Computer-aided X-ray screening for tuberculosis and HIV testing among adults with cough in Malawi (the PROSPECT study): A randomised trial and cost-effectiveness analysis.

BACKGROUND: Suboptimal tuberculosis (TB) diagnostics and HIV contribute to the high global burden of TB. We investigated costs and yield from systematic HIV-TB screening, including computer-aided digital chest X-ray (DCXR-CAD). METHODS AND
FINDINGS: In this open, three-arm randomised trial, adults (≥18 years) with cough attending acute primary services in Malawi were randomised (1:1:1) to standard of care (SOC); oral HIV testing (HIV screening) and linkage to care; or HIV testing and linkage to care plus DCXR-CAD with sputum Xpert for high CAD4TBv5 scores (HIV-TB screening). Participants and study staff were not blinded to intervention allocation, but investigator blinding was maintained until final analysis. The primary outcome was time to TB treatment. Secondary outcomes included proportion with same-day TB treatment; prevalence of undiagnosed/untreated bacteriologically confirmed TB on day 56; and undiagnosed/untreated HIV. Analysis was done on an intention-to-treat basis. Cost-effectiveness analysis used a health-provider perspective. Between 15 November 2018 and 27 November 2019, 8,236 were screened for eligibility, with 473, 492, and 497 randomly allocated to SOC, HIV, and HIV-TB screening arms; 53 (11%), 52 (9%), and 47 (9%) were lost to follow-up, respectively. At 56 days, TB treatment had been started in 5 (1.1%) SOC, 8 (1.6%) HIV screening, and 15 (3.0%) HIV-TB screening participants. Median (IQR) time to TB treatment was 11 (6.5 to 38), 6 (1 to 22), and 1 (0 to 3) days (hazard ratio for HIV-TB versus SOC: 2.86, 1.04 to 7.87), with same-day treatment of 0/5 (0%) SOC, 1/8 (12.5%) HIV, and 6/15 (40.0%) HIV-TB screening arm TB patients (p = 0.03). At day 56, 2 SOC (0.5%), 4 HIV (1.0%), and 2 HIV-TB (0.5%) participants had undiagnosed microbiologically confirmed TB. HIV screening reduced the proportion with undiagnosed or untreated HIV from 10 (2.7%) in the SOC arm to 2 (0.5%) in the HIV screening arm (risk ratio [RR]: 0.18, 0.04 to 0.83), and 1 (0.2%) in the HIV-TB screening arm (RR: 0.09, 0.01 to 0.71). Incremental costs were US$3.58 and US$19.92 per participant screened for HIV and HIV-TB; the probability of cost-effectiveness at a US$1,200/quality-adjusted life year (QALY) threshold was 83.9% and 0%. Main limitations were the lower than anticipated prevalence of TB and short participant follow-up period; cost and quality of life benefits of this screening approach may accrue over a longer time horizon.
CONCLUSIONS: DCXR-CAD with universal HIV screening significantly increased the timeliness and completeness of HIV and TB diagnosis. If implemented at scale, this has potential to rapidly and efficiently improve TB and HIV diagnosis and treatment. TRIAL REGISTRATION: clinicaltrials.gov NCT03519425.

Introduction

Despite being a leading infectious cause of adult mortality worldwide [1], tuberculosis (TB) remains challenging to diagnose, especially in low-resource settings [2]. In sub-Saharan Africa, where prevalence of active pulmonary TB can exceed 1% in some high HIV prevalence urban settlements [3], adults with TB symptoms frequently make multiple visits to primary healthcare services before TB testing is initiated [4]. Late and missed TB diagnosis is common, with severe consequences including hospitalisation, death, ongoing transmission, and catastrophic household expenditure associated with care-seeking and illness [4,5].

Guidelines recommend that adults attending health facilities be routinely screened for TB and investigated appropriately if symptomatic [6]. However, only a small percentage of clinic attenders successfully complete the TB screening cascade due to limited health worker numbers, high numbers of patient with symptoms of TB overwhelming testing capacity, and low availability and high cost of diagnostics [7].

Currently available TB diagnostics are not well suited to primary healthcare, and, consequently, patients are most likely to be lost during the diagnostic phase. Sputum smear microscopy has low sensitivity and is resource intensive [8]; Xpert—although more sensitive, especially for HIV–positive people [9,10]—is costly, and throughput is constrained by unit capacity [11]; lateral flow urine lipoarabinomannan assay (LF-LAM) is not currently recommended for HIV–negative people or HIV–positive outpatients without advanced immunosuppression [12]; and sputum mycobacterial culture remains costly, slow, and inaccessible to most primary clinics. Offering newer TB diagnostics such as Xpert to all adult primary clinic attenders with symptoms of TB (which can approach 60% [7]) could rapidly overwhelm clinic testing capacity and health systems’ budgets.

Rapid advances in digital X-ray technologies linked to computer-aided chest X-ray interpretation (DCXR-CAD) [13,14] mean that a “triage testing” approach to TB screening in primary care could remove barriers to high coverage of same-day, same-clinic TB diagnosis and treatment [15]. Digital chest X-ray provides a high-sensitivity and high-throughput initial screen for individuals with symptoms of TB [16], while computer-aided interpretation—software algorithms that provide an immediate probabilistic score for TB—remove the need for trained interpreters [17,18]. As specificity of chest X-ray for pulmonary TB is low, a positive X-ray triage screen should be confirmed with a high-specificity diagnostic test such as Xpert. Triage testing with DCXR-CAD could then substantially reduce the number of expensive Xpert tests that would have otherwise been required [19]. WHO has recently recommended that DCXR-CAD can be used for TB screening [18], but the impact on patient outcomes is unknown.

In this three-arm pragmatic randomised trial conducted in primary care in urban Blantyre, Malawi, we investigated whether a universal HIV testing and linkage to antiretroviral therapy (ART) intervention—either alone or combined with DCXR-CAD and subsequent sputum Xpert confirmation—could improve the timeliness and completeness of HIV and TB diagnosis and treatment. We additionally evaluated cost-effectiveness of implementation.

Methods

Study design

We conducted a three-arm, open, pragmatic randomised trial among adults recruited from an urban primary healthcare clinic in Blantyre, Malawi [20] (S1 Text). Bangwe Health Centre is located within a densely populated neighbourhood in the east of the city. Adult HIV prevalence in Blantyre is estimated to be 18% [21], and Malawi urban TB prevalence was estimated at 988 per 100,000 in the most recent national TB prevalence survey [22]. Comprehensive HIV care (including ART) and TB treatment are available at a primary care level through the national HIV and TB treatment programmes in Malawi.

All Blantyre-resident adults (≥18 years old) attending Bangwe Health Centre acute department who reported cough of any duration were eligible to participate and were screened for eligibility by study research assistants (6 in total, not medically trained, but with experience in community and clinic-based research) at the clinic registration desk. We excluded individuals who had taken TB treatment in the preceding 6 months, who were currently taking TB preventive therapy, or who planned to move out of Blantyre. All participants gave written (or, if illiterate, witnessed thumbprint) informed consent to participate. Ethical approval was granted by the College of Medicine, University of Malawi Research Ethics Committee, and the Liverpool School of Tropical Medicine.

Randomisation and masking

Participants were individually randomised without restriction to 1 of 3 arms—standard of care (SOC), HIV screening, or HIV-TB screening—in a 1:1:1 ratio using a computer-generated random number sequence running on study data collection electronic devices. Because of the nature of interventions, it was not possible to blind participants or study teams to allocation. However, investigator masking was maintained until after final statistical analysis. To minimise the risk of contamination between arms, a digital thumbprint (Simprints, Cambridge, United Kingdom) was recorded at recruitment and used to validate identity.

Procedures

All participants underwent a baseline questionnaire by study research assistants, conducted in a dedicated study research building within the grounds of the clinic.

Participants allocated to the SOC arm were directed to the clinic acute waiting area, where they were managed by clinic health workers without any further input from study staff. As this was a pragmatic trial, we intended that the SOC arm procedures would reflect the routine TB and HIV screening care delivered by a typical Ministry of Health primary care clinic in a low-resource setting. Available to participants in the SOC arm (if requested by a clinic health worker) were the following: sputum smear microscopy; sputum Xpert, TB treatment; HIV testing using rapid fingerprick diagnosis kits; and ART, as well as other routine primary health services. Malawi National Guidelines recommend HIV testing for all adults attending health facilities, although this is often not completed [23]. Clinic health workers included nurses, clinical officers, and HIV counsellors employed by the Ministry of Health of Malawi, but no physicians.

