improve survival chances [ 34 ]. The reality for many health workers in developing countries is to be underpaid, poorly motivated and increasingly dissatisfied and sceptical [ 35 ]. The relevance of motivation to migration is self-evident. There can be little doubt that for many health workers an improvement in pay and conditions will act as an incentive to stay in the country. Improved pensions, child care, educational opportunities and recognition are also known to be important [ 36 - 38 ]. In Ghana it is estimated that only 191 of the 489 doctors who graduated between 1985 and 1994 were still working in the country in 1997 [ 39 ]. Health system characteristics As the health workforce is part of the health care system, we shall also consider features of the health care system that are likely to have an impact on the demand and supply of health labour. In particular, we shall examine market failures, the diversity of stakeholders, the supply-demand adjustment time lag and hospitals' potential monopsony power. Market failures From an economic perspective, the health care market is characterized by market failures – that is, the assumptions for perfect competition are violated. From a societal perspective, in the presence of market failures such as externalities – imperfect knowledge, asymmetry of information and uncertainty – market mechanisms lead to a non-optimal demand and/or supply in health services. In other words, shortages and surpluses are likely to result from the health care market. Most markets are characterized by market failures, but what is unique to the health services market is the extent of these market failures [ 40 ]. Governments try to correct health care market failures through policy interventions. A classic example of public intervention in the presence of a positive externality is the introduction of a policy of mandatory vaccination. However, implementing such policies is sometimes difficult and may result in only partial correction of the market failures. Stakeholders The health care system is characterized by a wide range of institutional stakeholders involved in shaping human resources for health, all of whom may have different objectives [ 41 , 42 ]. The objectives of a union or professional association do not necessarily coincide, for example, with those of a government ministry, a hospital manager or the central government. Unions/professional associations seek to increase their members' market power, employment and income [ 43 ], whereas the ministry of finance will want more budget equilibrium and will favour measures to limit health care expenditures. In the case of Mozambique, whereas the policy of employing national professionals by cooperation agencies has met with warm support from national cadres, its effect on the health sector is problematic [ 44 ]. The prospect of immediate financial gains puts pressure on qualified professionals to leave their posts within the Mozambique National Health Service to take up management or consultant positions. The substantial investment in their training is therefore producing dubious direct returns to the National Health Service. More seriously perhaps, the presence of donor-paid jobs outside the health sector (as programme coordinators, researchers, etc.) is creating pressure on the Ministry of Health itself, exacerbating the imbalances in the National Health Service and creating incentives for trained Mozambicans to leave the public sector. Time lag Moreover, adjustments between the demand and supply for health personnel may take a long time. In the health care field; the time lag between education and practising may be quite substantial. To obtain licensure to practise medicine requires lengthy education and training, and the long lag time between a changed student intake and a change in supply has been noted [ 45 ]: supply adjustment for physicians is not immediate, but takes a long time. Hospitals' potential monopsony power A single entity that is the sole purchaser of labour is a monopsony . One example is the potential monopsony power of hospitals in hiring nurses or the ministry of health in hiring the health workforce. The amount of labour demanded will influence the price the monopsonist must pay for it. In contrast to the situation in a competitive market, the monopsony is a price maker, not a price taker. Monopsony results in lower wages and lower employment of nurses compared to a competitive market. A number of studies have tested whether or not hospitals possess monopsony power with respect to nurses, and the results are contradictory. Sullivan [ 46 ] and Staiger et al. [ 47 ] concluded that hospitals have a substantial degree of monopsony power. In contrast, Hirsch and Schumacher [ 48 ] did not find empirical support for the monopsony model. Nurses' wages were found not to be related to hospital density and to decrease rather than increase with respect to labour market size. Provider power/monopoly In contrast, providers' power may enable the latter to restrict the supply of human resources for health. Seldon, Jung and Cavazos [ 49 ] suggest that physicians in the United States have market power through such avenues as restricting supply and price-fixing. In France, trade unions are granted an institutional role at establishment level [ 50 ]. In India and Sri Lanka, a clear constraint to support-services contracting was the inability to counter the power of the public service unions in dictating employment terms and conditions [ 51 ]. The varying degree of homogeneity of the different professional groups may also explain their relative success in maintaining a monopoly of practice. In Iceland, one of the factors that contributed to breaking the professional monopoly of pharmacists was division within the profession [ 52 ]. Regulations The type off regulation associated with a profession plays an important role regarding the supply of members of a profession. Regulation has, by tradition, been achieved through a combination of direct government regulation and, to a large extent, through rules adopted by professional associations, whose self-regulatory powers enable them to establish both entry requirements and rules regarding professional conduct [ 53 ]. Such barriers to entry exist in particular for doctors, but also in other health professions, such as dentistry. Some argue that these barriers constitute a means to limit entry into the profession, and hence maintain high incomes. Muzondo and Pazderka [ 54 ] established, for Canadian professional licensing restrictions, a relationship between different variables of self-regulation and higher income. Seldon et al. [ 55 ] suggest that physicians in the United States have market power through such sources as restricting supply and price-fixing. However, the proponents of self-regulation claim that these barriers are a means to provide health care of quality and to protect patients from incompetent providers. In contrast, although most countries have a professional nursing association, nurses tend to have limited power to regulate entry to the profession. This could be associated with a large diversity of specialist groups in nursing failing to unite on issues related to professional regulation [ 56 ]. Health and non-health policies Health and non-health policies contribute to shaping the health care system and have an influence on the demand and supply of health labour. Health policy can be defined as a formal statement or procedure within institutions (notably government) that defines priorities and the parameters for action in response to health needs, available resources and other political pressures. Health policy is often enacted through legislation or other forms of rule-making that create regulations and incentives for providing health services and programmes and access to them. For instance, the decision to introduce or expand health insurance coverage is likely to have an impact on the demand for health services. This is illustrated by the RAND Health Insurance Experiment, a controlled experiment that increased knowledge about the effect of different insurance copayments on use of medical services. Insurance copayments ranged from zero to 95%. The RAND study concluded that as the co-insurance rose, overall use and expenditure fell for adults and children combined [ 57 ]. Non-health policies reflect state interventions in areas such as employment, education and regional development that contribute to shaping the health workforce. These policies do not directly address health issues, but have an indirect impact on such issues. In France, a controversial new regulation was introduced that reduced the workweek to a maximum of 35 hours in an attempt both to create hundreds of thousands of new jobs and to achieve greater flexibility in the labour force. Unions responded by demanding the creation of more posts in public hospitals. Financial, physical and knowledge resources Financial, physical and knowledge resources are crucial to any type of health care workforce. The level of resources attributed to the health care system, and how these resources are used, will have a significant impact on health workforce issues. In terms of financial resources, human resources account for a high proportion of national budgets assigned to the health sector [ 58 ]. Health expenditure claims an increasingly important share of the gross domestic product and, in most countries, wage costs (salaries, bonuses and other payments) are estimated to account for between 65% and 80% of the renewable health system expenditure [ 59 , 60 ]. Physical resources include human resources within the health sector and other sectors; buildings and engineering services such as sanitation, water and heating systems for community use and for the use of medical care institutions; and equipment and supplies. Finally, the health workforce is also constrained by its human capital. This human capital can be associated with the qualification and education of the health workforce. Education of the health workforce is the systematic instruction, schooling or training given in preparation for work. Global factors Economic, sociodemographic, cultural, and geographical factors contribute to shaping and transforming society and hence have a direct or indirect impact on health workforce issues. From an economic perspective, for instance, there is evidence of a correlation between the level of economic development of a country and its level of human resources for health. Countries with higher GDP per capita are said to spend more on health care than countries with lower income, as demonstrated by cross-sectional studies, [ 61 ] and hence would also tend to have larger health workforces. Moreover, both the demand and supply are likely to be affected by sociodemographic elements such as the age distribution of the population. On the demand side, the ageing of the population is giving rise to an increase in the demand for health services and health personnel, especially nurses for home care. On the supply side, the ageing of the health workforce, and in particular of nurses, has serious implications for the future of the nursing labour market. For example, the Institute of Medicine noted that older registered nurses have a reduced capacity to perform certain tasks [ 62 ]. It was found that between 1983 and 1998 the average age of practising registered nurses increased by more than four years, from 37.4 to 41.9 years [ 63 ]. In contrast, the average age of the United States workforce as a whole increased by less than two years during the same period. Furthermore, the proportion of the registered-nurse workforce younger than 30 years decreased from 30.3% to 12.1% during this period. Geographical and cultural factors also play a role in determining the demand and supply of human resources. Geographical characteristics affect