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Vol 4, No 1 (February 2014) /
In vivo evaluation of optic nerve aging in adult rhesus monkey by diffusion tensor imaging
Yumei Yan1, Longchuan Li2, Todd M. Preuss3, Xiaoping Hu4, James G. Herndon3, Xiaodong Zhang1,3
1Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia 30329, USA; 2Marcus Autism Center, Children's Healthcare of Atlanta, Emory University, Atlanta, Georgia 30322, USA; 3Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia 30329, USA; 4The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322, USA
Corresponding to: Xiaodong Zhang, Ph.D. 954 Gatewood Rd NE, Atlanta, GA 30329, USA. Email: xzhang8@emory.edu.
Abstract: Aging of the optic nerve can result in reduced visual sensitivity or vision loss. Normal optic nerve aging has been investigated previously in tissue specimens but poorly explored in vivo. In the present study, the normal aging of optic nerve was evaluated by diffusion tensor imaging (DTI) in non-human primates. Adult female rhesus monkeys at the ages of 9 to 13 years old (young group, n=8) and 21 to 27 years old (old group, n=7) were studied using parallel-imaging-based DTI on a clinical 3T scanner. Compared to young adults, the old monkeys showed 26% lower fractional anisotropy (P<0.01), and 44% greater radial diffusivity, although the latter difference was of marginal statistical significance (P=0.058). These MRI findings are largely consistent with published results of light and electron microscopic studies of optic nerve aging in macaque monkeys, which indicate a loss of fibers and degenerative changes in myelin sheaths.
Keywords: Optic nerve aging; white matter; mean diffusivity (MD); fractional anisotropy (FA); non-human primate; parallel imaging; GRAPPA
Submitted Jan 10, 2014. Accepted for publication Feb 17, 2014.
doi: 10.3978/j.issn.2223-4292.2014.02.04
The optic nerve is the continuation of the axons of ganglion cells in the retina, and the pathway by which visual information is transferred from the retina to the visual cortex. Aging of the optic nerve can result in reduced visual sensitivity or vision loss due to reduction of retinal ganlion cells and nerve fibers (1-3). As seen in glaucoma, progressive neurodegeneration of retinal ganlion cells and axons can lead to irreversible loss of visual sensitivity or blindness (4-7). Normal optic nerve aging has been studied previously using light and electron microscopic methods on specimens from humans (1,8), monkeys (9-11) and rats (12). In vivo investigation of the optic nerve, beyond the intraocular optic nerve head, has been hindered in the past because the nerve could not be assessed non-invasively. In recent years, high-field MRI and fast imaging techniques have facilitated both animal research and clinical investigation dramatically, and MRI has been increasingly used in various studies of visual pathways, thus helping to overcome these difficulties.
Diffusion tensor imaging (DTI) (13) is a non-invasive MRI technique for characterizing white matter fiber tracts and has been used widely to investigate white matter development, integrity and aging in human brains (14-23). In recent years, it has been applied in in vivo studies of the optic nerve of humans (24-34), monkeys (35,36), rodents (25,37-40), demonstrating that DTI can be a robust means to non-invasively examine optic nerve organization and abnormalities.
Although rodent models of eye-related diseases are widely used (12,25,37,39-42), their visual system is markedly different from that of humans. In contrast, the visual system organization of nonhuman primates (NHPs), while not identical to that of humans, is much more human-like than that of rodents (43). Therefore, NHP models are particularly valuable for investigating the aging and neuropathology of the visual pathway (9,10,42,44,45). The aim of the present study was to evaluate the diffusion property changes during the normal optic nerve aging in adult rhesus macaque monkeys by DTI in a clinical 3T setting.
Animal preparations
Fifteen young adult (9 to 13 years old, n=8) and old adult (21 to 27 years old, n=7) female rhesus monkeys were used in this study. Females were selected in order to minimize possible bias from gender difference because the nerve fiber degeneration with age may be different in male and female (46). Also, comparable sets of male macaques were not available to us for scanning at the time of the study.
The animals were initially anesthetized with ketamine (5-10 mg/kg, IM), then orally intubated and anesthetized with 1-1.5% isoflurane mixed with 100% O2 during scanning. An IV catheter was placed for delivering lactated ringers solution (3.5-10 mL/kg/hr). The body temperature was maintained at 37.5 °C by a feedback-regulated circulating warm-water blanket. The anesthetized animals breathed spontaneously, and were immobilized with a custom-made head holder and placed in the "supine" position in the scanner. Et-CO2, inhaled CO2, O2 saturation, blood pressure, the mean arterial pressure (MAP), heart rate, respiration rate, and body temperature were monitored continuously and maintained in normal range (47), in addition to visual inspection of animals at least every 30 minutes. All procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Emory University in accordance with the NIH Guide for Care and Use of Laboratory Animals.
MRI examination
MRI scans were performed on a Siemens TIM™ Trio 3T whole body scanner (Siemens Medical Solutions, PA, USA) with an 8-channel phase-array volume coil (INVIVO, Orlando, FL, USA). A double spin-echo single-shot echo-planar imaging (EPI) sequence with GRAPPA (Acceleration factor R =3) was utilized with the acquisition parameters: TR =7,000 ms/TE =108 ms, FOV =141 mm × 132.26 mm (93.8% phase FOV), data matrix =128×120, 1.1 mm isotropic resolution, b-value =0, 1,000 s/mm2, 60 gradient directions, and ten averages [5-pairs of phase reversal acquisition (48)] including five b0 scans in each repetition. The phase reversal acquisition was conducted by flipping the phase encoding direction by 180 degree in every other DTI scan. The total DTI acquisition time was about 86 minutes. High-resolution (0.5 mm isotropic resolution) T1-weighted images were acquired for structural identification of the optic nerve and for measuring optic nerve diameters. The T1-weighted images were collected using an MPRAGE sequence with GRAPPA (R =2) and the acquisition parameters: TR =2,600 ms, TE =3.37 ms, TI =900 ms, FOV =160 mm × 160 mm, data matrix =320×320, flip angle =8 degree, 0.5 mm slice thickness, 176 slices, and 3 averages.
DTI data from each animal were distortion-corrected, motion-corrected and averaged for generating MD (Figure 1A), FA (Figure 1B), axial diffusivity (AD or λ||) and radial diffusivity (RD or λ⊥) maps using FSL 4.1.3 (http://www.fmrib.ox.ac.uk/fsl/). All the maps were interpolated in the three dimensions by a factor of 2 to facilitate subsequent data analysis. Using MRIcro 1.4 (http://www.mricro.com), regions of interest (ROIs) were selected manually on the coronal MD maps (Figure 1A, bottom image). The ROI or pixels of interest on each optic nerve consisted of a set of consecutive pixels located in the central section of the optic nerve, between its anterior and posterior quarters (Figure 1C). Also, the ROIs in each animal were cross-verified visually in three-dimensional views of their corresponding FA maps and b0 images (Figure 1B and 1D). As no significant difference was observed between the left and right optic nerves (see Results for more details), the values of MD, FA, AD and RD in the corresponding ROIs of the two sides were averaged before statistical analysis. The interpolation and mean value calculation were implemented with Matlab (The MathWorks, Inc., MA, USA) scripts developed in-house. Probabilistic tractography analysis was performed using FSL with two masks defined on the coronal plane of FA map: one mask was set in the optic nerve at the midpoint between the eyeball and the optic chiasm, and the other one was 2.2 mm anterior to that point, toward the eyeball (Figure 1D). Also, the trackability of each optic nerve was examined by sending virtual streamlines from one of the two seed masks and counting the streamlines that reached to the other one and repeating the procedure with the seed mask order switched. The cross-sectional diameters at the middle section of the optic nerves were measured on T1-weighted images (Figure 1E) with ImageJ (http://imagej.nih.gov/ij). DTI measures for the left and right optic nerves were compared by means of the Student's paired t-test to determine whether there was a systematic side bias in the measures. Measures for the two optic nerves were then averaged for each subject, and Student's t-tests for independent samples were carried out to assess the differences between the young and old adult monkeys to uncover aging-related changes of MD, FA, AD and RD.
Figure 1 Illustration of optic nerve and regions of interest in the axial and coronal MRI images of an adult rhesus monkey. (A) Mean diffusivity (MD); (B) fractional anisotropy (FA) maps (both in 0.65 mm isotropic resolution after interpolation) of the same animal; (C) Region of interest (ROI) overlaid on the MD map. The ROI was selected manually between the anterior and posterior quarters of the optic nerve (see the axial image at the top), only including the central pixels of the optic nerve cross-sections (see the coronal image at the bottom) on the selected slices; (D) The two masks for optic nerve fiber tractography were selected on the middle slice (coronal image) of the optic nerves between the optic chiasm and retina, and 2.2 mm away from the middle slice, toward the eyeball; (E) T1 weighted images (0.5 mm isotropic resolution) of the optic nerve. The optic nerves are marked with arrows on the axial image at the top and on the coronal image at the bottom.
Paired t-tests of all 15 monkeys showed no significant differences in MD, AD, RD, FA, or optic-nerve diameter between the left and right optic nerves (P=0.81, 0.71, 0.81, 0.85, 0.19 and 0.64, respectively).