Participants allocated to the HIV screening arm who self-reported being HIV negative, or HIV positive but not taking ART, were offered HIV testing using oral kits (OraQuick HIV 1/2 Rapid Antibody Test, OraSure Technologies, Manufactured in Thailand), with confirmatory testing using serial testing with rapid kits (Determine HIV-1/2, Alere, Japan; Uni-gold HIV, Trinity Biotech, Ireland). HIV–positive participants were supported to register for ART at the onsite clinic by walking with them to the ART clinic registration area and making an appointment for treatment initiation assessment by the ART clinic staff (in practice usually done on the same day, in accordance with Malawi guidelines). Further investigations and management, including TB investigations, were provided by clinic health workers in accordance with national guidelines without any further input from study staff.

In addition to the HIV testing intervention, participants in the HIV-TB screening arm were offered a single posterior–anterior digital chest X-ray (MinXray Commander CMDR.T.120.60. S, United States of America) done by study radiographers with interpretation using CAD4TB v5 (Delft Imaging, the Netherlands). CAD4TB interpretation was done locally on a dedicated computer, with results automatically transferred to study data collection devices. Where the CAD4TB score was ≥45 (threshold selected based on pilot studies and in discussion with the software developer), participants’ sputum was tested onsite by Xpert; if positive, they were initiated on TB treatment by the clinic TB officer. Where the Xpert was negative or CAD4TB score was <45, they were directed to the clinic waiting area for further management by clinic health workers. Digital chest X-rays were additionally read remotely by a consultant radiologist, and participants with abnormal findings identified were recalled by telephone and referred to the clinic or city central hospital with results.

Participants were seen at the study clinic at 56 days, when they underwent questionnaire and inspection of health records to determine TB treatment status, HIV testing (if not known to be HIV positive and taking ART) and sputum collection for Xpert, MGIT culture and smear, with samples analysed at the TB Research Laboratory at the College of Medicine, University of Malawi. Participants who did not attend the clinic for this outcome assessment were traced to home, where possible. Loss to follow-up was defined as participants who did not attend their clinic day 56 outcome assessment appointment and could not be traced to home.

Outcomes

The primary outcome was time in days—up to, but not including, day 56—to TB treatment initiation. Secondary outcomes were the proportion of participants with same-day TB treatment initiation; proportion of participants with undiagnosed/untreated microbiologically confirmed pulmonary TB on day 56 (either sputum culture, or sputum Xpert, or sputum smear microscopy positive on day 56 sample); proportion with undiagnosed/untreated HIV; time in days—up to, but not including, day 56—to ART initiation among participants with positive confirmatory HIV test results at day 56 and who were not taking ART at day 0; mortality; and quality of life (assessed by difference in EuroQoL EQ5D utility score, a continuous variable that can take values between less than 0 and 1).

Statistical analysis

The statistical power was estimated for the primary outcome under the assumption that 17% of adults with TB symptoms in primary care in Blantyre would initiate TB treatment by 56 days [24] (S2 Text). A sample size of 1,455 participants (485 per arm) gave at least 80% power to detect a hazard ratio (HR) for TB treatment initiation of 1.5 comparing the HIV screening arm to SOC, a HR of 1.5 comparing HIV-TB screening arm to SOC, and a HR of 1.41 comparing the HIV-TB screening arm to HIV screening arm, at 5% significance level, allowing for 5% loss to follow-up and with no adjustment for multiplicity of testing.

This study is reported as per the Consolidated Standards of Reporting Trials (CONSORT) guideline (S3 Text). We performed analysis according to the intention to treat principle. To maintain investigator blinding, no inspection of data by trial arm was done until data cleaning was complete and the database locked; the trial statistician then undertook unblinded analysis of the locked trial database. Data are deposited in the Dryad repository doi: 10.5061/dryad.ffbg79ctb [25]. Baseline characteristics were summarised for the 3 trial arms using proportions, means (with standard deviations), and medians (with interquartile ranges (IQRs)). For analysis of the primary outcome, we compared survival times using log-rank tests and constructed Cox proportional hazard regression models to estimate HRs and 95% confidence intervals (CIs) for each pairwise comparison between arms. Participants lost to follow-up were censored at the last point of contact. Log–log plots were examined and tests of Schoenfeld residuals conducted to check the proportional hazards assumption. To analyse binary secondary outcomes (proportion with same-day TB treatment initiation, proportion with undiagnosed/untreated pulmonary TB, proportion with undiagnosed/untreated HIV, and proportion reported to have died), we constructed binomial regression models with a log link function to estimate relative risk ratios (RRs) and 95% CIs, comparing between pairs of arms. To evaluate the effect of interventions on health-related quality of life, we used linear regression to compare the mean EQ5D utility scores measured at day 56 between pairs of arms, adjusting for participants’ corresponding values measured at baseline. Residual plots were examined to check linear regression assumptions. For secondary outcomes with missing data, sensitivity analysis was conducted using multiple imputation with chained equations and 50 imputations.

Cost-effectiveness analysis

Cost–utility analysis was undertaken to estimate the incremental cost per quality-adjusted life year (QALY) gained from the Malawian Ministry of Health perspective (S4 Text). Questionnaires captured the health resources utilised by participants over the trial time horizon. This included any additional care received in hospitals and primary health clinics. Participant responses to the Chichewa version of the EQ-5D-3L were used to generate health utility scores and QALY profiles [26-28]. Incremental cost-effectiveness ratios (ICERs) were estimated to compare the 2 intervention arms to the SOC arm. Malawi does not have formal cost-effectiveness thresholds. We therefore compared the estimated ICERs to WHO-recommended thresholds using the gross domestic product (GDP) per capita for the country. Interventions that have an incremental cost per gain in QALY less than the national GDP per capita were defined as "very cost-effective," and those less than 3 times GDP per capita as "cost effective" [29]. The GDP per capital of Malawi is approximately US$400 per capital. We therefore used 1,000 bootstrapped replications to present the probability of the 2 interventions (HIV screening and HIV-TB screening) being cost-effective at increasing cost-effectiveness thresholds: US$400/QALY, US$800/QALY, and US$1,200/QALY. The probability represents the proportion of the 1,000 bootstrapped replications where the estimated ICER was below these cost-effectiveness thresholds. This analysis is reported as per the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guideline [30] (S5 Text).

Results

Between 15 November 2018 and 22 November 2019, 8,236 patients were screened, of whom 1,462 were randomly allocated to the SOC (473), HIV screening (492), or HIV-TB screening (497) arms (Fig 1). One participant withdrew consent after baseline interview, but before randomisation.

Fig 1

Trial profile.

IPT, isoniazid preventive therapy; TB, tuberculosis.

Characteristics of participants were balanced between arms (Table 1). Over half of participants were women in each arm, and mean ages ranged from 33 to 34 years old. Symptoms indicative of TB were common, with 40% to 43% reporting night sweats, 38% to 41% reporting weight loss, and 50% to 51% reporting fever. Between 4% and 5% of participants reported having been previously treated for TB. Knowledge of HIV status was very high (over 90%), with 18%, 19%, and 20% of participants in the SOC, HIV, and HIV-TB screening arms, respectively, self-reporting HIV–positive status. ART coverage was also very high at 95%, 97%, and 98%.

Table 1

Participant characteristics.

Characteristic	SOC arm (n = 473)	HIV screening arm (n = 492)	HIV-TB screening arm (n = 497)
Age in years (mean, SD)	33.5 (13.5)	32.8 (13.4)	34.3 (13.4)
Sex
Male (n, %)	208 (44%)	196 (40%)	228 (46%)
Female (n, %)	265 (56%)	296 (60%)	269 (54%)
Body mass index (mean kg/m2, SD)	22.8 (4.1)	22.7 (4.3)	22.9 (4.5)
Marital status
Married/cohabiting (n, %)	322 (68%)	347 (71%)	330 (66%)
Never married (n, %)	76 (16%)	74 (15%)	82 (16%)
Widowed/separated/divorced (n, %)	74 (16%)	71 (14%)	85 (17%)
Highest level of education
No schooling (n, %)	59 (12%)	59 (12%)	61 (12%)
Primary (n, %)	217 (46%)	234 (48%)	234 (47%)
Secondary no MSCE† (n, %)	130 (27%)	131 (27%)	125 (25%)
Secondary with MSCE† (n, %)	60 (13%)	56 (11%)	70 (14%)
Higher (n, %)	7 (1%)	12 (2%)	7 (1%)
Ever lost a spouse to death	44 (9%)	40 (8%)	46 (9%)
Literate	405 (86%)	426 (87%)	425 (86%)
Poverty quintileø
uintile 1 (least poor)	94 (20%)	99 (20%)	99 (20%)
Quintile 2	99 (21%)	106 (22%)	87 (18%)
Quintile 3	84 (18%)	102 (21%)	107 (22%)
Quintile 4	106 (22%)	90 (18%)	96 (19%)
Quintile 5 (poorest)	90 (19%)	95 (19%)	108 (22%)
TB symptoms
Cough (n %)	473 (100%)	492 (100%)	497 (100%)
Cough duration (median weeks, IQR)	1 (0.6, 2)	1 (0.4, 3)	1 (0.4, 3)
Night sweats (n, %)	203 (43%)	197 (40%)	200 (40%)
Weight loss (n, %)	194 (41%)	186 (38%)	195 (39%)
Fever (n, %)	242 (51%)	253 (51%)	250 (50%)
Previously treated for TB (n %)	17 (4%)	21 (4%)	26 (5%)
HIV status
HIV–positive (n, %)	86 (18%)	93 (19%)	101 (20%)
Taking ART (n, %)	82 (95%)	90 (97%)	99 (98%)
HIV–negative (n, %)	354 (75%)	365 (74%)	355 (71%)
Unknown (n, %)	33 (7%)	34 (7%)	41 (8%)
EQ5D§ utility score (mean, SD)	0.79 (0.14)	0.77 (0.15)	0.77 (0.14)
Self-rated health
Fair/good/very good	412 (87%)	440 (89%)	444 (89%)
Poor/very poor	61 (13%)	52 (11%)	53 (11%)

†Malawi Secondary Certificate of Education.