the organization of health services delivery. For instance, a country with many islands or with isolated population groups will face particular challenges in terms of health workforce issues. Similarly, significant climatic changes are likely to give rise to changes in health needs, which in turn will call for changes in health services and in the health workforce. Finally, both cultural and political values also affect the demand for and supply of human resources for health. Health workforce imbalances: a typology This section considers a typology of imbalances, and differentiates between the following: • Profession/specialty imbalances: Under this category, we consider imbalance in the various health professions, such as doctors or nurses, as well as shortages within a profession, e.g. shortage of one type of specialists. • Geographical imbalances: These are disparities between urban and rural regions and poor and rich regions. • Institutional and services imbalances: These are differences in health workforce supply between health care facilities, as well as between services. • Gender imbalances: These are disparities in female/male representation in the health workforce. Profession/specialty imbalances Imbalances have been reported for almost all health professions, and in particular for nurses. The United States General Accounting Office [ 64 ] reports a nursing shortage. However, the nursing shortage has not been institution-wide but is concentrated in specialty care areas, particularly intensive care units and operating rooms [ 65 ]. The shortage of registered nurses in intensive care units is explained in part by the sharp decline in the number of younger registered nurses, whom intensive care units have historically attracted. Shortages in operating rooms probably reflect that many registered nurses who work in this setting are reaching the age when they are beginning to reduce their hours worked or are retiring altogether. Major variations occur in the number of health care workers per capita population and in the skill mix employed across countries, as depicted in Fig. 2 . The nurse/doctor ratio varies widely from one country to another, as shown in Fig. 2 . The nurse/doctor skill mix is important and may have consequences for the respective tasks of nurses and doctors [ 66 ]. It is also interesting to note that these variations are taking place among countries with a relatively similar economic development level. Figure 2 Distribution of physicians, nurses, midwives, dentists and pharmacists in selected countries. WHO data, 2000. Geographical imbalances Virtually all countries suffer from a geographical maldistribution of human resources for health, and the primary area of concern is usually the physician workforce [ 67 ]. In both industrialized and developing countries, urban areas almost invariably have a substantially higher concentration of physicians than rural areas. Understandably, most health care professionals prefer to settle in urban areas, which offer opportunities for professional development as well as education and other amenities for themselves and their families. But it is in the rural and remote areas, especially in the developing countries, that most severe public health problems are found. The geographical maldistribution of doctors has been the object of particular attention. In general there is a higher concentration of general practitioners in the inner suburbs of the metropolitan areas. According to the Australian Medical Workforce Advisory Committee [ 68 ], the reasons for high concentration of general practitioners in inner city areas are: • historical • lifestyle-related: access to amenities • spouse/husband-related: greater employment opportunities • child-related: better access to secondary and tertiary education services • professional, family and social ties and professional ambitions. The geographical distribution of health care personnel is an important issue in many countries. Managua, the capital of Nicaragua, contains one-fifth of the country's population but around half of the available health personnel [ 69 ]. In Bangladesh, most of the doctors (35%) and nurses (30%) in health services are located in four metropolitan districts where only 14.5% of the population lives [ 70 ]. This concentration pattern is characteristic of developing countries. In Indonesia the geographical distribution of physicians is a particular concern, since Indonesia's vast size and difficult geography present a tremendous challenge to health service delivery [ 71 ]. It is difficult to place doctors in remote islands or mountain or forest locations with few amenities, no opportunities for private practice, and poor communications with the rest of the country. To improve the geographical distribution of physicians, governments often have used combinations of compulsory service and incentives. So far, there is virtually no country in the world that has solved the problem of a rural/urban imbalance of the physician workforce [ 67 ]. This does not necessarily mean that policies and programmes designed to reduce the imbalance have had no effect. For example, Thailand has successfully begun to stem the migration of health professionals from rural to urban areas and from public to private facilities with a range of strong financial incentives [ 72 ]. Institutional and services imbalances Institutional imbalances occur when some health care facilities have too many staff because of prestige, working conditions, ability to generate additional income, or other situation-specific factors, while others are understaffed [ 73 ]. Institutions such as magnet hospitals, for example, are hospitals characterized by adequate to excellent staffing, low turnover, rich nursing skill mix and greater job satisfaction, among other factors, even in the face of a general health personnel shortage [ 74 ]. Imbalance between the types of health services provided may also arise. In particular, we can consider the issue of curative versus preventive care. In effect, it has been estimated that most diseases (80%) and accidents are preventable through known methodologies, yet at present there is an imbalance in the funding of medical research, with only 1%-2% going to prevention and 98%-99% spent on curative approaches [ 75 ]. This imbalance in funding raises the question of a health workforce imbalance between preventive and curative care. Gender imbalances In many countries, women still tend to concentrate in the lower-status health occupations and to be a minority among more highly trained professionals and managers. In Bangladesh, the distribution by gender of the health workforce shows that the total proportion of women accounts for little more than one-fifth in health services [ 76 ]. The distribution of women by occupational category is biased in favour of nurses. Women are very poorly represented in other categories, such as dentists, medical assistants, pharmacists, managers/trainers and doctors. The underrepresentation of women in managerial and decision-making positions may lead to less attention to and poorer understanding of the problems specific to women and the particularities of their utilization patterns [ 77 ]. Female general practitioners have been shown to practise differently from males, managing different types of medical conditions, with some differences due to patient mix and patient selectivity, and others inherent in the sex of physician. In some more traditional areas, some women will not seek care for themselves or even for their children because they do not have access to a female provider [ 76 ]. Discussion This framework can be used to assess policy reforms and their impact on health workforce imbalances; it also provides a common framework for cross-country comparisons. This framework emphasizes the number and type of factors affecting health workforce imbalances, illustrating the complexity of this issue. From a policy perspective, it is particularly interesting to identify factors that policy-makers can influence in order to remedy imbalance problems. Various monetary and non-monetary incentives are used to influence the supply and/or demand for the health workforce. For example, subsidies, grants and scholarships are examples of incentives that can be used to attract more nursing students, whereas wage increases, additional benefits and working hours flexibility are examples of commonly used incentives to attract or retain the health workforce. The numerous factors and actors involved in the health workforce imbalance issue call for a coherent health workforce vision and policy. In that context, health planning plays an important role since it contributes to shaping the health care system. Moreover, since from a societal perspective market mechanisms alone do not allow an adequate demand/supply of health personnel to be reached, public interventions such as human resources planning are a means to correct for market failures. Health planning involves a time horizon. Forecasting the future number of health personnel needed and developing policies to meet such figures are common to any health care system. Physicians represent the profession for which more planning effort has been expended to achieve a workforce of appropriate size than for any other health profession. Countries' desire to meet population health needs and to avoid social welfare losses resulting from a shortage or an oversupply are factors explaining, to a large extent, the importance attributed to planning in the context of public health policies. The policy implications of forecasting either a shortage or a surplus of health care personnel are different, and hence attempts at projections must be rigorous. For instance, referring to previous studies predicting significant surpluses, Cooper [ 78 ] notes that such large surpluses have not occurred so far, because of a decrease in physician work effort. Factors such as age, sex and lifestyle contributed to this evolution. As a result of forecasted physician surpluses, various policy recommendations have been formulated. The United States Institute of Medicine [ 79 ] published a report recommending, among other things, that there be no new medical schools, that existing schools should not increase their class size and that the number of first-year residency positions should be reduced. The Pew Health Professions Commission Report [ 80 ] issued a report recommending more severe steps, such as the closing of some medical schools and tightening the visa process for international medical graduates. This framework also apprehends the different types of imbalances. This is important since the choice of a policy will also depend on the type of imbalance. Significant disparities in human resources for health between health occupation, regions, gender or health services are recognized as classic problems of imbalance. However, the question of a public/private imbalance is more debatable. One the one hand, we can argue that for equity and access, a health care system should have a strong public component. On the other hand, we can imagine a private-sector oriented health care system with mechanisms to ensure access to the poor. Conclusion In an attempt to contribute to a better understanding of imbalances in the health workforce, this paper has discussed a framework for human resources for health and proposed a typology of imbalances. Although the term "imbalance" is commonly used with respect to the health workforce, it is clear that imbalance in the health workforce encompasses a wide range of possible situations and is a complex issue. The use of a framework should facilitate the development of policy tools and their assessment. Competing interests None declared. Authors' contributions All authors participated in writing the original text and read and approved the final manuscript.