The age-related changes of the DTI-derived indices and the optic nerve diameters measured with T1-weighted images are shown in Figure 2. The most statistically reliable difference was in FA, which was 26.2% lower in the old monkeys than in the young adults (P=0.0087) (Figure 2A). The only other measure that approached significance was RD (λ⊥) (Figure 2D), which was 44.3% higher in the old than in the young monkeys (P=0.058) (Figure 2D). Meanwhile, in comparison with the young group, MD and AD (λ||) of the old group were 24% (P=0.11) and 6.5% (P=0.47) higher (Figure 2B,C), and the mean fiber tract number and optic nerve diameter in the old group were –4.8% (P=0.24) and –3.5% (P=0.55) lower (Figure 2E,F), respectively. Obviously, there were no significant group differences in MD, AD (λ||), fiber tract number, or optic nerve diameter.
Figure 2 Age-related quantitative MRI changes in (A) fractional anisotropy (FA); (B) mean diffusivity (MD); (C) axial diffusivity (AD or λ||); (D) radial diffusivity (RD or λ⊥); (E) reconstructed fiber numbers using DTI; and (F) diameters of optic nerves in the young and old groups of rhesus monkeys. Young, the young group; Old, the old group; mean, mean value; SD, standard deviation; y, year.
Light and electron microscopic methods have previously been used to evaluate optic nerve aging in humans (1,8,49) and monkeys (9,10). In vivo optic nerve studies of age-related eye diseases in rodents and humans have been explored with DTI in recent years. To our knowledge, this is the first DTI study to characterize the normal aging effect of the optic nerve in vivo in large animals or NHPs.
White matter integrity can be compromised by fiber demyelination and axonal degeneration, which lead to FA decrease and/or diffusivity increase in animal and human brains (50-53). As seen in the present data, FA was significantly lower in the old monkey group, while RD (λ⊥) was higher, although the latter difference statistically marginal (P=0.058). Experimental studies in rodent brains (37,54,55) have reported that degenerative changes in axons can result in decreased FA and decreased AD or λ||, the loss of axonal integrity and myelination yielding increased RD or λ⊥. Thus, our DTI results could be interpreted as reflecting the age-related loss of optic nerve axons along with degenerative changes in myelin sheaths. Viewed in this way, our results are consistent with some of the main findings of Sandell and Peters (10), in their light- and electron-microscopic studies of postmortem rhesus tissue. As shown in Figure 3, the total fiber loss (–44.4%, P<0.0001) and fiber density reduction (–41.1%, P <0.0001)from the previous microscopic study are in good agreement with the FA changes during aging in our DTI study. They reported that the average total number of optic nerve fibers was approximately 44% lower (0.9×106 versus 1.6×106) in a group of 27-30-year-old macaques compared to a group of 4-10-year-old macaques (10). In addition to fiber reduction, their study also reported degenerative changes in fiber sheaths that were most prevalent in the animals with the lowest fiber numbers. Similarly, aging in humans is associated with the loss of optic nerve fibers. For example, Dolman et al. revealed axon loss with increasing age that became particularly evident from 60 years upward (1). Jonas et al. investigated human optic nerve from 19 to 88 years old and found that the nerve fiber count decreased significantly with age with a linear annual loss of about 4,000 fibers staring at a calculated original population of about 1.4×106 fibers at birth (8).
Although our in vivo DTI results are largely congruent with results from postmortem histology, there are also disparities. For example, we observed no statistically significant difference in number of DTI-derived streamlines in the young versus old macaques, while the postmortem macaque and human studies report large differences in fiber numbers. This suggests that the numbers of DTI-derived streamlines may not be strongly correlated with the numbers of axons in a fiber tract as the DTI-based fiber tracking technique allows the visualization of fiber bundles and functional connectivity in brain central nervous system but cannot reconstruct the actual fibers (56). Furthermore, while we observed a robust difference in FA between the old and young group, the difference in RD or λ⊥ was statistically marginal, and there was no difference in AD or λ||. Based on the work of Song and colleagues (37,54,55), we would expect that loss of axons and myelin integrity would result in reduced AD. It is possible that the failure to find a difference in AD in the present study reflects small samples and inadequate statistical power or lack of subjects aged 27 years old or more in the old group. This could also account for the lack of an age-related difference in optic-nerve diameter in our MRI study (Figure 2F).
In addition, the scatterplots in Figures 2 and 3 may illustrate the tendency of the parameters from MRI and light and electron microscopy examination during optic nerve aging. However, due to the lack of middle-aged animals in the present or previous study, the linear fits suggesting the alteration of these parameters between the young and old animals during optic nerve aging should be considered tentative. Also, it should be noted that involuntary eye movement in isoflurane-anesthetized animals may cause motion artifact in MRI images of eyes and retinas (57,58). In the present study, the eyeball was nearly invisible in the diffusion-weighted raw images as the water signal in the eyeball was suppressed due to the applied diffusion gradient (b =1,000 s/mm2). In contrast, the eyeball movement was still visible in some raw b0 images (acquired with b =0 s/mm2) of DTI scans. But no motion artifact was observed in the diffusion weighted images of the interested optic nerve section, indicating the involuntary eye movement didn't affect the DTI examinartion of the optic nerve in the present study.
Figure 3 Data of Sandell and Peters (10) showing age-related changes in (A) total fiber number; (B) fiber area (cross-section area of optic nerve); and (C) fiber density of optic nerves in the young adult and old adult rhesus monkeys measured with light and electron microscopy. y, year.
Due to the limited spatial resolution of the DTI images, the DTI measures of optic nerve might be contaminated with the partial volume effect (PVE) from the surrounding cerebrospinal fluid (CSF) (59,60). In our present study, we used the proposed ROI selection procedure to mitigate the adverse effect. Also, as no significant reduction (–3.5%, P=0.55) was seen between the mean optic nerve diameters of the young and old adult monkeys (Figure 2F) in agreement with the fiber area change (–10%, P=0.16, Figure 3B), we believe that the DTI index changes observed in our study are age-related but not due to the PVE contributions from the diameter difference of optic nerve bundles between the young and old adult monkeys.
DTI is increasingly used in studying the optic nerve related diseases (24-27,35,37) and optic nerve development (36). With the advent of high field MRI and fast imaging technique, the DTI examination in diagnostic application of the optic nerve is becoming more widespread. Therefore, the present study of rhesus monkey optic nerves may provide a translationally relevant imaging protocol and also a normal reference baseline for studying the optic nerve related diseases in clinical settings.
In conclusion, this study demonstrates DTI can be a robust means to evaluate the optic nerve aging in a clinic setting. A substantial and significant decrease of FA (P<0.01) in the optic nerve was observed, highly consistent with the previous findings of fiber loss in a light and electron microscopic study, suggesting FA is sensitive to aging effect and may be used for quantitatively characterizing the optic nerve abnormality in aging or age-related vision diseases.
The authors are grateful to Wendy Williamson Coyne and Ruth Connelly for animal handling, Sudeep Patel for the MRI data acquisition. All animal protocols were approved by the Institutional Animal Care and Use Committee of Emory University. This work was supported by NIH/NIA PO1 AG026423-02 and was funded by the National Center for Research Resources P51RR000165 and is currently supported by the Office of Research Infrastructure Programs/ODP51OD011132.
Disclosure: The authors declare no conflict of interest.
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Cite this article as: Yan Y, Li L, Preuss TM, Hu X, Herndon JG, Zhang X. In vivo evaluation of optic nerve aging in adult rhesus monkey by diffusion tensor imaging. Quant Imaging Med Surg 2014;4(1):43-49. doi: 10.3978/j.issn.2223-4292.2014.02.04
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LEXINGTON PARK, Md. – Some celebrate March 14th, often referred to as Pi-Day, by eating delicious, flaky baked pies with airy whipped toppings. At AVIAN, we smash them in each other's faces to raise money for cancer research.
Last week, a total of seven AVIAN teammates, including the founder and president of the company, were pied in the face as part of a St. Mary's County Relay for Life fundraising event.
Every $314.00 donated to its relay team resulted in another pie to the face.
On one of Southern Maryland's first spring-like days, where temperatures peaked in the low-70's, AVIAN's President, Kevin Switick; its Chief Operating Officer and Founder, Jeff Sherman and its Vice President for Product Acquisition and Support, Bill Schaefer, all lined up for a smashing good time.
"Five, four, three, two, one," the group of excited AVIAN spectators chanted and then burst with excitement as the pies met their target faces.
"I knew provoking the company with some friendly email banter before March 14th would doom me," Switick said.
AVIAN has employees located throughout the United States, and although this was a fundraiser for its Maryland Relay for Life event, the donations poured in from regions near and far.
In just under one week, AVIAN's team raised $1,700 for the American Cancer Society. The team has a goal of raising at least $4,000 this year and is well on its way. There are several other fundraising events scheduled leading up to the relay event on June 1, 2019.
AVIAN has supported Relay for Life and the American Cancer Society for nearly eight years. In the past, AVIAN's team has raised upwards of $40,000.
(In article photo) Three AVIAN teammates at their Thomas Drive office received a pie in the face on Friday, March 15 as part of a Pi-Day fundraiser in support of the American Cancer Society Relay for Life. From left to right, Chris Sewell, Dan Lucarell, Greg Roth, Geoff McConnell, Vicky Perkins, Sandy Means.