øBased on urban proxy means test using assets derived from 2014–2015 Malawi Integrated Household Survey.

§EuroQOL EQ5D utility score (Zimbabwe tariff).

ART, antiretroviral therapy; IQR, interquartile range; SD, standard deviation; SOC, standard of care; TB, tuberculosis.

In the HIV-TB screening arm, 448 (90%) participants completed DCXR-CAD, of whom 305 (68%) had a CAD4TB score above the threshold of ≥45. CAD4TBv5 scores were similar when compared by participant characteristics (Fig 2A–2E). Of these, 279 (91%) successfully completed sputum Xpert examination. Approximately 4% (12/279) had TB confirmed on this initial sample, all of whom subsequently initiated TB treatment. CAD4TBv5 scores were higher among participants with Xpert-confirmed TB compared to participants with sputum-negative Xpert tests (Fig 2F). One further participant in the HIV-TB screening arm initiated TB treatment on day 48 with a positive Xpert result from the routine health system.

Fig 2

CAD4TBv5 scores by participant characteristics (HIV-TB screening arm only).

ART, antiretroviral therapy; TB, tuberculosis.

Of the 399 participants in the HIV screening arm and 396 participants in the HIV-TB screening arm who reported never having previously tested for HIV or being HIV negative, 336 (84%) and 362 (91%), respectively, received study HIV testing on the same day. A total of 1,320 (90%) participants had day 56 vital status ascertained, with similar proportions in all 3 arms (SOC: 420/473 [89%], HIV screening: 450/492 [91%], and HIV-TB screening: 450/497 [91%]), and 1,206 (82%) had day 56 TB/HIV outcomes ascertained (Fig 1). Participants lost to follow-up were slightly younger on average than those seen at day 56 and more likely to report unknown HIV status; otherwise characteristics were comparable (S1 Table).

A greater percentage of participants in the HIV-TB screening arm (15, 3.0%, 95% CI: 1.7% to 4.9%) initiated TB treatment by day 56 compared to participants in the HIV screening arm (8, 1.6%, 95% CI: 0.7% to 3.2%) or SOC (5, 1.1%, 95% CI: 0.3% to 2.4%) (Table 2), although this trend was not statistically significant.

Table 2

Effect of interventions on trial outcomes.

	SOC arm	HIV screening arm	HIV-TB screening arm	HIV screening vs. SOC arm	HIV-TB screening vs. HIV screening arm	HIV-TB screening vs. SOC arm
Primary outcomes				HR, 95% CI	HR, 95% CI	HR, 95% CI
Number initiating TB treatment (n, %)	5 (1.1%)	8 (1.6%)	15 (3.0%)
Time (days) to TB treatment initiation (median, IQR)	11 (6.5–38)	6 (1–22)	1 (0–3)	1.51 (0.49–4.62)	1.89 (0.80–4.46)	2.86 (1.04–7.87)
Secondary outcomes				RR, 95% CI	RR, 95% CI	RR, 95% CI
Undiagnosed/untreated microbiologically confirmed pulmonary TB (n, %)	2/382 (0.5%)	4/414 (1.0%)	2/410 (0.5%)	1.85 (0.34–10.02)	0.50 (0.09–2.74)	0.93 (0.13–6.58)
Same-day TB treatment initiation (n/N, %)	0 (0%)	1 (0.2%)	6 (1.2%)	--	--	--
Undiagnosed/untreated HIV (n, %)	10/377 (2.7%)	2/414 (0.5%)	1/415 (0.2%)	0.18 (0.04–0.83)	0.50 (0.05–5.48)	0.09 (0.01–0.71)
Mortality (n/N, %, OR, 95% CI)	3/420 (0.7%)	3/450 (0.7%)	4/450 (0.9%)	0.93 (0.19–4.60)	1.33 (0.30–5.92)	1.24 (0.28–5.53)
				AMD (95% CI) †	AMD (95% CI) †	AMD (95% CI) †
EQ5D§ utility score (mean, SD)	0.79 (0.18)	0.82 (0.19)	0.81 (0.18)	0.03 (0.004–0.05)	−0.003 (−0.03–0.02)	0.02 (0.002–0.05)
				0.03 (0.01–0.05)	−0.002 (−0.03–0.02)	0.03 (0.004–0.06)

†First row shows unadjusted results; second row shows results adjusted for baseline EuroQoL EQ5D utility score.

§EuroQoL EQ5D utility score (Zimbabwe tariff).

AMD, average mean difference; CI, confidence interval; HR, hazard ratio; IQR, interquartile range; OR, odds ratio; RR, risk ratio; SD, standard deviation; SOC, standard of care; TB, tuberculosis.

There was a statistically significant increased rate of TB treatment initiation among participants in the HIV-TB arm compared to SOC (HR: 2.86, 95% CI: 1.04 to 7.87, p = 0.04), but not for comparisons between other pairs of arms (Fig 3). Overall, 60%, 100%, and 100% of participants who initiated TB treatment in the SOC, HIV, and HIV-TB screening arms, respectively, had microbiologically confirmed disease at TB treatment initiation. In the HIV-TB arm, 6/15 (40%) of participants treated for TB achieved same-day treatment initiation, compared to 0/5 (0%) in SOC and 1/8 (12.5%) in the HIV screening arm (Fisher’s exact p = 0.03). At day 56 assessment, 2/382 (0.5%), 4/414 (1.0%), and 2/410 (0.5%) in the SOC, HIV, and HIV-TB screening arms, respectively, had previously undiagnosed or untreated microbiologically confirmed TB (RR for HIV-TB arm versus SOC: 0.93, 95% CI: 0.13 to 6.58, p = 0.94).

Fig 3

Time to initiation of TB treatment by trial arm.

TB, tuberculosis.

Only 1/415 (0.2%) participants in the HIV-TB arm who underwent HIV testing at day 56 assessment had previously undiagnosed/untreated HIV infection, compared to 10/377 (2.7%) in the SOC arm and 2/414 (0.5%) in the HIV screening arm (RR for HIV-TB arm versus SOC: 0.09, 95% CI: 0.01 to 0.71, p = 0.02; RR for HIV arm versus SOC: 0.18, 95% CI: 0.04 to 0.83, p = 0.03). Of the 20, 25, and 15 participants newly diagnosed with HIV in the SOC, HIV, and HIV-TB screening arms over the course of the study (including at the day 56 assessment), 6 (30%), 15 (60%), and 9 (60%) initiated ART before day 56, with a median (IQR) number of days to ART initiation of 5 days (1 to 36), 0 days (0 to 0), and 0 days (0 to 17). There were no significant differences between pairs of arms in all-cause mortality by day 56. A sensitivity analysis using multiple imputation to impute missing data for secondary outcomes found similar results to the primary analysis (S2 Table).

Mean EuroQoL EQ5D utility scores at day 56 were significantly higher in participants in the HIV-TB arm compared to SOC (adjusted mean difference [AMD]: 0.03, 95% CI: 0.004 to 0.06, p = 0.02), and in participants in the HIV screening arm compared to SOC (AMD: 0.03, 95% CI: 0.01 to 0.05, p = 0.01). If these differences were maintained for 1 year, at the population level, this would result in 3,000 QALYs gained per 100,000 people.

In the base–case analysis, the ICER for the HIV screening arm versus SOC was US$901.29 per QALY gained, and the ICER for HIV-TB screening versus SOC was US$4,620.47 per QALY gained (Table 3). At the cost-effectiveness thresholds of US$400, US$800, and US$1,200 per QALY, the probability of cost-effectiveness for HIV screening was 3.0%, 36.2%, and 83.9%, respectively. Across all these 3 cost-effectiveness thresholds, the probability of cost-effectiveness for HIV-TB screening was 0%.

