'Online' geriatric assessment procedure for older adults referred for geriatric assessment during an acute care episode for consideration of reliability of triage decisions

Len Gray
Lucy Dakin
Steven Counsell
Helen Edwards
Richard Wootton
Melinda Martin-Khan
'Online' geriatric assessment procedure for older adults referred for geriatric assessment during an acute care episode for consideration of reliability of triage decisions
S T U D Y P R O T O C O L Open Access
Background: Comprehensive geriatric assessment has been shown to improve patient outcomes, but the geriatricians who deliver it are in short-supply. A web-based method of comprehensive geriatric assessment has been developed with the potential to improve access to specialist geriatric expertise. The current study aims to test the reliability and safety of comprehensive geriatric assessment performed "online" in making geriatric triage decisions. It will also explore the accuracy of the procedure in identifying common geriatric syndromes, and its cost relative to conventional "live" consultations. Methods/Design: The study population will consist of 270 acutely hospitalized patients referred for geriatric consultation at three sites. Paired assessments (live and online) will be conducted by independent, blinded geriatricians and the level of agreement examined. This will be compared with the level of agreement between two independent, blinded geriatricians each consulting with the patient in person (i.e. "live"). Agreement between the triage decision from live-live assessments and between the triage decision from live-online assessments will be calculated using kappa statistics. Agreement between the online and live detection of common geriatric syndromes will also be assessed using kappa statistics. Resource use data will be collected for online and live-live assessments to allow comparison between the two procedures.Discussion: If the online approach is found to be less precise than live assessment, further analysis will seek to identify patient subgroups where disagreement is more likely. This may enable a protocol to be developed that avoids unsafe clinical decisions at a distance.

Background

Comprehensive geriatric assessment (CGA) is a core procedure in specialist geriatric care. There is evidence that this process improves functional recovery, reduces morbidity and attenuates demand for long term institutional care [1]. It is central to hospital triaging processes which direct patients to formal inpatient geriatric assessment, rehabilitation, long term institutional care and complex community support programs. Geriatric consultation services are the vehicle for delivering CGA to older hospitalised patients who are not located in specialist geriatric units.

Geriatric consultation is delivered by geriatricians, gerontic nurses -alone or in partnership -sometimes with support from other allied health specialists. The availability of these aged care specialists, and the specialist services within which they work, is currently inadequate. Furthermore, the undersupply is mal-distributed, with access considerably worse in provincial cities and rural communities [2].

An innovative web based model of comprehensive geriatric assessment has been developed by The Centre for Research in Geriatric Medicine (CRGM) at the University of Queensland (UQ) [3,4]. This system utilises the interRAI Acute Care (AC) instrument, a validated assessment tool developed by the interRAI international collaborative to support evaluation of older people in acute hospital settings [5]. The interRAI AC comprises 121 individual clinical items that profile the pre-morbid and current status of the patient. The individual items have been shown to be clinically relevant and to have overall "substantial" levels of inter-rater reliability [6]. The interRAI AC incorporates screeners for common geriatric syndromes, risk assessment tools for adverse outcomes and a range of scalar measures that enable assessment of severity or dependency in the domains of pain, cognition, communication, and activities of daily living. It also includes algorithms to identify patients most likely to benefit from preventive or curative interventions, along with guidelines for further management. The instrument is administered by trained nurses who collect standardised patient data, and enter it onto a web based database. This data is then used by the geriatrician to facilitate patient assessment and provide recommendations either "online" or in conjunction with an in-person consultation. The system has also been used to support case preparation for distance assessment using video-conferencing [7].

Research aims

This study aims to determine whether appropriate and reliable geriatric triage decisions can be made using CGA performed "online", by establishing whether agreement between "online" and "live" triage decisions is comparable (i.e. non-inferior) to inter-rater agreement for conventional "live" geriatrician assessment. It will also explore the accuracy of the procedure in identifying common geriatric syndromes, and its cost relative to conventional "live" consultations.

The key research question is: What is the level of agreement in triage decisions made by clinicians "online" and "live"?

Subsidiary research questions will provide further insight into reliability, safety and cost effectiveness:

• Is the number of patients recommended for a higher level of care (than their prior living arrangement) greater for "online" consultations?

• How often are "important" clinical diagnoses detected during live consultation that are not identified "online"?

• What is the level of agreement in detection of the common geriatric syndromes (delirium, dementia, depression) "online" compared to "live" consultation?

• What are the costs of "live" and "online" assessment procedures?

• What is the relationship between cost and effectiveness?

Methods/Design

Setting and participants

The study will be conducted at 3 large Australian hospitals: Princess Alexandra Hospital, Brisbane, Queensland; Box Hill Hospital, Box Hill, Victoria; and The Northern Hospital, Melbourne, Victoria. As the study is designed to evaluate the performance of the online approach when delivered by professionals with experience in using the interRAI AC instrument, at Box Hill Hospital and The Northern Hospital, where the nurses and geriatricians are new to the approach, there will be a "run in" series of 25 cases per geriatrician. This study has been approved by the Human Research Ethics Committee at each site and UQ.

All acutely hospitalised patients referred to the geriatric consultation service at each site will be considered for inclusion in the study. Patients who have previously been consented and reviewed for the study (in a former hospital episode) and those who are already known to one of the study geriatricians will be excluded.

Consent for participation in the study will be secured by the nurse administering the interRAI AC instument prior to the commencement of the consultation. Nonconsenting patients will receive the "normal" geriatric consultation for the hospital site. Full demographic and limited clinical information will be secured for non-consenting subjects to enable examination of potential bias. We anticipate consent rates in excess of 95% based on previous research experience with similar subjects, and on the experience of a previous pilot study [8].

Study design

The design aims to measure the level of agreement in triage decisions made "online" with those made for the same patient at "live" assessment, and to compare this with the level of agreement between two independent geriatricians each assessing a patient at a live consultation. The aim is to distinguish decision disagreement attributable to the online format of assessment from the 'usual' inter-geriatrician differences in clinical decision making. It is beyond the scope of this study to establish the "online-online" inter-rater reliability; this will be examined in a subsequent study and reported separately.

Each consenting patient will undergo sequential assessment by two independent geriatricians, designated "geriatrician 1" (G1) and "geriatrician 2" (G2), in one of the following randomly allocated configurations: live-live (G1-G2), live-live (G2-G1), online-live (G1-G2) or online-live (G2-G1). The patient recruitment and randomisation process is outlined in Figure 1.

The paired geriatricians will be blinded to the results of their colleague's assessment. In the case of live-live assessments the order in which the geriatricians consult the patient will be randomly allocated in order to minimise any systematic influence that the interaction of the first geriatrician with the patient may have on the second assessment.

In all cases, the interRAI AC instrument will be administered by a trained nurse. Once these findings have been entered by the nurse into the software system, a geriatrician consultation will follow. Paired geriatrician assessments will be completed in the time it usually takes for a geriatrician assessment to occur following referral to the geriatrician consultation service at that site.

For live assessments, the assessing geriatrician will have access to the interRAI AC instrument data online. The geriatrician will also be able to read the medical record. The nurse will not interact directly with the geriatrician, in order to reduce the risk of contamination of decisions when consecutive live assessments are made.

For online assessments, the geriatrician will have access to the interRAI AC instrument data online only. The geriatrician will conduct the assessment without direct contact with the nurse. The geriatrician will not have access to the medical record. However, any online test results (e.g. radiology and pathology) which are usually accessed electronically will be available to both doctors, replicating the process that would occur in a distance assessment.

