DOI: 10.5061/dryad.v9s4mw71p
title: Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria

description: Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB)
 screening of high-risk populations combining digital chest radiography
 with computer-aided automated detection (CAD) and chronic cough screening
 to identify presumptive TB clients in communities, health facilities, and
 prisons in Nigeria. The model evolves to address technical, political, and
 sustainability challenges. Screening methods were iteratively refined to
 balance TB yield and feasibility across heterogeneous populations.
 Performance metrics were compared over time. Screening volumes, risk mix,
 number needed to screen (NNS), number needed to test (NNT), sample loss,
 TB treatment initiation and outcomes. Efforts to mitigate losses along the
 diagnostic cascade were tracked. Participants with high likelihood on
 CAD4TB (≥80) who tested negative on a single spot GeneXpert were
 followed-up to assess TB status at six months. An experimental calibration
 method achieved a viable CAD threshold for testing. High-risk groups and
 key stakeholders were engaged. Operations evolved in real-time to fix
 problems. Incremental improvements in mean client volumes (128 to
 140/day), target group inclusion (92% to 93%), on-site testing (84% to
 86%), TB treatment initiation (87% to 91%), and TB treatment success (71%
 to 85%). Attention to those as highest risk boosted efficiency (the NNT
 declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added
 after follow-up among those with ≥ 80 CAD scores initially spot-sputum
 negative found 11 additional TB cases (6.3%) after 121 person-years of
 follow-up. Iterative adaptation in response to performance metrics foster
 feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD
 scores can identify subclinical TB and those at risk of progression to
 bacteriologically-confirmed TB disease in the near term. Policy makers,
 donors, and community advocates are hesitant to invest in the steep
 infrastructure costs for mobile digital chest x-ray and GeneXpert MTB/RIF
 (dCXR/GXP) laboratories without a better understanding of how to maximize
 and sustain their impact. It is rarely possible to conduct the months of
 local CAD calibration recommended by experts via costly universal testing
 with a reference standard.4,9 Stakeholder needs and resource limitations
 require a more rapid and cost-conscious means of setting a sustainable
 algorithm. Viable, field-robust methodologies are needed, and optimization
 strategies informed by routine field findings were lacking. A precise
 assessment of the contribution of routine mobile TB screening has been
 challenging because few authors fully disaggregate losses along the
 diagnostic cascade or track TB treatment outcomes. Publication bias has
 limited access to results of active case finding pilots with suboptimal
 risk group targeting, community engagement, yield, or treatment
 outcomes.10–14 Evaluations (and scrutiny) of routine data are needed that
 make the demands, constraints, costs and choices facing implementers more
 explicit.
description: These are data from digital LP chest x-ray images read by CAD4TB
 version 5 merged with data from 2 G4 Genexpert machines, with a data
 variable to indicate screening date, cough status, as well as age and
 sex.  Two large container trucks housing a lead-lined
 chest-x-ray suite, reading area, and mobile GeneXpert laboratory were
 sourced via competitive tendering. Solar panels, shade canopies,
 anti-theft features were added. CAD4TB version 4 by
 Delft Imaging interprets a digital image in less than a minute and can do
 so consistently during day-long screening events.24–26 The software
 generates a TB likelihood score between 0 and 100, indicating the extent
 of lung abnormalities. CAD4TB displays a heatmap pinpointing the size and
 location affected. The scores are used to set a threshold above which
 persons are invited for TB testing. Each team had a radiology
 technologist, laboratory technologists, data clerks, truck driver, and a
 clinical supervisor. Training was conducted on standard operating
 procedures (SOPs) drawn from procedural manuals of successful digital
 chest X-ray (CXR) TB screening programs.27 Experts in social mobilization
 crafted logos and messaging. The intervention was implemented by KNCV TB
 Foundation (KNCV) under a cooperative agreement, Challenge TB Project,
 funded by the United States Agency for International Development (USAID)
 from 2016 to 2019.  The community mobilization strategy
 was geared toward recruitment of men and persons over 30 years of age,
 because of their elevated vulnerability. Men tend to be less likely to
 participate in community TB screening, so dedicated efforts are often
 required to attain a high risk participant mix.20,28 Aiming for a
 high-risk pool with a pulmonary TB prevalence of 1,000/100,000 per
 population with an estimated 85% sensitivity of the algorithm, we expected
 an average daily yield of 1.7 persons with bacteriologically-confirmed
 TB. The intervention was carried out in three phases
 across four states (See Figure 2 of Eneogu et al. in PLOSGH).  First, a
 “Calibration phase” was undertaken to identify a feasible CAD4TB score
 threshold for GeneXpert test eligibility that would ensure a reasonable TB
 yield given varying micro-epidemic conditions and to ensure value for
 money.. Rigorous calibration would have required TB testing of
 approximately 30,000 people at low risk over a six-month period, at
 cartridge cost of 300,000 USD.  Instead, a sensitive algorithm comprised
 of a low testing threshold (CAD4TB score ≥ 40) and a classic symptom
 screen (cough of ≥two weeks) were trialed over 8 days (n=1875).  Persons
 with CAD4TB scores ≥ 80 and negative spot TB test results were followed up
 three to six months later to identify missed TB from GeneXpert MTB/RIF
 testing on a single spot sample. Emphasis was placed on implementation of
 a simple algorithm to facilitate the highest volume screening of highest
 risk adults while minimizing the participation burden and risk/benefit
 balance. Pilots were executed in two regions (North, South), to field test
 the approach. The third phase (“Scale-up”) leveraged learning from the
 calibration and pilots to refine strategy. Persons classified as
 presumptive for TB followed the national guidelines.  Before treatment
 initiation, a risk factor and clinical interview of bacteriologically
 confirmed PTB was added to preclude over-diagnosis.Findings from each
 phase informed the design of the subsequent phase.  Iterative modification
 of procedures, strategies, CAD test thresholds and targets occurred after
 review of prior phase results. 
