Background Validation of the use of the lognormal model for predicting long-term survival rates using short-term follow-up data.
Background
Validation of the use of the lognormal model for predicting long-term survival rates using short-term follow-up data.Methods 907 cases of laryngeal cancer were treated from 1973–1977 by radiation and surgery (248), radiation alone (345), and surgery alone (314), in registries of Connecticut and Metropolitan Detroit of the SEER database, with known survival status up to 1999. Phase 1 of this study used the minimum chi-square test to assess the goodness of fit of the survival times of those who died with disease to a lognormal distribution. Phase 2 used the maximum likelihood method to estimate long-term survival rates using short-term follow-up data. In order to validate the lognormal model, the estimated long-term cancer-specific survival rates (CSSR) were compared with the values calculated by the Kaplan-Meier (KM) method using long-term data.
Methods
907 cases of laryngeal cancer were treated from 1973–1977 by radiation and surgery (248), radiation alone (345), and surgery alone (314), in registries of Connecticut and Metropolitan Detroit of the SEER database, with known survival status up to 1999. Phase 1 of this study used the minimum chi-square test to assess the goodness of fit of the survival times of those who died with disease to a lognormal distribution. Phase 2 used the maximum likelihood method to estimate long-term survival rates using short-term follow-up data. In order to validate the lognormal model, the estimated long-term cancer-specific survival rates (CSSR) were compared with the values calculated by the Kaplan-Meier (KM) method using long-term data.Results The 25-year CSSR were predicted to be 72%, 68% and 65% for treatments by radiation and surgery, by radiation alone, and by surgery alone respectively, using short-term follow-up data by the lognormal model. Corresponding results calculated by the KM method were: 72+/-3%, 68+/-3% and 66+/-4% respectively.
Results
The 25-year CSSR were predicted to be 72%, 68% and 65% for treatments by radiation and surgery, by radiation alone, and by surgery alone respectively, using short-term follow-up data by the lognormal model. Corresponding results calculated by the KM method were: 72+/-3%, 68+/-3% and 66+/-4% respectively.Conclusions The lognormal model was validated for the prediction of the long-term survival rates of laryngeal cancer patients treated by these different methods. The lognormal model may become a useful tool in research on outcomes.
Conclusions
The lognormal model was validated for the prediction of the long-term survival rates of laryngeal cancer patients treated by these different methods. The lognormal model may become a useful tool in research on outcomes.

Background Literature review [ 1 ] indicated that local control, laryngeal preservation, and survival rates of larynegeal cancer patients were similar after transoral laser resection, open partial laryngectomy, and radiotherapy. Open partial laryngectomy was reserved for patients with locally recurrent tumors. There are still some unanswered questions. Will radiation combined with surgery give a better result than single modality treatment alone? Will treatment results from the community centers follow published data from prestigious centers? After radiotherapy, radio-resistant cells theoretically may take some time to grow before recurrence. Short-term data may not reflect long-term local control and survival rates. We attempt to address these questions in the present study. The lognormal distribution is defined as the distribution of a random variable whose logarithm is normally distributed. The purpose of this study is to validate the use of the lognormal model [ 2 - 4 ] by estimating the long-term survival from short-term follow-up data of laryngeal cancer treated by three different treatment methods: radiation and surgery, radiation alone, and surgery alone. We have previously validated the application of the lognormal model for small cell lung cancer [ 5 ], glottic laryngeal cancer [ 6 ], prostate cancer [ 7 ] and breast cancer [ 8 ]. This model may be useful for randomized clinical trials because it allows the prediction of long-term survival rates several years earlier than is possible by using the standard actuarial life table/Kaplan-Meier method of calculation [ 9 ]. The idea that long-term survival rates can be estimated from short-term follow-up data is attractive because this method shortens the delay in further research to improve cancer treatment. The validation of the lognormal model has two phases. Phase 1 tests the goodness of fit to a lognormal distribution of the survival times of those cancer patients who died with disease. Phase 2 attempts to verify the lognormal model, which uses short-term follow-up data to predict long-term survival rates. These survival rates are then compared with values calculated by the Kaplan-Meier life table method from available long-term data. The second phase has been difficult to implement because of the general lack of large number of patients with sufficiently long follow-up information. With the SEER database [ 10 ], the validation of the lognormal model is now possible.
Background
Literature review [ 1 ] indicated that local control, laryngeal preservation, and survival rates of larynegeal cancer patients were similar after transoral laser resection, open partial laryngectomy, and radiotherapy. Open partial laryngectomy was reserved for patients with locally recurrent tumors. There are still some unanswered questions. Will radiation combined with surgery give a better result than single modality treatment alone? Will treatment results from the community centers follow published data from prestigious centers? After radiotherapy, radio-resistant cells theoretically may take some time to grow before recurrence. Short-term data may not reflect long-term local control and survival rates. We attempt to address these questions in the present study.The lognormal distribution is defined as the distribution of a random variable whose logarithm is normally distributed. The purpose of this study is to validate the use of the lognormal model [ 2 - 4 ] by estimating the long-term survival from short-term follow-up data of laryngeal cancer treated by three different treatment methods: radiation and surgery, radiation alone, and surgery alone. We have previously validated the application of the lognormal model for small cell lung cancer [ 5 ], glottic laryngeal cancer [ 6 ], prostate cancer [ 7 ] and breast cancer [ 8 ].This model may be useful for randomized clinical trials because it allows the prediction of long-term survival rates several years earlier than is possible by using the standard actuarial life table/Kaplan-Meier method of calculation [ 9 ].The idea that long-term survival rates can be estimated from short-term follow-up data is attractive because this method shortens the delay in further research to improve cancer treatment. The validation of the lognormal model has two phases. Phase 1 tests the goodness of fit to a lognormal distribution of the survival times of those cancer patients who died with disease.Phase 2 attempts to verify the lognormal model, which uses short-term follow-up data to predict long-term survival rates. These survival rates are then compared with values calculated by the Kaplan-Meier life table method from available long-term data. The second phase has been difficult to implement because of the general lack of large number of patients with sufficiently long follow-up information. With the SEER database [ 10 ], the validation of the lognormal model is now possible.Methods We analyzed a total of 907 cases of laryngeal cancer treated by three treatment methods: radiation and surgery (248 patients), radiation alone (345 patients), and surgery alone (314 patients), registered in two registries, Connecticut and Metropolitan Detroit, from 1973–1977 with known survival status up to 1999 extracted from the SEER database. Fourty-seven patients with unknown survival time and 165 patients with missing treatment methods were excluded. A table of the patient characteristics was listed in Table  1 . The cause-specific survival time was defined as the interval from the date of diagnosis to the date of death from laryngeal cancer or the date of last follow-up for censoring purposes, if the patient was alive and still being followed at the time of analysis. Patients who died of other causes were also censored at date of death. Phase 1 – Test of goodness of fit for lognormality Testing for lognormality was done separately for laryngeal cancer patients who died with disease (as distinct from those who died of an intercurrent disease) treated by: radiation and surgery, radiation alone, and surgery alone. For Phase 1, a minimum chi-square method was used to estimate the standard deviation S and mean M of the log 10  (survival time) of the distribution of patients who died of the cancer. The minimum chi-square test was run by a Microsoft Excel program. A range of S-values and M-values were tested to reduce the chi-square values to a minimum. In order to determine if the observed survival times for a given cohort are lognormally distributed, the results are given in terms of probability levels of significance P for the chi-square estimates which correspond to a minimization of chi-square and hence a maximization of P. The test statistic of the minimum chi-square test was minimized by varying the parameters and the P-value gave the significance of the test. The class intervals were in the powers of 2 in months of the survival time, such as 0–2, >2–4, >4–8, >8–16, and so on. The number of cases in each interval should not be less than 5. The null hypothesis being tested is H 0 : that there is no difference between the observed survival times and the expected survival times calculated from a lognormal distribution with a specified S and M. If P < 0.05 the null hypothesis is rejected. Phase 2 – Validation of the lognormal model A second computer program for the Phase 2 of the study was also run using Microsoft Excel to estimate the cured fraction C by using a maximum likelihood method described by Boag [ 4 ]. Using the lognormal model, the standard deviation (S) was fixed, and only the two remaining parameters, the mean (M) and the cured fraction among all patients (C), were kept floating. Multiple iterations converged to a stable solution for C. The cause-specific survival rate (CSSR) at time τ = [C + (1-C) × Q] × 100%, where Q = the integral of the lognormal distribution between the limits τ and infinity, τ = long-term survival time, C was calculated by the maximum likelihood method. The five-year cohort extended from 1973 to the end of 1977. Prediction of the long-term CSSR were made after one year follow-up, i.e. at the end of 1978, for the laryngeal cancer patients with three different treatments: radiation and surgery, radiation alone, and surgery alone. The predicted CSSR were then validated by comparing with the results calculated by the Kaplan-Meier method using the actual follow-up data available up to 1999. Log-rank tests were performed for the three different treatment groups over the whole time period in order to see if there was a difference in CSSR between treatments.
