Checkley, William

Cárdenas, Maria Kathia

Hurst, John R

Mohan, Sakshi

Pollard, Suzanne L.

Ricciardi, Federico

Rykiel, Natalie A.

Siddharthan, Trishul

Soares, Marta O

White Rose Research Online

Cost-Accuracy Analysis of Chronic Obstructive Pulmonary Disease Screening in Low- and Middle-Income CountriesSakshi Mohan (1), Maria Kathia Cárdenas (2), Federico Ricciardi (3), Trishul Siddharthan (4), Suzanne L Pollard (5,6), Natalie A Rykiel (5,6), William Checkley (5,6), John R Hurst (7)*, Marta O Soares (1)* for the GECo Study Investigators.*: joint Senior AuthorThe GECo Study Investigators: Patricia Alupo, Julie A Barber, Maria Kathia Cárdenas, Ram K Chandyo, William Checkley, Santa Kumar Das, Oscar Flores-Flores, John R Hurst, Bruce Kirenga, J Jaime Miranda, Sakshi Mohan, Suzanne L Pollard, Shumonta A Quaderi, Federico Ricciardi, Natalie A Rykiel, Arun K Sharma, Laxman Shrestha, Trishul Siddharthan, Marta O Soares, Adaeze C Wosu1.  Centre for Health Economics, University of York, York, UK2.  CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru3.  Department of Statistical Science, University College London, London, UK4.  Division of Pulmonary and Critical Care, Miller School of Medicine, University of Miami, Miami, Florida, USA5.  Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, USA6.  Center for Global Non-Communicable Disease Research and Training, School of Medicine, Johns Hopkins University, Baltimore, USA7.  UCL Respiratory, University College London, London, UKFUNDING: Financial support was provided by the Medical Research Council (MR/P008984/1).  TS is supported under NIH K23HL126946. This research was conducted while SP was employed at Johns Hopkins University. The opinions expressed in this article are the author's own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. Neither the Medical Research Council nor the NIH had any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.WORDCOUNT: 1000IntroductionThe global burden of COPD disproportionately affects low- and middle-income countries (LMIC).  Among well-recognised challenges to the mitigation of COPD in LMIC include the absence[1] and poor implementation[2] of COPD guidelines, and lack of access to diagnostic spirometry.  Screening and case-finding tools may provide an effective way to target scarce resources to those most likely to benefit, and we have recently reported the diagnostic accuracy of three COPD screening tools in LMIC settings[3].  Cut-points on screening tools can be adjusted to prioritise sensitivity or specificity, which impacts diagnostic pick-up and the downstream need for confirmatory spirometry and thus further healthcare use and costs.  We now report a cost-accuracy analysis of three COPD screening tools in LMIC settings.MethodsThe trial methodology has been previously reported[4].  Individuals aged over 40 years were randomly selected from the population in semi-urban Bhaktapur, Nepal, urban Lima, Peru, and rural Nakaseke, Uganda.  We tested CAPTURE (COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk, which includes peak expiratory flow, PEF), COLA-6 (COPD in LMICs Assessment-6, which includes PEF) and the Lung Function Questionnaire (LFQ, which does not include PEF).  To characterise diagnostic pathways, we used a structured decision tree (Figure 1) to consider the likelihood of reaching individuals for the screening visit, prevalence of COPD, and screening tool accuracy.  We estimated the overall accuracy of the tools, and a sensitivity analysis used site-specific accuracy estimates with thresholds for the tools adjusted to return a sensitivity ≥90%.  The likelihood of individuals accessing the healthcare system for a spirometry-confirmed diagnosis, and the availability of spirometry were also considered.  