Data collection

The CGA software will collect patient level data and provide a web report for online assessors to view. At the conclusion of the assessment, the geriatricians will record geriatric triage decisions on a standard research form. Other data, such as randomisation and timing for assessments, will be collected on printed forms and entered into an appropriate database. The following data will be collected:

▪ InterRAI AC data (demographic and clinical information), collected by the nurse administering the interRAI AC instrument. ▪ Geriatrician recommendations for discharge destination: direct discharge to the community; transfer to inpatient rehabilitation or a geriatric unit; transfer to permanent residential care, including a distinction between the need for a high or low level of care (equivalent approximately to Skilled Nursing Facilities or Assisted Living in the North American context). ▪ Geriatrician prediction of discharge destination and location in 3 months: own home; residential care (high or low) or "likely to be deceased". This will provide an estimate of the ability to prognosticate using online assessment, as compared with live consultation. ▪ Presence of geriatric syndromes detected by the geriatrician (including delirium, dementia and depression). ▪ Diagnoses made by the geriatrician during assessment which were not recorded in the online report. These will be classified into those already documented in the medical record and those newly identified by the geriatrician. ▪ Costing data. Direct and indirect time contributions by relevant personnel will be recorded using time sheets. Estimates of software related costs will be calculated from data obtained from the UQ Centre for Online Health. This will be supplemented by costing information derived from existing working systems in Queensland. ▪ Three month follow up data. A research assistant will follow up each patient at three months to identify actual discharge destination and outcomes (location) at 3 months. This will be compared with the geriatrician's predictions.

Patient referred for geriatric consultation

Data analysis

Agreement between the triage decisions from live-online assessments will be calculated using percentage agreement and kappa statistics, reported with 95% confidence intervals. This will also be calculated for the triage decisions from live-live assessments. Comparison of the difference in levels of agreement between the live-online group and the live-live group will be the primary measure of the reliability of the online assessment and is the basis for the sample size calculation for this project. Online assessment will be taken to be non-inferior to live assessment if percentage agreement in the liveonline group is not more than 15% less than the percentage agreement in the live-live group. The online assessment can be regarded in a similar manner to a diagnostic test, where the interest is in the number of patients recommended for a higher level of care from the online assessment compared with live assessment (i.e. false positives, specificity), and in the number of patients where important clinical diagnoses are missed in an online assessment (i.e. false negatives, sensitivity).

These measures will therefore be calculated and reported. Exploratory analyses of patient characteristics will be undertaken to examine these cases where differences of triage decision are identified. Agreement between the online and live detection of common geriatric syndromes will also be assessed using kappa statistics.

Economic analysis

All resource use data will be collected for those who receive online assessment and those who receive the live-live assessment. Resource use will include resources associated with patient care, and resource use associated with delivery of the interventions (e.g. estimated travel time and cost of the clinician for a live assessment). The resources to be collected during the trial include staff time, diagnostic tests, inpatient stay, and patient deployment. Unit costs will be attached to each of these resource items to compare the difference in total costs between the two assessment procedures. Extrapolations, using Markov-chain MonteCarlo simulations will be undertaken to generate estimates of long-term outcomes and costs for both groups. These long-term outcomes and cost will include length of stay in hospital (using data from the observed index episode), estimates of subsequent hospitalisations, movement into residential care facilities, and mortality. The additional longer-term costs and benefits from online assessment will then be estimated to indicate any divergence between the two assessments.

Sample size and power analysis

The outcome of interest will be the difference in percentage agreement between paired assessments for livelive arm and online-live arm for the question: "is residential care recommended?", scored as "yes" (1) or "no" (2). A non-inferiority study tests whether the percentage agreement between clinicians in an intervention arm (online-live) does not lie beyond the lower limit of an acceptable range (a one-tailed area of clinical indifference) when compared with the standard clinical practice arm (live-live).

The sample size of 288 subjects (114 online-live, 114 live-live) was identified to provide the study with power exceeding 80% to detect a non-inferiority margin of 15% for levels of agreement between online-live when compared with live-live arms for the decision "yes, residential care is recommended". This is based on an assumption of inter-geriatrician agreement of 60% for geriatric triage decisions [8], allowing a two-sided type 1 error rate of 5%. With a 15% allowance for attrition, the adjusted sample size is 268 (i.e. 228/(1-0.15)). After rounding, 270 cases will be required in total between all sites.

Randomisation

The following process for randomisation will be carried out by the statistician. The < ralloc > command will be used to generate 2 separate randomisations in Stata version 10.1. These allocations will then combined to create the individual patient allocation to geriatrician/intervention and stored in individually numbered opaque envelopes for each site. Initially, patients will be equally randomised to the 4 intervention arms in blocks of 4 and 8. Patients will then be equally randomised to the 2 intervention arms (geriatricians 1 and 2) in blocks of 2 and 4. Due to the constraint that a maximum of two patients are to be randomised on any one day and the same geriatrician must not be used for both, the second of the two patients is deterministically allocated to the doctor who does not attend to the first of the two patients. Allocations will be approximately balanced across 3 studies centres.

Discussion

This study is designed to identify the strengths and weaknesses of an online method of CGA. It seeks to identify whether consistent, reliable and safe recommendations can be made online. Even if the online decisions prove to be deficient to some extent, the study will allow effectiveness to be weighed against cost and may develop a methodology to identify patients who can be safely assessed online as opposed to those who require in-person consultation. A clinical algorithm would then enable online clinical reviewers to quickly determine whether a case requires live assessment. If successful, online CGA has the potential to increase access to geriatrician expertise, especially in rural and remote locations or areas of urban isolation such as residential care.

Figure 1
1Procedure for patient recruitment and allocation to paired consultation method.
AcknowledgementsMs Elaine Beller, for her statistical support in the early stages of the planning and implementation of this project. The project is funded by the National Health and Medical Research Council of Australia (569688).Authors' contributionsLG led the design of the protocol and contributed to the preparation of the draft. LD contributed to the preparation of the draft. SC, HE and RW participated in the design of the protocol and contributed to the preparation of the draft. MMK assisted LG in conceptualisation and design of the study, participated in the design of the protocol and contributed to the preparation of the draft. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
. Princess Alexandra Hospital. 210Centre for Research in Geriatric Medicine, University of Queensland ; University School of MedicineBuildingCentre for Research in Geriatric Medicine, University of Queensland, Level 2, Building 33, Princess Alexandra Hospital, Ipswich Road, Woolloongabba, QLD 4102, Australia. 2 Indiana University School of Medicine, 1001 West 10th

USA. 3 School of Nursing and Midwifery. Opw-M200 Street, Foundation Building, Royal Children's Hospital. Indianapolis, Indiana; Victoria Park Road, Kelvin Grove, QLD 4059, Australia; Herston; Australia46202Queensland University of Technology ; University of Queensland4 Centre for Online HealthStreet, OPW-M200, Indianapolis, Indiana 46202, USA. 3 School of Nursing and Midwifery, Queensland University of Technology, Victoria Park Road, Kelvin Grove, QLD 4059, Australia. 4 Centre for Online Health, University of Queensland, Level 3, Foundation Building, Royal Children's Hospital, Herston 4029, Australia.

Comprehensive geriatric assessment: a meta-analysis of controlled trials. A E Stuck, A L Siu, G D Wieland, J Adams, L Z Rubenstein, Lancet. 3428878Stuck AE, Siu AL, Wieland GD, Adams J, Rubenstein LZ: Comprehensive geriatric assessment: a meta-analysis of controlled trials. Lancet 1993, 342(8878):1032-1036.

