I – The intervention being introduced is utilizing Sevoflurane (UltaneÂ®) as the inhaled anesthetic for surgeries requiring general anesthesia lasting less than one hour.
C – The comparison group will be those utilizing Desflurane (SupraneÂ®) as the inhaled anesthetic for surgeries requiring general anesthesia lasting less than one hour.
O – The use of Sevoflurane for surgery cases requiring general anesthesia for less than one hour will save money for the anesthesia department rather than using Desflurane.
PICO Question: Will the use of Sevoflurane rather than Desflurane in surgeries requiring general anesthesia lasting less than one hour save money for the anesthesia department?
Inhalation anesthetics are used millions of times every single day in surgeries requiring general anesthesia all over the world. Cost containment is anesthesia is no longer an option, it is an absolute necessity. Anesthetic drugs account for up to 6 percent of total hospital pharmacy costs and inhalation agents comprise over twenty percent of that as a whole. One of the areas most amenable to cost reduction in the anesthesia department budget is the use and choice of inhalational anesthetic. A quantitative quasi-experimental controlled trial was developed to determine if money could be saved in the anesthesia department by utilizing Sevoflurane as the inhalational anesthetic for surgeries requiring general anesthesia less than one hour rather than Desflurane. The study chose males and females, ages twenty to forty, requiring general anesthesia at a mid-sized, urban, teaching hospital in South-Western Pennsylvania. Prices of anesthetic agents were gathered from the same facility and calculated to determine the total cost savings that would ensue with the use of the interventional inhalation agent rather than that used by the control group. The use of Sevoflurane for two-thirds of the cases which Desflurane was used saved the institution over twenty thousand dollars.
Key Words: Inhalational Anesthetics, Pharmacoeconomics, Desflurane, Sevoflurane, Anesthesia
Introduction Research Problem:
The cost of health services and technologies continue to increase exponentially while resources are finite. Developing effective strategies to minimize costs without compromising patient safety and quality of care are the most difficult challenges medical professionals experience.13 Inhalational anesthetics comprise twenty percent of the drug expenses in anesthesia departments. The cost of inhalational anesthetics depend on their potency, which is their minimum alveolar concentration (MAC), the minimum amount of inhalational anesthetic needed in the lungs to facilitate the absence of movement to noxious stimuli, the fresh gas flows (FGF) used to deliver the anesthetic gas, the amount of anesthetic vapor released by each milliliter of liquid, and the price of the inhalational anesthetics themselves.2,16
Inhalational anesthetics are used when general anesthesia is needed for surgery and patients do not have diseases or conditions which would preclude them from being able to utilize them (e.g. those who have malignant hyperthermia). The anesthesia provider decides which inhalational anesthetic is best for a given patient, based on their history and needs for the surgery. The problem exists when providers just use whichever anesthetic is most easily accessible at that time without minding any consideration as to the cost of the particular gas and the level of FGF they are using, which is purely a waste of money as the patient will lose heat and humidity, waste high levels of inhaled anesthetic, and demise cost containment for the anesthesia department.
Statement of Purpose
The purpose of this research study is to determine whether the implementation of Sevoflurane as the choice inhalation anesthetic for surgery requiring general anesthesia for procedures lasting less than one hour rather than using Desflurane will provide cost containment for the anesthesia department.
The objective of this study is to provide an avenue of cost containment for the anesthesia department of a mid-sized, urban, teaching hospital and overall savings for the medical health system. This will allot more money in the department budget to be spent on technological advances, and equipment used to better develop opportunities for patient safety, the ultimate goal of all medical professionals.