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Back to Journals » Risk Management and Healthcare Policy » Volume 7
Adherence and health care costs
Authors Iuga A, McGuire M
Received 1 March 2013
Accepted for publication 6 August 2013
Published 20 February 2014 Volume 2014:7 Pages 35—44
DOI https://doi.org/10.2147/RMHP.S19801
Aurel O Iuga,1,2 Maura J McGuire3,4
1Johns Hopkins Bloomberg School of Public Health, 2Johns Hopkins University, 3Johns Hopkins Community Physicians, 4Johns Hopkins University School of Medicine, Baltimore, MD, USA
Abstract: Medication nonadherence is an important public health consideration, affecting health outcomes and overall health care costs. This review considers the most recent developments in adherence research with a focus on the impact of medication adherence on health care costs in the US health system. We describe the magnitude of the nonadherence problem and related costs, with an extensive discussion of the mechanisms underlying the impact of nonadherence on costs. Specifically, we summarize the impact of nonadherence on health care costs in several chronic diseases, such as diabetes and asthma. A brief analysis of existing research study designs, along with suggestions for future research focus, is provided. Finally, given the ongoing changes in the US health care system, we also address some of the most relevant and current trends in health care, including pharmacist-led medication therapy management and electronic (e)-prescribing.
Keywords: patient, medication, adherence, compliance, nonadherence, noncompliance, cost
In 2010 the costs of health care in the US exceeded $2.7 trillion and accounted for 17.9% of the gross domestic product. Projections indicate health care will account for 20% of the US gross domestic product by 2020.1–4 Twenty percent to 30% of dollars spent in the US health care system have been identified as wasteful.1,2,5 Providers and administrators have been challenged to contain costs by reducing waste and by improving the effectiveness of care delivered. Patient nonadherence to prescribed medications is associated with poor therapeutic outcomes, progression of disease, and an estimated burden of billions per year in avoidable direct health care costs.6–8 This review provides a general overview of nonadherence, its cost in specific illnesses, and approaches to improving medication adherence.
Definition and measurement of medication adherence
Patients are considered adherent to medications when they take prescribed agents at doses and times recommended by a health care provider and agreed to by the patient.9 As the health care community adopts the concepts of patient centeredness and activation, it is moving away from the term "compliance", which implies patient passivity in following the prescriber's recommendations.10 Medication persistence is the length of time from initiation to discontinuation of therapy.11,12
Adherence may be measured indirectly or directly as shown in Table 1. Two indirect adherence metrics used in research and administrative work are the medication possession ratio (MPR) and the proportion of days covered (PDC). MPR is calculated as the total number of days supplied, divided by the number of days between the first and last refills; while PDC is calculated as the total number of days supplied during an interval, divided by the total number of days during that interval.13 An MPR of 80% is often used as the cut off between adherence and nonadherence based on its ability to predict hospitalizations across selected high prevalence chronic diseases.14 These measures rely on pharmacy claims data, which does not account for the use of free drug samples, can miss coverage through a different insurance plans, and is insensitive to therapy changes.15,16 Insurance claims data also do not assess whether patients time doses, or use delivery devices, correctly. These protocols are important in conditions like COPD and asthma, where the way a patient uses inhaled therapy can also affect outcomes significantly.17
Table 1 Methods of measuring adherence
Note: *Generally not used in direct patient care.
Abbreviations: MPR, medication possession ratio; PDC, proportion of days covered; PBM, pharmacy benefit manager; EMR, electronic medical records.
In clinical settings, adherence may be indirectly assessed using patient recall. Because patients may significantly overestimate adherence during self-reports,18 patient recall is more effectively interpreted when combined with a validated questionnaire to assess adherence barriers.19–21 Other methods such as pill counting and reviewing pill bottles against medication lists may provide important clinician insights and an opportunity for patient education.22 Bidirectional electronic (e)-prescribing interfaces which provide clinicians data on medication refill intervals at the time of care, are available in settings with electronic medical records.23 Electronic and mechanical dose counters provide estimates of adherence that can be reviewed during clinician visits; these may also improve adherence by providing patient reminders.24 Finally, clinicians may assume patients are adherent with medications when therapeutic goals are achieved. Like claims data, clinical setting measures lack the ability to verify doses are taken but require less time and expense to implement, compared to directly measured adherence.
Direct methods, including observed therapy, and blood or urine drug and metabolite concentrations are most commonly used in research when therapy involves high risk medications, or when public health needs merit the additional costs, invasiveness, and resources required to implement them.9,25
Optimizing expenditures and outcomes
In 2010 spending for prescription drugs in the US was US$259 billion.26 Considering the prevalent rates of nonadherence, drug-related expenses could increase substantially if adherence improved. Medication nonadherence is widespread and varied by disease, patient characteristics, and insurance coverage, with nonadherence rates ranging from 25% to 50%.6,27 In the US, nearly half of all adults have at least one chronic disease28 and the percentage of Americans taking at least one prescription drug increased from 38% in the period 1988–1994 to 49% in the period 2007–2010; during the same time the number of adults taking three or more prescription drugs doubled.26 Prescription medication use will increase as the population ages. Based on these statistics, increasing adherence from current levels could increase medication expenses by billions of dollars.
Strategies to enhance adherence should consider the impact on overall health care costs, weighing increased drug expenditures against savings from improved outcomes. The majority of the costs attributed to medication nonadherence result from avoidable hospitalization.7 Additional direct costs are incurred by progression of controllable disease with: 1) increased service utilization at physician offices, emergency rooms, and urgent care and treatment facilities such as nursing homes, hospice, or dialysis centers; 2) avoidable pharmacy costs related to therapy intensification as comorbid conditions develop; and 3) diagnostic testing that could be avoided by controlling the primary illness.
Because the adverse consequences of most chronic illnesses may not present for years, it is argued that additional expenditure to increase medication adherence might not be economically attractive to payers. Even in illnesses where total health care costs are lower in adherent patients, savings might reflect the impact of patient characteristics, other than adherence, that make them healthier overall than nonadherers.29 If this were true, investing resources in activating "unhealthy nonadherers" might not be cost effective. However, as discussed in our review, there is substantial evidence that the long term costs of poor outcomes exceed costs of medications in many chronic illnesses.7
In contrast, increasing adherence in mild illness may not save costs. If the cost of the medication is relatively high, while the baseline rate of hospitalizations and emergency department visits is low (eg, mild asthma or early human immunodeficiency infection), total health care costs may increase with better adherence.17,30 In cases like these, using low-cost generic medications and targeting higher severity patients may shift the balance towards cost savings.
Increasing adherence in patients with higher acuity may be a better investment, especially when rates of hospitalization are high. An estimated 10% of hospitalizations in older adults may be caused by medication nonadherence.29,31 To prevent admissions and readmissions, payers and hospitals have implemented programs to improve medication adherence after discharge.32,33
Medication adherence and health care costs