Supply of inpatient geriatric medical services in Australia. L Gray, K Moore, R Smith, M Dorevitch, Intern Med J. 374Gray L, Moore K, Smith R, Dorevitch M: Supply of inpatient geriatric medical services in Australia. Intern Med J 2007, 37(4):270-273.

Comprehensive geriatric assessment 'online'. L Gray, R Wootton, Australas J Ageing. 274Gray L, Wootton R: Comprehensive geriatric assessment 'online'. Australas J Ageing 2008, 27(4):205-208.

Comprehensive geriatric assessment for the private hospital inpatient. S A Salih, O Wright, L Gray, Australas J Ageing. 283152Salih SA, Wright O, Gray L: Comprehensive geriatric assessment for the private hospital inpatient. Australas J Ageing 2009, 28(3):152.

Standardizing assessment of elderly people in acute care: the interRAI acute care instrument. L C Gray, R Bernabei, K Berg, H Finne-Soveri, B E Fries, J P Hirdes, J Am Geriatr Soc. 563Gray LC, Bernabei R, Berg K, Finne-Soveri H, Fries BE, Hirdes JP, et al: Standardizing assessment of elderly people in acute care: the interRAI acute care instrument. J Am Geriatr Soc 2008, 56(3):536-541.

Geriatric ward rounds by video conference: a solution for rural hospitals. L C Gray, O R Wright, A J Cutler, P A Scuffham, R Wootton, Med J Aust. 191Gray LC, Wright OR, Cutler AJ, Scuffham PA, Wootton R: Geriatric ward rounds by video conference: a solution for rural hospitals. Med J Aust 2009, 191(11-12):605-608.

Reliability of Online Geriatric Consultation Triage Decisions: A Pilot Study. L Dakin, A Cutler, O Wright, M Martin-Khan, P Varghese, L Gray, Australas J Ageing. 304Dakin L, Cutler A, Wright O, Martin-Khan M, Varghese P, Gray L: Reliability of Online Geriatric Consultation Triage Decisions: A Pilot Study. Australas J Ageing 2011, 30(4):239-240.


Iridescence impairs object recognition in bumblebees OPEN
2018

Karin Kjernsmo email@example.com
School of Biological Sciences
University of Bristol
BS8 1TQBristolUK

Joanna R Hall
School of Biological Sciences
University of Bristol
BS8 1TQBristolUK

School of Experimental Psychology
University of Bristol
BS8 1TUBristolUK

Cara Doyle
School of Biological Sciences
University of Bristol
BS8 1TQBristolUK

Nadia Khuzayim
School of Biological Sciences
University of Bristol
BS8 1TQBristolUK

Innes C Cuthill
School of Biological Sciences
University of Bristol
BS8 1TQBristolUK

Nicholas E Scott-Samuel
School of Experimental Psychology
University of Bristol
BS8 1TUBristolUK

Heather M Whitney
School of Biological Sciences
University of Bristol
BS8 1TQBristolUK

Iridescence impairs object recognition in bumblebees OPEN

SciEntific REPORTS |
88095201810.1038/s41598-018-26571-6Received: 13 December 2017 Accepted: 14 May 20181 Correspondence and requests for materials should be addressed to K.K.
Iridescence is a taxonomically widespread and striking form of animal coloration, yet despite advances in understanding its mechanism, its function and adaptive value are poorly understood. We test a counterintuitive hypothesis about the function of iridescence: that it can act as camouflage through interference with object recognition. Using an established insect visual model (Bombus terrestris), we demonstrate that both diffraction grating and multilayer iridescence impair shape recognition (although not the more subtle form of diffraction grating seen in some flowers), supporting the idea that both strategies can be effective means of camouflage. We conclude that iridescence produces visual signals that can confuse potential predators, and this might explain the high frequency of iridescence in many animal taxa.Iridescence is a form of structural coloration caused by interference of light reflected from nanostructures within a surface 1 . Iridescence can be generated though a wide range of mechanisms, including thin-film interference, diffraction gratings, multilayers and photonic crystals 1-6 . However, the two most common mechanisms underlying iridescence in both animals and plants are multilayers and diffraction gratings 7 and in all cases, the result of these optical phenomena is that the hue and intensity of the reflected light will vary depending on the angle of view or angle of illumination, as illustrated by the shimmering fruits of Pollia condensata, vivid green and orange breast feathers of the male Lawes's parotia (Parotia lawesii), a bird of paradise, the green and purple stripes of the Japanese jewel beetle (Chrysochroa fulgidissima), and the brilliant wings of the blue morpho butterflies (Morpho sp.) 6-9 .Due to its unique optical properties and striking appearance, iridescence in animals is, understandably, usually assumed to be a signal 2 . In species with strong sexual dimorphism, such as the Indian peafowl (Pavo cristatus), iridescence may serve an important function in mate choice 2,4,10-12 . However, it is also common in monomorphic animals, such as beetles, and in non-reproductive life stages such as butterfly chrysalises 2,3 . Here, the evolution of iridescence by sexual selection is a priori unlikely. Instead, the American artist and father of modern camouflage theory, Abbot Thayer, proposed that iridescence was an anti-predator adaptation. Thayer described his observations of iridescent insects: "Brilliantly changeable or metallic colors are among the strongest factors in animals' concealment" 13 (p.87). He continued: "Even without motion, the animal's surface, which would show all in its true place and plane if it were plainly colored, is by its iridescence made to appear 'dissolved' into many depths and distances".Thayer's statement is counter-intuitive: how can something that is both brilliant and changeable contribute to concealment? Camouflage generally works through matching the background, mimicking inedible objects (masquerade), or by utilizing colour patterns that break up the otherwise recognisable shape of a prey (disruptive coloration) 14,15 . The latter may impede identification by predators searching for a typical shape: even if individual colour patches are detected, they are not recognised as belonging to a single object and so the prey remains unrecognised13,14,16. In perceptual terms, disruptive coloration interferes with feature binding 17 . In a similar way, iridescence creates changing colour and intensity boundaries, thereby disrupting the stable edge features normally used in object recognition: the brightness of iridescence may make (varying) parts of objects more conspicuous, but the changing colour patterns and boundaries could also deceive and confuse potential predators. This effect might be particularly acute in animals that lack the extensive upstream processing characteristic of the primate visual cortex.Here, using artificial discs as prey and naïve Bombus terrestris as models for insect perception, we tested whether three forms of iridescence (multilayer and two types of diffraction grating: one cast from a flower, the other synthetic and perfectly regular) impair shape recognition. Bees are clearly not predators, but other

Iridescence is a taxonomically widespread and striking form of animal coloration, yet despite advances in understanding its mechanism, its function and adaptive value are poorly understood. We test a counterintuitive hypothesis about the function of iridescence: that it can act as camouflage through interference with object recognition. Using an established insect visual model (Bombus terrestris), we demonstrate that both diffraction grating and multilayer iridescence impair shape recognition (although not the more subtle form of diffraction grating seen in some flowers), supporting the idea that both strategies can be effective means of camouflage. We conclude that iridescence produces visual signals that can confuse potential predators, and this might explain the high frequency of iridescence in many animal taxa.