Table 3

Health-related quality of life outcomes by treatment allocation.

	Total cost (mean/SE)	Incremental cost (95% CrI)†	QALYs (mean/SE)	Incremental QALYs (95% CrI)†	ICER†	Probability cost-effective at cost-effectiveness threshold:
						US$400/QALY	US$ 800/QALY	US$ 1,200/QALY
Base–case analysis#
SOC	21.45 (3.18)		0.001 (0.002)
HIV screening	24.29 (1.61)	3.58 (1.70, 5.45)	0.007 (0.002)	0.004 (0.003, 0.005)	901.29	0.030	0.362	0.839
HIV-TB screening	41.01 (1.17)	19.92 (18.17, 21.68)	0.007 (0.002)	0.004 (0.003, 0.005)	4,620.47	0	0	0
Sensitivity analysis—Imputed data using UK Tarif to derive EQ-5D utility scores#
SOC	21.45 (3.18)		0.001 (0.003)
HIV screening	24.29 (1.61)	3.58 (1.70, 5.45)	0.010 (0.002)	0.005 (0.004, 0.007)	714.69	0.062	0.652	0.959
HIV-TB screening	41.01 (1.17)	19.92 (18.17, 21.68)	0.009 (0.002)	0.005 (0.004, 0.007)	3,841.67	0	0	0
Sensitivity analysis—Complete cases#
SOC	20.19 (2.77)		0.001 (0.001)
HIV screening	23.72 (1.31)	3.91 (−2.72, 10.53)	0.007 (0.002)	0.004 (0.0005, 0.008)	953.85	0.266	0.445	0.595
HIV-TB screening	40.33 (0.91)	19.94 (13.50, 26.38)	0.007 (0.002)	0.005 (0.001, 0.009)	4,139.92	0.007	0.008	0.09
Sensitivity analysis—Imputed data using lower cost for digital CXR (US$5)*#
SOC	21.11 (2.95)		0.001 (0.002)
HIV screening	24.16 (1.42)	3.57 (1.72, 5.41)	0.007 (0.002)	0.004 (0.003, 0.005)	961.12	0.020	0.292	0.748
HIV-TB screening	35.56 (1.09)	14.53 (12.74, 16.32)	0.007 (0.002)	0.004 (0.003, 0.005)	3,600.37	0	0	0

Incremental estimates are in comparison to SOC arm.

#Adjusted for age, sex, marital status, highest level of education, employment status, and poverty quintile.

Bootstrapped differences (1,000 replications).

*In base–case analysis cost of digital CXR US$10.98.

CrI, credible interval; ICER, incremental cost-effectiveness ratio; QALY, quality-adjusted life year; SE: standard error, SOC, standard of care; TB, tuberculosis.

Discussion

We have shown that the yield of HIV and TB among symptomatic adults attending primary care in Malawi was higher and that the time to TB treatment initiation was significantly shorter where participants received oral HIV testing plus DXCR-CAD TB screening with Xpert confirmation compared to clinician-directed TB/HIV screening. Although computer-aided TB diagnosis was not cost-effective over 8 weeks in the within-trial analysis, participant quality of life was significantly improved, and further economic analysis over longer time horizons is required. Universal offer of oral HIV testing was cost-effective and could offer substantial individual and public health benefits, even in settings like Malawi where UNAIDS 90–90–90 targets are close to being met [31]. If implemented at scale in primary care, DCXR-CAD combined with oral HIV screening has potential to improve diagnosis and treatment of symptomatic TB and HIV among persons presenting to the health facility with cough.

Diagnosis of TB in high HIV-TB burden and low-resource settings is extremely challenging. Under current recommendations, all clinic attendees should be asked about TB symptoms, interpreted according to HIV status, and followed by sputum testing with Xpert or microscopy of those with symptoms [6,32]. However, with nearly 60% of unselected acute care clinic attendees reporting TB symptoms [7], the current recommended screening approach for TB is not reliably implemented, reflecting workload, turnaround time, affordability, and throughput issues for sputum-based diagnostics. In the absence of an accurate, point-of-care diagnostic test for TB [33], alternative diagnostic algorithms that reduce demand for sputum-based diagnostics offer substantial potential benefits. For DCXR-CAD, these include minimal consumables, high patient throughput, and highly sensitive results available in minutes allowing large numbers to be screened for TB [34]. If DXCR-CAD accuracy is sufficiently high, there are potential cost-savings and infection control benefits from reducing workload and demand for sputum tests, as well as other clinical benefits from DCXR-CAD, including rapid screening for COVID-19 [35]. However, implementation of DCXR-CAD in high HIV/TB burden settings where they are most needed can be challenging and requires careful consideration by Ministries of Health, as well as technological optimisation for low-resource settings and operational research to support deployment. Particular challenges can include absent or intermittent power supplies, limited internet availability, availability of maintenance and servicing personnel, and laboratory capacity to handle potential increased sputum specimen samples for confirmatory testing, although these were all overcome in this trial.

Here, we investigated “triage testing” adults with cough using automated DCXR-CAD screening accepting a CAD4TB score cutoff that provided high sensitivity (but correspondingly low specificity) combined with highly specific sputum Xpert confirmation. We achieved our aim of increasing speed and completeness of TB diagnosis in patients with cough of any duration but still required sputum from 61% (305/497) participants, a proportion that would have been similar to that in the SOC arm had national algorithms been consistently applied, emphasising the need to further optimise DCXR-CAD thresholds for confirmatory testing.

Potential approaches to further reducing sputum test demand include increasing the CAD4TB score used to define presumptive TB, or adding in a further rapid screening step such as point-of-care C-reactive protein, or clinical risk prediction scores [36,37]. These should be implemented and evaluated in conjunction with health system and laboratory strengthening. DCXR-CAD screening software is rapidly evolving, exceeding expert radiological reference standards across a range of target diseases and use-cases [35,38]. However, evidence for health impact or cost-effectiveness under route programmatic conditions—arguably of greatest importance to health planners—is extremely limited. Moreover, data from low-resource settings, where computer-aided systems could have greatest benefit in overcoming limited health worker capacity, are scarce. The CAD4TBv6 system has reported sensitivity of 91% and specificity of 84% compared to sputum Xpert [17], performing significantly better than expert radiologists and meeting target product profiles for a community TB screening test [39]. Important questions include the extent to which performance of DCXR-CAD systems varies by level of health system and patient characteristics such as sex, age, HIV status, disease stage, and epidemiological setting. As DCXR-CAD costs fall and systems develop, implementation research will still be needed to evaluate and optimise accuracy and ensure equity in access to care and health benefits.

Our effectiveness and cost-effectiveness estimates need to be considered in the context of low levels of investigation for HIV and TB seen in the SOC arm. For example, higher utilisation of routine clinic HIV testing and less accurate TB sputum screening in the SOC arm would reduce the likelihood that the HIV screening arm was cost-effective but may conversely increase the likelihood that offering more accurate TB screening with DCXR-CAD would be cost-effective. Our economic analysis demonstrates that optimisation of this TB triage testing approach may be possible. We are undertaking further modelling to evaluate the impact of lowering DCXR-CAD implementation and running costs; refinement of CAD thresholds to reduce the number of confirmatory sputum Xpert tests conducted (the major cost-driver here), while maintaining low false negativity rates in settings with varying pretest TB probabilities; alternative TB confirmatory tests that maintain high specificity and capability to be implemented in low-resource settings; and task-shifting to allow DCXR-CAD scale-up where radiographers are scarce or not available. Importantly, although CAD thresholds would ideally be adapted to local epidemiological characteristics and available resources, the large numbers of participants required and cost of undertaking diagnostic accuracy studies often precludes this, as in this trial. To minimise the need for multiple diagnostic accuracy studies with microbiological reference standards prior to implementation, modelling of accuracy at varying thresholds—as well as repeated surveillance and threshold adaption under programmatic conditions—will be required.

Malawi, similar to many countries in sub-Saharan Africa, has made tremendous progress in increasing access to HIV testing and treatment [21]. In this trial conducted in a busy urban neighbourhood of Blantyre, where adult HIV prevalence approaches 20% [21], greater than 90% of participants reported knowing their HIV status at recruitment, and >95% of HIV–positive participants were taking ART. Modelling data suggests that where very high levels of population ART coverage are achieved, TB incidence will rapidly decline [40]. When planning this study, we assumed that 17% of participants would initiate TB treatment by 56 days based on data from a previous study [24]. However, we found TB treatment initiations and prevalence of microbiologically confirmed TB to be substantially lower than expected. We speculate, based on accelerated declines in Blantyre TB case notifications in our citywide TB surveillance system and from prevalence survey data, that public health interventions such as HIV testing and universal treatment, active case finding for TB, and scale-up of isoniazid preventive therapy may have caused this reduction. We did not estimate the diagnostic accuracy of DCXR-CAD, as baseline sputum samples were not collected from participants with a low CAD4TB score; if DCXR-CAD is widely implemented, surveillance of accuracy at DCXR-CAD thresholds using microbiological and clinical reference standards should be part of routine monitoring and evaluation activities. Nevertheless, the substantial individual and public health benefits offered by routine HIV testing of adults attending primary care with TB symptoms—even in a setting such as Malawi where HIV testing and treatment are extremely high—demonstrates that this should remain a central pillar of primary healthcare in countries with generalised HIV epidemics.