Methods
We analyzed a total of 907 cases of laryngeal cancer treated by three treatment methods: radiation and surgery (248 patients), radiation alone (345 patients), and surgery alone (314 patients), registered in two registries, Connecticut and Metropolitan Detroit, from 1973–1977 with known survival status up to 1999 extracted from the SEER database. Fourty-seven patients with unknown survival time and 165 patients with missing treatment methods were excluded. A table of the patient characteristics was listed in Table  1 .The cause-specific survival time was defined as the interval from the date of diagnosis to the date of death from laryngeal cancer or the date of last follow-up for censoring purposes, if the patient was alive and still being followed at the time of analysis. Patients who died of other causes were also censored at date of death.Phase 1 – Test of goodness of fit for lognormality Testing for lognormality was done separately for laryngeal cancer patients who died with disease (as distinct from those who died of an intercurrent disease) treated by: radiation and surgery, radiation alone, and surgery alone. For Phase 1, a minimum chi-square method was used to estimate the standard deviation S and mean M of the log 10  (survival time) of the distribution of patients who died of the cancer. The minimum chi-square test was run by a Microsoft Excel program. A range of S-values and M-values were tested to reduce the chi-square values to a minimum. In order to determine if the observed survival times for a given cohort are lognormally distributed, the results are given in terms of probability levels of significance P for the chi-square estimates which correspond to a minimization of chi-square and hence a maximization of P. The test statistic of the minimum chi-square test was minimized by varying the parameters and the P-value gave the significance of the test. The class intervals were in the powers of 2 in months of the survival time, such as 0–2, >2–4, >4–8, >8–16, and so on. The number of cases in each interval should not be less than 5. The null hypothesis being tested is H 0 : that there is no difference between the observed survival times and the expected survival times calculated from a lognormal distribution with a specified S and M. If P < 0.05 the null hypothesis is rejected.
Phase 1 – Test of goodness of fit for lognormality
Testing for lognormality was done separately for laryngeal cancer patients who died with disease (as distinct from those who died of an intercurrent disease) treated by: radiation and surgery, radiation alone, and surgery alone. For Phase 1, a minimum chi-square method was used to estimate the standard deviation S and mean M of the log 10  (survival time) of the distribution of patients who died of the cancer. The minimum chi-square test was run by a Microsoft Excel program. A range of S-values and M-values were tested to reduce the chi-square values to a minimum. In order to determine if the observed survival times for a given cohort are lognormally distributed, the results are given in terms of probability levels of significance P for the chi-square estimates which correspond to a minimization of chi-square and hence a maximization of P. The test statistic of the minimum chi-square test was minimized by varying the parameters and the P-value gave the significance of the test. The class intervals were in the powers of 2 in months of the survival time, such as 0–2, >2–4, >4–8, >8–16, and so on. The number of cases in each interval should not be less than 5. The null hypothesis being tested is H 0 : that there is no difference between the observed survival times and the expected survival times calculated from a lognormal distribution with a specified S and M. If P < 0.05 the null hypothesis is rejected.Phase 2 – Validation of the lognormal model A second computer program for the Phase 2 of the study was also run using Microsoft Excel to estimate the cured fraction C by using a maximum likelihood method described by Boag [ 4 ]. Using the lognormal model, the standard deviation (S) was fixed, and only the two remaining parameters, the mean (M) and the cured fraction among all patients (C), were kept floating. Multiple iterations converged to a stable solution for C. The cause-specific survival rate (CSSR) at time τ = [C + (1-C) × Q] × 100%, where Q = the integral of the lognormal distribution between the limits τ and infinity, τ = long-term survival time, C was calculated by the maximum likelihood method. The five-year cohort extended from 1973 to the end of 1977. Prediction of the long-term CSSR were made after one year follow-up, i.e. at the end of 1978, for the laryngeal cancer patients with three different treatments: radiation and surgery, radiation alone, and surgery alone. The predicted CSSR were then validated by comparing with the results calculated by the Kaplan-Meier method using the actual follow-up data available up to 1999. Log-rank tests were performed for the three different treatment groups over the whole time period in order to see if there was a difference in CSSR between treatments.
Phase 2 – Validation of the lognormal model
A second computer program for the Phase 2 of the study was also run using Microsoft Excel to estimate the cured fraction C by using a maximum likelihood method described by Boag [ 4 ]. Using the lognormal model, the standard deviation (S) was fixed, and only the two remaining parameters, the mean (M) and the cured fraction among all patients (C), were kept floating. Multiple iterations converged to a stable solution for C. The cause-specific survival rate (CSSR) at time τ = [C + (1-C) × Q] × 100%, where Q = the integral of the lognormal distribution between the limits τ and infinity, τ = long-term survival time, C was calculated by the maximum likelihood method.The five-year cohort extended from 1973 to the end of 1977. Prediction of the long-term CSSR were made after one year follow-up, i.e. at the end of 1978, for the laryngeal cancer patients with three different treatments: radiation and surgery, radiation alone, and surgery alone. The predicted CSSR were then validated by comparing with the results calculated by the Kaplan-Meier method using the actual follow-up data available up to 1999.Log-rank tests were performed for the three different treatment groups over the whole time period in order to see if there was a difference in CSSR between treatments.Results Using data from 1973–1977, the minimum chi-square tests verified that the survival times of the patients who died of laryngeal cancer and who were treated by three treatments followed three different lognormal distributions. At minimum chi-square, the S and M values, the numbers of patients N and the P values of the minimum chi-square tests for the three different treatments are listed in Table  2 . As in a prospective trial at interim analysis, Phase 2 was performed for each of the five-year cohort periods after one year of follow-up. Phase 2 predicted, using the maximum likelihood method, the long-term 10-, 15-, 20-, and 25-year CSSR. An S value was selected for the maximum likelihood method, so that the prediction curve obtained by the lognormal model would best fit the Kaplan-Meier graph  at one year  after the five-year cohort period. Hence at the time of prediction by the lognormal model, the long-term data were not known yet. Table  3  lists the results for each treatment arm and their comparison with calculation obtained by the Kaplan-Meier method. The predictions were validated by comparing them with the Kaplan-Meier calculation using available data up to 1999. The 25-year CSSR were predicted by the lognormal model after only short term follow-up to be 72%, 68% and 65% for treatments by radiation and surgery, radiation alone, and surgery alone respectively. The 25-year CSSR were found to be 72+/-3% (one standard error), 68+/-3% and 66+/-4% respectively by Kaplan-Meier method. Long-term survival rates at other years, e.g. 10-, 15-, and 20-year CSSR, were all within one standard error compared with the Kaplan-Meier calculations. There were no statistically significant differences between the CSSR of the three different treatment groups for all stages combined (p = 0.35 by log-rank test for the three treatments). Figures  1 ,  2  and  3  show the comparisons of the three different treatments: radiation and surgery, radiation alone, and surgery alone respectively for laryngeal cancer in the Kaplan-Meier graph at the year 1999 compared with the lognormal model prediction curve which could be obtained at 1978. The SEER database provides localized and regional disease stages, instead of T1N0 or T2N0 stages, etc. The lognormal model prediction of the 25-year CSSR for 454 patients with localized stage disease were 84%, 75%, and 80% for treatments by radiation and surgery, radiation alone, and surgery alone respectively. The 25-year CSSR calculated by the Kaplan-Meier method were 83+/-4%, 75+/-4%, and 77+/-6% respectively (p = 0.08, log-rank test for the three treatments). For 286 patients with regional stage disease, the 25-year CSSR were 62%, 58%, and 52% respectively, and compared with Kaplan-Meier method were 63+/-6%, 58+/-7%, and 46+/-6% respectively (p = 0.76, log-rank test for the three treatments).