Each step was costed from a health-system perspective using time taken to deliver CAPTURE, COLA-6 and LFQ, and externally sourced costs including the cost of spirometry, and of a clinician visit for those testing positive.  We present three metrics – 1) cost of diagnostic workup per 100 individuals screened, 2) cost per correctly identified negative or positive case, and 3) cost per correctly diagnosed positive case.  To reflect those who could benefit from treatment, metrics 2 and 3 considered only positive cases that accessed the health system.  Permissions were obtained and participants provided written consent as reported in the parent study[3,4]. A detailed description of the assumptions on costing and probabilities associated with the decision tree are provided in the supplement. FIGURE 1: Decision tree for screening using a case-finding questionnaire (CFQ), assuming that confirmatory diagnosis with spirometry subsequently takes place.ResultsThe patient demographic and diagnostic accuracy of the three tools at the three sites have been previously reported[3].  In brief, we screened 10709 people, with a mean (SD) age of 56.3(11.7) years, 50.3% female.  The overall unweighted prevalence of COPD was 9.4% but this varied by site and was 18.2% in Nepal, 2.7% in Peru and 7.4% in Uganda.  More than 95% of cases were previously unaware of their diagnosis despite 49.3% having GOLD group[5] B, C or D disease and therefore clinically significant COPD.  The AUC for the three screening tools were similar[3].Administration of the case-finding instruments in participant homes took a mean (SD) of 6.5 (0.28) minutes for LFQ, 7.8 (0.42) minutes for CAPTURE and 8.7 (0.61) minutes for COLA-6 inclusive of introductory conversations.  The cost-accuracy analyses are reported in Table 1 (2019 USD).  The total diagnostic pathway cost per 100 individuals screened was similar across the three instruments but lowest for COLA-6 (at around $700).  COLA-6 also had the lowest cost per correct diagnosis, negative or positive, because of its higher specificity.  Analysis by site showed that the costs of the diagnostic pathways were heterogeneous: highest in Peru ($1,567 per 100 individuals screened on average across the three questionnaires), followed by Nepal ($308 per 100 individuals screened), then Uganda ($295 per 100 individuals screened), associated with differences in prevalence and unit costs across sites.Results of the sensitivity analysis, where thresholds for the instruments were adjusted to return a sensitivity ≥90% indicate that, due to lower specificity, 74-89% of patients screened would be positive on the screening tools.  This would improve the identification of patients with COPD (from 2.8-3.6 to 5.8-6.0, per 100 screened), but significantly increase the downstream diagnostic workup costs (from around $700 to around $1,100, per 100 patients screened).TABLE 1: Cost-Accuracy Analysis of three COPD Screening Tools in three LMIC Settings.	Site	(number targeted/	100 screened)	Screening tool (ST)	Number of individuals per 100 screened...	Cost (2019 USD) per…		... positive to ST *	…with correct positive diagnosis **	… with correct negative diagnosis ***	…100 individuals screened	…correct  positive or negative diagnosis **,***	…correct positive diagnosisMain analysis for LFQ, CAPTURE and COLA-6 using trial estimates (pooled across sites) of sensitivity/specificity: 58.8%/78.9% (LFQ), 60.0%/81.5% (CAPTURE) and 45.6%/86.6% (COLA-6).Nepal	(159)	LFQ	28.0	6.9	68.8	305.4 	4.0 	44.0 	CAPTURE	26.0	7.1	70.4	315.6 	4.1 	44.5 	COLA-6	19.3	5.4	73.6	303.4 	3.8 	56.3 Uganda	(171)	LFQ	23.9	2.8	76.2	309.3 	3.9 	109.9 	CAPTURE	21.6	2.9	78.2	307.3 	3.8 	106.9 	COLA-6	15.8	2.2	82.2	269.2 	3.2 	123.2 Peru	(181)	LFQ	22.1	1.0	81.3	1,590.9 	19.3 	1,525.5 	CAPTURE	19.6	1.1	83.3	1,580.0 	18.7 	1,483.3 	COLA-6	14.3	0.8	87.1	1,526.7 	17.4 	1,886.3 Overall	(150)	LFQ	24.7	3.6	75.4	735.2 	9.1 	559.8 	CAPTURE	22.4	3.7	77.3	734.3 	8.9 	544.9 	COLA-6	16.4	2.8	81.0	699.8	8.1	688.6Sensitivity analysis for LFQ and COLA-6 using site-specific thresholds defined to return a sensitivity ≥90%.  