There were a number of limitations to this trial. In this pragmatic trial, alternative diagnoses to TB were not investigated, with participants referred to routine clinic or hospital care as required. If DCXR-CAD is widely implemented, clear clinical pathways for people who have an abnormal CAD4TB score, but who are sputum Xpert negative, will be required [41]. Although more participants initiated TB treatment in the HIV-TB screening arm compared to the other arms, the proportion with undiagnosed TB was similar across arms. It is not clear why this is the case but may be due to chance with small numbers of events here. We had planned subgroup analyses to investigate whether the effects of interventions differed by sex, an important determinant of delayed TB care-seeking. However, the fewer-than-expected number of events precluded this and also resulted in a loss of precision in treatment estimates. This means that our findings may not be robust to any differential missingness between groups. However, loss to follow-up was similar between arms, and characteristics of participants followed up were similar to those lost to follow-up. Some participants who were lost to follow-up may have died. We took extensive measures to limit contamination between arms (including fingerprint verification of all participants before randomisation, delivery of interventions, and at follow-up) and to avoid modifying health worker behaviour in the study clinic, but it is possible that the study presence in the clinic may have modified usual care practices. Future trials of DCXR-CAD may consider cluster randomisation at the clinic level to reduce these risks and to potentially increase the number of events observed. For safety reasons, remote radiologists (not usually available in similar primary care settings) provided a clinical interpretation of chest X-rays, although, given the very short median time to TB treatment initiation, this did not seem to influence TB treatment starts. Malawi has achieved very high levels of HIV testing and ART coverage; the effectiveness of these interventions should be evaluated in other settings.

In summary, DCXR-CAD with universal HIV testing for adults attending primary care with symptoms of TB increased TB diagnoses, shortened time to TB treatment, and was cost-effective at reducing undiagnosed HIV infection. If effective in larger trials and under programmatic conditions, this approach has potential to improve diagnosis and treatment of TB and HIV.

Characteristics of participants seen at day 56 visit compared to those lost to follow-up.

(DOCX)

Click here for additional data file.

Multiple imputation analysis of secondary outcomes with missing data.

(DOCX)

Click here for additional data file.

Protocol.

(DOCX)

Click here for additional data file.

Analysis plan.

(DOCX)

Click here for additional data file.

CONSORT Checklist.

(PDF)

Click here for additional data file.

Economic evaluation.

(DOCX)

Click here for additional data file.

CHEERS Checklist.

(PDF)

Click here for additional data file.

7 Jan 2021

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Requests from the editors:

Comments from the reviewers:

Reviewer #1: Computer aided diagnostic systems have been evaluated in my multiple studies and have been shown to have utility in triaging for TB diagnosis. The finding that use of CAD in the screening reduces the time to diagnosis is not a surprise finding.

Specific comments

1. Previous studies evaluating CAD systems are not adequately referenced

2. There is not much description given of the HIV and TB programs in the setting. For example in most high TB and HIV settings, opt out HIV testing is offered to all patients accessing services. The study suggests that this is not offered in routine services and that the study was offering this outside of routine services. Is this correct?

3. CAD threshold setting is not fully described. How was the threshold of >=45 arrived at . The threshold setting is based on the prevalence of TB in the population being studied. Was this taken into consideration. Were there any patients with CAD score below 45 on whom Xpert was positive?

4. Since there is limited CXR accessibility in most TB and HIV high burden countries, the authors could have considered a HIV-TB screening arm ( without CXR) and and HIV-TB screening arm( with CXR) and compare the findings. TB screening is recommended for every HIV positive patient whenever in contact with the health services- generating evidence for the impact of on time to TB treatment, TB diagnosis can support the scale up of routine TB screening in HIV positive patients even when there is no CXR with CAD.

5. It would be helpful to report the the retention at day 56 instead of leaving it to the readers to calculate. If there were any drop outs, what was the reason for drop out ( or not returning at day 56)- If unknown or due to death can the assumption be made that some patients could have died from undiagnosed TB?

Reviewer #2: The manuscript by Macpherson et al from the Malawi research program supported by the Liverpool and London Schools of Hygiene and Tropical Medicine investigates the impact of an enhanced approach to TB diagnosis in early detection of TB in PLHIV and early TB treatment. A single clinic site, Bangwe Health Centre in Blantyre was chosen for the study. The diagnostic enhancement is a study staff controlled digital chest xray with an interpretation software application followed by sputum Gene Xpert probe for all xrays likely to be TB above a pre-set cutoff. The impact was compared with 'Standard of Care' managed by Ministry of Health staff and a second comparator group of study staff-managed 'oral HIV screening'. The main finding was median time to TB treatment which was 11 days for SoC, 6 days for oral HIV and 1 day for HIV and TB. The Hazard Ratio for HIV-TB compared with SoC is calculated at 2.84 which was statistically significant. A cost analysis is reported in a subsidiary section and found no chance of cost-effectiveness for the HIV-TB intervention although oral HIV screening was probably cost effective in the short term.

The primary audience for such studies should be Departments of Public Health, in particular in this case the Malawi Ministry of Health, but they may not find this study as helpful as it could have been. My main criticism is that, although the study is described as 'pragmatic', it is designed to measure the impact of complex and expensive pieces of technology to diagnose TB. A number of 'pragmatic' studies over the last 5 years of 'breakthrough' molecular diagnostics for TB, implemented in LMIC health systems, have been disappointing. One of the main reasons is that the systems - human resources, facilities, supply chains etc. - are not reliable enough to deliver the increased sensitivity and specificity of new technology. In this study, performed at a single site that accommodated all three arms, two of the arms were supervised by 'study staff' that have a large impact on implementation and adherence to standards of care, compared with the Ministry public health workers supervising the SoC arm. Thus there was benefit observed in the oral HIV arm supervised by study staff over the SoC arm, even though no direct TB diagnostic enhancement was used. It is not mentioned how much the study staff added to the cost of care but the impact of 'oral HIV' arm combined with its cost-effectiveness may have been more impressive, sustainable, and attractive to public servants than the high technology solution.

As far as the report in itself - it is well written, pretty clear, and the figures and analysis seem clear and supportable. Some specific criticisms - the digital xray platform and interpretation software is not well described. The study staff are not described - how many people, what level of training, location within Bangwe Clinic. A brief discussion of 'cross-contamination' between the study and SoC within a single clinic is included but I think this could be a major issue. Finally, Malawi generally has implemented an efficient national ART program with excellent coverage and adherence and this may well be responsible for unexpectedly low rates of community TB and present the most cost-effective approach. A discussion of the use of enhanced TB diagnosis in places where there is a much lower ART program efficiency should be included.

Reviewer #3: General:

The paper reports an innovative approach to try and improve diagnosis of both TB and HIV in a high HIV prevalence setting among people presenting to the clinic with cough. The study provides additional useful information on the importance of oral HIV self-testing, however, the importance of oral HIV self-testing in Blantyre and similar high HIV prevalence settings and OPD settings has been well described [1,2]. The use of Computer-aided X-ray screening for TB is innovative, but findings are limited to patients with cough presenting to the facility, which needs to be clear in the title. In addition, the combined HIV and TB screening intervention was not cost-effective. The paper provides important data points in this field, but the authors could and, in my opinion, should do more to describe the considerable cost, feasibility, and human resource challenges associated with rolling out Computer-aided X-ray screening in low-and middle income countries like Malawi where the burden of undiagnosed TB is highest.

Title

1. The study population represents symptomatic persons attending acute primary care clinics:

- The title could include the population studied such as: "Computer-aided X-ray screening for tuberculosis and HIV testing among adults presenting with cough: a randomised trial and cost-effectiveness analysis in Malawi (PROSPECT)"

� This is important because a significant percentage of patients with active TB are asymptomatic [3]. In addition, the four-symptom TB screen is only able to detect 51% of active TB cases among PLHIV on ART [4]. Therefore, this is an intervention targeted at detecting symptomatic TB where the symptom is cough (and HIV infection among people with at least one symptom (cough))

Abstract:

2. The primary outcome was time to TB treatment. Is there a reason that the authors and investigators chose this as the primary outcome? Several studies have shown that even though new diagnostic approaches, like using Xpert instead of smear microscopy, have shortened the time to TB treatment, these approaches did not significantly improve patient important outcomes like morbidity or mortality [5].