The relationship between nonadherence and associated health care costs is shown in Figure 1. Medication nonadherence leads to poor outcomes, which then increase health care service utilization and overall health care costs. The financial pressure is passed to patients by payers through higher copayments, or via higher costs to employers for coverage. Increased patient cost sharing beyond a threshold negatively impacts the level of medication adherence.34 Figure 1 does not include other important drivers, such as the impact of reduced productivity, absenteeism, and increased disability on employers or society. It has been estimated that health-related productivity loss costs are 2.3 times higher than the direct health care costs.35 Therefore, the benefits of improved medication adherence may be even greater when considered at a societal level. However, most of the existing studies consider only direct health care costs when estimating the impact of nonadherence, and typically do not include productivity and disability costs.17 Medication waste accounts for a small percentage of these costs.36,37
Figure 1 Conceptual diagram displaying a mechanism that may contribute to the maintenance of the medication nonadherence problem within the US health care system.
Between $100 and $300 billion of avoidable health care costs have been attributed to nonadherence in the US annually, representing 3% to 10% of total US health care costs.7,38 While there is substantial information relating nonadherence to poor patient outcomes, relatively few high quality studies report the impact on costs. The cost of nonadherence is generally determined by using administrative data to evaluate health care costs in populations of patients who are adherent compared to costs of populations of patients who are nonadherent. Systematic reviews of adherence note that differences in design, cost definitions, and included diagnosis (International Classification of Diseases [ICD]-9) groups, have varying levels of attributable-cost and make comparison of outcomes challenging. Over the last decade, the impact of adherence has been evaluated in association with numerous illnesses, including cardiovascular, pulmonary, gastrointestinal, metabolic, infectious, and psychiatric diseases. Representative findings are presented in our review, including summaries of systematic reviews when available.
Approximately 50% of patients with cardiovascular disease have poor adherence to their prescribed medications.39 In a 2009 review, Ho et al concluded "Surprisingly little is known about the association between medication adherence and health care costs in cardiovascular populations".12 In Medicare and Medicaid beneficiaries with congestive heart failure, Esposito et al found that total health care costs showed a graded relationship to adherence measured by MPR, and, were as much as 23% less per year for adherent compared with nonadherent patients.40 A retrospective longitudinal analyses by Sun et al, of more than 1,300 patients with heart failure or myocardial infarction after acute hospitalization revealed that adherence and persistence with angiotensin receptor blockers and angiotensin converting enzyme inhibitors resulted in lower risk of rehospitalization and lower health care costs.41 Several studies have found strong associations between statin adherence and costs. In a retrospective cohort study of 381,422 patients using an integrated pharmacy and medical claims database, higher MPR was associated with reductions in subsequent total health care costs and cardiovascular disease-related hospitalizations.42 Another retrospective claims-based study of 1,705 patients with diabetes and hyperlipidemia showed that adherence decreased all-cause medical costs by 15%.43 Sokol et al reported that in a retrospective cohort observation of 137,277 benefit plan patients, high levels of adherence were significantly associated with lower overall health care costs for diabetes, hypercholesterolemia, and hypertension, but not with congestive heart failure.29
In a 7-year retrospective administrative claims study of 55,076 chronic obstructive pulmonary disease (COPD) patients, Toy et al demonstrated an association between a high proportion of days covered (PDC) and fewer emergency department visits, and hospitalizations, resulting in a 2.2% reduction in overall costs.44 A similar study of 33,816 Medicare beneficiaries diagnosed with COPD by Simoni-Wastila et al found a reduction (−$2,185 per patient) in the annual Medicare spending for patients with PDC higher than 80%, compared to patients with PDC below 80%.45 Stuart et al compared users and nonusers of maintenance medication for COPD, and found use of maintenance therapy was associated with significantly lower risks of hospitalization and rehospitalization and reduced Medicare expenditures.46
In contrast to COPD, the studies have shown better adherence in asthma cohorts was associated with higher total health care costs. For example, a retrospective observational study of 18,456 Medicaid children aged 2–18 years diagnosed with asthma found lower rates of emergency room utilization, but did not demonstrate improved health care costs.17 A retrospective observational study using 2 years of claims data for 41,234 commercially insured asthmatics found overall costs increased with better adherence, except for patients in high-risk subgroups with past emergency department visits or hospital admission.30 Both studies suggest that improving medication adherence is cost saving in patients with severe disease, but increases overall costs in patients with mild disease, and a corresponding lower baseline rate of hospitalizations.17,30
In a retrospective study of patients initiating selective serotonin reuptake inhibitor (SSRI) therapy for depression and/or anxiety between July 2001 and June 2002, in a large national managed care database, Cantrell et al found approximately 43% of patients were adherent to antidepressant therapy, and adherent patients were associated with the lowest yearly medical costs.48 Conversely, a study of 65,753 managed care patients between 2001 and 2002, found medical charges (mainly related to inpatient treatment), excluding pharmacy charges, were lower for patients remaining on antidepressant drug therapy for at least 90 days, but that when drug costs were added there was no difference between adherent and nonadherent patients.49 Another study of 60,386 adult patients with depression studies showed adherent patients incurred an additional $806 in overall health care expenditures compared to nonadherent patients, in the 6 months following initiation of antidepressant therapy.50
Multiple studies have evaluated the relationship of costs to diabetes, and generally support a correlation of increased adherence and reduced cost. One systematic review by Salas et al identified 209 studies, of which ten met inclusion criteria, and evidenced that despite significant methodological discrepancies among studies, low adherence was generally associated with higher costs.51 Another systematic review identified 449 relevant articles (12 met criteria) and reached similar conclusions.52 In a retrospective study of 57,687 patients, Hepke et al reported that overall health care costs were not lower among diabetic patients who were adherent with medications due to increased pharmaceutical costs, but suggested a threshold effect above which adherence was associated with reduced costs.16 Balkrishan et al found that each 10% increase in adherence was associated with an 8.6% to 28.9% decrease in total annual health care costs.48 More recently, in a longitudinal 4 year study of 740,195 veterans with type II diabetes, Egede found that nonadherent patients can have annual inpatient costs 41% higher compared to adherent patients, and concluded that significant costs could be avoided by increasing adherence.53 The level of cost savings varies with the type of diabetes medication considered.16,54
In a retrospective cohort study of 325 previously antiretroviral medication-naïve human immunodeficiency virus (HIV)-infected individuals initiating first antiretroviral therapy from 1997 through 2003, Gardner et al showed that better adherence to antiretroviral medication was associated with decreased health care utilization and associated costs; however, because of the high cost of antiretroviral therapy, total medical costs were increased.55 A cohort study of 2,234 Medi-Cal patients with HIV reviewed outcomes in a patient subset served by medication therapy management (MTM) pharmacies versus other pharmacies; this documented higher medication adherence in MTM patients but no significant differences in total cost per patient per group.56 Among HIV-infected veterans, some with drug resistant HIV and multiple comorbid conditions, high adherence with antiretroviral therapy was associated with lower rates of inpatient hospitalization but no difference in total annual health care costs.57