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Comparison of the NEI-VFQ and OSDI questionnaires in patients with Sjögren's syndrome-related dry eye

Background To examine the associations between vision-targeted health-related quality of life (VT-HRQ) and ocular surface parameters in patients with Sjögren's syndrome, a systemic autoimmune disease characterized by dry eye and dry mouth. Methods Forty-two patients fulfilling European / American diagnostic criteria for Sjögren's syndrome underwent Schirmer testing without anesthesia, ocular surface vital dye staining; and measurement of tear film breakup time (TBUT). Subjects were administered the Ocular Surface Disease Index (OSDI) and the 25-item National Eye Institute Vision Functioning Questionnaire (NEI-VFQ). Main outcome measures included ocular surface parameters, OSDI subscales describing ocular discomfort (OSDI-symptoms), vision-related function (OSDI-function), and environmental triggers, and NEI-VFQ subscales. Results Participants (aged 31–81 y; 95% female) all had moderate to severe dry eye. Associations of OSDI subscales with the ocular parameters were modest (Spearman r (ρ) < 0.22) and not statistically significant. Associations of NEI-VFQ subscales with the ocular parameters reached borderline significance for the near vision subscale with TBUT (ρ = 0.32, p = .05) and for the distance vision subscale with van Bijsterveld score (ρ = 0.33, p = .04). The strongest associations of the two questionnaires were for: ocular pain and mental function with OSDI-symptoms (ρ = 0.60 and 0.45, respectively); and general vision, ocular pain, mental function, role function, and driving with OSDI-function (ρ = 0.60, 0.50, 0.61, 0.64, 0.57, and 0.67, respectively). Conclusions Associations between conventional objective measures of dry eye and VT-HRQ were modest. The generic NEI-VFQ was similar to the disease-specific OSDI in its ability to measure the impact of Sjögren's syndrome-related dry eye on VT-HRQ.