3. One of the secondary outcomes is undiagnosed or untreated TB. Is this because the authors could not differentiate between undiagnosed and untreated TB based on available data in the medical records? If the authors could differentiate diagnosis from treatment, wouldn't undiagnosed TB be the cleaner outcome of interest because the intervention is designed to increase diagnosis of TB? Were there cases where an individual was diagnosed but not treated?

4. In the second last sentence of the abstract, the abstract talks about undiagnosed/untreated HIV. Again, can the authors indicate whether (1) it was impossible to differentiate between undiagnosed and untreated HIV, or (2) this represents a combined outcome.

- In the abstract, the intervention itself seems focused on screening for HIV and no mention of the linkage component is made. However, on line 136: it suggests that support for ART enrollment was provided as part of the intervention - this is important for the abstract and should ideally be included. E.g., "oral HIV testing (HIV screening) and linkage to treatment for those who screen positive."

5. In the conclusions of the abstract, the term "universal HIV testing" is used, but earlier in the abstract the term HIV screening is used. Wouldn't "universal HIV screening" more accurately reflect the intervention?

6. In reporting the primary outcome analysis, it is a little unclear which is the primary outcome comparison - is it the median time to TB treatment? The hazard ratio analysis compares rates of TB diagnosis between arms. Can the authors clarify why the statistical test used was Cox proportional hazards ratio analysis when the primary outcome comparison is of medians [6,7]?

7. In the conclusion, does the term "completeness of diagnosis" mean completeness of both HIV and TB diagnosis? For clarity suggest adding the word "HIV and TB diagnosis".

Methods:

8. Page 8 - line 125 "we intended that the SOC arm would reflect the routine TB and HIV screening"

- To what extent is it truly possible to ensure that SOC stayed the same. Surely the healthcare workers would have been aware of the study and its hypothesis, and could this have affected SOC (i.e., improved quality) through a type of Hawthorne effect?

9. Page 9 - line 148: How many of the suspect TB cases were identified by the consultant radiographer rather than the CAD4TB?

- Most primary healthcare clinics will not have ready access to a consultant radiographer, so the authorship team should ideally specify whether this radiologist checking the x-ray results was part of the intervention or not?

10. Page 11 -Line 181-182: As noted in the abstract comments, please can the authors explain and clarify the terminology "undiagnosed/untreated tuberculosis" and "undiagnosed/untreated HIV". The key questions are: (1) do these represent composite outcomes? (2) were the authors able to differentiate between diagnosis and treatment? And (3) given the intervention on the TB side, which seems focused on diagnosis and not linkage to treatment, is it appropriate to include the TB treatment component in the secondary TB outcome? If linkage to TB treatment was part of the intervention, this should be clarified in the abstract and methods section. If someone was diagnosed with TB but not treated for TB, would this contribute to the numerator of the percentages reported for each arm?

- Similarly, if someone was diagnosed with HIV but not treated for HIV, would this contribute to the numerator of the percentages reported for each arm?

- What is the rationale behind the decision the authors made?

11. Page 12 - cost-effectiveness analysis:

- Please include the following details in the main manuscript text in the methods section - without this information, it is hard to understand what the authors did. Since word limits are not a constraint, the following text should be included: "Malawi does not have formal cost-effectiveness threshold. We therefore compared the estimated ICERs to WHO-recommended thresholds using the gross domestic product (GDP) per capita for the country. Interventions that have an incremental cost per gain in QALY less than the national GDP per capita were defined as "very cost-effective", and those less than three times GDP per capita as "cost effective"11. The GDP per capital of Malawi is approximately US400 per capital. We therefore used the bootstrapped replications to present the probability the two interventions (HIV screening; TB-HIV screening) were cost-effective at increasing cost-effectiveness thresholds: US$400/QALY; US$800/QALY; US$1200/QALY. The probability represents the proportion of the 1000 bootstrapped replications where the estimated ICER was below these cost-effectiveness thresholds."

Results:

12. The Fig 1 did not show up well on the manuscript I reviewed.

13. Can the authors add the percentages for those seen/vital status and the % lost to follow-up

14. Seems like close to 20% were lost to follow-up? Will need to address this potential source of selection bias and measurement error.

15. Line 219 - the problem of asymptomatic TB among the +-20% of clients living with HIV and mostly on ART needs to be considered in the discussion section.

16. Line 231 - 232: the CAD4TB approach screened in over two thirds of the population for GeneXpert (68%), but only 4% of the screened in population tested postive for microbiologically confirmed TB. A positive predictive value of only 4% among a group of symptomatic patients among whom HIV prevalence was 20%, seems low. Is the specificity of the CAD4TB approach too low? Do you have any data on TB prevalence among those who screened negative with the CAD4TB?

17. Table 2: Can the 95% CI of the number initiating TB treatment be added?

18. Line 276: the EuroQoL EQ5D utility score increases of 0.03 in both arms - this seems a small number. Is it possible to contextualize what a 0.03 means in the discussion section?

19. The fact that the EuroQoL increases was 0.03 in both the HIV and HIV-TB arms, does this suggest that all the improvements were due to the HIV screening? Would the authors have expected a dose-response effect if additional improvements from undiagnosed TB on top of undiagnosed HIV were to be observed?

Discussion:

20. Page 23: Note: the line numbering is absent from the Discussion

21. Page 23: Laste sentence of first paragraph of the Discussion section: The following sentence should be revised to indicate that the intervention helped detect symptomatic TB, and helped detect HIV among those with at least one symptom "cough". In addition the words "rapidly and efficiently" should be removed. E.g., the sentence could be edited to read: "If implemented at scale in primary care, DCXRCAD combined with oral HIV screening has potential to improve diagnosis and treatment of symptomatic TB and HIV among persons presenting to the health facility with cough."

- The word "efficiently" needs to be deleted because (1) the paper did not assess the term "efficiency" from a costing perspective, and (2) from the cost-effectiveness analysis, the HIV-TB component was not cost-effective. If the HIV-TB screening component was not cost-effective, why would the authors consider it efficient? the word "rapidly" needs to be removed because the paper provided no evidence that either is shortened time for the patient at the clinic, or that DCXRCAD could be rapidly rolled out.

22. Page 23: The authors state: "However, with nearly 60% of unselected acute care clinic attendees reporting TB symptoms,[7] the current recommended screening approach for TB is not reliably implemented, reflecting workload, turnaround time, affordability and throughput issues for sputum-based diagnostics." The authors then list perceived advantages of the chest x-ray screening approach. However, the authors do not provide a ballanced view of the pros and cons of the chest radiography screening approach. There are substantial barriers facing widespread scale-up of the proposed intervention including:

(1) no abnormalities are definitive of TB and therefore specificity is low,

(2) special equipment with constant source of electricity needed,

(3) Equipment manintence is needed which is a challenge in LMIC,

(4) Trained personnel are needed to operate the equipment,

The authors observed the intervention was not cost-effective in their own analysis.

The authors need to better describe the challenges associated with wide scale-up of the propsoed intervention, especially in a very resource-contrained country like Malawi where only 10% of the population has access to electricity and less than 60% of health facilities have any supply of electricity. Even those facilities that have a supply of electricity have routine, unscheduled blackouts.

23. Page 23: The authors note that all persons attending these clinics should be screened for HIV and TB risk, given the very high prevalence of HIV. For those clients with HIV, the WHO 4-symptom screen is recommended. Instead of introducing more complicated TB screening approaches with chest radiography and other needs (e.g., stable electricity, maintenance, skilled personnel), what would be the cost and impact of improving current WHO-recommended approaches to screening for TB among PLHIV and HIV-negative persons by providing additional healthcare workers, and training in current algorithms? In at least one study, this was effective among PLHIV [8]. There is only limited discussion of other approaches to improving screening for TB, and a broader acknowledgement of other potential approaches (e.g., health system strengthening to improve implementation of current algorithms, and use of simple risk scores) could ideally be mentioned.

24. The authors state that CAD4TBv6 has high specificity (84%), without mentioning the population in which these data come from - can this be added? In addition, over 60% of persons attending the clinic with cough screened into Xpert screening after CAD4TBv6 screening with only 4% yield. Do the authors have any data on sensitivity and specificty of the TB screening approach taken in this study?

25. In the concluding sentence, the authors state: "If effective in larger trials and under programmatic conditions, this approach has potential to rapidly and efficiently improve diagnosis and treatment of TB and HIV.

- Recommend removing the words "rapidly and efficiently" because the data in this paper do not support this.

References

1. Choko AT, Corbett EL, Stallard N, Maheswaran H, Lepine A, Johnson CC, et al. HIV self-testing alone or with additional interventions, including financial incentives, and linkage to care or prevention among male partners of antenatal care clinic attendees in Malawi: An adaptive multi-arm, multi-stage cluster randomised trial. PLoS Med. 2019;16(1):e1002719. PubMed PMID: 30601823.