Results
Using data from 1973–1977, the minimum chi-square tests verified that the survival times of the patients who died of laryngeal cancer and who were treated by three treatments followed three different lognormal distributions. At minimum chi-square, the S and M values, the numbers of patients N and the P values of the minimum chi-square tests for the three different treatments are listed in Table  2 .As in a prospective trial at interim analysis, Phase 2 was performed for each of the five-year cohort periods after one year of follow-up. Phase 2 predicted, using the maximum likelihood method, the long-term 10-, 15-, 20-, and 25-year CSSR. An S value was selected for the maximum likelihood method, so that the prediction curve obtained by the lognormal model would best fit the Kaplan-Meier graph  at one year  after the five-year cohort period. Hence at the time of prediction by the lognormal model, the long-term data were not known yet. Table  3  lists the results for each treatment arm and their comparison with calculation obtained by the Kaplan-Meier method. The predictions were validated by comparing them with the Kaplan-Meier calculation using available data up to 1999.at one yearThe 25-year CSSR were predicted by the lognormal model after only short term follow-up to be 72%, 68% and 65% for treatments by radiation and surgery, radiation alone, and surgery alone respectively. The 25-year CSSR were found to be 72+/-3% (one standard error), 68+/-3% and 66+/-4% respectively by Kaplan-Meier method. Long-term survival rates at other years, e.g. 10-, 15-, and 20-year CSSR, were all within one standard error compared with the Kaplan-Meier calculations.There were no statistically significant differences between the CSSR of the three different treatment groups for all stages combined (p = 0.35 by log-rank test for the three treatments).Figures  1 ,  2  and  3  show the comparisons of the three different treatments: radiation and surgery, radiation alone, and surgery alone respectively for laryngeal cancer in the Kaplan-Meier graph at the year 1999 compared with the lognormal model prediction curve which could be obtained at 1978.The SEER database provides localized and regional disease stages, instead of T1N0 or T2N0 stages, etc. The lognormal model prediction of the 25-year CSSR for 454 patients with localized stage disease were 84%, 75%, and 80% for treatments by radiation and surgery, radiation alone, and surgery alone respectively. The 25-year CSSR calculated by the Kaplan-Meier method were 83+/-4%, 75+/-4%, and 77+/-6% respectively (p = 0.08, log-rank test for the three treatments). For 286 patients with regional stage disease, the 25-year CSSR were 62%, 58%, and 52% respectively, and compared with Kaplan-Meier method were 63+/-6%, 58+/-7%, and 46+/-6% respectively (p = 0.76, log-rank test for the three treatments).Discussion The current study demonstrates the application of the lognormal model to a population based study. The lognormal model is being applied in a manner that can be applied to prospective trials in practice. Our previous publication about the application of the lognormal model was for different stages of laryngeal cancer patients treated by one radiation oncologist, Dr. M. Lederman in the Royal Marsden Hospital, United Kingdom [ 6 ]. The current study shows that the lognormal model is applicable in different scenarios. Detroit and Connecticut registries were chosen because they have the earliest data starting from 1973 and they include a large population of both white and black patients. Generally cause-specific death rates underestimate the mortality associated with a diagnosis of the specific cancer, because some patients died of other causes[ 11 ]. Gamel  et al .[ 12 ] found that the follow-up time should be one standard deviation beyond the mean of the survival time so as to obtain stable results. In the current study, five-year cohorts extended from 1973 to the end of 1977 were used. Stable results for prediction of the long-term CSSR were obtained after one year of follow-up. In this study, for localized stage disease 5-year CSSR were 91%, 83%, 95% for treatments by radiation and surgery, radiation alone, and surgery alone respectively with Kaplan-Meier method. Jones  et al .[ 13 ] found that the 5-year tumor-specific survival for those treated by radiation was 87% and for those treated by surgery was 77% (p = 0.1022). Both radiation and surgery are equally effective for treating early stage laryngeal carcinoma. In the current study, there were marginal difference in the results of the three treatments for localized stage patients. Jorgensen  et al .[ 14 ] found that among patients with T1 glottic carcinomas the 5-year locoregional control rate was 88%, i.e. 88% of patients were cured by radiotherapy alone. The 5-year disease-specific survival (DSS) was 99%, i.e. salvage surgery added approximately 11% to the survival of T1 glottic patients. Only 4% (12/312) of T1 glottic patients underwent laryngectomies. Locoregional control among T2 glottic patients was 67% and the DSS 88%, and 18% (41/233) of patients underwent laryngectomies. The corresponding results among T3 glottic patients were 30% and 59%, about 50% of patients underwent laryngectomies. For T3 glottic carcinomas, initial surgery did not produce better survival rates. Franchin  et al .[ 15 ] studied T1 and T2 glottic carcinoma. The 5-year and 10-year overall survival rates were 83% and 63.5%, respectively. The overall 10-year local control rate for patients with T1 and T2 glottic carcinoma was 89%. In this study, the treatment results were marginally different for localized stage (p = 0.08, log-rank test for the three treatments) and the treatment results were similar for regional stage (p = 0.76, log-rank test for the three treatments). For localized stage combination radiation and surgery may have more certainty of disease control and hence long-term survival benefit. The treatment results were similar for regional stage because the patients were diagnosed late and disease control may be more difficult. The predicted survivals were within one standard error of the Kaplan-Meier estimations for both localized and regional stages. It shows that the prediction method can work for both good and poor prognosis cases. The practical value of this study is that this lognormal model may be used for prediction of the results of prospective trials earlier than the Kaplan Meier method. This lognormal model may become a useful tool in research about outcomes. Use of this lognormal model could result in more rapid advances in cancer treatment and have the potential benefit of a reduction of cost in cancer research.
Discussion
The current study demonstrates the application of the lognormal model to a population based study. The lognormal model is being applied in a manner that can be applied to prospective trials in practice. Our previous publication about the application of the lognormal model was for different stages of laryngeal cancer patients treated by one radiation oncologist, Dr. M. Lederman in the Royal Marsden Hospital, United Kingdom [ 6 ]. The current study shows that the lognormal model is applicable in different scenarios.Detroit and Connecticut registries were chosen because they have the earliest data starting from 1973 and they include a large population of both white and black patients. Generally cause-specific death rates underestimate the mortality associated with a diagnosis of the specific cancer, because some patients died of other causes[ 11 ]. Gamel  et al .[ 12 ] found that the follow-up time should be one standard deviation beyond the mean of the survival time so as to obtain stable results. In the current study, five-year cohorts extended from 1973 to the end of 1977 were used. Stable results for prediction of the long-term CSSR were obtained after one year of follow-up.et alIn this study, for localized stage disease 5-year CSSR were 91%, 83%, 95% for treatments by radiation and surgery, radiation alone, and surgery alone respectively with Kaplan-Meier method.Jones  et al .[ 13 ] found that the 5-year tumor-specific survival for those treated by radiation was 87% and for those treated by surgery was 77% (p = 0.1022). Both radiation and surgery are equally effective for treating early stage laryngeal carcinoma. In the current study, there were marginal difference in the results of the three treatments for localized stage patients.et alJorgensen  et al .[ 14 ] found that among patients with T1 glottic carcinomas the 5-year locoregional control rate was 88%, i.e. 88% of patients were cured by radiotherapy alone. The 5-year disease-specific survival (DSS) was 99%, i.e. salvage surgery added approximately 11% to the survival of T1 glottic patients. Only 4% (12/312) of T1 glottic patients underwent laryngectomies. Locoregional control among T2 glottic patients was 67% and the DSS 88%, and 18% (41/233) of patients underwent laryngectomies. The corresponding results among T3 glottic patients were 30% and 59%, about 50% of patients underwent laryngectomies. For T3 glottic carcinomas, initial surgery did not produce better survival rates.et alFranchin  et al .[ 15 ] studied T1 and T2 glottic carcinoma. The 5-year and 10-year overall survival rates were 83% and 63.5%, respectively. The overall 10-year local control rate for patients with T1 and T2 glottic carcinoma was 89%.et alIn this study, the treatment results were marginally different for localized stage (p = 0.08, log-rank test for the three treatments) and the treatment results were similar for regional stage (p = 0.76, log-rank test for the three treatments). For localized stage combination radiation and surgery may have more certainty of disease control and hence long-term survival benefit. The treatment results were similar for regional stage because the patients were diagnosed late and disease control may be more difficult. The predicted survivals were within one standard error of the Kaplan-Meier estimations for both localized and regional stages. It shows that the prediction method can work for both good and poor prognosis cases.The practical value of this study is that this lognormal model may be used for prediction of the results of prospective trials earlier than the Kaplan Meier method. This lognormal model may become a useful tool in research about outcomes. Use of this lognormal model could result in more rapid advances in cancer treatment and have the potential benefit of a reduction of cost in cancer research.Competing interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors' constributions PT: Data analysis and writing of the manuscript. EY, RS: Critical appraisal of the manuscript. JT: Data analysis and critical appraisal of the manuscript.
Authors' constributions
PT: Data analysis and writing of the manuscript.EY, RS: Critical appraisal of the manuscript.JT: Data analysis and critical appraisal of the manuscript.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Background Diabetes requires significant alterations to lifestyle and completion of self management tasks to obtain good control of disease. The objective of this study was to determine if patient trust is associated with reduced difficulty and hassles in altering lifestyle and completing self care tasks.
Background
Diabetes requires significant alterations to lifestyle and completion of self management tasks to obtain good control of disease. The objective of this study was to determine if patient trust is associated with reduced difficulty and hassles in altering lifestyle and completing self care tasks.Methods A cross-sectional telephone survey and medical record review was performed to measure patient trust and difficulty in completing diabetes tasks among 320 medically underserved patients attending diabetes programs in rural North Carolina, USA. Diabetes tasks were measured three ways: perceived hassles of diabetic care activities, difficulty in completing diabetes-related care activities, and a global assessment of overall ability to complete diabetes care activities. The association of patient trust with self-management was examined after controlling for patient demographics, physical functioning, mental health and co-morbidities.
Methods
A cross-sectional telephone survey and medical record review was performed to measure patient trust and difficulty in completing diabetes tasks among 320 medically underserved patients attending diabetes programs in rural North Carolina, USA. Diabetes tasks were measured three ways: perceived hassles of diabetic care activities, difficulty in completing diabetes-related care activities, and a global assessment of overall ability to complete diabetes care activities. The association of patient trust with self-management was examined after controlling for patient demographics, physical functioning, mental health and co-morbidities.Results Level of patient trust was high (median 22, possible max 25). Higher trust levels were associated with lower levels of hassles (p = 0.006) and lower difficulty in completing care activities (p = 0.001). Patients with higher trust had better global assessments of overall ability to complete diabetes care activities (p < 0.0001).