Review of Literature: Many studies explore different methods of cost containment in the anesthesia department. Almost all of the studies exclusively develop the notion of cost savings related to the fresh gas flows used to deliver inhalation anesthetics. “Cost containment requires primarily a decrease in FGFs, but it may also be influenced by a rational use of the available halogenated agents.”3 With the advances in technology and the ultra-modern anesthesia machines available, anesthesia providers should feel safe in using minimal FGFs to deliver anesthesia that would not harm their patients in any way.3 Anesthetists can directly influence healthcare costs through the patterns in which they practice, which includes reducing the FGFs chosen during inhalational anesthesia, which will directly affect the amount of inhaled anesthetic delivered to the patient and ultimately the costs which are attributable to inhaled anesthetics.10,15
Depending on breathing systems used, which are mostly semi-open today, up to ninety percent of the anesthetic gases delivered escapes into the atmosphere completely unused. By using low-flow anesthesia, most of the patient’s air they exhale is recycled and then returned to the patient’s lungs after the carbon dioxide from the exhaled air has been absorbed.15 There are also advantages of using low-flow anesthesia such as conserving the patient’s heat and humidity, making it less of a need to warm the patient externally, which will cut down costs on heating blankets and the like, as well as maximized the rebreathing of the exhaled anesthetic, which will cut down on the amount of gas vented into the environment,6 but there are cautions that anesthesia providers must take when using low FGFs, as titration in inhalation anesthetic is not as easily predicted.
Product labeling for Sevoflurane does include a warning that patient exposure should not exceed two hours using FGF rates greater than one and less than two liters per minute.7 Also, it is not recommended at all to maintain FGFs less than one when using Sevoflurane as the patient’s inhalational anesthetic of choice. These recommendations are to minimize the risk of exposure of the patient to compound A which in laboratory animals, has been shown to be linked to nephrotoxicity. There are no minimum flow rate designations that exist for Desflurane.7,8
Isoflurane is less expensive than Sevoflurane and Desflurane at equal dose and the same FGFs.7 When comparing Sevoflurane with Desflurane as they are being used for surgeries lasting less than two hours, there was a ninety percent decrement time for Sevoflurane which approached the short duration of anesthesia matching that of Desflurane. However, when the duration of anesthesia increased over two hours, Sevoflurane’s ninety percent decrement time approached that of Isoflurane.7
The cost of inhaled anesthetic agent can be estimated by using an equation that provides the cost of anesthetic per MAC hour:
“Cost per MAC hour ($) = [(Conc.)(FGF rate)(Duration)(MW)(Cost per mL)] / [(2412)(D)]”6
The cost of one milliliter of Isoflurane is twenty-three cents, Sevoflurane is forty-one cents, and Desflurane is eighty-three cents. Regardless of the fast onset and emergence Desflurane offers, the cost of administering Desflurane is greater than the cost of Sevoflurane at any FGF rate.10,11
The cost of inhaled anesthetic agent can be estimated by calculating the cost per MAC hour, which is the administration of inhaled anesthetic agent at 1 MAC for a one hour time interval. The cost can be calculated using the concentration percent on the anesthetic dial of gas delivered, the FGF rate chosen, the duration of the anesthetic (sixty minutes in this case), the molecular weight of the anesthetic agent (MW in grams), cost per mL of liquid agent (in dollars), a factor accounting for the molar volume of a gas at twenty-one degrees Celsius (a constant of two thousand four-hundred twelve), and the density of the agent (D in grams/mL).
“Cost per MAC hour ($) = [(Conc.)(FGF rate)(Duration)(MW)(Cost per mL)] / [(2412)(D)]”6
The cost per MAC hour can be seen in Table 2. Desflurane is associated with a shorter recovery as is Sevoflurane, but the differences between the two in terms of patient discharge cannot be consistently shown in research studies.13 Gupta evaluated n=246 patients for recovery time after undergoing surgeries less than two hours with either Sevoflurane or Desflurane. Patients given Desflurane were able to open their eyes sixty seconds before those who received Sevoflurane, P<0.00001. However, when the patients went to recovery, those who went under Sevoflurane were responding much quicker to command than those who had Desflurane by six minutes, P<0.00001. No other differences were found between the two groups of patients 14.