2. Dovel K, Shaba F, Offorjebe OA, Balakasi K, Nyirenda M, Phiri K, et al. Effect of facility-based HIV self-testing on uptake of testing among outpatients in Malawi: a cluster-randomised trial. The Lancet Global health. 2020;8(2):e276-e87. PubMed PMID: 31981557.

3. Drain PK, Bajema KL, Dowdy D, Dheda K, Naidoo K, Schumacher SG, et al. Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection. Clinical microbiology reviews. 2018;31(4). PubMed PMID: 30021818.

4. Hamada Y, Lujan J, Schenkel K, Ford N, Getahun H. Sensitivity and specificity of WHO's recommended four-symptom screening rule for tuberculosis in people living with HIV: a systematic review and meta-analysis. The lancet HIV. 2018;5(9):e515-e23. PubMed PMID: 30139576.

5. Auld AF, Fielding KL, Gupta-Wright A, Lawn SD. Xpert MTB/RIF - why the lack of morbidity and mortality impact in intervention trials? Trans R Soc Trop Med Hyg. 2016;110(8):432-44. PubMed PMID: 27638038.

6. Theron G, Zijenah L, Chanda D, Clowes P, Rachow A, Lesosky M, et al. Feasibility, accuracy, and clinical effect of point-of-care Xpert MTB/RIF testing for tuberculosis in primary-care settings in Africa: a multicentre, randomised, controlled trial. Lancet. 2014;383(9915):424-35. PubMed PMID: 24176144.

7. Spruance SL, Reid JE, Grace M, Samore M. Hazard ratio in clinical trials. Antimicrob Agents Chemother. 2004;48(8):2787-92. PubMed PMID: 15273082.

8. Auld AF, Agizew T, Mathoma A, Boyd R, Date A, Pals SL, et al. Effect of tuberculosis screening and retention interventions on early antiretroviral therapy mortality in Botswana: a stepped-wedge cluster randomized trial. BMC Med. 2020;18(1):19. PubMed PMID: 32041583.

Reviewer #4: MacPherson and colleagues describe the results of the PROSPECT trial in Malawi. The trial is both impressive and informative despite not having observed as many endpoints as it needed to be well-powered for the result. The manuscript is very well-written. I especially commend the many ways that the authors went above and beyond to make the manuscript transparent and clear.

It is really helpful to have the power analysis described, to help readers explain why the trial seemed to be adequately powered at the outset but ended up having so few events in the primary outcome.

The Appendix is a terrific addition, and having the unit cost assumptions broken out in great detail is an excellent addition to the literature and a leg up to future researchers.

Below are some suggestions for improving the manuscript.

Major points:

The article presents both HIV and TB outcomes, but the HIV component is presented as just a footnote. The Introduction only discusses TB, providing no context about HIV in this setting. To balance this, consider moving some of the HIV background (such as local HIV prevalence and ART coverage) from Discussion to Introduction and consider ways to balance the other sections of the paper.

There was a statistically significant reduction in undiagnosed HIV on on Day 56, but this finding does not seem to jive with the reported HIV diagnoses leading up to Day 56 (20, 25, and 15 new diagnoses in the SOC, HIV, and HIV-TB arms). How can one reconcile the finding of similar numbers diagnosed through Day 56 in each arm, and yet more undiagnosed cases of HIV on Day 56 in the SOC arm (10 compared to 2 and 1 in the other arms)? One would expect, if the arms were balanced in terms of number with undiagnosed HIV, that more HIV diagnoses would need to occur in the HIV and HIV-TB arm in order to "deplete" those arms of undiagnosed PLHIV.

It is surprising that a majority of patients received a positive result from the triage test at the cut-off value used in this study, yet the actual prevalence of Xpert-positive TB was quite low, given the previously published CAD4TB performance of 91% sensitivity and 84% specificity. Could the authors claculate the specificity of the CAB4TB test that was observed in the trial? It would also be helpful if the Discussion could provide any insights into why the specificity might be so much lower than expected.

Relatedly, the paper could provide more information about co-occurance of TB and HIV in the cohort. What was the HIV and ART status of those who screened positive with the CAB4TB test? Did CAD4TB specificity differ by HIV status? ART status?

Do the investigators have access to the raw CAB4TB score (rather than just the binary outcome of whether or not it met the cutoff)? It could provide insight if the authors could tabulate (or show in a bar graph) the number of patients with different CAB4TB scores colored according to whether they had a positive Xpert, negative Xpert, or did not receive Xpert testing after the x-ray. Augmenting the table/graph with symptom and smear data stratified by CAB4TB score may also help inform the critical issue of specificity.

Cost-effectiveness analysis appears (at least based on what I understood from the Appendix) to only take into account the EuroQoL results, but in general one would expect that the main health benefit of prompt diagnosis would be later avoidance of critical illness and death, with only a small proportion of the effect being immediate quality of life after diagnosis. Is there a way to up-weight or forecast undiagnosed HIV and TB cases to account for future QALYs? Many published mathematical models make assumptions about future mortality risk for HIV and TB accounting for prompt vs delayed diagnosis; some of these models are open-source and available for download free of charge.

Minor points:

Abstract:

The abstract uses the label "TB-HIV screening" but elsewhere in the manuscript it is referred to as "HIV-TB screening." Helpful to use consistent terminology.

"Undiagnosed/untreated" -- unclear if this means "undiagnosed OR untreated" or if it means "undiagnosed AND untreated." Please specify. If it is "OR" consider simply stating "undiagnosed" since all indiagnosed individuals would be untreated.

This sentence is difficult to understand -- "HIV screening reduced the proportion with undiagnosed/untreated HIV..."

Introduction:

"which can approach 60%" -- of all attendees?

Would be helpful to provide context and references/rationale for the CEA thresholds used.

Methods

The ART linkage intervention should be specified. "HIV-positive participants were supported to register for ART at the online clinic." -- what does the support entail?

The method of recall of participants whose X-ray was flagged by the radiologist should also be specified.

Has the EuroQoL EQ5D score been validated in Chichewa? Please site validation studies.

It isn't clear why linear regression is used to analyze the change in EQ5D by arm. What is the independent variable?

Typo: "CD4TB" rather than "CAD4TB"

Results:

What was the reason for the large drop-off in terms of screen-to-enroll ratio?

Important to qualify claims like "participants in the HIV-TB screening arm were more likely to have initiated TB treatment..." since this was not a statistically significant trend.

Figure 1 (Trial Profile) did not render correctly in the PDF. What I'm albe to see (but these might be incorrect due to the rendering issue) is of n=8,236 screened, n=6,773 were "excluded" (why?), n=159 say "Multiple inclusion criteria not met" (what if only one is not met?), n=70 "did not consent" and n=1 "Withdrew before randomization." Need info on why the 6,773 were excluded and what happened with the remaining 1,224.

Figure 2 would be helpful to overlay statistics on the figure itself.

Discussion:

Would be helpful for Discussion to address why it might be that the rates of undiagnosed TB on day 56 were similar across arms, despite 2-3x more TB diagnosis and treatment between arms. Why didn't the intervention reduce prevalence of undiagnosed TB on day 56? For example, do you suspect much of the undiagnosed TB in the SOC arm may have self-resolved? Or treatment may have been unsuccessful in the HIV-TB arm?

Typo: "target produce profiles" -> target product profiles

Reviewer #5:

Thanks for the opportunity to review your manuscript. My role is as a statistical reviewer, so my review is focused on the study design, data, and analysis (and the presentation of these). I have put general comments about the manuscript first, followed by comments specific to a section of the manuscript (with line numbers).

This trial compares two treatments (oral HIV tests, and HIV test + computer aided chest x-rays) with usual care in adults with cough attending primary health services in Malawi. Two key strengths are the study is the large size of patients enrolled, and how the study was embedded within normal clinical practice.

My first concern is about the final event rate (TB treatment initiated) in the study. The sample size for the study was based on 17% of patients initiating TB treatment, with a reasonably large effect size (HR of 1.5). The study recruited ~ 500 participants per arm (~1500) total and saw TB treatment in 28 participants (~2% initiated TB treatment). There is a 'significant'' difference between the HIV-TB screening arm and usual care, but the estimated upper limit of the 95% CI for this effect is 7.87. How should this effect be interpreted given this very high upper limit? A relatively small missing-not-random effect could diminish the difference very quickly - how robust is this main finding given the drop-out rate observed? The lower event rate is mentioned as a limitation of the study but this is only mentioned in the context of sub-group analyses not performed (I agree that this was a good decision). At the very least this should be discussed directly in the context of the assumed event rate in the sample size calculation.