Results
Level of patient trust was high (median 22, possible max 25). Higher trust levels were associated with lower levels of hassles (p = 0.006) and lower difficulty in completing care activities (p = 0.001). Patients with higher trust had better global assessments of overall ability to complete diabetes care activities (p < 0.0001).Conclusion Higher patient trust in physicians is associated with reduced difficulty in completing disease specific tasks by patients. Further studies are needed to determine the causal relationship of this association, the effect of trust on other outcomes, and the potential modifiability of trust
Conclusion
Higher patient trust in physicians is associated with reduced difficulty in completing disease specific tasks by patients. Further studies are needed to determine the causal relationship of this association, the effect of trust on other outcomes, and the potential modifiability of trust

Background Diabetes mellitus is a disease associated with significant morbidity and mortality [ 1 , 2 ]. Patients with diabetes have higher rates of coronary artery disease, retinopathy, neuropathy and nephropathy [ 1 ]. Many of these complications can be prevented with appropriate medical care[ 3 , 4 ] This care, however, often requires significant alterations in lifestyle and strict adherence to self-care tasks, such as checking blood sugars, and taking medications by the patient [ 5 ]. Previous research has shown that patients with diabetes and other diseases often have difficulty in adopting lifestyle changes and completing self-care tasks[ 6 ]. The cause of this poor adherence is multi-factorial and includes patient characteristics such as educational level, [ 7 - 9 ] treatment regimen characteristics, [ 10 ] and characteristics of the clinical setting [ 11 , 12 ]. Patient's perception on the intrusiveness of treatment regimens and their perceived self-efficacy in completing the task have also been demonstrated to affect adherence to diabetes treatment [ 13 , 14 ]. Aspects of the patient-physician relationship such as communication and empathy have been shown to be important to patient's adherence [ 15 - 18 ] and ability to complete self-care tasks [ 7 , 19 , 20 ]. Patient trust is another component essential to the doctor-patient relationship [ 21 ]. Defined variously as a set of beliefs or expectations that the doctor will act in the patient's best interest, or as a reassuring feeling of confidence in the doctor, [ 22 , 23 ] trust provides the foundation for many of the other aspects of the relationship such as communication and empathy [ 23 ]. Despite the significance of trust, there has been relatively little research on this aspect of the doctor-patient relationship. Most studies examining trust have focused on characteristics that predict trust levels [ 24 - 28 ] or the influence insurance characteristics have on trust [ 29 , 30 ]. Limited studies have been done examining the relationship between patient trust and medical outcomes. Safran et al found that adherence to physician recommended lifestyle changes was significantly associated with patient trust levels [ 31 ]. Higher levels of patient trust have also been associated with lower hemoglobin A1c levels in patients with type 2 diabetes [ 32 ]. None have measured trust and its effect on patient's ability in completing self-care tasks. Given the underlying importance of trust in the doctor-patient relationship and the effect of the doctor-patient relationship on adherence and self-care, we hypothesized that higher levels of patient trust in physicians may be associated with reduced difficulty of self-care tasks. To explore this hypothesis, we conducted a survey of patients with diabetes to determine if level of trust is associated with the outcomes of difficulty and hassles in completing self-care tasks.
Background
Diabetes mellitus is a disease associated with significant morbidity and mortality [ 1 , 2 ]. Patients with diabetes have higher rates of coronary artery disease, retinopathy, neuropathy and nephropathy [ 1 ]. Many of these complications can be prevented with appropriate medical care[ 3 , 4 ] This care, however, often requires significant alterations in lifestyle and strict adherence to self-care tasks, such as checking blood sugars, and taking medications by the patient [ 5 ]. Previous research has shown that patients with diabetes and other diseases often have difficulty in adopting lifestyle changes and completing self-care tasks[ 6 ]. The cause of this poor adherence is multi-factorial and includes patient characteristics such as educational level, [ 7 - 9 ] treatment regimen characteristics, [ 10 ] and characteristics of the clinical setting [ 11 , 12 ]. Patient's perception on the intrusiveness of treatment regimens and their perceived self-efficacy in completing the task have also been demonstrated to affect adherence to diabetes treatment [ 13 , 14 ].Aspects of the patient-physician relationship such as communication and empathy have been shown to be important to patient's adherence [ 15 - 18 ] and ability to complete self-care tasks [ 7 , 19 , 20 ]. Patient trust is another component essential to the doctor-patient relationship [ 21 ]. Defined variously as a set of beliefs or expectations that the doctor will act in the patient's best interest, or as a reassuring feeling of confidence in the doctor, [ 22 , 23 ] trust provides the foundation for many of the other aspects of the relationship such as communication and empathy [ 23 ]. Despite the significance of trust, there has been relatively little research on this aspect of the doctor-patient relationship. Most studies examining trust have focused on characteristics that predict trust levels [ 24 - 28 ] or the influence insurance characteristics have on trust [ 29 , 30 ]. Limited studies have been done examining the relationship between patient trust and medical outcomes. Safran et al found that adherence to physician recommended lifestyle changes was significantly associated with patient trust levels [ 31 ]. Higher levels of patient trust have also been associated with lower hemoglobin A1c levels in patients with type 2 diabetes [ 32 ]. None have measured trust and its effect on patient's ability in completing self-care tasks.Given the underlying importance of trust in the doctor-patient relationship and the effect of the doctor-patient relationship on adherence and self-care, we hypothesized that higher levels of patient trust in physicians may be associated with reduced difficulty of self-care tasks. To explore this hypothesis, we conducted a survey of patients with diabetes to determine if level of trust is associated with the outcomes of difficulty and hassles in completing self-care tasks.Methods Data Source Project IDEAL (Improving Diabetes Education, Access to Care and Living) was an initiative to increase the quality of care and quality of life for underserved patients with diabetes in North Carolina. Full details of the study have been published elsewhere [ 33 ]. Briefly, fourteen clinical sites across North Carolina were funded by the Kate B. Reynolds Charitable Trust to establish programs to improve the quality of care provided to underserved patients with diabetes. Each developed its own unique diabetes intervention. Evaluation of the initiative was conducted by the Wake Forest University School of Medicine Department of Public Health Sciences using both quality of care and quality of life indicators. To assess the effectiveness of the initiative, a random sample of patients from each program was selected for inclusion at each point in time (1998 pre-initiative and 2001 post-initiative). Quality of care was assessed through a chart review of randomly selected patient. Diabetes Quality of Care (DQIP) [ 34 ] and Health Plan Employer Data and Information Set (HEDIS) [ 35 ] measures were used as standards for quality of care. Quality of life was assessed through a telephone survey of the same patients, allowing both quality of care and quality of life indicators to be linked. The telephone survey was composed of portions of the Medical Outcomes Study Short Form 36 (SF-36) [ 36 ], the DQIP Patient Measures [ 34 ] and Diabetes-39 Health Related Quality of Life instruments [ 37 ]. To determine the relationship between self-management and level of patient trust, an established trust instrument was added to the post intervention survey. Trust Instrument To measure patient trust, we used the Wake Forest University Trust Scale. This scale has been tested in multiple populations and found to be reliable with good construct validity [ 38 , 39 ]. We used the 5 question institutional trust scale as many of the patients were cared for by multiple physicians. This trust measure is a summation of five items that are adapted from a longer trust instrument to ask the patient's view of doctors in general [ 39 ]. The items are: "The doctors at 'this clinic' will do whatever it takes to get patients all the care they need;" "The doctors at 'this clinic' are extremely thorough and careful;" "The medical skills of the doctors here are not as good as they should be;" "You have no worries about putting your life in the hands of the doctors at 'this clinic;" "All in all, you trust the doctors at 'this clinic' completely." A five point scale was used with answers ranging from strongly disagree to strongly agree (1–5), with a higher score indicating a higher level of trust. Self-Management Measures Patient self-care measures were taken from the telephone survey. The methods for this survey have been described elsewhere [ 40 ]. We examined 3 measures: patients' perceived hassle associated with self-care tasks (hassles), patients' difficulty in completing recommended care activities (difficulty), and a global assessment of their ability to care for their disease (care). Briefly, hassle was measured with a 7-item scale that asked patients to rate how much of a problem or hassle it has been to complete diabetes-related tasks over the past 4 weeks. Items in the scale included remembering to take medication and test blood sugar, making meal plans, avoiding particular foods, having to keep a care schedule in mind, organizing the daily routine around a medical care activity, and total time spent in managing their disease. This scale is part of the DQIP instrument [ 34 ]. All questions were measured using a 5-point scale from "no problem" (1) to "a major hassle" (5) with a higher value equalling more hassle. Respondents also had the choice of indicating the question "does not apply". This resulted in a variable number of questions being answered for each patient. To account for this variation, we calculated an average response for each patient by summing the responses and dividing by the number of questions answered by that patient. Our second outcome, difficulty in completing care activities, was measured in a 5-item scale also taken from the DQIP measures [ 34 ]. This scale asked patients about the difficulty they had in completing 5 specific care activities exactly as their doctor suggested. Activities included 1) taking their medication as prescribed, 2) exercising regularly, 3) following their diet, 4) checking their blood sugar, and 5) checking their feet for wounds or sores. Answers were scored on a 5-point scale from "not at all difficult" (1) to "extremely difficult" (5) with an option for "does not apply". Again, to account for variation in the number of questions answered, we calculated an average score, excluding measures that did not apply. The final adherence measure we examined was a global measure of care. This single item asked patients to rate how they "...are at taking care of their diabetes". Possible responses on a 5-point scale ranged from "I stay right on top of it at all times" (1) to "I let it slip way too much" (5). Independent Variables We considered several modifying factors that might influence the relationship between trust and self-care measures. These factors were selected either because they have been shown to influence patient self-care or because they have been used in previous studies on patient trust and outcomes. Modifying factors were obtained from either the survey or through the chart review. Patient's age, gender, race (white, non-white) and insurance (any, none) were obtained from chart review. A co-morbidity score (none, 1–2, greater than 2) was generated based on the number of additional diabetes-related diseases (hypertension, coronary artery disease, non-traumatic amputation, nephropathy, neuropathy, peripheral vascular disease and smoking) identified during chart review. The Medical Outcomes Study Short Form 36 (MOS SF-36) [ 36 ] subscales for physical and mental health were included as part of the patient interview and were used to assess health-related quality of life. The length of the relationship with the health care professional and the number of visits to a provider for diabetes care were obtained from the telephone survey. The use of insulin by the patient was collected from the chart review and was included in the analyses. Imputed values were created for missing measures and analyses were run with and without imputed variables to detect the effect of these variables. Statistical Analysis To determine the association of patient trust and our outcomes of patient hassles, difficulty in completing care activities and global assessment, we used Generalized Estimating Equation[ 41 ] methods to account for clustering of patients within IDEAL program sites. A regression analysis using the xtgee command in STATA/SE 7.0 (STATA Corporation, College Station, TX) was performed for each outcome of interest. Models adjusted for age, gender, race, insurance status, number of co-morbidities, insulin use, number of visits for diabetes in the last year, length of relationship with the provider, physical functioning, and mental health.