When comparing Sevoflurane and Isoflurane for patients who are undergoing surgery for more than two hours, Gupta evaluated n=634 patients. The patients who underwent Sevoflurane were able to open their eyes sooner, P<0.00001, time to obeying commands, and time for home discharge, P=0.05. There is only a very minute favoring of Sevoflurane, but it is important to note these discharge statistics are only based on two trial studies. There were no differences found in postoperative complications between Sevoflurane and Isoflurane.13,14
Savings of more than one hundred thousand dollars resulted from the change of using Desflurane to using Sevoflurane in the operating rooms at Montefiore Medical Center in the Bronx, New York. Traynor noted that three bottles of Desflurane are needed to maintain a level of anesthesia equal to that of one bottle of Sevoflurane, making this agent much less expensive for the hospital to utilize, with no difference in patient discharge times. Reviews of Literature indicated that patients who received Desflurane could be extubated in the operating room about two minutes sooner than the patients who received Sevoflurane; an advantage seen meaningless in the large scale of the operating room sequences.9,15
Methodology The research design chosen was a quasi-experimental study. It involved the use of an experimental group and a control group. It is important to mention that the control group was compiled from hospital anesthesia records. The study did not involve randomization as a medical facility’s computer database cannot throughput this information. Permission from a mid-sized, Southwestern Pennsylvania teaching hospital was granted and all information gathered from the facility involved no patient identifiers, including gender or age. The hospital’s Institutional Review Board (IRB) was provided all details of the research study and deemed the research experiment to be exempt and permission was granted to start the study without any changes in the study’s design. General anesthesia data spreadsheets were gathered from the hospital exemplifying a patient anesthesia case total for the month of July, 2012. From this information, it was determined there were a total of n0=1,459 general anesthesia cases and after careful analysis, it was found that n1=500 cases lasted less than one hour and n2=959 cases lasted equal to or more than one hour in length from the beginning of anesthesia delivery to the end of the surgical procedure, as seen in Chart 1 in Appendix A.
The Southwestern Pennsylvania hospital was asked to provide their costs for Desflurane, Sevoflurane, and Isoflurane inhalation anesthetics. These prices can be seen in Table 1 of Appendix A. Utilizing the equation to calculate the amount of inhaled anesthetic used during a general surgical case in a sixty minute time duration, MAC hour costs could be calculated as seen in Table 3. The pharmacy was also asked to provide how many bottles of inhalational anesthetic are bought and used annually in their operating rooms. These results are seen in Chart 2 and calculated annual costs of the anesthetics are shown in Chart 3 in Appendix C
The experimental group for the research procedure involved men and women, ages twenty to forty, with PS scores of I and II, who were undergoing general anesthesia for surgery cases lasting less than one hour. These patients were administered Sevoflurane as their inhalational anesthetic during surgery at a FGF rate of 2 liters per minute (LPM). To be sure all patients were given the same standard induction regimen, all drug doses were based on current body weight calculations. Propofol, a sedative hypnotic was given at 2 milligrams per kilogram (mg/kg). Fentanyl, an opioid analgesic was given at 5 micrograms per kilogram (mcg/kg). Midazolam, a benzodiazepine sedative was given at a standard dose of 2mg/kg. Lidocaine, a class 2 anti-arrhythmic agent was given at 1.5 mg/kg. All patients participating in the study were given their induction medications at these doses provided.
In the preoperative area, all patients were given the right to participate or not participate in the research experiment as they were provided a letter of informed consent, which described completely that they would be treated no differently than any other patient and they were voluntarily consenting to participate in a research study as an individual, and in no way obligated to participate if they were unwilling to do so.
Those who were not involved in the experimentation are those patients who were unwilling to consent to participate in the experiment as an individual. Those with malignant hyperthermia were excluded from the study as they could not receive volatile inhalational anesthetics. Patients who required additional narcotic for pain during surgery were excused from participation. Those with chronic kidney disease or kidney failure were also not involved in the experiment as Sevoflurane, the experimental variable could lead to kidney complications. Participants who only were given inhalational anesthetic for painful stimulation during intravenous sedation were also excluded from participation in this study.