Iridescence is a form of structural coloration caused by interference of light reflected from nanostructures within a surface 1 . Iridescence can be generated though a wide range of mechanisms, including thin-film interference, diffraction gratings, multilayers and photonic crystals [1][2][3][4][5][6] . However, the two most common mechanisms underlying iridescence in both animals and plants are multilayers and diffraction gratings 7 and in all cases, the result of these optical phenomena is that the hue and intensity of the reflected light will vary depending on the angle of view or angle of illumination, as illustrated by the shimmering fruits of Pollia condensata, vivid green and orange breast feathers of the male Lawes's parotia (Parotia lawesii), a bird of paradise, the green and purple stripes of the Japanese jewel beetle (Chrysochroa fulgidissima), and the brilliant wings of the blue morpho butterflies (Morpho sp.) [6][7][8][9] .

Due to its unique optical properties and striking appearance, iridescence in animals is, understandably, usually assumed to be a signal 2 . In species with strong sexual dimorphism, such as the Indian peafowl (Pavo cristatus), iridescence may serve an important function in mate choice 2,4,10-12 . However, it is also common in monomorphic animals, such as beetles, and in non-reproductive life stages such as butterfly chrysalises 2,3 . Here, the evolution of iridescence by sexual selection is a priori unlikely. Instead, the American artist and father of modern camouflage theory, Abbot Thayer, proposed that iridescence was an anti-predator adaptation. Thayer described his observations of iridescent insects: "Brilliantly changeable or metallic colors are among the strongest factors in animals' concealment" 13 (p.87). He continued: "Even without motion, the animal's surface, which would show all in its true place and plane if it were plainly colored, is by its iridescence made to appear 'dissolved' into many depths and distances".

Thayer's statement is counter-intuitive: how can something that is both brilliant and changeable contribute to concealment? Camouflage generally works through matching the background, mimicking inedible objects (masquerade), or by utilizing colour patterns that break up the otherwise recognisable shape of a prey (disruptive coloration) 14,15 . The latter may impede identification by predators searching for a typical shape: even if individual colour patches are detected, they are not recognised as belonging to a single object and so the prey remains unrecognised 13,14,16 . In perceptual terms, disruptive coloration interferes with feature binding 17 . In a similar way, iridescence creates changing colour and intensity boundaries, thereby disrupting the stable edge features normally used in object recognition: the brightness of iridescence may make (varying) parts of objects more conspicuous, but the changing colour patterns and boundaries could also deceive and confuse potential predators. This effect might be particularly acute in animals that lack the extensive upstream processing characteristic of the primate visual cortex.

Here, using artificial discs as prey and naïve Bombus terrestris as models for insect perception, we tested whether three forms of iridescence (multilayer and two types of diffraction grating: one cast from a flower, the other synthetic and perfectly regular) impair shape recognition. Bees are clearly not predators, but other hymenopterans are predators and parasitoids of many insects; furthermore, much is known about the bee visual system [18][19][20] , and they can easily be trained to perform discrimination tasks 21 .

Results and Discussion

Using absolute conditioning 21 , we compared the recognition of oval and circular discs that displayed matte, floral diffraction grating iridescence, synthetic diffraction grating iridescence or multilayer iridescence on their surface ( Fig. 1). All discs had equal surface areas and a blue ground colour ( Fig. 1). For each treatment, 15 naïve bees were pre-trained to associate either an oval or a circular disc (randomly assigned for each individual and counter-balanced so half the test group were conditioned to one shape or the other) with a food reward. During this conditioning phase four discs of one shape and treatment (matte, floral diffraction grating, synthetic diffraction grating or multilayer iridescence that the bee would experience in the following experiment), each with a sucrose reward, were placed at random positions in the arena. Bees were individually released into the arena and a minimum of 10 visits (defined as drinking from the sucrose well in the disc) were recorded for each. The bees made on average 10.3 (range 10-13, N = 15/treatment group) visits before undergoing the experiment. After conditioning, the bees returned to the hive while the arena and discs were cleaned with ethanol to remove scent marks. For the experiment, which took place within minutes after the conditioning phase, a conditioned bee was released back into the arena, where eight unrewarded discs (four oval and four circular) had been randomly positioned. A total of 20 visits to the discs was observed for each bee. At this stage a visit was defined as either landing with all feet on the discs or drinking. The shape of the disc was recorded for each visit (see Material and Methods for details).

There was a significant effect of treatment on the bees' ability to identify the previously rewarded stimulus (χ 2 = 11.32, d.f. = 3, P = 0.0101). Tukey-type pair-wise comparisons showed that the bees had a stronger preference for this stimulus when targets were matte, compared to both the synthetic diffraction grating (t = 2.97, P = 0.0158) and multilayer iridescent targets (t = 3.11, P = 0.0098). However, there was no difference in discrimination when the matte and floral iridescent conditions were compared (t = 2.01, P = 0.1837), nor synthetic diffraction grating vs multilayer (t = 2.01, P = 0.1837), synthetic diffraction grating vs floral (t = 0.97, P = 0.7640) or multi-layer vs floral (t = 1.11, P = 0.6810). For matte (73% correct, t = 5.70, P = 0.0001), and floral iridescent targets (62% correct, t = 4.12, P = 0.0010) shape recognition was significantly higher than would be expected for random foraging, but this was not the case for targets with iridescence produced by synthetic diffraction gratings (58% correct, t = 1.93, P = 0.0748), or multilayer (57% correct, t = 1.70, P = 0.1117). Our results demonstrate that iridescence impairs shape identification, in accordance with Thayer's seemingly counter-intuitive proposal for a protective value of "Brilliantly changeable or metallic colors". Both synthetic diffraction grating and multilayer iridescence impair shape recognition in bees, when compared to matte non-iridescent discs, suggesting that both strategies can be protective. Interestingly, the floral iridescence, where the iridescence is generated by disordered nanostructures that produces angle-dependent scattered light in a narrower range of wavelengths 4 did not significantly impair the bees' ability to discriminate shape. This difference in visual impact may explain why it is this subtle form of iridescence that has convergently evolved in several lineages of flowers 4 , rather than the more intense type of iridescence that leads to reduced discrimination. This suggests that functional aspects of iridescence, depending on the level of intensity, could be highly specific 22 .

We conclude that, just as it has deleterious effects on target tracking 23 'strong' iridescence, produced by multilayers or highly regular diffraction gratings, can act as camouflage by corrupting an otherwise recognisable shape. Future studies should investigate whether different types of iridescence have different functions and extend our results for insects to other species with different visual and cognitive systems.

Material and Methods

Target design. Following an established protocol 22,24 the artificial targets were made of two components:

(1) a colored disc made out of '2-Ton' epoxy (Devcon, Danvers, MA, USA) and mixed with "ultramarine blue" color pigment (L. Cornelissen and Son, London, UK), and (2) a sucrose well made of an upturned lid of 1.5 ml Eppendorf containers and glued onto the disc.

We used Elite HD + Light Body silicone dental mould (Zhermack, Badia Polesine, Italy) to make the negative copies of the target discs, with equal amounts of base and catalyst. The matte copy was made by gluing a paper disc ("A4 Sketch Pad Paper", W.H. Smith, London, UK) on top of a glass disc, and then gently push it down the dental mould with the paper-covered side of the glass disc faced downwards. The subtle "floral" diffraction-grating iridescent copy was made by gently pushing down a tulip ("Queen of the Night") tepal in the dental mould. To make perfectly regular iridescence diffraction grating discs, the dental mould was instead cast from 1000 lines per mm unmounted holographic diffraction grating film (Edmund Optics, York, UK).