Studies of adherence and its impact on health care costs have been completed for other illnesses including musculoskeletal conditions, gastrointestinal and metabolic disorders, neurologic conditions, and others, and suggest varying levels of impact.6,9,30,58
Determinants of patient adherence
In order to improve medication adherence, clinicians must understand why patients fail to take prescribed medications. There is a large body of literature that investigates the determinants of medication nonadherence. These determinants can be categorized as patient related, provider related, and external factors.9,11 Patient related factors can be further divided into demographic, sociocultural and behavioral factors.59 External factors include disease characteristics, medication properties and system components.9,11 Figure 2 depicts the relationship of patient, provider and external factors.
Figure 2 Selected determinants of medication adherence.
Among patients, forgetting is the most frequently reported reason for nonadherence.60,61 In a survey of 10,000 patients, the most common reported reason for missing medications was forgetfulness (24%), followed by perceived side effects (20%), high drug costs (17%), and perception that a prescribed medication would have little effect on their disease (14%).62 In another survey of 14,464 Medicare beneficiaries, patients who did not fill at least one prescription reported the following reasons: "thought it would cost too much" (55.5%), "medicine not covered by insurance" (20.2%), "didn't think medicine was necessary for the condition" (18.0%), and "was afraid of medicine reactions/contraindications" (11.8%).63 More sophisticated patient assessment tools can elucidate psychological determinants such as self-reported medication self-efficacy, beliefs about medications and motivation that may impact adherence.19,64
In Figure 2, providers are positioned as connectors between patient and external factors. In an effective provider–patient relationship, providers will engage patients in shared decision making regarding medication needs and expected efficacy. Communication is central to the effectiveness of this relationship. Zolnierek et al estimated in a recent meta-analysis, that poor communication results in a 19% higher risk of nonadherence.65 Conversely, training physicians to improve their communication abilities leads to an improved chance of patient adherence.65 Factors like racial concordance can affect physician–patient communication and clinical decision making. A qualitative study conducted by Snipes et al concluded that black physicians were more inclined than white physicians to take into account a patient's race and cultural beliefs about disease when deciding among treatment alternatives.66 From a provider perspective, inadequate time and communication tools were perceived to be the most important barriers to enhancing adherence.67
Health care systems determine adherence in a number of ways. Restrictive formularies may limit access to prescribed medications and magnify patient barriers.68 Systems should ensure patient benefits support appropriate access and utilization, and develop provider payment models and patient management services that encourage adherence. The impact of out-of-pocket patient cost on adherence is discussed below.
Numerous interdependencies exist between patient, provider, and system. For example, a perceived threat from a specific disease may be influenced by a patient's sociocultural background, as well as the patient's personal experience with the disease and its severity. Patient perceptions may be further influenced by clinician–patient communication, the strength of which may be a function of the patient's cultural concordance and by system and provider factors. These relationships also suggest strategies that may enhance adherence.
Strategies to increase patient adherence
Strategies that may be used to increase medication adherence are presented in Table 2. These can be seen as targeting the patient, provider, and external determinants of adherence. A 2012 systematic review by Viswanathan et al evaluated interventions to improve adherence to self-administered medications for chronic disease, and found that reduced out-of-pocket patient costs (ROPC), case management and patient education improved adherence in more than one condition.57 The review also found strong evidence for interventions such as collaborative care (in depression) and self-management (of asthma), but cautioned that benefits might not extend to other conditions. Disease self-management, patient engagement or "activation", as measured by the validated questionnaires, has been linked to better health outcomes.69,70
Table 2 Selected strategies for improving medication adherence
Abbreviation: EMR, electronic medical records.
Reducing out-of-pocket costs leads to better medication adherence across many diagnoses.58 There is a linear relationship between the magnitude of patient cost sharing and the level of adherence.34
This relationship persists from low to higher income levels. A large survey found that a significant percentage (14%) of high-income respondents also indicated cost-related nonadherence, despite relatively affordable copayments.71
Whether ROPC will result in overall reductions in health care costs is less clear. Choudhry et al demonstrated that providing full prescription coverage to patients discharged after acute myocardial infarction improved mediation adherence by 4 to 6 percentage points but did not affect total health care costs despite some improvement in rates of subsequent first major vascular events.72 A 2013 consensus panel review called for additional work to assess the effectiveness of ROPC studies.73
Several studies have specifically reviewed use of financial incentives to increase medication adherence. A recent systematic review by DeFulio and Silverman identified 692 articles, 13 of which met inclusion criteria; across all studies, incentives increased adherence by a mean of 20 percentage points, but effects varied widely.74 Providing incentives that are at risk for nonadherence may be an effective strategy, based on the behavioral construct of "loss aversion" which states that people experience more dissatisfaction from the loss of a certain dollar amount than the satisfaction from the gain of the same amount.75 Two studies looking at the effect of lottery based incentives on anticoagulant adherence have had mixed results, but this concept needs further assessment. The impact on long term adherence has not yet been investigated.76,77
In contrast to population-based approaches, patient-specific approaches to nonadherence have been suggested. Marcum et al recently described use of validated adherence assessment tools to "diagnose" patient specific reasons for nonadherence, with subsequent effort directed at the specific barriers represented by that diagnosis.20 This strategy suggests benefit for incorporating standard screening tools into patient interviews and reflects a need for patient centeredness in care delivery.
Additional work is needed to elucidate the cost and impact of effort to improve adherence of patients. Because many reports studying the impact of medication adherence on health care costs are based on retrospective observational studies; it is difficult to prove causality between changes in medication adherence and corresponding effects on health care costs. Studies may be affected by the "healthy adherer" phenomenon, which occurs because healthier people may be more adherent, but also have better outcomes independent of the drug's therapeutic impact.54 As a consequence, it has been argued that more clinical trials, where randomization could remove some sources of bias, should be designed to directly investigate the cost impact.29 Because of the insensitivity of current diagnosis groups to disease severity or stage, as in congestive heart failure or asthma, the planned national implementation of ICD-10, which has much more specificity, may improve identification of specific populations where efforts to increase adherence would achieve greatest gains.
Team-based case management is already recognized as an effective care strategy that improves adherence and health outcomes, and can decrease overall medical care costs. The guiding principles of patient-centered medical homes include the maintenance of a longitudinal patient relationship with a personal physician together with a multidisciplinary care team who together take a whole-person approach and coordinate care across settings is also a key component.78 Since the "medical home" model is relatively new and still under assessment, its impact on health care costs as related to improvements in medication adherence remains to be fully demonstrated.