It was important to determine which inhalational anesthetic was favored most by anesthesia providers at the Southwestern Pennsylvania hospital between Sevoflurane and Desflurane for general anesthesia cases lasting less than one hour. Surveys were sent out to all members of the anesthesia team as titled “Survey 1” in Appendix D.
Ethical considerations were regarded during the completion of the study from start to finish. All information gathered from a Southwestern Pennsylvania hospital was kept in confidence and destroyed at the finish of the study by professional paper shredding services. Participant names, ages, race, nationality, and medical histories were not gathered from the hospital and were blinded from the investigator, owing to complete confidence of all those who participated during a twelve month interval.
Results Results are pending until the quasi-experimental research study is completed.
Discussion Volatile inhalational anesthetics account for twenty percent of pharmacy costs in the anesthesia department.10,16 By using a pharmacoeconomic model, it can be seen that careful choice of anesthetic for patients can represent a large cost containment for the anesthesia department in the hospital, without compromising patient care. All patients need to be considered a new case and may not fit into the pharmacoeconomic model because of family histories (e.g. malignant hyperthermia). However, when possible, using Sevoflurane for the choice inhalation anesthetic for general surgery cases rather than Desflurane for those lasting less than one hour can result in large cost savings for the anesthesia department. Over twenty thousand dollars could be saved if the Southwestern Pennsylvania hospital would utilize Sevoflurane over Desflurane for two-thirds of their total surgeries requiring general anesthesia for less than one hour.
It is important to mention that as a professional anesthesia student, FGF commonly observed with Sevoflurane are 2 LPM and FGF with Desflurane commonly observed are 2 LPM. The nephrogenicity associated with Sevoflurane is not commonly observed when this amount of FGFs are used.7 With the use of 1 LPM FGF when using Desflurane, as there is no minimum FGF required to avoid physiological aberrance, 17,19 there is also a great cost savings maintained for the anesthesia department.
There are limitations in any study when research is conducted and from this study, results are limited to a single institution and this limits its external validity as a result. Randomization was not included in this research study as a convenience sample population was used at one hospital location. The study was not extremely descriptive as patients with higher PS scores were not studied in this particular research project. This study is also missing research on older age groups, as participants’ age twenty to forty were included only. As all of these limitations can be seen in the experimental study, the results from this research study cannot be imposed on larger populations and therefore generalizability is poor. Future research can make these findings representable and provide for better generalization.
Future Recommendations for Research
In future research, randomization should be used when developing research as this will strengthen the results. Larger sample populations should be used in defined, smaller age variances to be able to impose the results on a specific group of patients. Defined surgeries should be used in sample populations to show stronger significance when looking at varied surgical procedures their results. With the inclusion of the above criteria, the research study would be experimental rather than quasi-experimental, and results could be superimposed onto given populations with much stronger data observance.
This research project will appear in the May, 2013 edition of Anesthesia and Analgesia in its entirety with all results included. There will also be one hour lectures provided on these research findings at the University of Pittsburgh at Greensburg, Robert Morris University, and St. Vincent College, times and final adjusted locations are to be announced via bulletins, which be hung in the cafeterias of each of these locations.
Conclusion Inhalational anesthetics represent a significant cost for pharmaceutical costs in the anesthesia department. It is important for professional anesthesia providers to deliver cost-effective, safe anesthesia care to patients in surgery. When analyzing surgical procedures requiring general anesthesia for less than one hour, the use of Sevoflurane rather than Desflurane as the choice inhalation anesthetic in surgery can provide the anesthesia department an avenue of cost savings without compromising patient care. When Desflurane must be used for patients who cannot be administered Sevoflurane, it is important to keep in mind there is no recommendation for FGF rates, and cost savings can also be preserved by utilizing low FGFs when using Desflurane.