When the silicone dental mould mixture had hardened, the specimen was removed, leaving an inverted mould. The positive replicas of the floral iridescent, diffraction grating iridescent and matte discs were then made of the specimen by using the '2-Ton' epoxy. Equal amounts of the resin and catalyst were combine with pigment (7 g resin + 7 g catalyst with 50 mg of "ultramarine blue") and mixed. The inverted silicone mould was then filled with the pigmented epoxy. Once the cast was fully hardened (overnight at room temperature) it was removed from the mould. The multilayer iridescent targets were made by adding a sheet of multilayer thin film (Blue Iridescent Cellophane, Product code: AR01098, TTS, UK) on top of the epoxy resin immediately after pouring it into a dental mould cast by a glass disc (i.e. same as for the matte, but without the matte paper).

Target measurements.

Reflectance spectra of the four targets and of the bee arena background were measured using an Ocean Optics Flame-S-UV-VIS miniature spectrometer (Ocean Optics). A deuterium -halogen tungsten lamp (DH-2000-BAL UV-VIS-NIR, Ocean Optics) was used as a standardized light source, and measurements were taken using a premium-grade reflection probe (QR400-7-UV-VIS, Ocean Optics). We used a Stanley 1-77-153 FatMax CL2 Cross Line Laser to set the axis of the illuminating and reflection probe so that it was perpendicular (i.e. 90°) to the plane of the targets and background (BG) for targets at θ = 0°, and we also measured the targets, but not the background at θ = 15°, 30° and 45° (Fig. 2). A white spectralon standard (WS-1-SL, Ocean Optics) was used to calibrate the spectrometer. eSEM magnification images (Fig. 1) were taken of the surface structure of each of the four targets using a Zeiss EVO HD1 machine, at a working distance of 9 mm, EHT of 10 kV, and the I probe at 30 pA. Prior to the eSEM imaging, all samples were coated with 5 nm gold using a QUORUM Q150RES machine. For low magnification samples were at 800-808x mag, for high magnification samples were at 3.5 k-3.52 k x mag.

Experimental design. Prior to the experiments, each bee (supplied by Syngenta Bioline, Little Clacton, Essex, UK) was individually marked using coloured Queen Marking Paints (EH Thorne, Beehives Ltd., Wragby, UK) for identification. Following the protocol of Whitney et al. 22,24 , each colony of bees were housed in plastic nesting boxes connected to a flight arena via a transparent plastic tube. The tube was regulated by a series of gates such that we could control the bees' access to the flight arena. We used seven identical (l × w × h: 112 × 75 × 30 cm) wooden framed flight arenas topped with UV-transparent acrylic (Perspex TM ) sheet for these experiments. The floor of each flight arena was covered in green AT 202 Advance Gaffa tape (Stage Electrics, Patchway, Bristol, UK). Illumination in the laboratory was provided by 46 Sylvania Activa 172 Professional 36 W fluorescent tubes (Osram Sylvania Inc., Wilmington, MA, USA) powered by Philips high frequency-ballasts (Philips NV, Amsterdam, Netherlands) with a flicker frequency higher than 20 KHz, on a 12:12 hour light/dark schedule. Temperature was automatically regulated and held between 21-23 °C. The bees were fed daily ad libitum with 30% sucrose solution and 3 times weekly with 15 g of pollen/colony. In order to motivate the bees to forage from the targets during the behavioural experiments, the sucrose solution was provided using wells (1.5 ml Eppendorf containers) fitted to epoxy targets of varying colours (albeit not the same as in the experiments). The flowers were randomly distributed inside the flight arena and redistributed each day so that the bees wouldn't learn any specific positions of the flowers within the arena. For each behavioural experiment, a "rewarded" target means that it contained a 30% sucrose solution, whereas an unrewarded target means that it contained only water. Targets that were re-used in experiments were always cleaned with pure ethanol (Ethanol, absolute, >99.8% (GC), Sigma-Aldrich Ltd., St. Louis, Missouri, USA) to remove any possible scent marks from the bees 25  Statistical analysis. All data were analyzed using R v. 2.13 26 . To test for the effect of target type (fixed effect with 4 levels: matte, floral iridescence, "perfect" iridescence produced by diffraction gratings and an iridescent multilayer) on the response variable "lands on conditioned and unconditioned stimuli" over the consecutive visits (20 in total), we used Generalized Linear Mixed Models (function glmer in the package lme4) 27 to model the binomial error distribution. Individual bumblebee (N = 60) was treated as a random effect. We then carried out pair-wise comparisons between treatment groups with Tukey-type adjustment for multiple testing (function glht in the multcomp package) 28 . Finally, we tested whether the probability of landing on the pre-conditioned stimuli deviated from that expected by random choice (i.e. the expected value of 50%) for each treatment separately using Binomial GLMM's. Data availability. The dataset supporting this article will be made available from the Dryad Digital Repository upon acceptance.

Figure 2.

Reflectance spectra of the four treatment groups. Shown are estimated means and 95% CI from Generalized Additive Models with 20-degree-of-freedom spline smoothers. The triangles indicate the wavelength of maximum reflectance for each of the four treatment groups (each N = 9) at θ = 0°, 15°, 30° and 45°. The background (BG) is included in all graphs for comparison but was only measured at the relevant angle of θ = 0°.

SciEntific
REPORTS | (2018) 8:8095 | DOI:10.1038/s41598-018-26571-6

Figure 1 .
1Experimental stimuli and results. Mean (±95% CI) proportion of visits to the conditioned stimuli for each treatment group, with corresponding photos (top two rows) and high magnification surface eSEM images (bottom row) of the four different treatment groups (from left to right; Matte, Floral diffraction grating, synthetic Diffraction grating and Multi-layer interference). The dashed line indicates the 50% expectation (i.e. the expected success rate for random foraging). SciEntific REPORTS | (2018) 8:8095 | DOI:10.1038/s41598-018-26571-6

.
SciEntific REPORTS | (2018) 8:8095 | DOI:10.1038/s41598-018-26571-6
School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, UK. 2 School of Experimental Psychology, University of Bristol, Bristol, BS8 1TU, UK. Correspondence and requests for materials should be addressed to K.K. (email: email@example.com)
© The Author(s) 2018
AcknowledgementsFunding for this study was provided by the Biotechnology & Biological Sciences Research Council, UK (grant BB/ M002780/1 to H.M.W., N.S.-S. and I.C.C.). We are grateful to Sally Hobson and Henry D. Knowles for technical assistance in the laboratory and to two anonymous referees for helping to improve the clarity of the manuscript.Author ContributionsH.M.W., I.C.C. and N.E.S. designed the experiment; K.K., J.R.H., C.D. and N.K. performed the experiment; K.K. and I.C.C. analysed the data and wrote the first draft of the ms, with contributions by all authors.Additional InformationCompeting Interests: The authors declare no competing interests.Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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Estimating the lifelong health impact and financial burdens of different types of lung cancer