Background Dry eye is a common disorder of the ocular surface and tear film and is estimated to affect from 2% to over 15% of persons in surveyed populations, depending on the definition used [ 1 - 6 ]. Symptoms of dry eye are a major reason to seek ophthalmic care: a study by Nelson and co-workers found that 1.3% of Medicare patients had a primary diagnosis of keratoconjunctivitis sicca or dry eye [ 7 ]. Dry eye can range from mild to severe disease; although the majority of patients with dry eye experience ocular discomfort without serious vision-threatening sequelae, severe dry eye can compromise corneal integrity by causing epithelial defects, stromal infiltration, and ulceration, and can result in visually significant scarring [ 8 ]. Moderate to severe dry eye disease can adversely affect performance of visually demanding tasks due to pain and impaired vision [ 9 ]. In addition, corneal surface irregularity due to epithelial desiccation, quantified by using corneal topography, can decrease visual acuity [ 10 ]. Patient-reported measurements used to evaluate the specific impact of eye disease and vision on symptoms (discomfort), functioning (the ability to carry out activities in daily life), and perceptions (concern about one's health) are referred to as vision-targeted health-related quality of life (VT-HRQ) instruments. Valid and reliable measurements of VT-HRQ have become essential to the assessment of disease status and treatment effectiveness in ocular disease [ 11 ]. There are two general categories of VT-HRQ instruments: generic, which are designed to be used for a broad spectrum of visual disorders and ocular disease; and disease-specific, which are tailored toward particular aspects of a specific ocular disorder. In general, disease-specific instruments tend to be more sensitive than generic ones in detecting VT-HRQ impairments [ 12 ]; however, generic instruments allow comparisons across more diverse populations and diseases [ 13 ]. In addition, generic instruments may be able to capture additional aspects of systemic disease, related to the ocular disorder in question, providing a broader characterization of health-related quality of life [ 14 , 15 ]. There is therefore no clear-cut basis in a given study or population for choosing a generic versus a disease-specific measure: if possible, both should be utilized to determine whether one or the other is more consistent with clinical indicators, or if one appears to obtain additional, relevant information on patient status [ 16 ]. However, it may be the case that weak-to-moderate associations between clinical indicators and quality-of-life measures indicates that the VT-HRQ measure is capturing elements of disease above and beyond those that can be measured clinically (for example, visual acuity may be good but a patient may have problems with functioning related to problems with contrast sensitivity or glare disability). Again, depending on the characterization of the disease desired and the goal of the study, a researcher might choose an instrument that either is or is not strongly correlated with clinical signs. The measurement of the impact of dry eye on a patient's daily life, particularly symptoms of discomfort, is a critical aspect of characterizing the disease [ 17 ]. Despite the fact that most studies have found weak or no correlations between symptoms and signs of dry eye [ 18 - 20 ], symptoms are often the motivation for seeking eye care and are therefore a critical outcome measure when assessing treatment effect [ 7 ], and hence are increasingly used as a surrogate for ocular surface disease in many epidemiologic studies. Indeed, recent studies have focused on developing more robust ways of measuring patient-reported symptoms of dry eye [ 21 - 23 ]. The Ocular Surface Disease Index (OSDI) © [ 24 ] was developed to quantify the specific impact of dry eye on VT-HRQ. Sjögren's Syndrome is an autoimmune systemic disease characterized by dry mouth and dry eye signs and symptoms [ 25 , 26 ]. Its manifestations include fatigue, arthritis, neuropathy, and pulmonary and renal disease. Histopathologic evidence of salivary gland inflammation and the presence of serum autoantibodies SSA or SSB are important diagnostic features of the disease [ 27 ]. Sjögren's Syndrome has been stated to be the second most common autoimmune disease, ranking between rheumatoid arthritis and systemic lupus erythematosus [ 27 ]. In the U.S., it is estimated that between 1 and 4 million persons (approximately 1–2 in 200) have Sjögren's Syndrome [ 28 ]. Prevalence estimates for other countries range from 0.3 to 4.8% [ 29 ]. Female gender and older age are known risk factors for Sjögren's syndrome [ 30 ]. A wide range of studies have assessed the ocular manifestations of Sjögren's syndrome [ 31 - 33 ]; however, assessment of symptoms and quality of life have been limited and, in most cases, generic measures of well-being, psychological distress, and fatigue without ocular dimensions have been employed [ 34 - 40 ]. Further, while there are many published studies of VT-HRQ in mild to moderate dry eye, there are few publications on VT-HRQ in Sjögren's syndrome, which is characterized by dry eye causing significant ocular irritation as well as systemic disease factors that could have their own additional significant impact on VT-HRQ. Our purpose in this study was to examine VT-HRQ in patients with primary Sjögren's syndrome, using a generic and a dry-eye-disease-specific instrument. We examined the associations of ocular surface parameters with the VT-HRQ scores, hypothesizing that the disease-specific instrument would be more closely related than the generic to the clinical markers of disease. We also examined the association of the generic and disease-specific VT-HRQ scores with each other. Methods The study protocol was approved by the National Eye Institute Internal Review Board. All patients completed an informed consent prior to examination. Consecutive patients with diagnosed primary Sjögren's syndrome were recruited from the NIH Clinical Center, Bethesda, MD. The diagnosis of primary Sjögren's syndrome was based on European-American criteria, which requires at least four of the following six features: signs and symptoms of dry eye and of dry mouth, histopathologic evidence of inflammation on minor salivary gland biopsy, and positive anti-Ro or anti-La antibodies. Before the clinical examination, a trained interviewer administered two questionnaires (described further below) to measure VT-HRQ to each patient. The subsequent clinical examination included a comprehensive anterior segment evaluation, including slit lamp biomicroscopy, evaluation of lid margin thickness and hyperemia, conjunctival erythema, chemosis, tear film debris and mucus, and extent of meibomian gland plugging. Tests of tear function and ocular surface status were performed as described below. The OSDI [ 24 ] (provided by Allergan, Inc. Irvine, CA) was used to quantify the specific impact of dry eye on VT-HRQ. This disease-specific questionnaire includes three subscales: ocular discomfort (OSDI-symptoms), which includes symptoms such as gritty or painful eyes; functioning (OSDI-function), which measures limitation in performance of common activities such as reading and working on a computer; and environmental triggers (OSDI-triggers), which measures the impact of environmental triggers, such as wind or drafts, on dry eye symptoms. The questions are asked with reference to a one-week recall period. Possible responses