References Weinberg L, Story D, Nam J,
Estimate Waterfowl Nests on Monte Vista National Wildlife
USING DISTANCE SAMPLING TO ESTIMATE WATER FOWL NESTS ON MONTE VISTA NATIONAL WILDLIFE REFUGE, COLORADO, USA Principal Investigator
Nicole J. Traub, College of Arts and Sciences, University of Colorado at Boulder, 275 UCB,
A Research Proposal Project Justification
Measuring nest success is extremely important in order to determine the well-being of avian populations. Biologists have been attempting to infer the status of avian species by estimating rates of births and deaths to determine population growth and stability (Johnson, 1979; Newson et al., 2008). One measure of avian birth rate that is easy to gauge is the percentage of nests that hatch, which is used as an indirect measure of reproduction (Johnson, 1979). Nest success rates can also be used to hypothesis causes for declines in avian populations, i.e. habitat degradation, predation, overhunting, disease, environmental contaminants, etc. (Beauchamp et al., 1996). Nest success is defined as a nest in “which at least one egg hatched” and the “presence of detached shell membranes is the best evidence that eggs hatched” (Klett et al., 1986). Nest failure usually results from predation but they may have been abandoned if the hens are disturbed during the early stages of egg laying (Klett et al., 1986).
Transect sampling is widely used by wildlife managers and researchers to estimate population sizes of inanimate and animate objects (Newson et al., 2008). Transect studies designed to estimate inanimate object population size, such as waterfowl nests, usually proceed as follows: the area to be sampled is defined; random (or systematic) transect lines are placed throughout the area; transects are searched to record the detection of the study object (Anderson and Pospahala, 1970). Bias is unavoidable in population size (density) estimates; therefore, it is important to recognize the source(s) of bias and adjust for them. An important source of bias lies in the transect sampling methods themselves. If some objects are not detected, then the expanded population estimate will be lower than the true population size unless adjustments are made (Burnham et al., 1980; Buckland et al., 2001). This source of bias is very important when detecting objects that are small, secretive, or well concealed; however, when detecting large or inanimate objects, this source of bias may be of little importance (Anderson and Pospahala, 1970).
The basic output from line transect sampling is the encounter rate, which is the number of detections per distance walked. This method can be used to estimate relative density but it does not account for detectability which can vary depending on the study object and habitat (Marshall et al., 2008). In order to compensate for incomplete counts and problems with detectability, one can measure the distance from the transect to each observation (distance sampling) (Burnham and Anderson, 1984). The sample population is then the area sampled rather than the objects of interest. For example, the population sampled is a population of line transects in a given area, each line transect is a sample unit, and the object of interest (waterfowl nests) is the variate associated with each transect (Anderson and Posahala, 1970; Marshall et al., 2008).
Four assumptions must be met in order to make valid inferences about population densities using distance sampling (in order of importance): (1) all objects that fall on the transect line are detected with certainty; (2) objects do not move either away from or towards the observer prior to detection; (3) perpendicular distance data are measure accurately; and (4) all detections are independent of each other (Burnham and Anderson, 1984; Buckland et al., 2001). These assumptions can be violated in many ways including, but not limited to, inexperienced or untrained observers, lack of interest in the observer, fatigue, speed of travel down the transect, transect width, habitat type, time of day, season, sun angle, inclement weather, object size, shape, coloration, and habits (Burnham and Anderson, 1984; Buckland et al., 2001; Marshall et al., 2008).
Both strip transects and line transects can be useful measures of population density. However, the key difference between them is that density can be estimated using line transects based on distance without some of the bias innate to strip transects. Line transects require only the perpendicular distance to the object. In contrast, strip transect density estimates are usually low because not all objects in the strip are detected (Burnham et al., 1980; Burnham and Anderson, 1984; Buckland et al., 2001).