The Cox method is a robust for large samples, but I am also concerned that the very small number of events makes this inappropriate with the data collected. There are proposed modification of the log-rank test that should be more robust in these situations (e.g. Mehrotra + Roth, Stats in Medicine 2001: doi: 10.1002/sim.854.) that would be appropriate to apply here to ensure the event-rate does not cause a bias in the hazard ratio.

Was the study prospectively registered? Was a SAP created? Having access to the SAP would help with the review process.

With regards to the main outcome, what is the interpretation of comparisons against the oral HIV test arm? Is this interpreted as a more 'active' control?

L113. Was block randomisation used? One arm (497) has 24 more participants than one of the other study arms (473)

L183. What exact type of binomial models were these? i.e. what link function was used in the generalised linear model?

L183. Three pairwise-comparisons between the three arms was made - was any adjustment for multiplicity performed?

L187. How was this adjustment made? By change score or including baseline value as a covariate?

L187. It looks like approximately 256 participants in total were able to be contacted at day 56. How was this missing data treated? Were these censored in the main analysis or were they excluded?

L187. How were the assumptions in the models checked (e.g. PH assumption for Cox model, distribution of residuals in linear regression?)

Any attachments provided with reviews can be seen via the following link:

[LINK]

24 Mar 2021

Submitted filename: 2021-03-15_response-R1_PROSPECT.pdf

Click here for additional data file.

21 Jul 2021

Dear Dr. MacPherson,

Thank you very much for re-submitting your manuscript "Computer-aided X-ray screening for tuberculosis and HIV testing among adults with cough: a randomised trial and cost-effectiveness analysis in Malawi (PROSPECT)" (PMEDICINE-D-20-05974R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by five reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are expecting to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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Requests from Editors:

Please update your title to ‘Computer-aided X-ray screening for tuberculosis and HIV testing among adults with cough in Malawi (the PROSPECT study): a randomised trial and cost-effectiveness analysis’.

Abstract Methods and Findings:

* Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text (e.g. line 52 ‘83%’ is 83.9% on line 321).

* Line 38: Please either update to ‘Secondary outcomes included’ if opting to only include the main outcomes, or list and report all of your secondary outcomes.

* Please specify who was blinded/masked.

* Please state that analysis was intention to treat.

* Please provide the number of participants lost to follow up in each group.

* In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Please provide titles and legends for all figures (including those in Supporting Information files).

Please provide the unadjusted comparisons as well as the adjusted comparisons in Table 2.

Please update the CONSORT checklist to use section and paragraph numbers, rather than page numbers. Please add the following statement, or similar, to the Methods: "This study is reported as per the Consolidated Standards of Reporting Trials (CONSORT) guideline (S1 Checklist)."

Per CONSORT guidelines, please describe how investigator masking was maintained until after statistical analysis. Please also define "lost to follow-up" as used in this study. Other reasons for exclusion should be defined. Please reference these when updating the CONSORT checklist.

* When completing the checklist, please use section and paragraph numbers, rather than page numbers.

Please report your cost-effectiveness analysis according to the CHEERS checklist provided at https://www.equator-network.org/wp-content/uploads/2013/04/Revised-CHEERS-Checklist-Oct13.pdf. Please provide a copy of the completed checklist as a supplementary file and reference this in the Methods (and when completing, use section and paragraph numbers rather than page numbers).

Please include the study protocol document, with any amendments, as Supporting Information to be published with the manuscript if accepted.

On line 312, please clarify what you mean by "significantly". If statistical significance is intended, please provide the relevant p-value.

Citations should be in square brackets, and preceding punctuation.

Please remove the ‘Role of the funding source and data availability’ section from your methods – in the event of publication this information will be published as metadata based on your responses to the submission form.

Comments from Reviewers:

Reviewer #1: The use and role of digital CXR with CAD systems has previously been evaluated and shown to have utility in reducing time to diagnosis and increasing TB diagnosis. This work presented in this manuscript further adds to this existing body evidence though unfortunately does not adequately reference previous reports.

However, the manuscript is well written and easy to follow and the authors have adequately responded to previous reviewer comments

Some limitations/ criticisms

1. The enhanced arms (HIV screening and HIV-TB screening )depended on trained study staff whilst the SOC depended on routine ministry of health staff. This limitation was pointed out a previous reviewer. The outcome of the enhanced arms could have been influenced by use of dedicated study staff. Dedicated staff should have been used for SOC arm too

2. When done well, symptom screening can result in increased TB case detection in PLHIV, as previously pointed by another reviewer, the study should have included this with dedicated staff to provided the opportunity for comparison with CAD TB screening. This is especially important in settings that will in the foreseeable future still have very limited access to digital x-rays. CAD systems also come at a cost and thus we do not expect increased access to CAD systems immediately despite the new recommendations from WHO

3. Did calculations of cost effectiveness take into consideration the lifespan of digital x-ray machines? By how much did use of CXR reduce the number needed to test with Xpert and what was the cost savings on Xpert cartridges?

4. Ascertainment of mortality and lost to follow up is not well described in the methods, were participants asked to visit the facility at day 56? or did they come through at predefined follow up time points? And those lost to follow up, any effort made to determine reason, could some be moralities?

5. Were any of the mortalities among those who were initiated on TB treatment? If so , though numbers are small, were they any differences that might indicate some differences in the different arms?

Reviewer #2: I have read the detailed responses of Dr MacPherson on behalf of his co-authors to the 5 reviewers of this submission. I am satisfied with the responses which show an appreciation of the issues raised by the reviewers and the limitations of the single-site Bangwe Clinic study. The results and conclusions of this necessarily limited study remain valid and the need for a larger and more robust study design in settings with different epidemiological and health system characteristics has been recognized.I think the updated manuscript has addressed the reviewers comments and and can be published without further revision.

Reviewer #3: Thanks for the opportunity to review the revision. Thanks to the authors for making many of the suggested changes. I have no additional comments.

Reviewer #4: The authors have addressed the reviewer's comments well. Just minor follow-up:

Reviewer #4 items 5 and 6: The authors requested guidance about whether to icnlude in the paper the additional data on HIV and ART status by CAB4TB score, as well as the new figure showing the distribution of numeric scores by important strata. These do seem appropriate to incorporate into the manuscript. The figure should use bar graphs or line graphs showing the number or proportion of participants with each score. It is difficult to ascertain the density from the jittered dots in the graphs provided.

Reviewer #4 item 15: Please cite the 2012 validation study of the EuroQoL in Chichewa.

Reviewer #4 item 19: Please qualify with something like "but this trend was not statistically significant."

Reviewer #5: Thanks for the revised manuscript. Your manuscript had had a thorough set of reviews and I appreciate the thoughtful replies to my original queries. Most of my queries have been resolved - the only things outstanding were some additional planned sensitivity analysis (from the SAP) that should be presented, along with some clarification about interim visits (see further down).

I agree that the Cox model appears to be the best option here - the low event rate is acknowledged completely in the discussion and thank for pointing me to the simulation study on event rates, that was helpful.

Unrestricted randomisation is fine - I would just add a few words as a qualifier in the info about randomisation (e.g. L123).

The thee pairwise comparisons are ok as well - 3 pre-planned comparisons is perfectly reasonable, but I would just add a clarifying phrase to the methods stating that there was no adjustment for multiplicity.

Apologies for not picking up the SAP in the first round of reviews, this for the most part matches the analysis described in the manuscript. The main difference I noted was that there was a planned sensitivity analysis using MICE for missing outcomes was planned, but these aren't reported. This would provide reassurance that missing data (at least under the missing at random assumption) does not affect the main results.

Going from p-values in the Supp table, it may look as though there are some key differences between the complete-case data and those lost to follow-up. Education/literacy and age are moderately different but the p-values reflect the sample size here more than any strong differences. A HIV-positive status was less likely in those lost to follow-up, but again this was not strong (13% vs 20%). Presenting the planned sensitivity analysis showing similar effects as the main analysis along with this descriptive data would settle the issue about missing outcome data in this study. Strictly speaking, censoring or excluding participants with missing outcome data means the analysis can't be considered 'intention-to-treat'.

For those who didn't make the Day 56 outcome assessment visit, what was the average (mean, median) length of follow-up? I wasn't clear from the description if there were interim visits between baseline and the Day 56 visit.

Any attachments provided with reviews can be seen via the following link:

[LINK]

28 Jul 2021

Submitted filename: 2021-07-23_response-R2_PROSPECT.docx

Click here for additional data file.

3 Aug 2021

Dear Dr MacPherson,

On behalf of my colleagues and the Academic Editor, Dr Ruanne Barnabas, I am pleased to inform you that we have agreed to publish your manuscript "Computer-aided X-ray screening for tuberculosis and HIV testing among adults with cough in Malawi (the PROSPECT study): a randomised trial and cost-effectiveness analysis" (PMEDICINE-D-20-05974R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

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* Line 56: Please update ‘84%’ to ’83.9%’ for consistency with the main text.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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