refer to the frequency of the disturbance: none of the time, some of the time, half of the time, most of the time, or all of the time. Responses to the OSDI were scored using the methods described by the authors [ 24 ]. Subscale scores were computed for OSDI-symptoms, OSDI-function, and OSDI-triggers, as well as an overall averaged score. OSDI subscale scores can range from 0 to 100, with higher scores indicating more problems or symptoms. However, we subtracted the OSDI overall and subscale scores from 100, so that lower scores would indicate more problems or symptoms. The 25-item NEI Visual Function Questionnaire (NEI-VFQ) [ 41 , 42 ] is a non-disease-specific (i.e., "generic") instrument designed to measure the impact of ocular disorders on VT-HRQ. Depending on the item, responses to the NEI-VFQ pertain to either frequency or severity of a symptom or functioning problem. A recall period is not specified in the questionnaire. Responses to the NEI-VFQ were scored using the methods described by the authors [ 43 ]. Subscale scores for general vision, ocular pain, near vision, distance vision, social functioning, mental functioning, role functioning, dependency, driving, color vision, and peripheral vision, as well as an overall score, were computed. The NEI-VFQ scores can range from 0–100, with lower scores indicating more problems or symptoms. Schirmer tests of tear production without and with anesthesia were performed by inserting a Schirmer tear test sterile strip (35 mm, Alcon Laboratories, Inc, Fort Worth, TX) into the inferior fornix, at the junction of the middle and lateral third of the lower eyelid margin, for 5 minutes with the eyes closed. The extent of wetting was measured by referring to the ruler provided by the manufacturer on the envelope containing the strips. Possible scores range from 0 to 35 mm, with lower scores indicating greater abnormality in tear production. This test was repeated after instillation of topical anesthetic, 0.5% proparacaine [ 44 ]. A Schirmer without anesthesia score of ≤ 5 mm in at least one eye is one required element of dry eye, as defined by the European-American Sjögren's syndrome diagnostic criteria [ 45 ]. The assessment of ocular surface damage was performed by a cornea specialist using vital dye staining with 2% unpreserved sodium fluorescein and then 5% lissamine green dye. The corneal, temporal, and nasal regions of the conjunctiva were scored individually from 0–5 (for fluorescein) and 0–5 (for lissamine green) using the Oxford grading scheme [ 46 ]. The Oxford score was derived by adding the scores for corneal fluorescein and nasal plus temporal conjunctival lissamine green staining. Total Oxford score could range from 0–15. The van Bijsterveld score [ 47 ] (VB) was assessed using lissamine green staining of the cornea (0–3) and conjunctiva (0–3). Total VB score could range from 0–9. For all staining tests, higher scores indicate worse ocular surface damage. Tear film stability was assessed using fluorescein tear film breakup time (TBUT). Five microliters of 2% sodium fluorescein was instilled into the inferior fornix and the patient was asked to blink several times. Using the cobalt blue filter and slit lamp biomicroscopy, the duration of time required for the first area of tear film breakup after a complete blink was determined. If the TBUT was less than 10 seconds, the test was repeated for a total of 3 values and the average was calculated. For analysis, for each individual, the maximum (worse) score for the two eyes was used for Oxford score and VB, and the minimum (worse) score for the two eyes was used for Schirmer with and without anesthesia and for TBUT. TBUT values greater than or equal to 10 seconds [ 48 ] were coded as 10 (normal) and < 10 seconds was defined as abnormal. Schirmer without anesthesia score result of ≤ 5 mm or VB ≥ 4 were used as objective evidence of dry eye, following the European / American criteria for the diagnosis of dry eye for Sjogren's syndrome [ 49 ]. Hypotheses of specific associations were formulated based on the areas and domains assessed by the two VT-HRQ instruments. Scatterplots and Spearman's correlation coefficient (ρ) [ 50 ] were used to examine associations between pairs of variables. Multiple linear regression [ 51 ] was used to assess the strength of association between pairs of variables while adjusting for confounders (e.g., age). Results Characteristics of participants A total of 42 patients, 40 female and 2 male, were included in this study. The average age was 55 years (range, 31–81 y). Most (81%) were of European descent. Visual acuity in the better eye was 20/20 or better for 68% of the patients; the remainder had 20/25 or better in the better eye, except for one patient who was 20/32 in both eyes. Ocular examination (Table 1 ) showed that, on average, the participants suffered from moderate to severe dry eye: mean Oxford score was 7.2, mean VB score was 5.3. Average Schirmer without anesthesia score was 4.8 mm, with nearly all (79%) having scores less than 10 mm and the majority (59%) having scores less than 5 mm. Mean TBUT was 2.9 seconds, with nearly all (87%) having scores less than 5 seconds. Table 1 Characteristics of participants (n = 42) Mean, sd [range] N (%) Age (y) 54.9 (12.7) [31–81] Ethnicity European-derived 34 (81%) African-derived 3 (7%) Other 5 (12%) Gender Female 40 (95%) Male 2 (5%) Visual acuity* 20/20 + OU 18 (44%) 20/20+, better eye 10 (24%) 20/25+, better eye 12 (29%) <20/25, better eye 1 (2%) Vital dye staining Oxford score 7.2 (3.4) [1–14] -- 5+ -- 34 (81%) Van Bijsterveld score** 5.3 (2.7) [0–9] -- 4+ -- 28 (74%) Tear production Tear film break-up time (s)** 2.9 (1.7) [1–8] -- < 5 sec -- 33 (87%) Schirmer without anesthesia (mm) 4.9 (5.4) [0–20] -- 0–5 -- 25 (60%) 5-<10 -- 8 (19%) 10+ -- 9 (21%) Meibomian gland disease** None -- 10 (26%) 1 -- 8 (21%) 2+ -- 20 (53%) European-American dry eye criteria -- 37 (90%) *One person had missing visual acuity information; **Four persons had missing information for some components of the clinical examination Association of OSDI © with ocular surface parameters OSDI scores (all subtracted from 100) indicated moderate problems with symptoms, functioning, and adverse environmental conditions. Mean OSDI-symptoms score was 62.5, mean OSDI-function score was 78.2, and mean OSDI-triggers score was 60.2. However, some patients had no problems with these areas: 12% reported no problems with irritation symptoms, 21% reported no problems with functioning, and 24% had no problems with environmental triggers. Associations of the OSDI subscale and overall scores with ocular surface parameters (Oxford score, VB, TBUT, and Schirmer score with and without anesthesia) are shown in 2 . In general, no substantive associations were found, except for visual functioning with TBUT (r = 0.22), and none of the observed associations reached statistical significance. Median scores on OSDI were compared between normal/abnormal categories of ocular surface variables (Schirmer without anesthesia score < 5, 5-<10, versus 10+; TFB < 5 versus > = 5; VB < 4 versus 4+, Oxford score < 5 versus 5+; European-American criteria, yes versus no). Considerable overlap in the distributions between categories was observed for all subscales, with no significant differences in median values (data not shown). Table 2 Association of OSDI (scores subtracted from 100) with ocular surface parameters (Spearman ρ) Oxford score van Bijsterveld score Tear film breakup time Schirmer without anesthesia score