A previous study completed on the Monte Vista National Wildlife Refuge (Anderson and Posahala, 1970) estimated waterfowl nest density using strip transects with a narrow width (8.25 ft. each side). This method is impractical and inefficient for sampling large areas since an insufficient number of objects may be detected after covering great distances (Anderson and Posahala, 1970). In contrast, this project proposes to utilize distance sampling with systematically placed line transects to obtain a full waterfowl nest census in order to determine nest distribution, nest success, and nest density.
Objectives The purpose of this study is to test the possibility of employing a distance-based sampling protocol utilizing line transects to estimate waterfowl nest density. Specifically, the objectives are to:
1. Evaluate and expand upon previous density estimates of waterfowl nests in the Monte Vista National Wildlife Refuge.
2. Determine if line transect sampling is more efficient than strip transect sampling for calculating waterfowl nest density.
3. Implement a distance-based line transect approach to calculating:
a. Number of successful nests b. Number of depredated nests c. Total number of nests
Methods and Study Design The general survey design will follow Anderson and Pospahala (1970). Thus, the survey design will involve at least 20 transects that will be oriented north to south across the Refuge and spaced 150 feet apart. Total transect length will depend on the desired coefficient of variation (described below). Transects will be systematically overlaid a map of the Refuge prior to the start of the project to avoid bias in the way of vegetation or land use gradients (Figure 1). A transect will be randomly selected and a subsequent transect 150 feet away will be walked. This method will be followed in a sequential manner until all transects have been walked (Anderson and Pospahala,
1970; Buckland et al., 2001).
Figure 1: Potential configuration of line transects throughout the Monte Vista National Wildlife Refuge
The Monte Vista National Wildlife Refuge is home to several species of migratory waterfowl such as ducks and geese that rely on the refuge for breeding. Some species arrive on the refuge earlier than others. To mitigate the possibility of not detecting nests due to waterfowl arrival, this project will collect data twice a year, once during mid-May and once between mid-July to mid- August (Monte Vista, 2017).
Sampling effort, and consequently cost, depends on the acceptable amount of uncertainty (randomness) in the density estimates. The coefficient of variation (CV) measures the uncertainty of the density estimate. Meaning that it measures how much the density estimate would change if the data were collected again (Burnham et al., 1980; Buckland et al., 2001; Schnupp, 2017a). The greater the variation in the estimate, the farther the estimate is from the true value. To control for fluctuations in variation, this project will utilize a systematic survey design with many transects (large sample size) and each transect will aim to have similar encounter rates (Figure 1).
For ease of navigation and repeatability, pre-established transects will be uploaded through Mapwel 2016 to Garmin Etrex GPS units (Garmin International Incorporated, Olathe, Kansas). For each nest detected, the perpendicular distance from the center of the nest to the transect line, nest state (depredated or successful), and waterfowl type (duck or non-duck) will be recorded. Program DISTANCE 7.0 (Buckland et al., 2001) will be used to calculate overall nest density, density of successful nests, and density of depredated nests for both waterfowl types. If strong habitat differences are encountered during the survey, stratification will be used in post- processing of the data to reduce variation and improve the precision of density estimates. Data will be pooled from all transects to increase model robustness. Data pooling helps even out minor fluctuations in object density between transects and lead to more precise density estimations (Fewster et al., 2005). Various detection functions will be evaluated in DISTANCE, including uniform, half-normal, hazard rate, and negative exponential with simple polynomial, hermite polynomial, or cosine adjustments. A detection function will be selected from the competing models using Akaike’s Information Criterion (AIC) values and goodness of fit using Chi-square analysis (Buckland et al., 2001).
Expected Results and Benefits Given that nest success is viewed as empirical evidence for reproduction success and population status, it is imperative that estimates of density be as accurate as possible. The proposed research will (1) analyze the effectiveness of line transect distance sampling versus strip transect sampling and (2) provide an accurate, efficient, and cost-effective method to determine waterfowl nest success and distribution on the Monte Vista National Wildlife Refuge, Colorado, USA.