Sensitivity/specificity values for LFQ were: Nepal 95.7%/27.5%, Peru 92.6%/30.4% and Uganda 90.4%/25.2%. Sensitivity/specificity values for COLA-6 were: Nepal 98.4%/11.6%, Peru 97.9%/12.4% and Uganda 96.5%/10.7%.Nepal	(159)	LFQ	76.7	11.3	37.2	 403.8 	 8.3 	 35.7 	COLA-6	90.2	11.6	27.4	 446.5 	 11.4 	 38.4 Uganda (171)	LFQ	75.9	4.3	34.3	 665.1 	 17.2 	 153.6 	COLA-6	89.8	4.6	23.0	 775.4 	 28.0 	 167.7 Peru	(181)	LFQ	70.2	1.6	44.5	 2,109.4 	 45.7 	 1,284.4 	COLA-6	87.9	1.7	30.9	 2,320.6 	 71.2 	 1,335.2 Overall	(150)	LFQ	74.3	5.8	38.7	 1,059.4 	 23.7 	 491.2 	COLA-6	89.3	6.0	27.1	 1,180.8 	 36.9 	 513.8 Costs presented here consider i) the resources required to perform screening (in the community) and ii) any subsequent procedures/resources needed to achieve a diagnosis (at health facilities).* includes false positives** a correct positive diagnosis includes individuals that were identified as positive to the screening tool, accessed the health system and have conducted spirometry where available (hence, excludes false positives identified via spirometry). Note that it was assumed that only a proportion of those testing positive to the screening questionnaire would follow-through to the health system.*** a correct negative diagnosis includes individuals that were true negatives to the screening questionnaire and any false positives which received spirometry.DiscussionWe have recently reported the diagnostic accuracy of three screening tools for COPD in LMIC settings[3].  The tools tested had similar, moderate accuracy.  Here we report the cost-accuracy of the tools, which are dependent on tool performance (and selected thresholds), COPD prevalence, and local costs.In choosing among screening instruments, a policymaker needs to consider the trade-offs between specificity (reducing health-system burden from false positives) and sensitivity (identifying more cases).  Our cost-accuracy analysis makes these trade-offs explicit by quantifying diagnostic-workup savings from the increased specificity of COLA-6.  However, consideration needs to be given to long-term benefits arising from the increased detection of COPD using CAPTURE and LFQ (which had a higher sensitivity), and whether these offset the additional costs arising from reduced specificity.  Part of the cost of administering screening tools is incurred attempting to reach targeted individuals.  Programs designed to better target and reach individuals at risk of COPD could improve cost-accuracy. We demonstrate that maximising sensitivity to >90% increased identification of COPD to 6%, but resulted in 74-89% of all screened cases retuning a positive result.  Even if only 65% of these individuals accessed the health-system (as considered in our analyses), this is likely unaffordable.Arguments against screening for COPD in high-income settings have centred around the lack of disease-modifying interventions in milder disease[6].  This does not hold in LMIC settings where many participants have clinically significant disease[3].  Moreover, availability of cost-effective interventions for COPD in LMIC is increasing, including pulmonary rehabilitation[7], and with the recent inclusion of long-acting anti-muscarinic drugs on the WHO Essential Medicines List[8].In conclusion, COPD screening in LMIC identifies clinically significant, previously undiagnosed disease, which presents a high-burden to patients and health systems.  