Pharmacists can play integral roles within medical homes.79 Pharmacist led multi-pronged interventions have indeed been demonstrated to improve outcomes.58 From a legislative perspective, the Medicare Modernization Act of 2003 required Medicare part D plans to implement medication therapy management services provided by clinical pharmacists, while the 2010 Affordable Care Act provided more guidance and expanded patient eligibility.80 These services include a comprehensive assessment of current medications, which includes counseling regarding the importance of adherence, and the design of a medication related action plan.81 A recent review of studies evaluating medication therapy management programs has identified key elements that improve outcomes, such as providing services to patients with specific therapeutic problems, having routine communications with primary care providers, and having regular follow-up to encourage medications adherence after changes in the medication regimen.82
E-prescribing is another intervention with the potential to improve medication adherence and total health care costs, and is becoming more widely used as electronic medical records are integrated into medical practices. E-prescribing differs from simple transmission of prescriptions directly to pharmacies and allows prescribers to receive an electronic notice from the pharmacy telling them that a patient's prescription has been picked up, not picked up, or has been partially filled, to help monitor medication adherence in patients with chronic conditions. A recent study, conducted by Surescripts in collaboration with pharmacies and pharmacy benefit managers, analyzed over 40 million prescription records, observing that e-prescribing increased the percentage of new prescriptions that were picked up by patients from the pharmacy by 10%, compared to paper prescriptions. In the absence of e-prescribing, only 73.2% of paper prescriptions make it to the pharmacy and an even smaller percentage of the total prescriptions written are picked up by patients. The same study estimated that e-prescribing and the corresponding increase in first-fill medication adherence would save at least $140 billion over the following decade.83 Additional studies to validate the impact of e-prescribing systems and electronic medical records on adherence and related cost savings are needed.84
Medication nonadherence is a significant contributor to avoidable health care costs in this country. While impact on disease outcome and cost is more pronounced in some illnesses than others, stakeholders agree that increasing medication adherence would improve health outcomes and save billions of dollars. Improved adherence can be achieved through better education, value-based insurance designs, and thoughtful use of patient incentives. Ultimately collaboration between patients, payers, policy makers and providers, and redesigning systems to employ more team-based care, use lower cost medications, and target highest cost illnesses, will be needed to achieve better adherence and optimize spending.
The authors report no conflicts of interest in this work.
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Stuart B, Davidoff A, Lopert R, Shaffer T, Samantha Shoemaker J, Lloyd J. Does medication adherence lower medicare spending among beneficiaries with diabetes? Health Serv Res. 2011;46(4):1180–1199.
Gardner EM, Maravi ME, Rietmeijer C, Davidson AJ, Burman WJ. The association of adherence to antiretroviral therapy with health care utilization and costs for medical care. Appl Health Econ Health Policy. 2008;6(2–3):145–155.
Hirsch JD, Gonzales M, Rosenquist A, Miller TA, Gilmer TP, Best BM. Antiretroviral therapy adherence, medication use, and health care costs during 3 years of a community pharmacy medication therapy management program for medi-cal beneficiaries with HIV/AIDS. J Manag Care Pharm. 2011;17(3):213–223.
Barnett PG, Chow A, Joyce VR, et al. Determinants of the cost of health services used by veterans with HIV. Med Care. 2011;49(9):848–856.
Viswanathan M, Golin CE, Jones CD, et al. Interventions to improve adherence to self-administered medications for chronic diseases in the United States: A systematic review. Ann Intern Med. 2012.
Krueger KP, Berger BA, Felkey B. Medication adherence and persistence: A comprehensive review. Adv Ther. 2005;22(4):313–356.
Boskovic J, Leppee M, Culig J, Eric M. Patient self-reported adherence for the most common chronic medication therapy. Scand J Public Health. 2013;41(4):333–335.
Barfod TS, Sorensen HT, Nielsen H, Rodkjaer L, Obel N. 'Simply forgot' is the most frequently stated reason for missed doses of HAART irrespective of degree of adherence. HIV Med. 2006;7(5):285–290.
Boston Consulting Group. The hidden epidemic: finding a cure for unfilled prescriptions and missed doses. 2003. Available from: http://www.bcg.com/documents/file14265.pdf. Accessed September 10, 2013.
Kennedy J, Tuleu I, Mackay K. Unfilled prescriptions of medicare beneficiaries: Prevalence, reasons, and types of medicines prescribed. J Manag Care Pharm. 2008;14(6):553–560.
Voils CI, Maciejewski ML, Hoyle RH, et al. Initial validation of a self-report measure of the extent of and reasons for medication nonadherence. Med Care. 2012;50(12):1013–1019.
Zolnierek KB, Dimatteo MR. Physician communication and patient adherence to treatment: A meta-analysis. Med Care. 2009;47(8):826–834.
Snipes SA, Sellers SL, Tafawa AO, Cooper LA, Fields JC, Bonham VL. Is race medically relevant? A qualitative study of physicians' attitudes about the role of race in treatment decision-making. BMC Health Serv Res. 2011;11:183–6963.
Brunton SA. Improving medication adherence in chronic disease management. J Fam Pract. 2011;60(4):S1–S8.
Braithwaite S, Shirkhorshidian I, Jones K, Johnsrud M. The role of medication adherence in the US healthcare system. 2013. Available from: http://static.correofarmaceutico.com/docs/2013/06/24/adher.pdf. Accessed September 10, 2013.
Greene J, Hibbard JH. Why does patient activation matter? an examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med. 2012;27(5):520–526.
Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the patient activation measure (PAM): Conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4 Pt 1):1005–1026.
Piette JD, Beard A, Rosland AM, McHorney CA. Beliefs that influence cost-related medication non-adherence among the "haves" and "have nots" with chronic diseases. Patient Prefer Adherence. 2011;5:389–396.
Choudhry NK, Avorn J, Glynn RJ, et al. Full coverage for preventive medications after myocardial infarction. N Engl J Med. 2011;365(22):2088–2097.
Community Preventive Services Task Force. Annual report to congress. 2013. Available from: http://www.thecommunityguide.org/annualreport/2013-congress-report-full.pdf. Accessed September 21, 2013.
DeFulio A, Silverman K. The use of incentives to reinforce medication adherence. Prev Med. 2012;55 Suppl:S86–S94.
Kimmel SE, Troxel AB. Novel incentive-based approaches to adherence. Clin Trials. 2012;9(6):689–695.
Volpp KG, Loewenstein G, Troxel AB, et al. A test of financial incentives to improve warfarin adherence. BMC Health Serv Res. 2008;8:272.
Kimmel SE, Troxel AB, Loewenstein G, et al. Randomized trial of lottery-based incentives to improve warfarin adherence. Am Heart J. 2012;164(2):268–274.
Rosenthal TC. The medical home: Growing evidence to support a new approach to primary care. J Am Board Fam Med. 2008;21(5):427–440.
Smith M, Bates DW, Bodenheimer T, Cleary PD. Why pharmacists belong in the medical home. Health Aff (Millwood). 2010;29(5):906–913.
Lee-Wilson C. MA. Establishing a medication therapy management (MTM) practice: Obtaining compensation for clinical services. Maryland Pharmacist. 2011:3.
National Conference of State Legislatures. Medication therapy management: Pharmaceutical safety and savings. National Conference of State Legislatures Web site. http://www.ncsl.org/issues-research/health/medication-therapy-management.aspx. Accessed December 19, 2012.
Kucukarslan SN, Hagan AM, Shimp LA, Gaither CA, Lewis NJ. Integrating medication therapy management in the primary care medical home: A review of randomized controlled trials. Am J Health Syst Pharm. 2011;68(4):335–345.
E-prescribing shown to improve outcomes, save billions. Study quantifies relationship between e-prescribing and medication adherence, with potential savings of $140 billion over the next 10 years. Health Manag Technol. 2012;33(4):22–23.
Blumenthal D, Tavenner M. The "meaningful use" regulation for electronic health records. N Engl J Med. 2010;363(6):501–504.
McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: Scientific review. JAMA. 2002;288(22):2868–2879.
Granger BB, Bosworth HB. Medication adherence: Emerging use of technology. Curr Opin Cardiol. 2011;26(4):279–287.
Cooper LA, Roter DL, Carson KA, et al. A randomized trial to improve patient-centered care and hypertension control in underserved primary care patients. J Gen Intern Med. 2011;26(11):1297–1304.
Kripalani S, Yao X, Haynes RB. Interventions to enhance medication adherence in chronic medical conditions: A systematic review. Arch Intern Med. 2007;167(6):540–550.