Schirmer with anesthesia score OSDI Mean (sd); % floor Symptoms 62.5 (25.7); 12% 0.02 0.16 -0.10 -0.04 0.02 Visual function 78.2 (21.4); 21% 0.15 0.17 0.22 0.12 0.05 Environmental triggers 60.2 (34.3); 24% -0.01 0.13 -0.02 0.04 0.12 Overall 70.0 (20.2); 10% 0.07 0.19 0.06 0.04 0.08 Association of NEI-VFQ with ocular surface parameters Overall, scores on the NEI-VFQ subscales tended to be high. Average scores for near and distance vision, social and mental functioning, dependency, driving, and peripheral vision were over 80, and a substantial percentage reported no problems at all with any of the items on the subscale: 26% for near vision, 24% for distance vision, 83% for social functioning, 17% for mental functioning, 74% for dependency, 38% for driving, and 79% for peripheral vision. The subscale indicating the most impairment was the ocular pain subscale, with a mean score of 66.7. Associations of the NEI-VFQ subscale and overall scores with ocular surface parameters (Oxford score, VB, TBUT, and Schirmer score with and without anesthesia) are shown in Table 3 . Overall, associations were weak to moderate, and none attained statistical significance. General vision showed moderate correlations with Oxford score, VB, and TBUT scores (r values from 0.20–0.27). Ocular pain showed a moderate correlation with TBUT (r = 0.23) and Schirmer with anesthesia score (r = 0.22). Near vision was associated with VB (r = .20) and to a greater extent with TBUT (r = 0.32). Distance vision showed moderate associations with Oxford score, TBUT, and Schirmer with anesthesia score (r values from 0.21 – 0.26) and a stronger association with VB (r = 0.33). Social functioning was moderately associated with VB (r = .24). Role functioning was associated with Schirmer scores both with and without anesthesia, more strongly so with Schirmer with anesthesia score (r = 0.31). Dependency was associated with TBUT (r = .29) and Schirmer with anesthesia score (r = .21). An anomalous finding was that peripheral vision showed moderate association with VB score (r = .29). Mental functioning and driving showed no associations with any of the ocular surface parameters. Median scores on NEI-VFQ scales were compared between normal/abnormal categories of ocular surface variables (Schirmer without anesthesia score < 5, versus 10+; TFB < 5 versus > = 5; VB < 4 versus 4+, Oxford score < 5 versus 5+; European-American criteria, yes versus no). Considerable overlap in the distributions between categories was observed for all subscales, with no significant differences in median values (data not shown), with the exception of the European-American criteria, where, counterintuitively, scores were higher (better) for near vision for those with dry eye (45.8) than for those without (83.7; p = .03). However, only 4 patients were in the "no dry eye" category, so this result may be the consequence of unstable small sample size. Table 3 Association of NEI-VFQ with ocular surface parameters (Spearman ρ) Oxford score van Bijsterveld score Tear film breakup time Schirmer without anesthesia score Schirmer with anesthesia score NEI-VFQ Mean (sd); % floor General vision 78.6 (12.8); 14% 0.22 0.20 0.27 -0.04 0.08 Ocular pain 66.7 (22.2); 12% 0.06 0.06 0.23 -0.06 0.22 Near vision 80.4 (19.4); 26% 0.18 0.20 0.32 -0.02 0.16 Distance vision 80.2 (18.4); 24% 0.25 0.33 0.26 -0.04 0.21 Social function 96.1 (11.2); 83% 0.14 0.24 0.15 -0.07 -0.09 Mental function 83.1 (17.5); 17% 0.15 0.18 0.19 -0.10 0.17 Role function 73.2 (25.4); 29% 0.07 -0.02 0.16 0.22 0.31 Dependency 94.4 (10.5); 74% -0.09 -0.04 0.29 0.03 0.21 Driving 84.9 (15.5); 38% -0.02 0.04 0.19 -0.07 -0.06 Peripheral vision 91.7 (19.6); 79% 0.11 0.29 0.15 -0.15 -0.13 Overall 83.6 (12.8); 2% 0.19 0.20 0.24 -0.04 0.19 Association of OSDI © with NEI-VFQ subscales In general, stronger associations were observed between subscales of the OSDI and NEI-VFQ (Table 4 ) than between ocular surface parameters and either the OSDI or the NEI-VFQ. Because of the large number of potential comparisons, we restrict discussion to associations that were hypothesized based on clinical plausibility. To test whether the overall (i.e., combined) OSDI and NEI-VFQ scales were related, we examined their linear relationship (Figure 1 ). Indeed, the association of these scales was strong (r = 0.61) and remained statistically significant after age adjustment. We hypothesized that the OSDI-symptoms subscale and the NEI-VFQ ocular pain subscale should show strong association, and in fact this was observed (r = 0.60, p < .001 after adjustment for age). A scatterplot of the data is shown in Figure 2 . We also hypothesized that the OSDI-triggers measure should be associated with the NEI-VFQ ocular pain subscale. This association was moderate (r = 0.46, Figure 3 ) and did not remain statistically significant after age adjustment. The OSDI-function subscale measures a domain that has theoretical overlap with the NEI-VFQ subscales for general, near, and distance vision, as well as driving, so we hypothesized that these correlations should also be relatively strong. This was true in particular for general vision (r = 0.60, Figure 4 ) and driving (r = 0.57, Figure 7 ), both of which remained highly statistically significant after adjustment for age (p < .001). The correlations of OSDI-function with NEI-VFQ near and distance vision were not as strong (0.45, Figures 5 and 6 ) and were not statistically significant after adjusting for age. Table 4 Associations of OSDI © subscales (subtracted from 100) with NEI-VFQ subscales (Spearman ρ). OSDI Symptoms OSDI Visual function OSDI Environmental triggers OSDI Overall NEI-VFQ General vision 0.34 0.60*† 0.28 0.51* Ocular pain 0.60*† 0.50* 0.46† 0.62* Near vision 0.08 0.46† 0.23 0.33 Distance vision 0.37 0.45† 0.27 0.46 Social function 0.16 0.26 0.17 0.22 Mental function 0.45* 0.61* 0.20 0.53* Role function 0.19 0.64* 0.33 0.48* Dependency 0.17 0.42* 0.17 0.33 Driving 0.28 0.57*† 0.33 0.48* Peripheral vision 0.18 0.02 0.04 0.14 Overall 0.43 0.67* 0.37 0.61*† †Associations hypothesized at the start of the study; *statistically significant after age adjustment (p < 0.001) Figure 1 Association between OSDI (scores subtracted from 100) and NEI-VFQ overall scales. Spearman ρ: 0.61*. Figure 2 Association between OSDI ocular discomfort subscale (scores subtracted from 100) and NEI-VFQ ocular pain subscale. Spearman ρ: 0.60* Figure 3 Association between OSDI environmental triggers subscale (scores subtracted from 100) and NEI-VFQ ocular pain subscale. Spearman ρ: 0.46. Figure 4 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ general vision subscale. Spearman ρ: 0.61. Figure 7 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ driving subscale. Spearman ρ: 0.57. Figure 5 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ near vision subscale. Spearman ρ: 0.46. Figure 6 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ distance vision subscale. Spearman ρ: 0.45. Table 4 shows that, in fact, several other significant associations not conjectured in our original hypotheses were observed. In particular, the OSDI-function subscale, in addition to the associations hypothesized above, showed substantial and statistically significant associations with ocular pain (r = 0.50), mental function (r = 0.61), role function (r = 0.64), and dependency (r = 0.42). The OSDI-symptoms subscale showed a moderate and statistically significant association with NEI-VFQ mental health (r = 0.45). The overall OSDI scale showed significant associations with