Szu-Chun Yang
Wu-Wei Lai
Wu-Chou Su
Shang-Yin Wu
Helen Hw Chen
Yi-Lin Wu
Mei-Chuan Hung
Jung-Der Wang
Estimating the lifelong health impact and financial burdens of different types of lung cancer
R E S E A R C H A R T I C L E Open Access
Background: Owing to the high mortality and rapidly growing costs related to lung cancer, it is worth examining the health benefits of prevention for major types of lung cancer. This study attempts to quantify the quality-adjusted life expectancy (QALE), loss-of-QALE, and lifetime healthcare expenditures of patients with different pathological types of lung cancer. Methods: A national cohort consisting of 66,535 patients with pathologically verified lung cancer was followed for 13 years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) to obtain the survival function, which was further extrapolated to lifetime. Between 2011 and 2012, EuroQol 5-dimension questionnaires were used to measure the quality of life (QoL) for 1,314 consecutive, cross-sectional samples. After multiplying the lifetime survival function by the utility values of QoL, we estimated the QALE and loss-of-QALE. We also collected the monthly healthcare expenditures, which included National Health Insurance-reimbursed and out-of-pocket direct medical costs, for 2,456 patients from 2005 to 2012. These values were multiplied by the corresponding survival probabilities to calculate lifetime healthcare expenditures after adjustments with medical care inflation rates and annual discount rates. Results: The QALE for patients with small cell lung cancer, squamous cell carcinoma, and adenocarcinoma were 1.21, 2.37, and 3.03 quality-adjusted life year (QALY), with the corresponding loss-of-QALE of 13.69, 12.22, and 15.03 QALY, respectively. The lifetime healthcare expenditures were US$ 18,455 ± 1,137,20,599 ± 1,787, and 36,771 ± 1,998,  respectively.    Conclusions: The lifelong health impact and financial burdens in Taiwan are heavier for adenocarcinoma than for squamous cell carcinoma. The cost-effectiveness of prevention programs could be directly compared with that of treatment strategies to improve patient value. And the methodology could be applied to other chronic diseases for resources planning of healthcare services.

Background

Over the past two decades, mortality attributed to lung cancer has increased [1], and it has become the leading cause of cancer deaths [1,2]. Owing to the development of new interventions to diagnose and treat lung cancer, lung cancer care costs are continuously rising [3]. Therefore, healthcare providers are now facing an increased burden of caring for lung cancer patients. It is thus worth examining the health benefits of prevention for major types of lung cancer.

For assessing health benefits, both survival and quality of life (QoL) should be taken into consideration, and quality-adjusted life expectancy (QALE) using qualityadjusted life year (QALY) as the unit may be more suitable than estimating survival alone for the purpose of comparison of various types of healthcare services [4][5][6]. Previous studies mostly have focused on cross-sectional analyses of the economic burden of lung cancer [7,8].

However, lifetime medical costs should be estimated to evaluate the cost-utility of prevention programs. Although almost all cancer-specific medical costs are reimbursed by the National Health Insurance (NHI) available in Taiwan, there are out-of-pocket medical costs that must be estimated to obtain the lifetime healthcare expenditures attributable to lung cancer. Transportation costs, payments to caregivers, home adaptation due to illness and human capital loss were not taken into consideration in this analysis.

Since treatments and prognoses for different pathological subtypes of lung cancer are different, we hypothesized that they may impact mortality, QoL, and medical costs to different degrees. Based on data from the National Cancer Registry, NHI reimbursements, and National Cheng Kung University Hospital (NCKUH), this study is aimed at quantifying the QALE, loss-of-QALE and lifetime healthcare expenditures that occur in patients with different subtypes of lung cancer that would be regained through successful prevention initiatives.

Methods

The Institutional Review Board of NCKUH approved this study before its initiation (Approval number: ER-100-079), and every patient interviewed provided written informed consent. This study abstracted data from the National Cancer Registry for survival analysis, combined this with the national life tables to extrapolate the survival function to lifetime, collected QoL and costs data from lung cancer patients in NCKUH, and integrated the survival function with the QoL and costs to estimate the life expectancy, QALE, loss-of-QALE, and lifetime healthcare expenditures per case of lung cancer. QALE was estimated using the following equation [9][10][11]:
QALE ¼ ∫E QoL t=x ð Þ ½ S t=x ð Þdt E[QoL(t/x)]
denotes the expected value of the QoL function for condition x at time t, and S(t/x) denotes the survival function for condition x at time t. The entire design is summarized in Figure 1.

National Cancer Registry for estimation of the survival function

All patients with lung cancer during the period from 1998 to 2007 were abstracted from the National Cancer Registry database. The diagnosis of lung cancer and its subtypes was based on either histology or cytology codes taken from the International Classification of Diseases [12]. The survival status for each patient was verified by linking the patient's identification information to the National Mortality Registry database. Each patient underwent follow-up from the day of diagnosis until the end of 2010.

Extrapolating the survival to lifetime

After obtaining the survival function of the lung cancer cohort, a method proposed by Huang and Wang [13] was used to extrapolate this curve beyond the end of the follow-up period. This approach assumes that lung cancer generates a constant excess hazard (i.e., mortality) after a certain follow-up period, and its calculation is carried out in three steps. First, we took the hazard functions from the life tables of the National Vital Statistics of Taiwan to generate an age-and sex-matched reference population using the Monte Carlo method and then estimated the survival function. Second, we calculated the survival ratio between the lung cancer cohort and the reference population at each time t and performed a logit transformation of this. Third, the logit transformation of the ratio was fitted by simple linear regression up to the end of the follow-up period. The estimated regression line, together with the survival function of the reference population beyond the follow-up limit, was used to extrapolate the lifetime survival function of the lung cancer cohort, and thus the life expectancy of the lung cancer cohort (up to 600 months) after diagnosis could be estimated. Expected years of life lost in the lung cancer cohort were defined as the survival difference between the cohort and the reference population. The method described above has been demonstrated to be effective using computer simulations [13], proven mathematically [14], and corroborated by several examples of cancer cohorts [15,16]. The iSQoL statistical package [17] was used to facilitate the computation. To validate the extrapolation method described above, we used the survival data of patients who were diagnosed during the first seven years and then extrapolated them to 13 years. Because these patients were actually followed until the end of 2010, the mean survival duration within the 13-year follow-up period, using the Kaplan-Meier method, was considered as the gold standard. The relative bias was computed to compare the difference in values between the extrapolation and the Kaplan-Meier estimation.

Measuring the QoL from a cross-sectional sample

From May 2011 to April 2012, all consecutive patients with lung cancer from the outpatient oncology, chest surgery, and chest medicine departments of NCKUH were invited to participate in this study. To enrich our sample, we also screened patients admitted to the wards between November 2011 and January 2012. The inclusion criteria included the realization of a lung cancer diagnosis in the participants, the absence of malignancy at another site, and the subjects' ability to understand and answer the questionnaire. In some individuals, measurements were performed repeatedly; however, each measurement took place 3 months apart.

The EuroQol 5-dimension questionnaire (EQ-5D) [18,19], which is a preference-based generic instrument, was used to estimate the QoL utility values. The five dimensions assessed by EQ-5D are mobility, self care, usual activities, pain/discomfort, and anxiety/depression, each with three levels of severity. Using the scoring function from the United Kingdom [20], these health state parameters were transformed into a utility value ranging from 0 to 1, in which 0 represents death and 1 indicates perfect health.

The duration-to-date for each measurement was defined as the period between the date the lung cancer diagnosis was made and that of the interview. A kernelsmoothing method (i.e., the moving average of the nearby 10%) was used to estimate the mean QoL function [10,11]. The QoL utility values beyond the followup period were assumed to be the same as the average of the last 10% of patients near the end of follow-up.

Estimating the QALE and loss-of-QALE

The lifetime survival function of the lung cancer cohort was adjusted using the corresponding mean QoL function to obtain a quality-adjusted survival curve, in which the sum of the area under the curve was the QALE of the lung cancer patients [10]. The loss-of-QALE was calculated by subtracting the area under the qualityadjusted survival curve of the lung cancer patients from that of the reference population. Because the EQ-5D scoring function for Taiwanese residents is still under development, we tentatively assumed the QoL score of the reference population to be 1 [10,13]. However, we also tested the assumption in sensitivity analysis assuming the average QoL values of 0.9 and 0.95 for the reference population. We used the iSQoL software [17], which can be downloaded for free, to facilitate the computation.