Methods
Data Source Project IDEAL (Improving Diabetes Education, Access to Care and Living) was an initiative to increase the quality of care and quality of life for underserved patients with diabetes in North Carolina. Full details of the study have been published elsewhere [ 33 ]. Briefly, fourteen clinical sites across North Carolina were funded by the Kate B. Reynolds Charitable Trust to establish programs to improve the quality of care provided to underserved patients with diabetes. Each developed its own unique diabetes intervention. Evaluation of the initiative was conducted by the Wake Forest University School of Medicine Department of Public Health Sciences using both quality of care and quality of life indicators. To assess the effectiveness of the initiative, a random sample of patients from each program was selected for inclusion at each point in time (1998 pre-initiative and 2001 post-initiative). Quality of care was assessed through a chart review of randomly selected patient. Diabetes Quality of Care (DQIP) [ 34 ] and Health Plan Employer Data and Information Set (HEDIS) [ 35 ] measures were used as standards for quality of care. Quality of life was assessed through a telephone survey of the same patients, allowing both quality of care and quality of life indicators to be linked. The telephone survey was composed of portions of the Medical Outcomes Study Short Form 36 (SF-36) [ 36 ], the DQIP Patient Measures [ 34 ] and Diabetes-39 Health Related Quality of Life instruments [ 37 ]. To determine the relationship between self-management and level of patient trust, an established trust instrument was added to the post intervention survey.
Data Source
Project IDEAL (Improving Diabetes Education, Access to Care and Living) was an initiative to increase the quality of care and quality of life for underserved patients with diabetes in North Carolina. Full details of the study have been published elsewhere [ 33 ]. Briefly, fourteen clinical sites across North Carolina were funded by the Kate B. Reynolds Charitable Trust to establish programs to improve the quality of care provided to underserved patients with diabetes. Each developed its own unique diabetes intervention. Evaluation of the initiative was conducted by the Wake Forest University School of Medicine Department of Public Health Sciences using both quality of care and quality of life indicators. To assess the effectiveness of the initiative, a random sample of patients from each program was selected for inclusion at each point in time (1998 pre-initiative and 2001 post-initiative). Quality of care was assessed through a chart review of randomly selected patient. Diabetes Quality of Care (DQIP) [ 34 ] and Health Plan Employer Data and Information Set (HEDIS) [ 35 ] measures were used as standards for quality of care. Quality of life was assessed through a telephone survey of the same patients, allowing both quality of care and quality of life indicators to be linked. The telephone survey was composed of portions of the Medical Outcomes Study Short Form 36 (SF-36) [ 36 ], the DQIP Patient Measures [ 34 ] and Diabetes-39 Health Related Quality of Life instruments [ 37 ]. To determine the relationship between self-management and level of patient trust, an established trust instrument was added to the post intervention survey.Trust Instrument To measure patient trust, we used the Wake Forest University Trust Scale. This scale has been tested in multiple populations and found to be reliable with good construct validity [ 38 , 39 ]. We used the 5 question institutional trust scale as many of the patients were cared for by multiple physicians. This trust measure is a summation of five items that are adapted from a longer trust instrument to ask the patient's view of doctors in general [ 39 ]. The items are: "The doctors at 'this clinic' will do whatever it takes to get patients all the care they need;" "The doctors at 'this clinic' are extremely thorough and careful;" "The medical skills of the doctors here are not as good as they should be;" "You have no worries about putting your life in the hands of the doctors at 'this clinic;" "All in all, you trust the doctors at 'this clinic' completely." A five point scale was used with answers ranging from strongly disagree to strongly agree (1–5), with a higher score indicating a higher level of trust.
Trust Instrument
To measure patient trust, we used the Wake Forest University Trust Scale. This scale has been tested in multiple populations and found to be reliable with good construct validity [ 38 , 39 ]. We used the 5 question institutional trust scale as many of the patients were cared for by multiple physicians. This trust measure is a summation of five items that are adapted from a longer trust instrument to ask the patient's view of doctors in general [ 39 ]. The items are: "The doctors at 'this clinic' will do whatever it takes to get patients all the care they need;" "The doctors at 'this clinic' are extremely thorough and careful;" "The medical skills of the doctors here are not as good as they should be;" "You have no worries about putting your life in the hands of the doctors at 'this clinic;" "All in all, you trust the doctors at 'this clinic' completely." A five point scale was used with answers ranging from strongly disagree to strongly agree (1–5), with a higher score indicating a higher level of trust.Self-Management Measures Patient self-care measures were taken from the telephone survey. The methods for this survey have been described elsewhere [ 40 ]. We examined 3 measures: patients' perceived hassle associated with self-care tasks (hassles), patients' difficulty in completing recommended care activities (difficulty), and a global assessment of their ability to care for their disease (care). Briefly, hassle was measured with a 7-item scale that asked patients to rate how much of a problem or hassle it has been to complete diabetes-related tasks over the past 4 weeks. Items in the scale included remembering to take medication and test blood sugar, making meal plans, avoiding particular foods, having to keep a care schedule in mind, organizing the daily routine around a medical care activity, and total time spent in managing their disease. This scale is part of the DQIP instrument [ 34 ]. All questions were measured using a 5-point scale from "no problem" (1) to "a major hassle" (5) with a higher value equalling more hassle. Respondents also had the choice of indicating the question "does not apply". This resulted in a variable number of questions being answered for each patient. To account for this variation, we calculated an average response for each patient by summing the responses and dividing by the number of questions answered by that patient. Our second outcome, difficulty in completing care activities, was measured in a 5-item scale also taken from the DQIP measures [ 34 ]. This scale asked patients about the difficulty they had in completing 5 specific care activities exactly as their doctor suggested. Activities included 1) taking their medication as prescribed, 2) exercising regularly, 3) following their diet, 4) checking their blood sugar, and 5) checking their feet for wounds or sores. Answers were scored on a 5-point scale from "not at all difficult" (1) to "extremely difficult" (5) with an option for "does not apply". Again, to account for variation in the number of questions answered, we calculated an average score, excluding measures that did not apply. The final adherence measure we examined was a global measure of care. This single item asked patients to rate how they "...are at taking care of their diabetes". Possible responses on a 5-point scale ranged from "I stay right on top of it at all times" (1) to "I let it slip way too much" (5).
Self-Management Measures
Patient self-care measures were taken from the telephone survey. The methods for this survey have been described elsewhere [ 40 ]. We examined 3 measures: patients' perceived hassle associated with self-care tasks (hassles), patients' difficulty in completing recommended care activities (difficulty), and a global assessment of their ability to care for their disease (care). Briefly, hassle was measured with a 7-item scale that asked patients to rate how much of a problem or hassle it has been to complete diabetes-related tasks over the past 4 weeks. Items in the scale included remembering to take medication and test blood sugar, making meal plans, avoiding particular foods, having to keep a care schedule in mind, organizing the daily routine around a medical care activity, and total time spent in managing their disease. This scale is part of the DQIP instrument [ 34 ]. All questions were measured using a 5-point scale from "no problem" (1) to "a major hassle" (5) with a higher value equalling more hassle. Respondents also had the choice of indicating the question "does not apply". This resulted in a variable number of questions being answered for each patient. To account for this variation, we calculated an average response for each patient by summing the responses and dividing by the number of questions answered by that patient.Our second outcome, difficulty in completing care activities, was measured in a 5-item scale also taken from the DQIP measures [ 34 ]. This scale asked patients about the difficulty they had in completing 5 specific care activities exactly as their doctor suggested. Activities included 1) taking their medication as prescribed, 2) exercising regularly, 3) following their diet, 4) checking their blood sugar, and 5) checking their feet for wounds or sores. Answers were scored on a 5-point scale from "not at all difficult" (1) to "extremely difficult" (5) with an option for "does not apply". Again, to account for variation in the number of questions answered, we calculated an average score, excluding measures that did not apply.The final adherence measure we examined was a global measure of care. This single item asked patients to rate how they "...are at taking care of their diabetes". Possible responses on a 5-point scale ranged from "I stay right on top of it at all times" (1) to "I let it slip way too much" (5).Independent Variables We considered several modifying factors that might influence the relationship between trust and self-care measures. These factors were selected either because they have been shown to influence patient self-care or because they have been used in previous studies on patient trust and outcomes. Modifying factors were obtained from either the survey or through the chart review. Patient's age, gender, race (white, non-white) and insurance (any, none) were obtained from chart review. A co-morbidity score (none, 1–2, greater than 2) was generated based on the number of additional diabetes-related diseases (hypertension, coronary artery disease, non-traumatic amputation, nephropathy, neuropathy, peripheral vascular disease and smoking) identified during chart review. The Medical Outcomes Study Short Form 36 (MOS SF-36) [ 36 ] subscales for physical and mental health were included as part of the patient interview and were used to assess health-related quality of life. The length of the relationship with the health care professional and the number of visits to a provider for diabetes care were obtained from the telephone survey. The use of insulin by the patient was collected from the chart review and was included in the analyses. Imputed values were created for missing measures and analyses were run with and without imputed variables to detect the effect of these variables.