description: # Iterative evaluation of mobile computer-assisted digital chest x-ray
 screening for TB improves efficiency, yield, and outcomes in Nigeria
 Rupert. A. Eneogu, Ellen M.H. Mitchell* , Chidubem Ogbudebe3 , Danjuma
 Aboki4 , Victor Anyebe3 , Chimezie B. Dimkpa3 , Daniel Egbule4 , Bassey
 Nsa5 , Emmy van der Grinten6 , Festus O. Soyinka7 , Hussien Abdur-Razzaq8
 , Sani Useni3 , Adebola Lawanson9 , Simeon Onyemaechi9 , Emperor
 Ubochioma9 , Jerod Scholten6 , Johan Verhoef6 , Peter Nwadike6 , Nkemdilim
 Chukwueme9 , Debby Nongo1 , Mustapha Gidado6 These de-identified data are
 from routine program evaluation, not a study. The evaluation was exempted
 from ethical review It's an Excel file. There are 14 variables: ###
 Data dictionary * PatientIDCAD: random ID * phase: 1, 2, 3 * Cough_Code:
 0= no cough, 1= cough &lt; 2 weeks, 2= cough =&gt; 2 weeks * CAD_Score:
 CAD SCORE is a number from 0-100 representing the likelihood of having
 present pulmonary TB.CAD4TB version 5 was used to score x-rays for TB-
 scores range from -10-100. A negative score means a gross difference from
 normal lung shape. * Presumptive: 0= no, 1=yes * MTB_detect:0= no, 1=yes *
 Region: 1=SOUTH, 2=nORTH * TESTED: 0= no, 1=yes * Client_Age = age in
 years * Older30: 0= no, 1=yes * shouldtest3: 0= no, 1=yes * recodesex 0=
 F, 1=M * Sex_qual: F OR M * TESTED_TB: 0= no, 1=yes Each row represents a
 person who was screened for TB. The vast majority of individuals screened
 had very low risk of TB and were not tested for TB. Missing values are
 either 99 or 999999. Data were collected according to the following
 algorithm: All persons were initially queried age,sex, and cough
 status.Then all were offered digital chest x-ray. If individuals had
 chronic cough or a CAD score = &gt; 60 (or =&gt; 57 in N region phase 3),
 they were invited to give a single spot sputum sample to be tested with G4
 GeneXpert. Those who tested positive on G4 GeneXpert were referred for TB
 treatment. In phase 3, those who tested negative, but had a high CAD score
 were offered further clinical examination for possible diagnosis of
 clinical TB. The data were collected in 3 phases and data quality improved
 over the phases. In the pilot phase (0), all persons scoring 40+ on CAD
 were to be tested, but this was not feasible (note high levels of missing
 test outcomes) because excess samples had to be tested off-site and
 results were recorded at the site. ### Description of the data and file
 structure This is an Excel file. Each row represents a person who was
 screened for TB. The vast majority of individuals screened had very low
 risk of TB and were not tested. CAD SCORE is a number from 0-100
 representing the likelihood of having present pulmonary TB. Missing values
 are either 999 or 999999. ### Sharing/Access information These data should
 not be linked or plotted so we have removed linking vars to preclude
 deductive disclosure. No HIV, geolocation, dates or treatment information
 is retained. Links to other publicly accessible locations of the data: N/A
 Data was derived from the following sources: CAD4 TB VERSION 5 G4
 GENEXPERT MTB/RIF SCREENING QUEX ### Code/Software We used SPSS for some
 of the analyses and R later. You can use whatever you want.