Estimating the lifetime healthcare expenditures

The reimbursement database at NCKUH was used to obtain spending details on medical services for lung cancer patients between January 2005 and December 2012. As these data included not only medical costs paid by the NHI but also out-of-pocket money, most direct medical costs attributable to lung cancer could thus be obtained to calculate the total monthly healthcare expenditures. These were then divided by the effective sample sizes, namely, the number of lung cancer patients who survived that month, to obtain the average monthly healthcare expenditures per case. These values were subsequently multiplied by the corresponding monthly survival probabilities and summed up to obtain the lifetime healthcare expenditures per case. All the payments in different calendar years were adjusted based on the related consumer price indices [21] and made equivalent to those in 2012. To discount costs in future years, we also adjusted the healthcare expenditures, using an annual discount rate of 3%. Because patients might incur expenses outside the hospital, we compared the costs we estimated with the costs reimbursed in the national database by using the same group of patients (2005)(2006)(2007). The proportion of reimbursement at NCKUH was estimated. A total of 1,002 patients ever cared at the NCKUH were linked with the reimbursement database of NHI, of which 11 cases could not be successfully connected. There were 4 cases (2 cases of small cell lung cancer (SCLC) and 2 cases of non-small cell lung cancer (NSCLC)) with the difference on the date of diagnosis larger than 1,000 days and were not included for the above calculation.

Results

The lung cancer cohort from the National Cancer Registry database used to obtain the survival function consisted of 66,535 patients. A total of 964 lung cancer patients visited NCKUH between 2011 and 2012, and 676 patients met the criteria for inclusion. Individuals who declined to answer the questionnaire were excluded, leaving 635 patients. However, 17 of these had incomplete data, and thus the cross-sectional sample for measuring the QoL consisted of a total of 618 patients, and 1,314 QoL measurements were performed. The average number of measurements per person was 2.1. In addition, the reimbursement database in NCKUH included details of healthcare expenditures for 2,456 lung cancer patients from 2005 to 2012. Table 1 summarizes the descriptive statistics of the national cohort for survival function and the NCKUH cohort for healthcare expenditures. By using the same method, patients with NSCLC in the NCKUH cohort were found to have longer life expectancy and greater NHI-reimbursed lifetime healthcare expenditures than those in the national cohort, which can be partially explained by the advancement of NSCLC treatment in the past five years and different frequency distributions of pathological subtypes in the two groups.

The QALE and loss-of-QALE

After multiplying the survival probability with the mean QoL at each time t (duration-to-date), we obtained the quality-adjusted survival curve, as shown in Figure 2. The sum of the shaded area under the curve represents the QALE. Assuming that the QoL of age-and sexmatched referents was 1, the difference between the quality-adjusted survival curve of patients and that of the referents was the loss-of-QALE ( Figure 3). The QALE for patients with SCLC and NSCLC was 1.21 and 2.65 QALY, respectively, while the loss-of-QALE for the two groups was 13.69 and 14.08 QALY, respectively. Compared with NSCLC patients with squamous cell carcinoma (SqCC), those with adenocarcinoma had a longer QALE (3.03 vs. 2.37 QALY, p < 0.001, Table 2). Moreover, we found that the loss-of-QALE of adenocarcinoma patients was significantly greater than that of SqCC patients (15.03 vs. 12.22 QALY, p < 0.001), probably because of the younger mean age at diagnosis (65.60 vs. 69.35 years, p < 0.001).

The lifetime healthcare expenditures

Using the kernel-smoothing method, the average healthcare expenditures per case of patients with SCLC and NSCLC are depicted in Figure 4. It shows that the medical costs reimbursed by the NHI decreased gradually after the diagnosis; however, the out-of-pocket medical costs in the terminal phase of NSCLC increased. Compared NHI-reimbursed costs of the 987 patients in our hospital with those in the national database, we found that healthcare costs of the NCKUH for SCLC and NSCLC patients accounted for 74.3% and 84.1%, respectively, of the total costs reimbursed by the NHI. Table 2 also shows the lifetime healthcare expenditures per case for patients with different NSCLC subtypes, adjusted for medical care inflation rates up to the year 2012 and for annual discount rates in future years. Compared with SqCC patients, adenocarcinoma patients had greater lifetime healthcare expenditures per case (US$ 36,771 ± 1,998 versus US$ 20,599 ± 1,787), especially in regard to out-of-pocket medical costs (US$ 18,620 ± 1,857 versus US$ 6,350 ± 741). Moreover, we found that both the annual healthcare expenditures and cost-per-QALY attributable to adenocarcinoma were higher than those for SqCC.  Table 3). The relative biases of the extrapolation ranged between + 7.71% (p = 0.122) and + 1.29% (p = 0.551).

Validation of the extrapolation method

Discussion

Although the incidence of adenocarcinoma (50.60%) was about twice that of SqCC (25.24%) during the period 1998-2007, according to the data from the National Cancer Registry, the former has not attracted as much attention as the latter, which is associated with cigarette   smoking [22] and has been the subject of much greater preventative efforts for the last few decades. There is a general impression that long-term survival is similarly poor with all types of NSCLC, a view that is corroborated by the results of this study, which found a life expectancy of 2.73 versus 3.46 years for patients with SqCC and adenocarcinoma, respectively ( Table 2). However, after adjustment for the age at onset, the latter group suffered from 2.77 (= 14.61-11.84) additional years of life lost in comparison with the former, which was equivalent to 2.81 (= 15.03-12.22) QALY more loss-of-QALE (Table 2). Moreover, the average healthcare expenditures per life-year for patients with adenocarcinoma were 46.9% (= (13,636-9,281)/9,281) higher than that of SqCC patients, while the average lifetime healthcare expenditures were 78.5% (= (36,771-20,599)/20,599) higher. As this study is limited to cases with pathological evidence that were followed for 13 years, sex-and agematched for estimating the life expectancy, and adjusted for the QoL and costs of an actual cohort, the estimation is not confounded by the factors listed above. In addition, the validation of our extrapolation method has shown that the relative biases were all less than 7.71% after 6 years of extrapolation (Table 3). We thus tentatively conclude that, in Taiwan, the lifelong health impact and financial burdens of adenocarcinoma are heavier than those of SqCC and deserve effort with regard to prevention. Several risk factors for adenocarcinoma have been identified. In addition to cigarette smoking, indoor exposure to radon gas is perhaps one of the most important [23,24]   and may be easily preventable. The risk of lung cancer among non-smoking women also appears to be associated with cooking fume exposure and lack of ventilation control [25][26][27]. By carefully studying and controlling these risk factors, more life-years and QALY lost, as well as healthcare expenses, might be saved.

Unlike previous studies that have used internationallyselected life tables together with expert determination of QoL values to calculate the disease burden of lung cancer with the disability-adjusted life year (DALY) unit [28,29], our study used the national life tables of Taiwan and a cross-sectional sample of patients for measurement of QoL to estimate the QALE and loss-of-QALE using QALY. While the DALY method makes international comparisons more feasible, the QALY unit calculated by our method can directly compare different prevention, diagnosis, and treatment strategies, and would probably be more feasible for national health policy decisions. However, the utility values of NSCLC patients reported by our cross-sectional sample were higher than those reported in other studies [30,31], and this may be due to several reasons. First, all patients must be healthy enough to accept our interview invitations and most (85.4%, 528 of 618 patients) of our subjects were recruited from outpatient departments. They were less likely to have any severe adverse effect and would have higher utility scores [30]. In addition, 59.1% (365 of 618 patients) of our subjects did not have any evidence of metastasis, which would make the average scores of our patients be higher than those with 63.2% of metastasis [31]. Second, because insight into the diagnosis of lung cancer was one of the inclusion criteria required by the Institutional Review Board, the utility values of our patients were usually higher [32]. Third, while extrapolating the QoL function to lifetime, it was assumed that patients remained at the same level of QoL near the end of the follow-up period. Such an assumption could result in an overestimated or higher QoL score because actual utility value usually declines with age [33]. Hence, the QALE would be overestimated while the loss-of-QALE would be underestimated.