COPD prevalence varies by setting and this cost-accuracy analysis highlights the trade-offs between sensitivity and specificity, number of cases accessing downstream services and costs.  Further research should be conducted to quantify the burden of unidentified COPD, and the potential for reducing this with cost-effective interventions.  This would allow extending our analyses to consider full cost-effectiveness which, by determining the net impacts of the sensitivity/specificity profiles of the different screening tools, would be able to explicitly inform policy decisions on the value of the alternative approaches.References1.  Tabyshova A, Hurst JR, Soriano JB, Checkley W, Wan-Chun Huang E, Trofor AC, Flores-Flores O, Alupo P, Gianella G, Ferdous T, Meharg D, Alison J, Correia de Sousa J, Postma MJ, Chavannes NH, van Boven JFM. Gaps in COPD Guidelines of Low- and Middle-Income Countries: A Systematic Scoping Review. Chest. 2021 Feb;159(2):575-584. doi: 10.1016/j.chest.2020.09.260. Epub 2020 Oct 8. PMID: 33038390; PMCID: PMC7856534.2.  Hurst JR, Buist AS, Gaga M, Gianella GE, Kirenga B, Khoo EM, Mendes RG, Mohan A, Mortimer K, Rylance S, Siddharthan T, Singh SJ, van Boven JFM, Williams S, Zhang J, Checkley W. Challenges in the Implementation of Chronic Obstructive Pulmonary Disease Guidelines in Low- and Middle-Income Countries: An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc. 2021 Aug;18(8):1269-1277. doi: 10.1513/AnnalsATS.202103-284ST. PMID: 34328399; PMCID: PMC8513652.3.  Siddharthan T, Pollard SL, Quaderi SA, Rykiel NA, Wosu AC, Alupo P, Barber JA, Cárdenas MK, Chandyo RK, Flores OF Kirenga B, Miranda JJ, Mohan MA, Ricciardi F, Sharma AK, Das SK, Shrestha L, Soares MO, Checkley W, Hurst JR, for the GECo Study Investigators.  Discriminative Accuracy of Chronic Obstructive Pulmonary Disease Screening Instruments in 3 Low- and Middle-Income Country Settings.  JAMA 2022;327:151-1604.  Siddharthan T, Pollard SL, Quaderi SA, Mirelman AJ, Cárdenas MK, Kirenga B, Rykiel NA, Miranda JJ, Shrestha L, Chandyo RK, Cattamanchi A, Michie S, Barber J, Checkley W, Hurst JR; GECo Study Investigators. Effectiveness-implementation of COPD case finding and self-management action plans in low- and middle-income countries: global excellence in COPD outcomes (GECo) study protocol. Trials. 2018 Oct 19;19(1):571. doi: 10.1186/s13063-018-2909-8. PMID: 30340648; PMCID: PMC6194571.5.  2022 GLOBAL STRATEGY FOR PREVENTION, DIAGNOSIS AND MANAGEMENT OF COPD.  Available at: https://goldcopd.org/2022-gold-reports/ last accessed December 31st 2021.6.  Guirguis-Blake JM, Senger CA, Webber EM, Mularski RA, Whitlock EP. Screening for Chronic Obstructive Pulmonary Disease: Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA. 2016 Apr 5;315(13):1378-93. doi: 10.1001/jama.2016.2654. PMID: 27046366.7.  Bickton FM, Fombe C, Chisati E, Rylance J. Evidence for pulmonary rehabilitation in chronic respiratory diseases in sub-Saharan Africa: a systematic review. Int J Tuberc Lung Dis. 2020 Oct 1;24(10):991-999. doi: 10.5588/ijtld.19.0526. PMID: 33126930.8.  WHO Model List of Essential Medicines 22nd List (2021).  Available at https://apps.who.int/iris/bitstream/handle/10665/345533/WHO-MHP-HPS-EML-2021.02-eng.pdf last accessed December 31st 2021.6

Cost-Accuracy Analysis of Chronic Obstructive Pulmonary Disease Screening in Low- and Middle-Income Countries

Cost-Accuracy Analysis of Chronic Obstructive Pulmonary Disease Screening in Low- and Middle-Income Countries

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