Upon confirmation of funding, research protocols will be refined in consultation with Monte Vista National Wildlife Refuge personnel and Colorado Parks and Wildlife. Annual progress reports will be submitted and a final report detailing findings and recommendations will be submitted within 1 year of contract completion. Research results will be presented at professional scientific meetings and published in peer-reviewed scientific journals where Monte Vista
National Wildlife Refuge will be acknowledged as a major funding contributor. Additionally, if desired, one or more Monte Vista National Wildlife Refuge employees will be listed as a coauthor in all presentations and publications.
Project deliverables will include:
Ph.D. dissertation and corresponding scientific publications
Scientific presentations at state, regional, and international conferences (undergraduate and graduate)
Spreadsheets for calculation of density estimates
Technical bulletin comparing the efficacy of estimating nest density using distance sampling with line transects and strip transects.
Endangered Species Considerations
This section is not applicable to the proposed project.
Necessity and Ethical Use of Animals
This study will determine nest success and estimate of density of waterfowl on the Monte Vista
National Wildlife Refuge, Colorado, USA. All necessary precautions will be utilized to avoid harm to waterfowl during this study; however, an Animal Care and Use Form is being submitted with this proposal for research approval.
The principal investigator of this study will be Nicole J. Traub, M.S. and the project will involve
1 Ph.D. candidate. Additionally, 5 part-time student workers will be hired to assist with research activities and data collection.
All items are budgeted for 2x year sampling
Fringe (0.7% salary)
Literature Cited Anderson, D.R. and R.S. Pospahala. 1970. Correction of bias in belt transect studies of immotile objects. The Journal of Wildlife Management 34(1):141-146.
Beauchamp, W. D., R.R. Koford, T. D. Nudds, R. G. Clark, and D.H. Johnson. 1996. Long-term declines in nest success of prairie ducks. The Journal ofWildlife Management 60 (2): 247-257.
Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001.
Introduction to distance sampling estimating abundance of biological populations. Oxford University Press, New York, USA. 432p.
Burnham, K. P., D. R. Anderson. 1984. The need for distance data in transect counts. The Journal ofWildlife Management 48 (4):1248-1254.
Burnham, K. P., D. R. Anderson, and J. L. Laake. 1980. Estimation of density from line transect sampling of biological populations. Wildlife Monographs. (72):3-202.
Fewster, R.M., J. L. Laake, and S. T. Buckland. 2005. Line transect sampling in small and large regions. Biometrics. 61 (3):856-859.
Johnson, D.H. 1979. Estimating nest success: The Mayfield Method and an alternative. TheAuk 96 (4):651-661.
Klett, A.T., H.F. Duebbert, C. A. Faanes, and K.F. Higgins. 1986. Techniques for studying nest success of duck in upland habitats in the Prairie Pothole region. Resource Publication 158. 24 p.
Marshall, A.R., J. C. Lovett, and P.C.L. White. Selection of line-transect methods for estimating the density of group-living animals: lessons from primates. 2008. AmericanJournal of Primatology70:452-462.
Monte Vista. 2017. Monte Vista National Wildlife Refuge. https://www.fws.gov/refuge/Monte_Vista/wildlife_and_habitat/index.html.
Newson, S. E., K. L. Evans, D. G. Noble, J. J. D. Greenwood, and K. J. Gaston. 2008. Use of distance sampling to improve estimates of national population sizes for common and widespread breeding birds in the UK. Journal of Applied Ecology45:1330-1338.
Schnupp, M. 2017a. Sample units and transect design. PowerPoint presentation. Estimating Wildlife Populations course-WSCI 6390. http://schnuppconsulting.com/wp- content/uploads/2017/01/2-Sample-Units-Transect-Design.pdf.
Schnupp, M. 2017b. Distance Sampling Assumptions. PowerPoint presentation. Estimating Wildlife Populations course-WSCI 6390. http://schnuppconsulting.com/wp- content/uploads/2017/01/4-Distance-Sampling-Assumptions.pdf.