NEI-VFQ general vision (r = 0.51, ocular pain (r = 0.62), mental and role functioning (r = 0.53 and 0.48, respectively), and driving (r = 0.61). Discussion We compared subscale scores for an ocular surface disease-specific instrument (OSDI) with a generic VT-HRQ instrument (NEI-VFQ-25) in patients with a systemic autoimmune disease associated with moderate to severe dry eye. We found that patients with primary Sjögren's syndrome had OSDI scores (mean, 30, before subtraction from 100) similar to those previously published [ 24 ] for moderate to severe dry eye patients (mean score was 36 for severe cases). Despite the fact that all of our patients had Sjögren's syndrome, with moderate to severe dry eye, we found that correlations of ocular surface parameters with VT-HRQ (i.e., patient-reported) parameters tended to be weak or nonexistent, consistent with several other studies demonstrating poor correlations between signs and symptoms of dry eye [ 18 - 20 ]. Indeed, contrary to our expectations, NEI-VFQ correlations with objective ocular surface parameters tended to be higher than those of OSDI, although all were relatively modest (all < 0.35) and none reached statistical significance. One explanation could be that the nature of the items for each of these instruments is quite different. The OSDI queries the frequency of a symptom or difficulty with an activity, over a one week recall period. The NEI-VFQ incorporates questions both the frequency and intensity of symptoms and their impact on activities, with no specified recall period. Perhaps this added element of capturing both the frequency and intensity of a symptom or impact accounts for some of the differences we found. For subscales that are similar, agreement was higher but still moderate, possibly due to differences in the nature of the questions or response options. The OSDI is targeted to assess how much the symptoms of dry eye affect the patient's current status (i.e., in the past week), whereas the NEI-VFQ may be more suited to capturing the overall impact of a chronic ocular disease on VT-HRQ. In this group of primary Sjögren's syndrome patients, associations between subscales of the NEI-VFQ and OSDI were moderate to strong (< 0.70) and in hypothesized directions. Significant associations were seen between OSDI and NEI-VFQ overall scales; OSDI-symptoms and NEI-VFQ ocular pain; and OSDI-function and NEI-VFQ general vision and driving. This suggests that both instruments are capturing important aspects of VT-HRQ. It is not surprising that the highest correlations were observed between subscales with similar domains, which serves to validate the use of alternate methodologies. On the other hand, it is counter-intuitive that the generic and disease-specific instruments appeared similar (or that the generic seemed to do a little better) with respect to their association with objectively measured clinical signs of dry eye, as the NEI-VFQ was designed to capture broader aspects of VT-HRQ. For the NEI-VFQ, we found moderate correlations (greater than 0.3) of distance vision with VB and near vision with TBUT. This was surprising, as one may have expected that subscales measuring ocular discomfort or pain (i.e., more disease-specific for dry eye) would have the strongest correlations with clinical measures of dry eye. Clinical signs of dry eye include measures of tear production, ocular surface staining, and tear film break-up; visual acuity and other aspects of visual function are not generally as widely used. However, some investigators have reported that visual acuity in dry eye patients is correlated with decreased spatial contrast sensitivity [ 52 ] and is functionally reduced with sustained eye opening due to increased surface irregularity which can be detected with corneal topography [ 53 , 10 ], which could explain our finding of moderate associations of ocular surface measures with near and distance vision. It has been proposed [ 10 ] that "subtle visual disturbance" is an important reason for dry eye patients to seek care. Indeed, improvement in blurred vision symptoms was one of the most frequently reported benefits of topical cyclosporine treatment for dry eye in a large, multicenter clinical trial [ 54 ]. The impact of the quality of vision or functional visual acuity on VT-HRQ has not been a focus of studies of the subjective aspects of dry eye. Our data indicate that the impact of dry eye on VT-HRQ is only partially accounted for by ocular pain in patients with severe dry eye, such as in Sjogren's syndrome. Would we expect the associations to be different in Sjögren's patients? Sjögren's syndrome is an autoimmune exocrinopathy and effects of its systemic nature and chronicity on dry eye may have been more readily captured by the NEI-VFQ's ability to measure both frequency and intensity of problems with VT-HRQ. In contrast, although the OSDI includes items to measure function, responses are limited to the frequency of problems. Because the type of dry eye in Sjögren's syndrome is more likely to be severe, and all patients in our study had Sjögren's-related dry eye, we speculated that somewhat stronger associations between signs and symptoms might be observed. On the other hand, ocular surface inflammation and decreased corneal sensation are features of severe dry eye which might alter a patient's perception of symptoms of ocular irritation and might be the cause of weaker correlations between signs and symptoms [ 48 , 55 ]. Indeed, reduced corneal sensation could provide inadequate feedback through the ophthalmic nerve to the central nervous system, resulting in less efferent stimulation to the lacrimal gland with reduced tear production and promotion of a vicious cycle. In addition, meibomian gland dysfunction plays a key role in dry eye in Sjögren's syndrome [ 56 ]. Therefore, aqueous and evaporative tear deficiency may combine to produce a particularly diseased ocular surface. Conclusions In addition to clinical signs, it is important to include assessments of VT-HRQ and visual function to fully characterize the impact of dry eye on health status. The correlation between signs and VT-HRQ are modest at best, indicating that VT-HRQ is capturing an additional component of disease that is not captured by the clinical assessment. This does not necessarily mean that the measures of VT-HRQ or the methods of detecting clinical signs are deficient, but rather that VT-HRQ is an additional element of the overall impact of this disease process on affected individuals. Furthermore, in diseases with systemic manifestations, such as Sjögren's syndrome, that may have an influence on quality of life independent of dry eye symptoms, appropriate tests of VT-HRQ are critical to completely characterize quality of life in these patients. It may also be valuable to explore possible differences in associations of clinical signs with VT-HRQ in patient populations with different manifestations or causes of dry eye. List of abbreviations VT-HRQ: Vision-targeted health-related quality of life; TBUT: Tearfilm breakup time; OSDI: Ocular Surface Disease Index; NEI-VFQ: National Eye Institute Visual Function Questionnaire; VB: van Bijsterveld Authors' contributions SV helped to design the study and performed all analyses and took the lead in writing the manuscript. LG performed the patient interviews and assisted with data analyses. GFR provided advice on statistical methods and presentation of the results. JA conceived and helped to design the study and assisted with writing the manuscript.