redpajama_set_name: RedPajamaCommonCrawl


text:
About Leslie Coff
Learning About Chinese Medicine
Sportsmedicine/Injuries
Women's Health & Fertility
Pain & Neurological Disorders
Chronic Illness, Stress & Autoimmune Disease
Asian Dietary Medicine
Find me in other places:
We Breathe Out Stars:
Narratives on Hope
www.lesliecoff.com
Leslie Coff, MTOM, L.Ac., Dipl. Acu.
Coff holds a Diplomate of Acupuncture from the National Commission for the Certification of Acupuncture and Oriental Medicine (NCCAOM). In addition, she is licensed to practice Acupuncture and Traditional Chinese Medicine in the states of California, Georgia and Wisconsin.
After completing her undergraduate work at the University of California at Los Angeles, Coff received her Masters Degree in Traditional Oriental Medicine after four years of intensive study at the Emperor's College of Traditional Oriental Medicine in Santa Monica, California. Her training included Chinese Herbal Medicine, Western Clinical Medicine, Clinical Nutrition, Acupuncture and Western Pharmacology.
Coff has been in private practice since 1988 in Los Angeles, California, St. Louis, Missouri, Atlanta, Georgia and now Madison, Wisconsin. She is a past-President of the Acupuncture Association of Missouri and worked to pass acupuncture licensing laws in both Missouri and Georgia.
Coff taught Acupuncture and Chinese Herbal Medicine in Atlanta and contributes to the NCCAOM licensing examination as a member of the Academy of Item Writers.
How can we help you? Email Leslie


text:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Text.RegularExpressions;

namespace MipsSharp.Sys
{
    public class Float
    {
        private static readonly Regex _roundUpParts =
            new Regex(@"(\d+)\.(\d+?)(9{4,})(\d+)", RegexOptions.Compiled);

        private static readonly Regex _truncateParts =
            new Regex(@"(\-?\d\.\d+?)(0{4,}(\d+))", RegexOptions.Compiled);

        public static bool BinaryCompare(float a, float b) =>
            BitConverter.GetBytes(a).SequenceEqual(BitConverter.GetBytes(b));

        public static bool BinaryCompare(double a, double b) =>
            BitConverter.GetBytes(a).SequenceEqual(BitConverter.GetBytes(b));

        public static (string Result, bool Success) FloatRoundDecimalUp(double input)
        {
            var hit = false;
            var str = _roundUpParts.Replace(
                input.ToString(),
                m =>
                {
                    hit = true;

                    return string.Join(
                        "",
                        m.Groups[1].Value,
                        ".",
                        ((int.Parse(m.Groups[2].Value) + 1) + "").PadLeft(m.Groups[2].Value.Length, '0')
                    );
                }
            );

            return (str, hit);
        }

        public static (string Result, bool Success) FloatTruncateDecimal(double input)
        {
            var hit = false;
            var str = _truncateParts.Replace(
                input.ToString(),
                m =>
                {
                    hit = true;

                    return m.Groups[1].Value;
                }
            );

            return (str, hit);
        }

        public static (string Approximation, bool Successful) GetClosestApproximation(float input)
        {
            var truncatedAsString = FloatTruncateDecimal(input);
            var truncated = float.Parse(truncatedAsString.Result);

            if (truncatedAsString.Success && BinaryCompare(truncated, input))
                return (truncatedAsString.Result, true);

            var strApprox = FloatRoundDecimalUp(input);

            if (strApprox.Success && BinaryCompare(float.Parse(strApprox.Result), input))
                return (strApprox.Result, true);

            return (
                input.ToString(),
                BinaryCompare(float.Parse(input.ToString()), input)
            );
        }

        public static IEnumerable<(string left, string right)> GenerateAssemblyLine(float input)
        {
            var approx = GetClosestApproximation(input);

            if (!approx.Successful)
            {
                yield return ($"/* Couldn't approximate 32-bit float. Was: {input} */", null);
                yield return (
                    ".word",
                    string.Format("0x{0:X8}", BitConverter.ToUInt32(BitConverter.GetBytes(input), 0))
                );
            }
            else
            {
                yield return (".single", approx.Approximation);
            }
        }

        public static IEnumerable<(string left, string right)> GenerateAssemblyLine(double input)
        {
            if (BinaryCompare(double.Parse(input.ToString()), input))
            {
                yield return (".double", input.ToString());
            }
            else
            {
                yield return ($"/* Couldn't approximate 64-bit float. Was: {input} */", null);
                yield return (
                    ".quad",
                    string.Format("0x{0:X16}", BitConverter.ToUInt64(BitConverter.GetBytes(input), 0))
                );
            }
        }

        public static IEnumerable<(string left, string right)> GenerateAssemblyLine(UInt64 input) =>
            GenerateAssemblyLine(BitConverter.ToDouble(BitConverter.GetBytes(input), 0));
    }
}

redpajama_set_name: RedPajamaGithub


Sustainability, Peter Senge, and the Necessary (Supply Chain) Revolution.
I just finished reading an interview with Peter Senge in the October Harvard Business Review. Senge, for those of you that are unfamiliar, founded the Society for Organizational Learning, is a faculty member at MIT Sloan School of Management, and the author the The Fifth Discipline and The Necessary Revolution. Senge maintains that to make progress on environmental issues, organizations must understand that they're part of a larger system. Senge also makes a great point that companies will be in a better competitive position if they understand the larger system that they operate within and to work with people you haven't worked with before. And while these two skills might seem distinct, in practice they're interwoven. This is generally because systems are often too complicated for one person to grasp, crossing over many boundaries, both internal and external. It's these external boundaries that supply chain management issues begin to become apparent.
According to Senge, and as I mentioned last month in an earlier post about Starbucks, supply chains support whole systems thinking because they focus on the "nature of the relationships". In the HBR article, Senge maintains that in most supply chains, 90% of them are still transactional. Manufacturer or retailers still pressure upstream suppliers to get their costs down and little incentive is given toward innovating together. This in turn erodes trust, however, as I have mentioned in this space, changes are everywhere. Some companies like Starbucks, Coca-Cola and Walmart are also partnering with Non-Governmental Organizations (NGO) and working in an open source manner with industry associations to innovate. Successful ventures like Walmart/Environmental Defense Fund, Unilever/Oxfam and Coca-Cola/World Wildlife Fund are taking a collaborative approach to problem solving that drives innovation, breeds trust and industry "cred" and offer NGO's a wider voice in addressing social, environmental performance issues in the supply chain.
Research and practice in supply chain management is beginning to prove once and for all that supply chain as a "practice" offer unique learning opportunities related to triple bottom line based sustainability. Learning experiences can range from relatively simple, incremental modifications to a current knowledge set – for example, new environmental regulations like REACH and RoHS – through to complex new approaches which will involve experimentation, small scale piloting and larger scale adaptation (such as those designed to help transporters manage their carbon emissions).
How does your company use "whole systems" thinking to manage supply chain issues? In coming weeks I will begin exploring supply chain learning and management through a sustainability lens, and share some findings from various manufacturing sectors. It's my hope that readers can then begin to understand how to apply whole systems approaches across enterprises in the supply chain. It's my grand plan that these ideas will gel into practical steps that add value and become a core operating principle in your company.


It has been a bit hot here lately. And by a bit, I mean "cook you brain/even the cactus looks PO'd hot". So when B & I were thinking about what to drink, it had to be something on the lighter side, but as it was evening, we wanted something a with a bit of complexity as well.
This leads us to our third of four installments where we try out the winners of Imbibe Magazine's "Simple 4 Ingredient Cocktails" challenge. So far we have taken the High Noon and The Graduate for a spin and both were impressive. This time we decided to try the Django Reinhardt, a creation of Erick Castro of Polite Provisions in San Diego.
The choice of dry vermouth as the main ingredient played off of the citrus is a pretty unique idea.
Gently muddle the orange slices in the shaker . Add the remaining ingredients and fill with ice cubes; shake until chilled, about 10 seconds. Fine-strain into a rocks glass filled with fresh ice; garnish with a slice of orange.
This is an interesting drink. The large amount of aromatic infused wine in the vermouth instead of a hard liquor base leaves the drink a bit lighter than many on this blog. So while always important, the freshness of the vermouth is even more critical for this drink. Also, it's probably best to make sure the slices of orange are pretty thin or the drink can incline toward the overly sweet. All in all, it's a very nice, lighter summer evening drink. A fact made even more impressive considering it's made vermouth and orange juice; two ingredients that would cause lesser bartenders to blanch.