Independent Variables
We considered several modifying factors that might influence the relationship between trust and self-care measures. These factors were selected either because they have been shown to influence patient self-care or because they have been used in previous studies on patient trust and outcomes. Modifying factors were obtained from either the survey or through the chart review. Patient's age, gender, race (white, non-white) and insurance (any, none) were obtained from chart review. A co-morbidity score (none, 1–2, greater than 2) was generated based on the number of additional diabetes-related diseases (hypertension, coronary artery disease, non-traumatic amputation, nephropathy, neuropathy, peripheral vascular disease and smoking) identified during chart review. The Medical Outcomes Study Short Form 36 (MOS SF-36) [ 36 ] subscales for physical and mental health were included as part of the patient interview and were used to assess health-related quality of life. The length of the relationship with the health care professional and the number of visits to a provider for diabetes care were obtained from the telephone survey. The use of insulin by the patient was collected from the chart review and was included in the analyses. Imputed values were created for missing measures and analyses were run with and without imputed variables to detect the effect of these variables.Statistical Analysis To determine the association of patient trust and our outcomes of patient hassles, difficulty in completing care activities and global assessment, we used Generalized Estimating Equation[ 41 ] methods to account for clustering of patients within IDEAL program sites. A regression analysis using the xtgee command in STATA/SE 7.0 (STATA Corporation, College Station, TX) was performed for each outcome of interest. Models adjusted for age, gender, race, insurance status, number of co-morbidities, insulin use, number of visits for diabetes in the last year, length of relationship with the provider, physical functioning, and mental health.
Statistical Analysis
To determine the association of patient trust and our outcomes of patient hassles, difficulty in completing care activities and global assessment, we used Generalized Estimating Equation[ 41 ] methods to account for clustering of patients within IDEAL program sites. A regression analysis using the xtgee command in STATA/SE 7.0 (STATA Corporation, College Station, TX) was performed for each outcome of interest. Models adjusted for age, gender, race, insurance status, number of co-morbidities, insulin use, number of visits for diabetes in the last year, length of relationship with the provider, physical functioning, and mental health.Results Three hundred and twenty-six individuals with diabetes had information from both the telephone survey and chart review portions of the study. The response rate for the telephone survey was 67%. The mean age of the respondents was 60, over two-thirds were female and forty percent were non-white (see table  1 ). Most respondents (75%) had some sort of insurance, primarily Medicare or a Medicare HMO. Most (79%) had least one co-morbid condition and the average number of co-morbid diseases was one. Average score on the MOS-SF 36 physical functioning and mental health scale was 57.0 (sd 29.8) and 73.5 (sd 21.2), respectively. Diabetes control was generally good with the average hemoglobin A1c 7.3%, although the range was wide (4.4%–14.6%). Twenty four percent of the sample had insulin listed as a treatment in their medical records. When asked about the length of time they had been seeing a provider or clinic, 15% responded less than 1 year, 21% 1–2 years, and 65% more than 2 years. The average number of visits to a provider for diabetes care in the last 12 months was 3. The level of patient trust in their physician was high, with an average and median level of trust of 22 (sd 3, range 11–25). Table 1 Description of the Sample (n = 326) Age, mean 60 (sd 12.0, range 19–89) Gender, female 67% (n = 217) Race, non-white 40% (n = 120) Insurance, none 25% (n = 82) Co-morbidities, any (%) 79% (n = 228) Co-morbidities, mean (%) 1.1 (sd 1.0, range 0–4) Hemoglobin A1c, mean 7.3% (sd 1.7, range 4.4–14.6) Physical Health (max 100) 57.0 (sd 29.8) Mental Health (max 100) 73.5 (sd 21.0) Number of years with provider/clinic*   Less than 1 year 13% (n = 44)   1 to 2 years 19% (n = 62)   More than 2 years 58% (n = 190) Number of visit in last 12 months 3 (sd 1.0) Patient Trust (max 25) 22 (sd 3, range 11–25) * Number does not equal 100% due to participant who responded that they did not have a regular source of diabetes care When asked about hassles associated with their diabetes, respondents reported the highest degree of hassle with avoiding foods they enjoyed (higher score equals more hassle: mean 2.4, SD 1.4, range 1–5) (table  2 ). Taking medication (mean 1.2, SD 0.7, range 1–5) and testing blood sugar (mean 1.4, SD 0.9, range 1–5) were the care activities associated with the lowest level of hassle. Similarly, patients reported low levels of difficulty when asked about taking their medication for their diabetes (higher score equals more difficulty: mean 1.2, SD 0.5, range 1–4), while they reported higher levels of difficulty in following a diet for their diabetes (mean 2.1, SD 1.0, range 1–5) and exercising regularly (mean 2.5, SD1.3, range 1–5). Overall, the patients surveyed reported high global assessments of their self care for their diabetes (higher score equals worse self care: mean 2.0, SD 0.9, range 1–5). Table 2 Average score for global outcome measures and individual components of each measure Outcome Measure Mean (SD, Range) Hassles associated with diabetes care*  Average for entire scale 1.6 (0.7, 1–4.5)   Remembering to take diabetes medicine 1.2 (0.7, 1–5)   Remembering to test blood for sugar 1.4 (0.9, 1–5)   Making meal plans 1.6 (1.2, 1–5)   Avoiding foods you enjoy 2.4 (1.4, 1–5)   Keeping schedule in mind at times 1.7 (1.2, 1–5)   Organizing daily routine around diabetes care 1.4 (0.9, 1–5)   Total time spent managing diabetes 1.5 (1.0, 1–5) Difficulty in performing self care activities**  Average of entire scale 1.7 (0.5, 1–3.8)   Taking medications as prescribed 1.2 (0.5, 1–4)   Exercising regularly 2.5 (1.3, 1–5)   Following diet 2.1 (1.0, 1–5)   Checking blood for sugar 1.5 (0.9, 1–5)   Checking feet for wounds or sores 1.2 (0.5, 1–5) Global assessment of ability to care for diabetes*** 2.0 (0.9, 1–5) * Higher equals more hassles associated with care activity ** Higher equals more difficulty in completing care activities *** Higher equals lower assessment of ability to care In bivariate analyses, a higher levels of hassles was associated with younger age (p < 0.001), non-white race (p = 0.03), and lower score for mental health. There was no association with gender, insurance status, co-morbidities, use of insulin, physical health, length of the relationship with the provider, or the number of visits for diabetes in the past year. A higher difficulty in completing care activities was associated with younger age (p = 0.002), female gender (0.003), non-white race (p = 0.03), worse physical health (p < 0.001) and worse mental health (p = 0.001). There was no association with insulin use, co-morbidities, length of relationship, or number of visits in the past year. Better self rated ability to care for diabetes was also associated with older age (p = 0.004), better physical health (p = 0.02), and better mental health (p < 0.0001). There was no association with gender, race, co-morbidities, use of insulin, length of relationship, or number of visits in the past year. In multivariate regression analysis, level of patient trust was significantly associated with each of the outcomes we examined. Patients with higher levels of trust in their physician reported lower levels of hassles with disease (GEE parameter -0.03, 95% CI -0.05 to -0.008, p = 0.006). Similar finding were seen when self-reported difficulty was examined. Higher level of patient trust was again associated with less reported difficulty in caring for diabetes (GEE parameter -0.02, 95% CI -0.03 to -0.01, p = 0.001). Our final patient outcome, global assessment of ability to care for diabetes, also showed a significant association with higher levels of patient trust associated with higher self-reported ability to care for diabetes (GEE parameter -0.05, 95% CI -0.08 to -0.02, p < 0.0001). Thus, if a patient responded "strongly agree" to all the trust questions rather than "agree" (representing a 5-point increase in level of trust), self-reported hassles would decrease by approximately 0.2 points (about 30% of a standard deviation in hassles). Similarly, for a 5-point increase in trust, self-reported difficulty would decrease by 0.1 points (about 20% of a standard deviation in difficulty) and global ability to care for diabetes would decrease by 0.3 (about 33% of a standard deviation in global ability to care for diabetes). (table  3 ) Table 3 General estimating equations regression parameters for difference in outcomes associated with a 5-point difference in trust Outcome GEE parameter estimates (95% CI) Hassles associated with diabetes care -0.2 (-0.3 to -0.05) Difficulty in performing self care activities -0.1 (-0.20 to -0.05) Global assessment of ability to care for diabetes -0.3 (-0.40 to -0.10)
Results