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527875

Statistical design considerations for pilot studies transitioning therapies from the bench to the bedside

Pilot studies are often used to transition therapies developed using animal models to a clinical setting. Frequently, the focus of such trials is on estimating the safety in terms of the occurrence of certain adverse events. With relatively small sample sizes, the probability of observing even relatively common events is low; however, inference on the true underlying event rate is still necessary even when no events of interest are observed. The exact upper limit to the event rate is derived and illustrated graphically. In addition, the simple algebraic expression for the confidence bound is seen to be useful in the context of planning studies.

Introduction In the translational research setting, statisticians often assist in the planning and analysis of pilot studies. While pilot studies may vary in the fundamental objectives, many are designed to explore the safety profile of a drug or a procedure [ 1 , 2 ]. Often before applying a new therapy to large groups of patients, a small, non-comparative study is used to estimate the safety profile of the therapy using relatively few patients. This type of investigation is typically encountered in the authors' experiences as collaborating biostatisticians at our General Clinical Research Center as well as developing applications addressing the National Institutes on Health Roadmap Initiative . In the context of pilot studies, traditional levels of α (the Type I error rate) and β (the Type II error rate) may be inappropriate since the objective of the research is not to provide definitive support for one treatment over another [ 3 ]. For example, the null hypothesis in a single arm pilot study might be that the tested intervention produces a safety profile equal to a known standard therapy. A Type I error (rejecting the null hypothesis when it is false) in the context of this preliminary investigation would encourage additional examination of the treatment in a new clinical trial. This is in contrast to a Type I error in a Phase III/IV clinical trial in which the error could result in widespread exposure of an ineffective treatment. Allowing for a less stringent Type I error rate is critical when trying to transition therapies from the animal models to clinical practice since it identifies a greater pool of potential therapies that could undergo additional research in humans. Similarly, power (1 - β ) is of less practical importance in a single arm, non-comparative (or historically controlled) pilot study since the results would almost always require confirmation in a controlled trial setting. Shih et al [ 4 ] extend the deviations from traditional hypothesis-driven analyses to suggest preliminary investigations should focus on observing responses at the subject level rather than testing a treatment's estimated mean response. In the section that follows, we will relate these notions under the context of safety data analysis and provide interpretations that can be used for sample size considerations. Methods For ease of presentation, assume the pilot study will involve n independent patients for which the probability of the adverse event of interest is π , where 0 < π < 1. A 100 × (1 - α )% confidence interval is to be generated for π and an estimate of the sample size, n , is desired. Denote X as the number of patients sampled who experience the adverse event of interest. Then, the probability of observing x events in n subjects follows the usual binomial distribution. Namely, Denote π u as the upper limit of the exact one-sided 100 × (1 - α )% confidence interval for the unknown proportion, π [ 5 ]. Then π u is the value such that A special case of the binomial distribution occurs when zero events of interest are observed. In pilot studies with relatively few patients, this is of practical concern and warrants particular attention. When zero events are realized (i.e., x = 0), equation (1) reduces to (1 - π u ) n = α . Accordingly, the upper limit of a one-sided 100 × (1 - α )% confidence interval for π is π u = 1 - α 1/ n .     (2) The resulting 100 × (1 - α )% one-sided confidence interval is (0, 1 - α 1/ n ). Graphically, one can represent this interval on a plot of π against n as illustrated in Figure 1 for α = 0.05, 0.10 and 0.25. As the figure illustrates, for relatively small sample sizes, there is a large amount of uncertainty in the true value of π . It is critical to convey this uncertainty in the findings and to guard against inferring a potential treatment is harmless when no adverse effects of interest are observed with limited data. Louis [ 6 ] also cautioned the clinical observation of zero false negatives in the context of diagnostic testing stating that zero false negatives may generate unreasonable optimism regarding the rate, particularly for smaller sample sizes. Figure 1 Upper limit of the 100 × (1 - α )% one-sided confidence interval for the true underlying adverse event rate, π , for increasing sample sizes when zero events of interests are observed Furthermore, one can consider using (2) in other clinically important manners. For instance, an investigator may be planning a pilot study and want to know how large it would need to be to infer with 100 × (1 - α )% confidence that the true rate did not exceed a pre-specified π , say π 0 , given that zero adverse events were observed. Using (2), it follows that: To illustrate the utility of this solution, consider the following example. Ototoxicity is well documented with increasing doses of cisplatin, a platinum-containing antitumoral drug that is known to be effective against a variety of solid tumors. It is of clinical interest to identify augmentative therapies that can alleviate some of the cell death since up to 31% of patients receiving initial doses of 50 mg/m 2 cisplatin are expected to have irreversible hearing loss [ 7 , 8 ]. Therefore, it is desirable to rule out potential treatments not consistent with this rate of hearing loss before considering more conclusive testing. Using equation (3), we would conclude that the augmentative therapy has a hearing loss rate less than 0.31, at the 90% confidence level, if a total of 7 patients are recruited and all 7 do not experience ototoxicity. Therefore, an initial sample size of 7 patients would be sufficient to identify augmentative therapies, such as heat shock or antioxidant supplements, that demonstrate preliminary efficacy in humans. In the event one or more ototoxic events are observed, then the results in relationship to the historical rate (31% in this example) may not be statistically different. The results of several of these pilot studies could then be used to rank-order potential therapies thereby proving an empirically justified approach to therapy development. Conclusions In translational research, it is common to explore the adverse event profile of a new regimen. In this note, we illustrate how a simple expression has utility for the generation of confidence intervals when zero events are observed. A more comprehensive and methodological treatment of inference with zero events can be found in Carter and Woolson [ 9 ], and Winkler et al [ 10 ], which treats the issue from a Bayesian statistical viewpoint. This commentary and related works have implications as a practical finding for the interpretation of clinical trial safety data and offer clinicians advice on the range of adverse event rates that can be thought to be consistent with the observation of zero events. The presented formula offers more flexibility than the "rule of 3" approximation [ 11 ] since it allows for the specification of significance levels other than α = 0.05. The ability to choose the significance level might be important when designing or interpreting preliminary data obtained from a pilot study. In summary, small sample sizes and a focus on safety are often associated with translational research, and the statistical approaches to these studies may need to deviate from traditional, hypothesis-driven designs. Competing interests The author(s) declare that they have no competing interests. Authors' Contributions RC and RW contributed to the conceptualization, writing and editing of this manuscript.

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548295

"Expression pattern and regulation of genes differ between fibroblasts of adhesion and normal human (...TRUNCATED)

"Background Injury to the peritoneum during surgery is followed by a healing process that frequently(...TRUNCATED)

"Background Peritoneal adhesions resulting from surgical injury are often associated with pelvic pai(...TRUNCATED)

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535530

"A trial design for evaluation of empiric programming of implantable cardioverter defibrillators to (...TRUNCATED)

"The delivery of implantable cardioverter defibrillator (ICD) therapy is sophisticated and requires (...TRUNCATED)

"Background Over the past decade ICD implantation has become increasingly straightforward, yet ICD p(...TRUNCATED)

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545941

Differential regulation of Aβ42-induced neuronal C1q synthesis and microglial activation

"Expression of C1q, an early component of the classical complement pathway, has been shown to be ind(...TRUNCATED)

"Introduction Alzheimer's disease (AD) is the most common form of dementia in the elderly. Its main (...TRUNCATED)

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