Have a question about file manager.
Discussion in 'General Discussion' started by prettydolphins7, Sep 16, 2008.
When I get into file manager and I upload a HTML document (like a new page for my website, it loads. But the thing is when I go and edit a page to add the new one in (With the Hyperlink) and the page i loaded into file manager isn't showing.So what is going on? I've had no problems til today.
Have you tried checking if the permission is set correctly to 755 (rwxr-x-r-x)? You may want to check on it and try to load your page again.


The Universal Asynchronous Receiver/Transmitter (UART) format supports a raft of different protocols. Perhaps the best-known of the lot is the venerable RS-232 protocol. The UART bus is an asynchronous type with rates that vary widely across different applications. It comprises a receive line, transmit line, and various handshaking communication lines that depend on the use case. The basic UART packet format consists of a start bit; 5 to 9 data bits; an optional parity bit; and either 1, 1.5, or 2 stop bits (Figure 2).
Because the UART protocol is so broadly configurable, any trigger/decode software used for debugging must be equally malleable. That means a completely customizable trigger configurable for any UART signal; a preset RS-232 mode is a handy attribute.
A fully customizable trigger means that the oscilloscope can trigger on any UART-based signal, even if the bus protocol is proprietary, by selecting the appropriate bit rate, parity, bus polarity, bit order, and stop-bit settings. The software also should support both 8- and 9-bit formats.
With properly equipped serial trigger/decode software, you can trigger on specific data values on a UART-based bus. The trigger may be set up by entering a data pattern in either binary or hex formats. With binary triggering, you gain the flexibility of triggering on individual bits in a pattern, while hex triggering simplifies the setup of long data-pattern triggers. Patterns of up to 12 bytes are supported. Another feature of a powerful serial trigger/decode package is conditional trigger settings such as less than, greater than, in range, and out of range. These can really help you get a handle on abnormalities in a UART signal.


Jeff For Banks: Politics: Can We All Just Get Along?
I do not venture into politics much, either in this blog or in person. But our environment is so toxic, I would like to take a crack at identifying shared goals by most of us.
1. We all want to reduce the number of impoverished people. We have different ideas on how to do it. I think capitalism is a better solution than socialism, as the latter creates so much more of an underclass. Except for the bureaucrats. They tend to do well in socialism. The more you disperse economic power in a society, the better, in my opinion.
2. Many successful capitalists turn into jerks. I think, by and large, this is because they want to solidify their position, and the by-product is keeping others from achieving it. That is why in large corporations executives might make it difficult for up and comers, fearing they might be unseated by them. This is also why top executives get paid so much. I wouldn't stop companies from paying executives so much, but would insist on transparency and not allow a company to deduct executive comp that is greater than some multiple of company average compensation on their Federal taxes. But better to have many, many successful capitalists, than a few successful bureaucrats. Successful capitalists are the "do-ers" of society. They create jobs. Not government. If you don't trust me on this, study economics. At most colleges. Not all. I also want to encourage a society where capitalists do well, and give their excess to charitable endeavors. Like Warren Buffet is trying to make happen.
3. Our tax system is way too complex. I would make the personal and corporate marginal tax rate the same. Somewhere around 20%. I put a simple tax solution in a post way back in 2012. See it here. This will cause disruption among accountants and tax lawyers. Taxes would be so easy and transparent, these professionals wouldn't be needed by individuals or corporations. Think of all the tax compliance savings!
4. Government spending. Until we reverse the alarming trend of national debt to GDP, we must spend less than revenue growth, and balanced budgets have to be the norm rather than the exception. I wouldn't do a balanced budget amendment, because elevating infrastructure investments and spending during recessionary periods makes sense to me. But until that time, Federal spending growth should be less than GDP growth. Oh, and the last balanced budget under President Clinton was spurred, in part, by Pay-Go. That system where new "programs" would have to be paid for by eliminating other programs that cost the same or more. I would put that structural discipline in place right away.
5. Federal government operation. Our rules are ridiculous. Bills would be cleaner, and more linear. No slapping on stupid amendments for pork. If it has enough support, have the pork get its own bill. And bringing a bill to the floor for vote would be easier. One requirement I would insist on is having a litmus test for programs designed to help society. If they don't meet a pre-agreed upon social objective over a reasonable time period, they automatically die. No vote needed. We tried to fix a social ill. It didn't work. Let's move on. And not worry about those that lose funding sending out press releases that we don't care "for the children".
6. Speaking about lawmaking, there are way too many laws and regulations for society to follow. Nobody, and I mean nobody, knows them all. In fact, most if not all of us break the law every day. This creates a ripe environment for tyranny, that we see playing out in front of our eyes. Don't like someone? Figure out a law they broke and go after 'em. Think about it. If I were in charge, there would be less, and the objective would be far less, rules and regs to follow, reducing the ability of powerful law enforcement and government bureaucrats to move against its citizens. And making it easier to enforce and comply, both individually and economically. Watch the bureaucrats squirm about this one.
7. World relations. We want to influence societies to be free. But our own freedoms are being eroded by the growing body of laws and regs, mentioned above, and political correctness which has curtailed our ability to solve problems. So we should take care of our own problems to show the world that, as our society matures, we make corrections to enhance freedom. And create a worthy example that other countries would like to emulate. But dictators. We have to be active in keeping them in check. If we were isolationist, our world would be a different, and much worse place, in my opinion. But our international forays would be selective, proportional, and given the resources and the fortitude to win. Isolated cells of terrorists need not worry about a US Army battalion. But they would have to worry if they actively seek to harm our citizens. Their end will not be pleasant. But it may not be all over the news media, either. Oh, and trade. Free trade works. Few economists believe otherwise. The rub is that they must be enforceable and punitive for cheaters, making cheating so unpalatable that parties to the agreements abide by what they signed. There is a lot of angst against free trade now, but as a society we voted for free trade by buying less expensive stuff that must be made by labor that is less expensive than our own. And lets face it, union work rules made us noncompetitive in manufacturing. Shame on us.
8. Elections. Any candidate that wants to run for office, must complete a two-page resume to a non-partisan website. Page one denotes the candidate's experience. Page two has the candidate's answers to five key questions for the office sought (i.e. federal questions for federal office), in 50 words or less for each question. This is so voters like me can review candidate's qualifications and positions before getting into the voting booth. In PA, where I live, there is a tough US Senate race underway, and the ads the candidates run are ridiculously irrelevant and designed to stir up emotion, and not make us better voters. I say ignore that idiocy. Read the two-page, vote smartly.
9. Safety Net. I'm all for a transitional safety net to help our fellow citizens pick themselves up and get on their feet again. I'm all against turning families into lifelong government dependents, which I think is the consequence of a safety net without the transitional philosophy. If someone hurts their elbow and can no longer do the manual job they once did, we don't re-train to do other jobs not dependent on the elbow. We put them on disability for life. C'mon. This makes so much common sense, that the cynic in me thinks those that support lifelong government assistance (either in word or deed) are just bribing people for their vote using other peoples' money. And relegating the lifelong "drawers" to the lower economic rung for life. Sad.
Why doesn't the media cover much of the above? Instead, they assemble a panel of talking heads to discuss a tweet.
Not sure there would be many that object to the above. Ok, accountants and lawyers, and union leaders. Other than them, why is everyone else shouting at each other?