Moving beyond a limited follow-up in cost-effectiveness analyses of behavioral interventions

Background Cost-effectiveness analyses of behavioral interventions typically use a dichotomous outcome criterion. However, achieving behavioral change is a complex process involving several steps towards a change in behavior. Delayed effects may occur after an intervention period ends, which can lead to underestimation of these interventions. To account for such delayed effects, intermediate outcomes of behavioral change may be used in cost-effectiveness analyses. The aim of this study is to model cognitive parameters of behavioral change into a cost-effectiveness model of a behavioral intervention. Methods The cost-effectiveness analysis (CEA) of an existing dataset from an RCT in which an high-intensity smoking cessation intervention was compared with a medium-intensity intervention, was re-analyzed by modeling the stages of change of the Transtheoretical Model of behavioral change. Probabilities were obtained from the dataset and literature and a sensitivity analysis was performed. Results In the original CEA over the first 12 months, the high-intensity intervention dominated in approximately 58% of the cases. After modeling the cognitive parameters to a future 2nd year of follow-up, this was the case in approximately 79%. Conclusion This study showed that modeling of future behavioral change in CEA of a behavioral intervention further strengthened the results of the standard CEA. Ultimately, modeling future behavioral change could have important consequences for health policy development in general and the adoption of behavioral interventions in particular

Renal clearance of the thyrotropin-releasing hormone-like peptide pyroglutamyl-glutamyl-prolineamide in humans

TRH-like peptides have been identified that differ from TRH (pGlu-His-ProNH2) in the middle amino acid. We have estimated TRH-like immunoreactivity (TRH-LI) in human serum and urine by RIA with TRH-specific antiserum 8880 or with antiserum 4319, which binds most peptides with the structure pGlu-X-ProNH2. TRH was undetectable in serum (< 25 pg/mL), but TRH-LI was detected with antiserum 4319 in serum of 27 normal subjects, 21 control patients, and 12 patients with carcinoid tumors (range 17-45, 5-79, and 18-16,600 pg/mL, respectively). Because serum was kept for at least 2 h at room temperature, which causes degradation of TRH, pGlu-Phe-ProNH2, and pGlu-Tyr-ProNH2, serum TRH-LI is not caused by these peptides. On high-performance liquid chromatography, serum TRH-LI coeluted with pGlu-Glu-ProNH2 (< EEP-NH2), a peptide produced in, among others, the prostate. Urine of normals and control patients also contained TRH-LI (range 1.14-4.97 and 0.24-5.51 ng/mL, respectively), with similar levels in males and females. TRH represented only 2% of urinary TRH-LI, and anion-exchange chromatography and high-performance liquid chromatography revealed that most TRH-LI in urine was < EEP-NH2. In patients with carcinoid tumors, increased urinary TRH-LI levels were noted (range 1.35-962.4 ng/mL). Urinary TRH-LI correlated positively with urinary creatinine, and the urinary clearance rate of TRH-LI was similar to the glomerular filtration rate. In addition, serum TRH-LI was increased in 17 hemodialysis patients (43-373 pg/mL). This suggests that serum < EEP-NH2 is cleared by glomerular filtration with little tubular resorption. The possible role of the prostate as a source of urinary TRH-LI was evaluated in 11 men with prostate cancer, showing a 25% decrease in urinary TRH-LI excretion after prostatectomy (0.19 +/- 0.02 vs. 0.15 +/- 0.01 ng/mumol creatinine, mean +/- SEM). However, TRH-LI was similar in spontaneously voided urine and in urine obtained through a nephrostomy cannula from 16 patients with unilateral urinary tract obstruction (0.15 +/- 0.01 vs. 0.14 +/- 0.01 ng/mumol creatinine). These data indicate that: 1) TRH-LI in human serum represents largely < EEP-NH2, which is cleared by renal excretion; 2) part of urinary < EEP-NH2 is derived from prostatic secretion into the blood and not directly into urine; and 3) urinary < EEP-NH2 can be used as marker for carcinoid tumors

Renal clearance of the thyrotropin-releasing hormone-like peptide pyroglutamyl-glutamyl-prolineamide in humans

TRH-like peptides have been identified that differ from TRH (pGlu-His-ProNH2) in the middle amino acid. We have estimated TRH-like immunoreactivity (TRH-LI) in human serum and urine by RIA with TRH-specific antiserum 8880 or with antiserum 4319, which binds most peptides with the structure pGlu-X-ProNH2. TRH was undetectable in serum (< 25 pg/mL), but TRH-LI was detected with antiserum 4319 in serum of 27 normal subjects, 21 control patients, and 12 patients with carcinoid tumors (range 17-45, 5-79, and 18-16,600 pg/mL, respectively). Because serum was kept for at least 2 h at room temperature, which causes degradation of TRH, pGlu-Phe-ProNH2, and pGlu-Tyr-ProNH2, serum TRH-LI is not caused by these peptides. On high-performance liquid chromatography, serum TRH-LI coeluted with pGlu-Glu-ProNH2 (< EEP-NH2), a peptide produced in, among others, the prostate. Urine of normals and control patients also contained TRH-LI (range 1.14-4.97 and 0.24-5.51 ng/mL, respectively), with similar levels in males and females. TRH represented only 2% of urinary TRH-LI, and anion-exchange chromatography and high-performance liquid chromatography revealed that most TRH-LI in urine was < EEP-NH2. In patients with carcinoid tumors, increased urinary TRH-LI levels were noted (range 1.35-962.4 ng/mL). Urinary TRH-LI correlated positively with urinary creatinine, and the urinary clearance rate of TRH-LI was similar to the glomerular filtration rate. In addition, serum TRH-LI was increased in 17 hemodialysis patients (43-373 pg/mL). This suggests that serum < EEP-NH2 is cleared by glomerular filtration wit

Computational Nuclear Physics and Post Hartree-Fock Methods

We present a computational approach to infinite nuclear matter employing Hartree-Fock theory, many-body perturbation theory and coupled cluster theory. These lectures are closely linked with those of chapters 9, 10 and 11 and serve as input for the correlation functions employed in Monte Carlo calculations in chapter 9, the in-medium similarity renormalization group theory of dense fermionic systems of chapter 10 and the Green's function approach in chapter 11. We provide extensive code examples and benchmark calculations, allowing thereby an eventual reader to start writing her/his own codes. We start with an object-oriented serial code and end with discussions on strategies for porting the code to present and planned high-performance computing facilities.Comment: 82 pages, to appear in Lecture Notes in Physics (Springer), "An advanced course in computational nuclear physics: Bridging the scales from quarks to neutron stars", M. Hjorth-Jensen, M. P. Lombardo, U. van Kolck, Editor