Three hundred and twenty-six individuals with diabetes had information from both the telephone survey and chart review portions of the study. The response rate for the telephone survey was 67%.The mean age of the respondents was 60, over two-thirds were female and forty percent were non-white (see table  1 ). Most respondents (75%) had some sort of insurance, primarily Medicare or a Medicare HMO. Most (79%) had least one co-morbid condition and the average number of co-morbid diseases was one. Average score on the MOS-SF 36 physical functioning and mental health scale was 57.0 (sd 29.8) and 73.5 (sd 21.2), respectively. Diabetes control was generally good with the average hemoglobin A1c 7.3%, although the range was wide (4.4%–14.6%). Twenty four percent of the sample had insulin listed as a treatment in their medical records. When asked about the length of time they had been seeing a provider or clinic, 15% responded less than 1 year, 21% 1–2 years, and 65% more than 2 years. The average number of visits to a provider for diabetes care in the last 12 months was 3. The level of patient trust in their physician was high, with an average and median level of trust of 22 (sd 3, range 11–25).Description of the Sample (n = 326)* Number does not equal 100% due to participant who responded that they did not have a regular source of diabetes careWhen asked about hassles associated with their diabetes, respondents reported the highest degree of hassle with avoiding foods they enjoyed (higher score equals more hassle: mean 2.4, SD 1.4, range 1–5) (table  2 ). Taking medication (mean 1.2, SD 0.7, range 1–5) and testing blood sugar (mean 1.4, SD 0.9, range 1–5) were the care activities associated with the lowest level of hassle. Similarly, patients reported low levels of difficulty when asked about taking their medication for their diabetes (higher score equals more difficulty: mean 1.2, SD 0.5, range 1–4), while they reported higher levels of difficulty in following a diet for their diabetes (mean 2.1, SD 1.0, range 1–5) and exercising regularly (mean 2.5, SD1.3, range 1–5). Overall, the patients surveyed reported high global assessments of their self care for their diabetes (higher score equals worse self care: mean 2.0, SD 0.9, range 1–5).Average score for global outcome measures and individual components of each measure* Higher equals more hassles associated with care activity** Higher equals more difficulty in completing care activities*** Higher equals lower assessment of ability to careIn bivariate analyses, a higher levels of hassles was associated with younger age (p < 0.001), non-white race (p = 0.03), and lower score for mental health. There was no association with gender, insurance status, co-morbidities, use of insulin, physical health, length of the relationship with the provider, or the number of visits for diabetes in the past year. A higher difficulty in completing care activities was associated with younger age (p = 0.002), female gender (0.003), non-white race (p = 0.03), worse physical health (p < 0.001) and worse mental health (p = 0.001). There was no association with insulin use, co-morbidities, length of relationship, or number of visits in the past year. Better self rated ability to care for diabetes was also associated with older age (p = 0.004), better physical health (p = 0.02), and better mental health (p < 0.0001). There was no association with gender, race, co-morbidities, use of insulin, length of relationship, or number of visits in the past year.In multivariate regression analysis, level of patient trust was significantly associated with each of the outcomes we examined. Patients with higher levels of trust in their physician reported lower levels of hassles with disease (GEE parameter -0.03, 95% CI -0.05 to -0.008, p = 0.006). Similar finding were seen when self-reported difficulty was examined. Higher level of patient trust was again associated with less reported difficulty in caring for diabetes (GEE parameter -0.02, 95% CI -0.03 to -0.01, p = 0.001). Our final patient outcome, global assessment of ability to care for diabetes, also showed a significant association with higher levels of patient trust associated with higher self-reported ability to care for diabetes (GEE parameter -0.05, 95% CI -0.08 to -0.02, p < 0.0001). Thus, if a patient responded "strongly agree" to all the trust questions rather than "agree" (representing a 5-point increase in level of trust), self-reported hassles would decrease by approximately 0.2 points (about 30% of a standard deviation in hassles). Similarly, for a 5-point increase in trust, self-reported difficulty would decrease by 0.1 points (about 20% of a standard deviation in difficulty) and global ability to care for diabetes would decrease by 0.3 (about 33% of a standard deviation in global ability to care for diabetes). (table  3 )General estimating equations regression parameters for difference in outcomes associated with a 5-point difference in trustDiscussion Diabetes is a disease requiring many types of interventions to prevent the associated morbidity and mortality. These interventions include medications to control elevated glucose levels and finger sticks to check blood sugars. Additionally, significant alterations in lifestyle such as increasing exercise and changing the type of food one eats are an essential portion of the treatment regimen. In our sample of low-income patients with diabetes, we found low reported hassle of completion of diabetes tasks, low reported difficulty in completing diabetes related tasks and good self reported ability to stay on top of their disease. The patients in this study did report more difficulty in making lifestyle changes such as exercising regularly and following recommended diets than taking medications. Other studies examining adherence in diabetic patients have found similar findings. Ruggiero et al, in a nationwide survey of individuals with diabetes, found that over 90% reported always or usually taking their medication but only 64% always or usually followed dietary recommendations and less than half always or usually exercised [ 6 ]. Higher levels of trust were associated with lower reported levels of hassles, lower self-reported difficulty in completing care activities, and improved self-reported global ability to take care of diabetes. These results support our hypothesis of increased completion of self-management tasks with higher levels of patient trust in physicians. There are several possible causes for this association. Patients who are actively engaged in their medical care and jointly make health care decisions with their provider may have less difficulty and hassles in performing self-care activities that they had input on. These patients may also have higher trust levels because they have been engaged as an active participant in the health care decision by their provider. Additionally, medical regimens used to treat chronic disease are complicated. Patients may not fully understand the medical rationale behind particular recommendations such as exercise and diet. Additionally, exercise and diet may not result in immediate improvement in symptoms and often cause initial discomfort or feelings of deprivation, thereby providing little positive feedback and reinforcement. Patients who have high levels of trust may be willing to overcome the initial discomfort and maintain faith that these difficult changes will ultimately be beneficial to them. Our results indicate that an intervention to improve trust that resulted in an increase of 5-points (moving from agree to strongly agree on all questions) would result in an improvement of 0.1–0.3 in our outcome scores. This improvement is equivalent to the difference in self care scores seen between a 60 year old patient and a 30 year old patient. Additionally, trust was significantly associated with all self care measures while insulin use was not. Few studies have examined the association of completion of self-care tasks with patient trust. Safran et al surveyed adults employed by the state of Massachusetts using the Primary Care Assessment Survey which includes a section on patient trust [ 31 ]. They found that patient trust was significantly associated with patient satisfaction and self-reported adherence to lifestyle changes. Thom et al conducted a longitudinal survey of patients cared for in primary care clinics and found that higher levels of patient trust were associated with greater self-reported adherence to prescribed medications [ 42 ]. Our study differs from these by its focus on low-income patients and on measurements of difficulty of self-care in a common, chronic disease. These results add to growing evidence that patient trust is important to patient outcomes, but are limited by the cross-sectional design of the study. Although the results support the contention that higher levels of trust result in decreased hassles and difficulty in completing self-management task, it is possible that the reverse is true. Longitudinal studies of individuals with newly diagnosed chronic diseases such as diabetes are needed to define the temporal nature of this association. Additionally, our outcome measures did not assess whether patients were actually performing the activities, rather, the instruments were designed to determine the level of difficulty and hassles. It is possible that individuals viewed particular activities as very difficult and having a high degree of hassle and still completed the task. Future studies with measurements of both patient perception and actual completion rates are needed. The shortened trust instrument we used lacked the important dimension of patient-centered care. Patients who feel their concerns are listened to and work in partnership with their health care provider may be more likely to attempt new self care activities. It is likely that the inclusion of this dimension through questions such as the providers' ability to listen to or advocate for the patient would strengthen the association of trust with reduced difficulty in completing self care tasks. We also did not collect information on several important mediators of patient trust and self-care such as the empathy level of the health care provider, educational level of the patient, and patients' overall perceived self-efficacy. These additional factors may provide further elucidation into the relationship of patient trust and ability to complete self care task. Despite these limitations, this study adds to our understanding of the predictors of patient self-management of disease. Further studies comparing strategies for measuring trust, measurement of trust at multiple points in time and the linkage of patient trust with clinical outcomes are needed.
Discussion
Diabetes is a disease requiring many types of interventions to prevent the associated morbidity and mortality. These interventions include medications to control elevated glucose levels and finger sticks to check blood sugars. Additionally, significant alterations in lifestyle such as increasing exercise and changing the type of food one eats are an essential portion of the treatment regimen. In our sample of low-income patients with diabetes, we found low reported hassle of completion of diabetes tasks, low reported difficulty in completing diabetes related tasks and good self reported ability to stay on top of their disease. The patients in this study did report more difficulty in making lifestyle changes such as exercising regularly and following recommended diets than taking medications. Other studies examining adherence in diabetic patients have found similar findings. Ruggiero et al, in a nationwide survey of individuals with diabetes, found that over 90% reported always or usually taking their medication but only 64% always or usually followed dietary recommendations and less than half always or usually exercised [ 6 ].Higher levels of trust were associated with lower reported levels of hassles, lower self-reported difficulty in completing care activities, and improved self-reported global ability to take care of diabetes. These results support our hypothesis of increased completion of self-management tasks with higher levels of patient trust in physicians. There are several possible causes for this association. Patients who are actively engaged in their medical care and jointly make health care decisions with their provider may have less difficulty and hassles in performing self-care activities that they had input on. These patients may also have higher trust levels because they have been engaged as an active participant in the health care decision by their provider. Additionally, medical regimens used to treat chronic disease are complicated. Patients may not fully understand the medical rationale behind particular recommendations such as exercise and diet. Additionally, exercise and diet may not result in immediate improvement in symptoms and often cause initial discomfort or feelings of deprivation, thereby providing little positive feedback and reinforcement. Patients who have high levels of trust may be willing to overcome the initial discomfort and maintain faith that these difficult changes will ultimately be beneficial to them. Our results indicate that an intervention to improve trust that resulted in an increase of 5-points (moving from agree to strongly agree on all questions) would result in an improvement of 0.1–0.3 in our outcome scores. This improvement is equivalent to the difference in self care scores seen between a 60 year old patient and a 30 year old patient. Additionally, trust was significantly associated with all self care measures while insulin use was not.Few studies have examined the association of completion of self-care tasks with patient trust. Safran et al surveyed adults employed by the state of Massachusetts using the Primary Care Assessment Survey which includes a section on patient trust [ 31 ]. They found that patient trust was significantly associated with patient satisfaction and self-reported adherence to lifestyle changes. Thom et al conducted a longitudinal survey of patients cared for in primary care clinics and found that higher levels of patient trust were associated with greater self-reported adherence to prescribed medications [ 42 ]. Our study differs from these by its focus on low-income patients and on measurements of difficulty of self-care in a common, chronic disease.These results add to growing evidence that patient trust is important to patient outcomes, but are limited by the cross-sectional design of the study. Although the results support the contention that higher levels of trust result in decreased hassles and difficulty in completing self-management task, it is possible that the reverse is true. Longitudinal studies of individuals with newly diagnosed chronic diseases such as diabetes are needed to define the temporal nature of this association. Additionally, our outcome measures did not assess whether patients were actually performing the activities, rather, the instruments were designed to determine the level of difficulty and hassles. It is possible that individuals viewed particular activities as very difficult and having a high degree of hassle and still completed the task. Future studies with measurements of both patient perception and actual completion rates are needed. The shortened trust instrument we used lacked the important dimension of patient-centered care. Patients who feel their concerns are listened to and work in partnership with their health care provider may be more likely to attempt new self care activities. It is likely that the inclusion of this dimension through questions such as the providers' ability to listen to or advocate for the patient would strengthen the association of trust with reduced difficulty in completing self care tasks. We also did not collect information on several important mediators of patient trust and self-care such as the empathy level of the health care provider, educational level of the patient, and patients' overall perceived self-efficacy. These additional factors may provide further elucidation into the relationship of patient trust and ability to complete self care task. Despite these limitations, this study adds to our understanding of the predictors of patient self-management of disease. Further studies comparing strategies for measuring trust, measurement of trust at multiple points in time and the linkage of patient trust with clinical outcomes are needed.Conclusions Trust is an integral component of the doctor-patient relationship. Increasingly, external forces such as changes in reimbursement for medical services and increases in the cost of malpractice insurance place pressure on this relationship [ 43 ]. These pressures may erode trust [ 43 ]. If trust is associated with self-care tasks such as adherence to medication or lifestyle changes, the turbulent changes in the health care industry may result in worse health outcomes. Efforts to define the impact of trust on patient ability and comfort in performing self-care activities and to identify the modifiable determinants of trust may translate into improved health outcomes, especially for vulnerable populations.
Conclusions
Trust is an integral component of the doctor-patient relationship. Increasingly, external forces such as changes in reimbursement for medical services and increases in the cost of malpractice insurance place pressure on this relationship [ 43 ]. These pressures may erode trust [ 43 ]. If trust is associated with self-care tasks such as adherence to medication or lifestyle changes, the turbulent changes in the health care industry may result in worse health outcomes. Efforts to define the impact of trust on patient ability and comfort in performing self-care activities and to identify the modifiable determinants of trust may translate into improved health outcomes, especially for vulnerable populations.Competing interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors' contributions DEB wrote the text and conducted with the statistical analysis. FB assisted with the statistical analysis and the writing of the paper. RAB was responsible for selection of the quality of care markers, oversaw data abstraction and assisted with writing the article. VTD was responsible for data collection and clinic interventions and assisted with editing the article. RTA was responsible for selection of the quality of life markers and assisted with editing the article. DCG was responsible for the scientific direction of the study, design, and assisted with editing the article All authors read and approved the final manuscript
Authors' contributions
DEB wrote the text and conducted with the statistical analysis.FB assisted with the statistical analysis and the writing of the paper.RAB was responsible for selection of the quality of care markers, oversaw data abstraction and assisted with writing the article.VTD was responsible for data collection and clinic interventions and assisted with editing the article.RTA was responsible for selection of the quality of life markers and assisted with editing the article.DCG was responsible for the scientific direction of the study, design, and assisted with editing the articleAll authors read and approved the final manuscriptPre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


NCEAS -- an ecological synthesis center -- is changing the way ecological research is conducted by fostering new forms of collaboration and interdisciplinary research

A substantial portion of research occurs in places where scholars congregate—in campus laboratories, in libraries, or in large specialized facilities, such as oceanographic ships, astronomical observatories, or accelerators. Under any of these circumstances, researchers can interact easily, exchanging ideas and information.Ecological research, especially the large component occurring in the field, takes place in disparate locations all over the world—in the air, on the surface, and below the oceans, lakes, and crust of the planet. Although there are many biological field stations where scientists and students gather, much of the research in ecology takes place in isolation. In addition to being highly dispersed geographically, ecology encompasses a disparate range of disciplines at scales from molecules to the globe, making the exchange of ideas and information even more difficult.Recognizing the benefits of interaction and collaboration, the ecological community began considering a synthesis center where researchers from many fields could meet to address important ecological questions. Several organizations held discussions about the nature of such a center, which culminated in two workshops hosted by the National Science Foundation (NSF) in the early 1990s, to set the scope. In 1994, NSF conducted an open competition for a synthesis center, eventually granting the University of California, Santa Barbara, the award for the National Center for Ecological Analysis and Synthesis (NCEAS). In addition to funding from NSF, the center is supported by the University of California system and its Santa Barbara campus and by several foundations.The center employs various types of research approaches. A primary approach is through Working Groups ( Figure 1 ) where scientists come to NCEAS to concentrate on specific issues requiring synthesis of ideas, in-depth analysis of data, development of models, and preparation of results. The groups generally visit NCEAS two to four times over two years and stay for three to ten days at a time. Working Group topics range from microbial diversity to global change and have included projects in sociology, economics, and computer science. The Center hosts about 100 meetings a year, involving hundreds of participants.NCEAS also supports up to six visiting Center Fellows (sabbatical visitors) each year. These scientists often integrate their own research into a broader context or synthesize what is known about certain areas in ecology. Concurrently, the Center houses 15–18 Postdoctoral Associates for one to three years each. These postdoctoral positions are distinctive in that there are no permanent mentors for the larval scientists—rather, they interact with the other Associates, the resident Fellows, and the hundreds of individuals who annually visit the Center as part of Working Groups.The Center also conducts training activities, including a distributed graduate seminar program. In this approach, graduate students around the world become involved in seminars on specific topics using data from their region (e.g., the relationship between productivity and diversity) and then representatives from each of the seminars are brought to NCEAS for a grand synthesis.As would be expected for a discipline as broad as ecology, the participants at NCEAS are extremely diverse. Over 3,000 individuals have visited NCEAS in just over eight years, representing 43 countries and 49 states in the United States. They come from over 800 institutions, many non-biology departments, and 397 non-academic organizations (e.g., agencies and companies). An interesting measure of their breadth is that participants belong to more than 180 professional societies.Proposals are solicited twice a year and reviewed by a Science Advisory Board. The Board looks for topics that would benefit from synthesis and analysis and that would make significant contributions to our understanding of ecological relationships. While many proposals pertain to core ecological questions, others deal with economics or sociology (e.g., how metaphors affect the way we conduct research). Approximately 40% of the projects have some applied component, many influencing resource management practices and conservation policies.Because the Center is based on the use of existing data, access to highly dispersed and profoundly heterogeneous ecological information is essential, but also very difficult. Recognizing the need for open access to a wide variety of data—versus project-specific data solutions—NCEAS and several collaborators (see  http://www.ecoinformatics.org ) have embarked on a major research program in developing tools to characterize data and make them available in standardized formats. The initial research effort, called the Knowledge Network for BioComplexity (KNB) is yielding tools to generate metadata (precise information about the data) and to make all the data available. The current research thrust, called Science Environment for Ecological Knowledge, will expand the capabilities of KNB by employing grid technology (in particular, EcoGrid, a network of networks), semantic mediation, knowledge representation, and workflow models for analysis and synthesis.The Center has supported almost 200 projects, the results of which are published in top scientific journals (see project results at  http://www.nceas.ucsb.edu ). Furthermore, some of the projects have had direct influence on conservation and resource management. For example, scientists at NCEAS developed theories for the design of marine reserves that were soon thereafter applied to the placement of reserves directly off the coast of Santa Barbara.In addition to scientific results, NCEAS is changing the way we conduct ecological research through novel means of encouraging productive collaborations. Sociologists studying the NCEAS model of collaboration have identified several important factors in its success. These include a distant, neutral location facilitating periodic, highly focused opportunities to concentrate on the issues under consideration; logistic support that lowers the activation energy required to develop collaborations; and the proximity of scientists from many disciplines having the opportunity to interact in ways otherwise not possible.Many significant contributions to our understanding of the patterns and processes of the natural world have emerged from NCEAS research activities. In addition, the Center is fostering new forms of collaboration and interdisciplinary research by providing a place where scientists from many disciplines can productively interact and by working to make eclectic ecological data available to many users. This is an extremely simple model for the scientific enterprise—but one not captured in most existing institutional and organizational structures. As recognition of the success of NCEAS spreads, other institutions are attempting to incorporate some of the traits of the Center into their operations, and new centers are being proposed. For example, the NSF is in the midst of a review of proposals for an evolution synthesis center.It is clear that the complexity of ecological systems, as well as the importance of understanding and maintaining them, requires information and knowledge from many disciplines. This is true even at a time when disciplines are becoming more specialized and scientists have less time to concentrate on broader issues. By facilitating interactions among many scholars and practitioners, NCEAS provides both time when and a place where far-reaching topics can be addressed.