The lifetime healthcare expenditures for adenocarcinoma, even if we look at the expenditures per life-year, were higher than those for SqCC. This can be at least partially explained by the fact that the patients with adenocarcinoma were 3.75 years younger than those with SqCC and usually received more aggressive forms of treatments, which included newly developed chemotherapeutic agents (such as premetrexed, gefitinib, erlotinib, and so on). Most costs of these treatments were out-of-pocket medical costs, which were administered in the later and/or terminal phase of the disease, as also shown on Figure 4. When estimating the lifetime healthcare expenditures, we adjusted the dollar values throughout the 2005-2012 period to that of 2012. In addition, the current estimates were adjusted for an annual discount rate of 3% in future years. Comparisons with studies from other countries [34,35] reveal that the lifetime healthcare expenditures reimbursed by the NHI in Taiwan seem relatively small. This could be explained by the different prices of medical care in different countries.

Several limitations must be acknowledged in our study. First, the QoL score of the reference population was assumed to be 1, whereas the reference population might include people with other illnesses and, as a result, the loss-of-QALE due to lung cancer might be overestimated. However, we have tested the assumption in sensitivity analysis and assessed its impact on the results (Table 2). In addition, since both reference groups for the two different subtypes of lung cancer were treated in the same way, the difference between them would not be confounded. Second, although the estimated lifetime healthcare expenditures represented not only the medical costs paid by the NHI but also the out-of-pocket money, there were still other expenses paid by the patients and/or their families, including transportation costs, payments made to caregivers, and human capital loss (i.e. the foregone earnings), which we did not take into consideration. Nevertheless, our estimations could be considered as a lower bound and may be useful for future lung cancer prevention programs planned by the related health authorities. Third, as the data abstracted from the National Cancer Registry for survival analysis did not have detailed information of tumor stages, we could not do further subgroup analysis regarding the staging issue. Because about threefourth of NSCLC and all SCLC patients are inoperable,

Conclusions

In conclusion, this study successfully estimated the QALE, loss-of-QALE, and the lifetime healthcare expenditures of patients with different lung cancer subtypes. We found that adenocarcinoma produces higher loss-of-QALE and lifetime healthcare expenditures than is seen with SqCC. Future research could focus on the costeffectiveness of different prevention programs targeted at different types of lung cancer to obtain the cost-perlife year and/or cost-per-QALY, and provide policy recommendations. In addition, the methodology could be applied to other cancers or chronic diseases for resources planning of healthcare services.

Figure 1
1Flow diagram of the inclusion of subjects and their relevant information for estimation. EQ-5D: EuroQol 5-dimension questionnaire; NCKUH: National Cheng Kung University Hospital; QALE: quality-adjusted life expectancy.

There were 4,425 patients with SCLC and 38,509 patients with NSCLC diagnosed during the first seven years, between 1998 and 2004, of which the survival curves were extrapolated to 2010 and compared with the Kaplan-Meier estimates based on the 13-year follow-up (

Figure 2
2The QALE (quality-adjusted life expectancy) of patients with small cell lung cancer (SCLC, upper panel) and non-small cell lung cancer (NSCLC, lower panel). The survival curves (dashed lines), mean utility functions (dotted lines), and quality-adjusted survival curves (solid lines) of patients with lung cancer are depicted, and the shaded area represents the QALE.

Figure 3
3The QAS (quality-adjusted survival) curves of patients with squamous cell carcinoma (SqCC), adenocarcinoma and the corresponding referents. The difference between the QAS curve of patients and that of the corresponding referents was the loss-of-QALE (quality-adjusted life expectancy). SqCC patients: solid line; age-and sex-matched referents of SqCC: dotted line; adenocarcinoma patients: long-dashed line; age-and sex-matched referents of adenocarcinoma: short-dashed line.

Figure 4
4The healthcare expenditures per case of patients with small cell lung cancer (SCLC, upper panel) and non-small cell lung cancer (NSCLC, lower panel). The medical costs reimbursed by the National Health Insurance (NHI) are depicted in solid lines and out-of-pocket medical costs are depicted in dotted lines.

Table 1
1Comparison between the national cohort and the cohort from National Cheng Kung University Hospital (NCKUH)The national cohort
The NCKUH cohort
P value *

Table 2
2The QALE (quality-adjusted life expectancy), loss-of-QALE, healthcare expenditures (in US dollars) of lung cancer patients SD: standard deviation; SE: standard error; NSCLC: non-small cell lung cancer; QALY: quality-adjusted life year; SqCC: squamous cell carcinoma.Large cell carcinoma, adenosquamous cell carcinoma, adenoid cystic carcinoma, mucoepidermoid carcinoma, carcinoid tumor and unspecified carcinoma.NSCLC (n = 59,787)

SqCC (n = 16,796)
Adenocarcinoma (n = 33,669)
Other NSCLCs * (n = 9,322)

Table 3
3Validation of the extrapolated estimates by 13 years of follow-up and the Kaplan-Meier method Cohort size 13-year follow-up Kaplan-Meier estimate, mean (SE) months Estimate using the extrapolation based on the first 7 years of follow-up, mean (SE) months Relative bias,% P value * SE: standard error; NSCLC: non-small cell lung cancer; SCLC: small cell lung cancer; SqCC: squamous cell carcinoma. Comparison using Z-statistic.the magnitude of potential bias may not be too large. Lastly, the estimation of QALE would have been more accurate if we had obtained the QoL follow-up for every patient in the lung cancer cohort. Future longitudinal studies are indicated to corroborate our results based on cross-sectional samples.SCLC
4,425
14.75 (0.45)
15.42 (0.38)
+4.54
0.424

NSCLC
38,509 25.27 (0.24)
25.89 (0.16)
+2.45
0.165

SqCC
11,507 25.51 (0.43)
26.21 (0.31)
+2.74
0.528

Adenocarcinoma 20,926 27.05 (0.30)
27.40 (0.23)
+1.29
0.551

Other NSCLCs
6,076
18.68 (0.44)
20.12 (0.37)
+7.71
0.122

*

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AcknowledgementsThis work was supported in part by the National Cheng Kung University Hospital (DOH100-TD-C-111-003 and NCKUH-10206012) and the National Science Council (NSC99-2628-B-006-036-MY3 and NSC101-3114-Y-006-001). The funders did not have a role in the study design, in the collection, analysis and interpretation of data, in the writing of the manuscript, or in the decision to submit the manuscript for publication. We are indebted to the kind assistance of the Collaboration Center of Health Information Application, Ministry of Health and Welfare, Executive Yuan and the Cancer Registry Databank of National Cheng Kung University Hospital Cancer Center for providing the data used in this research.Competing interestsThe authors declare that they have no competing interests.
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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Yang , BMC Cancer. 13579Estimating the lifelong health impact and financial burdens of different types of lung cancer. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Yang et al.: Estimating the lifelong health impact and financial burdens of different types of lung cancer. BMC Cancer 2013 13:579. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit