Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia

Youngji Jo; Mary Kagujje; Karl Johnson; David Dowdy; Peter Hangoma; Lophina Chiliukutu; Monde Muyoyeta; Hojoon Sohn

doi:10.1371/journal.pone.0256531

. 2021 Sep 9;16(9):e0256531. doi: 10.1371/journal.pone.0256531

Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia

Youngji Jo ¹, Mary Kagujje ², Karl Johnson ³, David Dowdy ¹, Peter Hangoma ⁴, Lophina Chiliukutu ², Monde Muyoyeta ², Hojoon Sohn ^1,^*

Editor: Kevin Schwartzman⁵

PMCID: PMC8428570 PMID: 34499668

Abstract

Introduction

Active-case finding (ACF) programs have an important role in addressing case detection gaps and halting tuberculosis (TB) transmission. Evidence is limited on the cost-effectiveness of ACF interventions, particularly on how their value is impacted by different operational, epidemiological and patient care-seeking patterns.

Methods

We evaluated the costs and cost-effectiveness of a combined facility and community-based ACF intervention in Zambia that utilized mobile chest X-ray with computer-aided reading/interpretation software and laboratory-based Xpert MTB/RIF testing. Programmatic costs (in 2018 US dollars) were assessed from the health system perspective using prospectively collected cost and operational data. Cost-effectiveness of the ACF intervention was assessed as the incremental cost per TB death averted over a five-year time horizon using a multi-stage Markov state-transition model reflecting patient symptom-associated care-seeking and TB care under ACF compared to passive care.

Results

Over 18 months of field operations, the ACF intervention costed $435 to diagnose and initiate treatment for one person with TB. After accounting for patient symptom-associated care-seeking patterns in Zambia, we estimate that this one-time ACF intervention would incrementally diagnose 407 (7,207 versus 6,800) TB patients and avert 502 (611 versus 1,113) TB-associated deaths compared to the status quo (passive case finding), at an incremental cost of $2,284 per death averted over the next five-year period. HIV/TB mortality rate, patient symptom-associated care-seeking probabilities in the absence of ACF, and the costs of ACF patient screening were key drivers of cost-effectiveness.

Conclusions

A one-time comprehensive ACF intervention simultaneously operating in public health clinics and corresponding catchment communities can have important medium-term impact on case-finding and be cost-effective in Zambia. The value of such interventions increases if targeted to populations with high HIV/TB mortality, substantial barriers (both behavioral and physical) to care-seeking exist, and when ACF interventions can optimize screening by achieving operational efficiency.

Introduction

Annually, an estimated 30% of tuberculosis (TB) incident cases are not notified, and this large pool of undetected diseases fuels ongoing transmission [1]. Missed diagnosis often results from underlying economic, geographic, socio-cultural and health system barriers to accessing TB care [2, 3]. Particularly, there remain large gaps in both the detection and treatment of HIV-associated TB; out of the almost 815,000 new cases of TB among people living with HIV (PLWH) in 2019, only 56% were diagnosed and reported [1]. As TB symptoms in this population may be nonspecific, many PLWH with TB may seek care late in their disease course [4]. Moreover, diagnosis of TB in PLWH is complicated by atypical chest radiography and a higher prevalence of sputum-smear negative disease [5–7]. In 2019, Zambia had an estimated 59,000 incident TB cases (333 per 100,000 per year), and of these, 28,000 were co-infected with HIV [1].

Given patient-level and health system barriers to accessing TB care [8, 9], improvements in routine TB diagnostic infrastructure alone (e.g. scaling-up rapid molecular tests) may not be sufficient to strengthen the TB care cascade in high TB burden settings such as Zambia. Active case finding (ACF) strategies (e.g. mass chest X-ray screening, household surveys, outpatient symptom screening, targeting high-risk populations) may therefore have an important role in facilitating early case detection and addressing gaps in the TB care cascade [10, 11]. To comprehensively address case detection gaps and improve linkage to care, the Centre for Infectious Disease Research in Zambia (CIDRZ) developed and implemented a one-time intensive active-case finding (ACF) program in both a health facility and the surrounding communities [12]. This combined ACF strategy included programmatic components to improve community awareness, access to timely diagnosis using mobile X-ray screening and rapid molecular testing (Xpert MTB/RIF), and facilitate linkage to care, including further clinical evaluation or initiation of TB treatment.

The resource-intensive nature of ACF interventions represents a major barrier to implementation and scale-up [13]. Previous modeling studies have shown that TB prevalence [14], human resource constraints [15], patients’ care seeking behavior [16], and linkage to care [17] may substantially affect the cost-effectiveness of ACF. As such, costs and cost-effectiveness of ACF will depend on how intervention components are designed, integrated, and implemented. Moreover, it is also important to understand how a range of local epidemiological and operational factors such as TB prevalence, underlying patient care-seeking behaviors, health system capacity, and community acceptance can influence programs’ scope, operations, and value [18].

In this study, we empirically assessed costs of key programmatic components of the CIDRZ ACF program. Then, we used a multi-stage Markov state-transition model representing patients’ symptom-associated care-seeking patterns and corresponding TB care algorithms to estimate cost-effectiveness if a similar intervention were to be implemented in a setting with TB epidemiology and economic conditions consistent with national averages.

Methods

Study setting and operations

The CIDRZ ACF project operated in the catchment area of the George primary health care centre (GPHC, covering a peri-urban settlement of 172,550 people), a Lusaka province public-sector facility offering TB diagnostic and treatment services. The intervention was operationalized by two distinct teams—community-based outreach and facility-based—which screened individuals for presumptive TB and facilitated linkage to care for diagnosis and treatment of TB. Diagnostic procedures included mobile X-ray followed by laboratory-based Xpert MTB/RIF (Xpert) testing. The community-based outreach team (which rotated through different areas of the GPHC catchment region, 3–4 days per area) employed a group of trained community health workers (CHWs) and drama teams who conducted community TB awareness events, community and door-to-door symptom screening, and chest X-rays taken in a mobile X-ray (mCXR) unit installed in a truck and interpreted by a computer-aided reading/interpretation software (CAD4TB Version 1.5, Delft Images, hereafter CAD4TB). The facility-based team operated out of an open-access tent at GPHC where four trained staff members conducted TB symptom screening and patient referral to TB services (including treatment initiation). In addition to screening people presenting (or referred) to the tent, the facility-based team also made regular visits to the antiretroviral therapy (ART) clinic and other departments (e.g., maternal health or general outpatient clinics) at GPHC to identify patients with TB symptoms and/or those patients otherwise indicated for TB screening (e.g., people living with HIV). Both community-based outreach and facility-based team collected sputum samples on-spot from the patients showing abnormal symptoms or Chest Xray results for Xpert testing in the GPHC laboratory once a patient was identified as a presumptive TB patient after initial symptom and mCXR screening. When on-site mCXR was not available (either in-use by one of the teams or inoperable), all symptomatic clients received Xpert without CXR screening. Patients with a positive Xpert test result were immediately followed-up by the ACF team and referred to the TB clinic for treatment initiation.

Cost analysis

Cost and resource-use data were collected using a standardized cost data collection and analysis tool developed by our team. This tool allows for cost and resource-use data collection by key activity component (i.e. training, screening, diagnosis, and treatment) for analysis using a top-down method [19]. Human resource costs for each activity component were estimated based on their estimated level of effort (LOE), approximated as proportional time allocation (%) of their full-time work spent on each activity during the program operations, assessed periodically (each quarter) using a workload survey. For the costs of goods, equipment, and services, we divided direct costs into capital and recurrent costs. Common programmatic costs (indirect and overhead costs that were shared across various ACF activities) were first calculated as total cost and were apportioned into each major ACF activity category based on direct human resource contribution (ratios of LOE across all ACF activities, weighted based the total person-time contribution assessed for each activity category). Cost data and program operation statistics were collected on a quarterly basis from July 2017 to December 2018 based on program financial documents and interviews with program managers. We evaluated these data against the respective service utilization and program statistics (e.g. number of patients) both quarterly and over the entire program period (total of 18 operational months). The main outcomes of the analysis were 1) unit costs of key service/activity components of the ACF intervention (calculated based on direct total costs of each discrete service/activity divided by the total number of patients who received the service); and 2) cost per direct program yield (cost per presumptive TB patient identified and per confirmed TB diagnosis). Capital assets were annualized based on the relevant expected life years and discounted at a 3% annual rate. All costs were assessed as economic costs from the health system perspective and reported in 2018 United States Dollars (USD) with cost data in local currencies converted based on the average United Nations operational exchange rates for 2018 [20].

Cost-effectiveness analysis

Costs and effect estimates were estimated using a multi-stage Markov state-transition model (Fig 1) using monthly time steps. Two stages included 1) a symptom transition model to calculate symptom-based care-seeking probabilities without ACF and 2) a decision-analytic model representing ACF and Passive Case Finding (PCF) as a status quo diagnostic and treatment algorithm. In the symptom transition model (developed using Microsoft Excel), we calibrated the underlying monthly care-seeking probabilities and symptom transition rates to reproduce a population consistent with current TB epidemiology in Zambia for each of three symptom levels: 1) TB-asymptomatic (patients unaware of or without any TB-specific symptoms); 2) non-TB-specific symptoms (symptoms for which an Xpert test would be ordered, if patients were to present passively to the clinic) and 3) classical TB symptoms (symptoms for which TB treatment would be started empirically, even if Xpert testing were negative). Additional details of this symptom transition model are described in the Supporting Information (S2 and S3 Tables in S1 File and S2 and S3 Figs in S1 File) and elsewhere [21]. Symptom-specific care-seeking probabilities from the symptom transition model were incorporated into the decision-analytic model to estimate the incremental effect (diagnosis and averted TB mortality) of the ACF intervention over the five-year time horizon compared to the status quo. The decision-analytic model, built-in TreeAge Pro 2018 (Williamstown, MA, USA), represented the overall TB care cascade for both passive and active case finding for a simulated population of 100,000 individuals. Epidemiologic parameters for our symptom-transition model were obtained from national estimates and published literature [22, 23–27]. Cost-effectiveness of the CIDRZ ACF operation in a setting with TB epidemiology and economic conditions consistent with national averages was evaluated based on the incremental cost-effectiveness ratio (ICER), calculated as incremental cost per TB death averted over a five-year time horizon (chosen as the minimum interval that might occur between serial intensive ACF campaigns in practice), relative to the status quo (PCF). Complete list of parameters used in the model can be found in Table 1 and S3 Table in S1 File.

Table 1. Key model parameters.

Model Parameter		Base Value	Distribution	Low Value	High Value	Source
Disease Epidemiology
People living with Human Immunodeficiency Virus (HIV) prevalence		0.113	Beta	0.1	0.13	22
Proportion of Tuberculosis (TB) occurring in HIV+ patients		0.59	Beta	0.46	0.70
Proportion of TB cases who have not been treated in the past		0.95	Beta	0.90	1.0
TB prevalence in general population		0.00346	Beta	0.0029	0.0043
Proportion of MDR+ TB cases who are previously treated ^a		0.18	Beta	0.14	0.22
Proportion of MDR+ TB cases, in treatment-naive patients		0.028	Beta	0.025	0.031
Efficacy of Diagnostic Tests
Sensitivity of chest X-ray		0.77	Beta	0.70	0.90	24
Specificity of Xpert		0.98	Beta	0.97	0.99	24
Sensitivity of Xpert for High Bacterial Loads ^b		0.98	Beta	0.97	0.99	26
Sensitivity of Xpert for Low Bacterial Loads ^b		0.68	Beta	0.59	0.75	27
Monthly Untreated TB Mortality ^c (per 1000 person-years)
HIV positive, Smear positive		0.06	Beta	0.0408	0.0799	25
HIV positive, Smear negative		0.054	Beta	0.0408	0.0799
HIV negative, Smear positive		0.021	Beta	0.0176	0.0288
HIV negative, Smear negative		0.0083	Beta	0.0071	0.0095
Connection to Health Care System
Proportion of (nonspecific/classic) symptomatic patients who attend active case finding program		0.86	Beta	0.5	1.0	S3 Table in S1 File
Monthly probability of a patient passively contacting the health system for TB diagnosis	Asymptomatic	0	Beta	0	0
	Nonspecific	0.2	Beta	0	0.5
	Classic	0.4	Beta	0.2	0.6
Monthly Symptom Level Transition Rate
Probability of transition from no symptom to cure		0.050	Beta	0.04	0.06	S3 Table in S1 File
Probability of transition from mild symptom to no symptom		0.120	Beta	0.1	0.2
Probability of transition from no symptom to mild symptom		0.240	Beta	0.2	0.3
Probability of transition from mild symptom to strong symptom		0.800	Beta	0.7	0.9
Probability of transition from strong symptom to mild symptom		0.400	Beta	0.3	0.8
Probability of transition from strong symptom to death		0.050	Beta	0.4	0.6

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^a. While multi-drug resistant (MDR) TB is not a part of standard monitoring indicators of the TB REACH program in Zambia, we incorporated a probability of MDR TB for the Markov state-transition model based on the country estimates.

^b. “High bacterial load” is defined as TB that, if tested with a single sputum smear under programmatic conditions, would test positive; “Low bacterial load” is defined as TB that, if tested with a single sputum smear under programmatic conditions, would test negative.

^c. Monthly mortality rate was estimated based on 1-EXP(-annual rate/12 months) from Vassall et al.[25] and WHO Zambia TB country profile (TB case fatality ratio as 31% in 2018)²⁰.

Sensitivity analysis

To test the robustness of our cost-effectiveness estimates, we performed a suite of sensitivity analyses (one-way, three-way, and probabilistic sensitivity analyses) based on the uncertainty estimates of each parameter. Parameters for the three-way sensitivity analysis were selected based on the ranking of the top three most influential parameters identified from one-way sensitivity analysis. For the Probabilistic Sensitivity Analysis (PSA), all model parameter values were randomly sampled over 10,000 Monte Carlo simulations based on pre-specified distributions of each data parameter to generate 95% uncertainty ranges. ICER estimates were evaluated against different willingness-to-pay (WTP) thresholds representing a range of financial and budgetary constraints in Zambia for public health interventions [28].

Ethical statement

Neither ethical approval nor informed consent was required for this analysis which did not involve human subjects research. Neither patients nor the public were involved in the design, conduct, reporting, or dissemination plans of our research.

Results

In our observation of program operations (July 2017 to December 2018), the comprehensive ACF program registered and screened 20,386 patients (mean 1,133 per month) and detected 943 new TB cases (mean 52 per month), incurring a total cost of $433,078 (S1 Table in S1 File). Various operational and external factors influenced service volume and unit costs over time, as described in S1 Fig in S1 File. As shown Table 2, unit cost per screening varied between $2 and $6 (mean $3.33) as quarterly service volume varied between 1724 and 6601; unit cost per mCXR-CAD4TB screening varied between $8 and $26 (mean $13.31) as service volume varied between 464 and 6038; and unit cost per Xpert test varied between $10 and $28 (mean $16.34) as service volume varied between 718 and 1179. On the other hand, the number of confirmed TB cases was reasonably constant varying between 151 and 190 per quarter. Overall, the ACF intervention cost an estimated $435 per TB treatment initiated by all diagnostic methods including mCXR-CAD4TB, Xpert, and empiric diagnosis.

Table 2. Service volumes and unit costs associated with an active case finding program for tuberculosis in Zambia.

	Types of services	Number of beneficiaries	Unit cost	Ranges ^a
Cost per activity ^b	Cost per patient screened	18,662	$3.33	($1.53, $5.79)
	Cost per patient diagnosed by Chest X-ray	12,679	$13.31	($7.54, $25.62)
	Cost per patient diagnosed by Xpert	4,511	$16.34	($9.68, $27.89)
Cost per yield	Cost per patient treated based on positive Chest X-ray	359	$553	($314, $1004)
	Cost per patient treated based on positive Xpert	471	$755	($509, $985)
	Cost per treatment initiated (by all diagnostic method)	847	$435	($313, $659)

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^a. Ranges represent the highest and lowest unit cost in a given quarter, accounting for observed variability from quarter to quarter over an 18-month period (S1 Fig and S1 Table in S1 File)

^b. Cost per activity estimates were used as cost parameters for the Markov state transition model.

Our model estimated that the one-off comprehensive ACF intervention implemented to cover 100,000 people in a generalized Zambian adult population (i.e., reflective of the epidemiology and economic conditions of Zambia as a whole) would incrementally diagnose 407 TB cases (7,207 vs. 6,800), at an incremental diagnostic cost of $822,000 (incremental cost of $2,020 per TB patient diagnosed), compared to passive case finding over a five-year horizon. This incremental and early diagnosis of TB patients by the ACF intervention would avert 498 TB deaths (612 vs 1,110) at incremental total health systems cost of $1,110,000 (ICER: $2,284 per death averted). Full cost-effectiveness analysis outcomes are available in Table 3.

Table 3. Five-year epidemiological and economic outcomes of 100,000 individuals exposed to status quo or active tuberculosis case finding.

Diagnostic Strategy	TB Diagnoses (95% uncertainty interval)	TB Deaths (95% uncertainty interval)	Total Diagnostic and Treatment Cost (95% uncertainty interval)	ICER (USD/Death Averted)
Active Case Finding (ACF)	7,207 (6,342, 8,488)	612 (405, 1111)	$3,897,000 ($3,314,000, $4,932,000)
Passive Case Finding (Status quo)	6,800 (5,912, 8,090)	1,110 (700, 1994)	$2,787,000 ($2,339,000, $3,610,000)
Incremental (ACF—Status quo)	407 (397, 430)	-498 (-296, -883)	$1,110000 ($975,000, $1,322,000)	$2,284 ($1,497, $3,298)

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Our one-way sensitivity analyses found that the incremental cost-effectiveness of ACF was most sensitive to estimated mortality among people with HIV and smear-positive TB, followed by the monthly probability of passive care-seeking and the unit cost of ACF screening (Fig 2). In three-way sensitivity analysis (Fig 3), ACF was most cost-effective when HIV/TB mortality was higher, symptom-associated passive care-seeking was less common, and the unit cost of ACF screening was lowest. Our probabilistic sensitivity analysis demonstrated that the likelihood of ACF cost-effectiveness substantially improved when evaluated over longer time horizons (Fig 4). If we assume a five-year time horizon, the probability of the cost-effectiveness of the ACF intervention was > 90% when policymakers in Zambia are willing to consider a value of $4,000 or higher for each death averted by the intervention. This would fall to 70% when considering only a one-year time horizon (Fig 4). In a longer-term assessment (five years), our model estimated that incremental diagnoses made by a one-off intensive ACF intervention may fall in the latter years as individuals with active TB subsequently seek routine care. Nevertheless, a substantial number of deaths were averted because individuals with TB were diagnosed earlier through ACF; these incremental benefits were mostly achieved within the first two years of ACF implementation (Fig 4). In settings where patients are more likely to passively seek care upon symptom onset (e.g., increase in the probability of symptom-associated care-seeking), the same ACF intervention would avert less than half as many TB deaths (reduced from 750 to 300 per 100,000 population) and would take a longer time to realize the full health benefit (16 vs. 28 months) as compared to settings with lower care-seeking probabilities (S4 Fig in S1 File). Depending on the costs of ACF and HIV/TB mortality levels, estimated ICERs varied by a factor of three (Fig 3).

Fig 2 — The parameters shown had the greatest quantitative influence on the incremental cost-effectiveness of ACF relative to routine care in one-way sensitivity analysis. Bars show the incremental cost-effectiveness (2018 US dollars per death averted averted) of ACF relative to routine care under the high value (dark blue bar) and low value (light blue bar) of the parameter in question, holding all other parameters constant. For example, when the untreated monthly mortality of HIV/TB (HIV positive and smear positive) is low, the ICER increased from the base value of $2,284 to $3,287, suggesting that the ACF intervention is less cost-effective compared to the routine program. The vertical line corresponds to the reference scenario (values as in Table 1, corresponding to $2,284 per death averted).

Fig 3 — This heat map displays the incremental cost-effectiveness of active TB case finding (ACF) compared to routine care, in units of cost per death averted. Each panel corresponds to a relative HIV/TB mortality rate (0.75 times base case, base case, 1.25 times of base case), with each column representing a different monthly probability of routine care-seeking behavior and each row depicting a different level of unit cost per ACF screening (as described as a measure of productivity level in ACF screening). The base case in our analysis had a HIV/TB mortality rate of 0.0598 per 1000 person years, 40% of individuals with classic symptoms seeking care to the clinic per month and cost per ACF screening of $3.33, resulting in an ICER of $2,284 per death averted.

Fig 4 — In this figure, the horizontal axis denotes the willingness to pay (WTP) per death averted (incremental cost-effectiveness ratio, ICER), and the vertical axis indicates the probability of ACF being cost-effective based on the proportion of simulations in which the comparison of the ACF intervention to the routine program falls below the WTP threshold. Costs are expressed in 2018 US dollars. At a WTP threshold of $4,800 per TB death averted (about three times Zambia’s GDP per capita in 2018), 97% of simulations suggest that the ACF intervention will be cost-effective compared to the routine program in Zambia over 5 years. This percentage declines to 95% over a three-year horizon, 93% over two years, and 83% in the first year of implementation.

Discussion

In this study, we evaluated the costs and cost-effectiveness of a one-time intensive ACF intervention in Zambia that included combined community and facility-based screening program using mCXR-CAD4TB followed by Xpert testing, and a linkage-to-care program to facilitate TB diagnosis and treatment initiation. In an average Zambian setting, represented by HIV/TB burden and care-seeking patterns similar to the national averages, we expect similar ACF interventions can avert a TB death at a cost of $2,284 (95% uncertainty: $1,497 to $3,298). This would translate to $99 per year of life saved (assuming average life expectancy of Zambian TB patients to be 40 years, discounted at 3% at the time of diagnosis), which is likely below Zambia’s country-specific cost-effectiveness threshold for health interventions (e.g., $68-$768 per quality-adjusted life year gained) [29], when considering earlier and incremental case detection by ACF interventions can also Our findings suggest that a comprehensive ACF intervention that can effectively close gaps between steps of TB screening, diagnosis, and treatment initiation may have greater value in settings with higher HIV/TB burdens and high patient care-seeking barriers compared to settings with lower HIV/TB disease burdens (e.g., ICER: $4,284 in Cambodia vs $2,284 in Zambia) [21]. However, the cost-effectiveness of any ACF intervention will vary depending on 1) operational factors influencing the costs of key intervention components (e.g., screening and use of mCXR); 2) level of patient symptom-associated care seeking—an area in which empiric data are lacking; and 3) the duration over which patient-relevant outcomes (e.g. TB case detection and death) are assessed.

Earlier studies evaluating the cost-effectiveness of ACF interventions largely focused on examining programmatic components and strategies (e.g. use of more accurate screening diagnostic tools such as Xpert) [14, 30–32]. While many of these analyses suggest ACF interventions can be highly cost-effective in many different settings, they do not consider supply (e.g. ACF intervention capacity, operational and implementation efficiency that may determine the costs of the intervention) and demand (e.g. patient symptom-associated care-seeking behavior) factors that may be important determinants of allocative efficiency. In this analysis, costs of key programmatic components of the ACF intervention (e.g. patient screening and mCXR) varied considerably depending on patient volumes; these may be contextualized to an intervention’s implementation and operational conditions (S1 Fig in S1 File). Unit costs were likely highest during early (with low operational efficiency) and late intervention periods (as the CIDRZ ACF project operated in a single operational catchment area, the number of individuals eligible for screening in the area may have fallen). Measuring the extent and trajectory of service volume and unit cost variation over time (as the product of HIV/TB prevalence, patients’ care-seeking and diagnostic sensitivity under both routine and ACF interventions) can provide important insight in evaluating key drivers of cost and cost-effectiveness in specific contexts.

Using an earlier modeling framework [21], we also explored the mechanisms of patient-level factors (symptom-associated care-seeking behavior) in determining the value of ACF interventions in high HIV burden settings. As shown in S4 Fig in S1 File, the timing and magnitude of incremental diagnosis and TB deaths averted differed by rates of symptom-associated care-seeking in the absence of ACF. In settings where patients seek TB care less readily, incremental health benefits (case detection and death aversion) achieved by ACF were twice as large and fast compared to settings where patients sought care more rapidly. Subsequently, if a shorter analytic time frame was used (less than 2 years) to assess the cost-effectiveness of the ACF intervention, it would not fully capture the future benefits of early and incremental case detection (Fig 4). As with an earlier modeling study [33], our results reiterate the importance of considering a longer analytic time frame to assess the potential health population benefits of early and incremental case detection achieved by ACF interventions. Furthermore, our study findings also highlight the significance of the relationship between TB natural history and care-seeking behavior in determining the cost-effectiveness of a one-time ACF intervention. As the factors and causality of patient care-seeking patterns will vary from one setting to another, understanding the local contexts in which TB care cascade gaps influence patient care-seeking patterns will help optimize the design and implementation strategy of ACF interventions.

Our study findings illustrate two distinctive implications for the dynamics of cost-effectiveness of ACF over time and place. First, S4 Fig in S1 File demonstrates how the cost-effectiveness may change over time among the same (fixed) population in a given catchment area, driven by their symptom progression and associated care-seeking. This suggests that cost-effectiveness may differ by stage of implementation. For example, in the initial stage of program implementation, HIV/TB prevalence, care-seeking patterns, and associated symptom progression may be key drivers of cost-effectiveness, while operational efficiency may have greater influence on cost-effectiveness at later stages (as prevalence decreases and service volume increases over time). Our study estimated that incremental cost-effectiveness might fall about 30% (i.e. improved cost-effectiveness) from $3,200 in the first year to $2,284 in the fifth year (Fig 4). On the other hand, Fig 3 demonstrates to what extent the cost-effectiveness of ACF may differ across different regions and populations, driven by underlying heterogeneity in risks or behaviors within those populations. For example, another study that evaluated ACF in Cambodia estimated cost-effectiveness at $5,300 per death averted [30], more than two times greater than estimated here ($2,284 per death averted). Since HIV/TB mortality and symptom-associated passive care seeking may not be easily modifiable, efforts to increase ACF service utilization in high prevalence areas through demand creation among key populations (e.g. people living with HIV or household contacts of people diagnosed with active TB) and/or optimization of intervention duration may be key strategies for program managers to improve the cost-effectiveness of ACF programs.

As with any modeling analysis, our study has some limitations. First, we calibrated care-seeking behavior to national estimates of prevalence and symptom duration but did not model underlying factors affecting patients’ care-seeking behavior that may vary within and across countries [34, 35]. These factors may include a wide range of health systems and patient-level barriers [36, 37] that may be difficult to model without empiric data in the context of TB case finding. Moreover, the extent of care seeking and the value of ACF interventions can also be influenced by other health system factors (e.g. quality of care) and other patient factors (e.g. community awareness of TB and available services). Given the importance of these factors in determining the value of ACF interventions, we encourage future observational and modeling studies to investigate how and to what extent different health system and patient factors might impact the incremental benefits and costs of ACF. Second, we assessed the costs and cost-effectiveness of the CIDRZ ACF intervention as a combined package, including multiple programmatic components that were deployed in both the community and the facility. Our analysis of effectiveness was limited to individuals diagnosed by the intensified case-finding and thus may underestimate the value of this intervention in encouraging individuals to also seek routine care for TB diagnosis. For example, a fast track point operating within GPHC was found to be an important entry point for TB diagnosis [38]; if continued, such interventions could—with minimal ongoing cost and effort—help to encourage ongoing care-seeking for TB diagnosis through routine services as well. Third, our empiric cost estimates were assessed using a top-down method only and were limited to one semi-urban health clinic in Zambia. Economic cost estimates of health services may vary depending on settings in which the services are deployed, methods used to evaluate costs, and how costs associated with implementation are accounted for in the analysis [39, 40]. While our findings may not generalize to epidemiological and health system contexts that are very different from our study, costs assessed at various points provide empiric uncertainty estimates of the cost of ACF and can be contextualized to different levels of operational and implementation efficiencies. Lastly, we did not include the effects of ACF on TB transmission; therefore, longer-term effects of ACF on TB incidence were not assessed. Instead, we focused on the trajectory of service output and impact of early case detection on TB death up to five years and demonstrate that the overall cost-effectiveness was robust to a range of sensitivity analyses. Likewise, if the effects of ACF on TB transmission were included, it would further strengthen the economic and epidemiologic case of ACF interventions in high HIV/TB burden settings [33].

In conclusion, our study demonstrates that a comprehensive ACF intervention model explored by CIDRZ can be cost-effective in populations representative of the epidemiological and economic conditions in Zambia—and likely in other contexts where HIV/TB burden is high. The value of ACF may be optimized in settings where HIV/TB mortality is high, existing care seeking is infrequent, and when ACF interventions can optimize patient screening by achieving operational efficiency. As these conditions may dynamically change throughout program implementation, individual ACF programs should carefully assess and actively monitor these indicators to identify the optimal timing and duration of operations. Better understanding of patients’ symptom-associated passive care-seeking patterns can also help improve the operational focus of ACF intervention and optimization of resource allocation for TB diagnosis and linkage to care in resource-limited settings.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(279.5KB, docx)}

Acknowledgments

We would like to thank the data collection team at the University of Zambia (Adam Silumbwe, Maio Bulawayo, Acklas Phiri, Tikurirekuti), and team members at CIDRZ and George Health Clinic in Lusaka who oversaw and conducted the day-to-day operations of the comprehensive active case finding for TB intervention.

Disclaimer: The findings and conclusions presented in this report are those of the authors and do not necessarily represent the official position of the authors’ affiliated institutions.

Abbreviations

ACF: Active-case finding
CHW: community health workers
ICER: incremental cost-effectiveness ratio
PCF: Passive Case Finding
PLWH: people living with human immunodeficiency virus
TB: Tuberculosis
WTP: willingness-to-pay

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

This study was funded by the Stop TB Partnership at the UNOPS through the TB REACH wave 5 grant. TB REACH – an initiative of Stop TB Partnership – is funded by Global Affairs Canada [grant number CA-3-D000920001] and The Bill and Melinda Gates Foundation [OPP1139029]. This study was also funded by the Korea Health Technology Research and Development Project through support from the Korea Health Industry Development Institute and the Ministry of Health and Welfare, Republic of Korea, in the form of funds to HS [H19C1235]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0256531.r001

Decision Letter 0

Kevin Schwartzman

20 May 2021

PONE-D-21-10534

Costs and Cost-Effectiveness of a Comprehensive Tuberculosis Case Finding Strategy in Zambia

PLOS ONE

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Reviewer #1: This paper describes a cost-effectiveness analysis of active case finding for tuberculosis, in a Zambian setting. This is an excellent paper that fills a real gap in the literature – the analysis is high-quality and the writing is very clear.

Minor comments

Line 105: typo ‘available’

Line 102: It would be useful to clarify ‘other departments’ here – was the target audience for the facility-based case finding restricted to people at higher risk for TB (ie. people with HIV), or did it include everybody attending the health clinic? How did the facility-based active case finding differ from ‘intensified case finding’ or a symptom screen at the front door?

Line 109: A bit more detail on methods for cost data collection would be useful. For example, was this a full or incremental costing? How was resource use measured or estimated - did data collection include observation, interviews, records review, or other methods? How were shared costs or overhead costs allocated across cost centres?

Appendix 1: Your description of the impact of factors such as service volume and time on unit costs is great. It might be useful to separate the graphs to show facility-based and community-based ACF separately, as they are quite different interventions and operated at different times so would probably be useful for readers to see the different costs. Can you also clarify how you have a unit cost for CXR during the period the Xray van was broken down (Jul-Dec 2018)?

Line 138: possible typo, I think you mean “Epidemiologic parameters for our symptom-transition model…”?

Line 175: typo ‘effectiveness’

Line 177: You mention the DALYs averted from the intervention in your results, but this is not in tables or in the discussion. It also looks like the only DALYs you include are related to YLLs, and you don’t include any from YLDs – is this right? If your main outcome is deaths, and not DALYs, it might not be necessary to include this sentence on DALYs in the results, especially as it is highly conservative as doesn’t include any reduction in disability through treatment of TB.

Line 206: I think you need to say a bit more here to justify why your incremental cost/death averted would be considered ‘cost-effective’. Most ‘universal’ thresholds use cost per DALY averted, not per death averted. Do you have a working threshold for cost-effectiveness per death averted, or can you give some examples of the cost/death averted for funded interventions?

Line 206: typo ‘expanded’

Line 236: typo ‘likely to seek care passively’

Discussion section: Did you capture any information on changes to passive case finding in the context of the community TB awareness events etc? Would it be worth mentioning these possible broader contextual impacts of ACF schemes if they have a knock-on effect of (for example) increased care-seeking behaviour in the general population?

Reviewer #2: General Comments:

Jo and colleagues evaluate the costs and cost-effectiveness of a large active TB case-finding initiative carried out in Zambia in 2017-18 using a multi-stage Markov transition model. The analysis compared conventional facility-based passive case-finding to facility- plus community-based TB screening using symptoms and automated digital chest radiography plus GeneXpert testing with linkage to TB treatment. The topic is important because a growing body of evidence suggests that active case finding is an effective and likely an essential strategy to be added to passive case finding in order to improve TB control and achieve TB elimination. However, there is a scarcity of information relevant to implementing these programs in real-world settings.

The underlying ACF program increased the number of TB cases detected by about 5%, at a cost of $435 per new treatment, and reduced deaths by almost 50%, at a cost of $2284 per death; these represent an excellent return on investment based on prior studies. The authors highlight several key implications of their analysis. First, their estimates of the unit costs and overall cost-effectiveness of the active case-finding program appeared to be highly sensitive to the available public health capacity and efficiency of implementation in a particular setting (“supply” factors), as well as to the volume of undiagnosed and non-care-seeking TB patients (“demand” factors). Second, similar to previous studies, they found that the benefits of active case finding are likely to accrue over a more extended time period than is commonly realized – at least five years. Both these considerations are likely to be important as countries consider where and how they will or will not adopt such programs and evaluate initial implementation.

Major Comments:

The analyses are well-presented and the figures and tables are very accessible to the reader. I have a few clarifying questions, as well as some questions about how these findings are packaged and presented to policymakers, as this is presumably one of the expected outcomes of publication.

Could the authors define what they mean by “generalized Zambian settings,” as compared with the settings of the CIDRZ ACF project? Are there contextual differences between them, or does this simply refer to nationwide scale-up?

The manuscript presents the differences in unit costs per process measure between different time periods, but how variable were the costs per outcome (i.e. per TB diagnosis, per TB death) by quarter?

Which of the factors influencing the temporal variations in cost are likely to be most easily modifiable? This information is contained in Figure 3, but it might be worth expanding on since HIV/TB mortality and symptom-associated passive care seeking may not be modifiable in ways that make ACF more cost-effective.

While interesting, I wonder if the insights into the important effects of care-seeking behaviors on ACF cost-effectiveness tell the whole story. For example, the model implies that active case finding will be most cost-effective in settings where passive care-seeking behaviors and/or the efficiency of facility-based care is lowest. I imagine that this may commonly occur in facilities where the quality of care and service are poor, and often the low quality of care may also be well-known in the community. Yet, the treatment outcomes of patients identified during active case-finding may also depend on the quality of care in these facilities. If the goal is high rates of treatment coverage and cure, this kind of analysis may not capture these complexities. The authors hint at this in their call for more empiric data across the diagnosis and treatment cascade but I wonder if this specific aspect might be highlighted since the influence of care-seeking behaviors is such a salient finding of this analysis.

In the last paragraph of the Results (Lines 189-198) and in the Discussion (Lines 227-230), the analysis uses temporal differences in cost and cost-effectiveness to generalize about such differences between settings. This seems potentially problematic since the word “settings” implies differences defined on factors other than time (e.g. geography). While time-based comparisons yields insights about how care can be delivered more efficiently to a fixed population (assumed stable, at least over the short term), comparisons between geographic settings should arguably account for selection differences between populations and for underlying heterogeneity in risks or behaviors within those populations, especially to the extent that the boundaries used to define populations geographically are arbitrary yet influence estimates of cost-effectiveness (i.e. different boundaries lead to different conclusions).

Minor Comments:

The manuscript would benefit from a close reading for grammar and usage to improve its readability and clarity.

Line 104: It is implied but not directly stated that specimens were collected for abnormal symptoms or abnormal mCXR; a little more detail on the ACF algorithm at this point in the Methods would be helpful for clarification.

Table 1: The proportion of TB occurring in HIV patients is estimated at 59% (range 46-70%) is justified by an unpublished manuscript (Reference 21). While the Introduction cites similar numbers from the WHO 2020 TB control report, and certainly this estimate reflects case notification data, recent prevalence studies seem to suggest that the contribution of HIV-patients to TB may be lower in active case finding settings, perhaps because of the success of HIV programs in increasing HIV status awareness, and linkage to care which includes TB screening. See for example N. Kapata et al. PLOS ONE (2016).

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PLoS One. 2021 Sep 9;16(9):e0256531. doi: 10.1371/journal.pone.0256531.r002

Author response to Decision Letter 0

23 Jul 2021

July 17, 2021

Responses to the reviewers comments provided for PONE-D-21-10534

Manuscript Title: Costs and Cost-Effectiveness of a Comprehensive Tuberculosis Case Finding Strategy in Zambia

Dear Editors and Editorial Team members,

We are grateful for the opportunity to revise and submit an improved manuscript, reflecting valuable feedback from the editors and reviewers. Specific responses to the editorial and reviewers’ comments are provided in blue, with references (with line numbers) to specific revisions made in the main manuscript (provided with italicized text with double quotes). A track change version of the revised manuscript and supplementing documents are uploaded with ‘R1’ designation.

We believe that the editor and reviewers’ feedback have resulted in a much improved manuscript and hope the editorial team and the reviewers agree.

Thank you.

Sincerely,

Hojoon Sohn, PhD MPH

Department of Epidemiology

Johns Hopkins Bloomberg of Public Health

615 N. Wolfe Street E. 6039

Baltimore, MD, 21205

United States of America

hsohn6@jhmi.edu

+1-443-517-8145

Additional Editor Comments:

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When submitting your revision, we need you to address these additional requirements.

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Thank you for this information. We have reviewed and updated the manuscript following the PLOS ONE styling.

2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

We’ve revised the text describing the grant information to match the ‘funding information as well as ‘financial disclosure’ sections.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

We have added the captions for the Supporting Information at the end of the manuscript.

5. Review Comments to the Author

Minor comments

Line 105: typo ‘available’

Line 175: typo ‘effectiveness’

Line 206: typo ‘expanded’

Line 236: typo ‘likely to seek care passively’

Line 138: possible typo, I think you mean “Epidemiologic parameters for our symptom-transition model…”?

Thank you for noting these typos. We have corrected typos throughout the manuscript, including those mentioned above.

Thank you for raising these questions. The target population for the facility-based case finding was all patients visiting the George primary health care center (GPHC) with either symptoms relating to tuberculosis or people living with HIV (PLHIV) who were making routine care visits to the ART clinic of GPHC. The facility-based team operated from an open access tent in the common grounds of GPHC to facilitate linkage (i.e. conducting initial symptom screening and patient referral for sputum collection and testing) to TB care for those accessing the open access tent. The main difference between facility-based case finding and teams that conducted the door-to-door screening was the location of operation (facility vs. community).

To further clarify the roles of facility-based case finding and what constitutes ‘other departments’, we have revised the text as the following (line 100-105):

“The facility-based team operated out of an open-access tent at GPHC where four trained staff members conducted TB symptom screening and patient referral to TB services (including treatment initiation). In addition to screening people presenting (or referred) to the tent, the facility-based team also made regular visits to the antiretroviral therapy (ART) clinic and other departments (e.g. maternal health or general outpatient clinics) at GPHC to identify patients with TB symptoms and/or those patients otherwise indicated for TB screening (e.g., people living with HIV).”

Thank you for this comment. For our empiric cost analysis, we primarily focused on the costs of ACF intervention service delivery, up to the point of treatment initiation, as incremental program to the routine care. Costs of the ACF intervention were assessed primarily based on our earlier work (also cited in the manuscript – reference #19)[1] but we have provided additional details on the methods used for data collection for resource use and apportionment of common costs across key ACF activities as following (lines 115-122):

“Human resource costs for each activity component were estimated based on their estimated level of effort (LOE), approximated as proportional time allocation (%) of their full-time work spent on each activity during program operation. Estimated LOE was assessed periodically (each quarter) using a workload survey. For the costs of goods, equipment, and services, we divided direct costs into capital and recurrent costs. Common programmatic costs (indirect and overhead costs that are shared across various ACF activities) were first calculated as total cost and were the apportioned into major ACF activity categories based on direct human resource contribution (ratios of LOE across all ACF activities, weighted based the total person-time contribution assessed for each activity category).”

In the cost-effectiveness analysis, we accounted for costs of routine care (status quo) with some of these costs empirically assessed from our costing study (routine symptom screening and facility based Xpert testing for which testing capacity was shared with the ACF program). Other routine care costs (e.g. costs of smear and treatment) were extrapolated from published estimates.[2, 3]

In our analyses, we considered community and facility-based components of the ACF program as a single intervention package to be compared to the status quo (absence of ACF intervention). This was primarily done due to limitations in collecting activity-based data as would be needed to assess costs using a bottom-up approach. Therefore, we used top-down approach to conduct cost analysis and could not distinguish costs of activities performed at the facility and in the community. This limitation extends to calculating costs of X-ray. While the truck with the mobile X-ray unit installed was not operational during the months between July and December of 2018, the truck was parked at GPHC (facility) to provide X-ray services for those patients being screened by the facility-based teams and those who were being referred to the clinic from the community-based activities (e.g. drama clubs).

Thank you for noting this. You are correct that our main cost-effectiveness outcome was assessed based on incremental deaths averted by the ACF intervention. Therefore, we have removed the references to DALYs in the Results section accordingly. Instead, we have included a statement in the Discussion section (with corrected notion of life years saved vs. DALYs) so that readers can contextualize our ICER estimates to effectiveness measures (e.g. life years saved) compared to Zambia’s country-specific cost-effectiveness threshold for health interventions (e.g., $68-$768 per quality-adjusted life year gained)[4]. Revisions in the text reference here are shown in our response to your next comment below.

Thank you for your suggestion. We have now added language that would help readers convert a cost per death averted into a cost per year of life saved. This latter quantity is still a conservative estimate of cost per DALY averted or cost per QALY gained (as we would expect the intervention to also avert TB-related morbidity, in the same vein as the Reviewer’s comment about YLDs vs YLLs above). But given that the cost per year of life saved is below most standard estimates of Zambia’s willingness to pay, we would expect the cost per DALY averted (or QALY gained) to be even more favorable to the intervention.

We have now added the following text, benchmarked to country-specific estimates of cost-effectiveness in Zambia, to help clarify these concerns (lines 211-217):

“In an average Zambian setting, represented by HIV/TB burden and care-seeking patterns similar to the national averages, we expect similar ACF interventions can avert a TB death at a cost of $2,284 (95% uncertainty range: $1,497 to $3,298). This would translate to $99 per year of life saved (assuming average life expectancy of Zambian TB patients to be 40 years, discounted at 3% at the time of diagnosis), which is likely below Zambia’s country-specific cost-effectiveness threshold for health interventions (e.g., $68-$768 per quality-adjusted life year gained), especially when considering earlier and incremental case detection by ACF interventions can also avert TB morbidity in addition to mortality.”

Thank you for the valuable comment. Our study was limited to assessing costs and cost-effectiveness of the CIDRZ ACF intervention; therefore, we did not capture or evaluate changes to passive case finding the context of the community TB awareness events conducted by the ACF project. While it is difficult to ascertain whether community-based activities had a direct effect on changing care-seeking behaviors, our co-authors have previously demonstrated that a fast track point – an access point designed to facilitate linkage to care for both the regular patients visiting the clinic and those being referred from the community – was an important access point for people ultimately diagnosed with bacteriologically confirmed TB (see Table 2 of Kagujje et al., 2020, below).[5]

Area of ACF activity Bacteriologically confirmed TB (%, out of 563 identified by the entire project)

Community 48 (8.5)

Overall facility 515 (91.5)

ART 49 (8.7)

MCH 3 (0.5)

OPD 232 (41.2)

Fast track 214 (38.0)

TB clinic 2 (0.4)

VCT 9 (1.6)

We have subsequently updated our discussion to highlight that ACF may also have positive “knock-on” effects of encouraging people to seek care passively as well (lines 285 – 290):

“Our analysis of effectiveness was limited to individuals diagnosed by active case-finding and thus may underestimate the value of this intervention in encouraging individuals to also seek routine care for TB diagnosis. For example, a fast track point operating within GPHC was found to be an important entry point for TB diagnosis[5]; if continued, such interventions could – with minimal ongoing cost and effort – help to encourage ongoing care-seeking for TB diagnosis through routine services as well.”

Reviewer #2: General Comments:

Thank you for the positive comments.

Major Comments:

Thank you for this question. We have clarified this on lines 87 and 150-151 of our revised manuscript:

“a setting with TB epidemiology and economic conditions consistent with national averages”

The manuscript presents the differences in unit costs per process measure between different time periods, but how variable were the costs per outcome (i.e. per TB diagnosis, per TB death) by quarter?

Thank you for this question. Using quarterly program yield data and expenditure/cost data, we calculated cost per programmatic yield (cost per TB diagnosis) for each quarter and presented these in S2 Table (also shown below). Cost per TB case detected/diagnosed and initiated on TB treatment varied from $312 to $658. While we did not estimate cost per death averted for each quarter, we performed probabilistic sensitivity analysis to ascertain uncertainty ranges (between $1,497 and $3,298 per TB death averted) of our primary incremental cost-effectiveness ratio (Table 3)

Types of services Average

unit cost Cost per activity

Jul-Sep, 2017 Oct-Dec, 2017 Jan-Mar 2018 Apr-Jun, 2018 Jul-Sep, 2018 Oct-Dec, 2018

Diagnosed as TB cases by X-ray $553 $409 $409 $575 $1,004 $461 $314

Diagnosed as TB cases by Xpert $755 $509 $509 $648 $980 $651 $985

Treatment initiated $435 $329 $329 $340 $533 $312 $658

Thank you for these important comments. We have included a discussion on this matter in line 269-273:

“Since HIV/TB mortality and symptom-associated passive care seeking may not be easily modifiable, efforts to increase ACF service utilization in high prevalence areas through demand creation among key populations (e.g. people living with HIV or household contacts of people diagnosed with active TB) and/or optimization of intervention duration may be key strategies for program managers to improve the cost-effectiveness of ACF programs.”

Thank you for the valuable comments. We revised the text in our discussion section to reflect the reviewer comments as the following (lines 279 – 283):

“Moreover, the extent of care seeking and the value of ACF interventions can also be influenced by other health system factors (e.g. quality of care) and other patient factors (e.g. community awareness of TB and available services). Given the importance of these factors in determining the value of ACF interventions, we encourage future observational and modeling studies to investigate how and to what extent different health system and patient factors might impact the incremental benefits and costs of ACF.”

Thank you for the valuable comments. In response to this comment, we have extensively revised the last paragraph of the Results to discuss differences in “settings” as reflective of our sensitivity analyses (e.g. differentiated by patient care-seeking patterns, costs of ACF, and HIV/TB mortality, rather than relying on temporal differences to distinguish settings (lines 200-205):

“In settings where patients are more likely to passively seek care upon symptom onset (e.g., increase in the probability of symptom-associated care-seeking), the same ACF intervention would avert less than half of TB deaths (reduced from 750 to 300 per 100,000 population) and would require a longer time-frame to observe the full health benefit (16 vs. 28 months) as compared to the settings with lower care-seeking probabilities (S7 Figure). Depending on the costs of ACF and HIV/TB mortality levels, ICERs may vary as much as three times compared to the lowest ICER estimate (Figure 3).”

Similarly, in the last paragraph of the Discussion, we differentiate discussion on settings (as being distinguished by HIV/TB mortality, care seeking patterns, and operational context) and temporal dynamics (as might be considered by individual ACF programs), lines 305-311:

“The value of ACF may be optimized in settings where HIV/TB mortality is high, existing care seeking is infrequent, and when ACF interventions can cost-optimize patient screening by achieving operational efficiency. As these conditions may dynamically change throughout program implementation, individual ACF programs should carefully assess and actively monitor these indicators to identify the optimal timing and duration of operations.”

While the definition of boundaries may change the estimates of key epidemic or care seeking parameters (as it may change the patients’ profile), it will not change our conclusion (incremental cost effectiveness ratio of ACF vs PCF in which ACT is more cost effective compared to PCF and the cost effectiveness may differ by setting specific conditions). Nonetheless, we agree with the reviewer’s point and added the following text in the discussion section (line 257-273):

“Our study findings illustrate two distinctive implications for the dynamics of cost-effectiveness of ACF over time and place. First, S7 Figure demonstrates how the cost-effectiveness may change over time among the same (fixed) population in a given catchment area, driven by their symptom progression and associated care-seeking. This suggests that cost-effectiveness may differ by stage of implementation. For example, in the initial stage of program implementation, HIV/TB prevalence, care-seeking patterns, and associated symptom progression may be key drivers of cost-effectiveness, while operational efficiency may have greater influence on cost-effectiveness at later stages (as prevalence decreases and service volume increases over time). Our study estimated that incremental cost-effectiveness might fall about 30% (i.e. improved cost-effectiveness) from $3,200 in the first year to $2,284 in the fifth year (Figure 4). On the other hand, Figure 3 demonstrates to what extent the cost-effectiveness of ACF may differ across different regions and populations, driven by underlying heterogeneity in risks or behaviors within those populations. For example, another study that evaluated ACF in Cambodia estimated cost-effectiveness at $5,300 per death averted [6], more than two times greater than estimated here ($2,284 per death averted). Since HIV/TB mortality and symptom-associated passive care seeking may not be easily modifiable, efforts to increase ACF service utilization in high prevalence areas through demand creation among key populations (e.g. people living with HIV or household contacts of people diagnosed with active TB) and/or optimization of intervention duration may be key strategies for program managers to improve the cost-effectiveness of ACF programs.”

Minor Comments:

The manuscript would benefit from a close reading for grammar and usage to improve its readability and clarity.

Thank you for this suggestion. We have carefully screened our manuscript for typos and corrected them accordingly.

Thank you for picking up on this issue. We have revised the wording as the following (lines 106 - 108):

“Both community-based outreach and facility-based team collected sputum samples on-spot from the patients showing abnormal symptoms or Chest Xray results for Xpert testing in the GPHC laboratory once patient was identified as a presumptive TB patient after initial symptom and mCXR screening.”

Thank you for this suggestion. We replaced the original unpublished reference with the suggested references.

References

1. Jo Y, Mirzoeva F, Chry M, Qin ZZ, Codlin A, Bobokhojaev O, et al. Standardized framework for evaluating costs of active case-finding programs: An analysis of two programs in Cambodia and Tajikistan. PLoS One. 2020;15(1):e0228216. doi: 10.1371/journal.pone.0228216. PubMed PMID: 31986183; PubMed Central PMCID: PMCPMC6984737.

2. Pooran A, Theron G, Zijenah L, Chanda D, Clowes P, Mwenge L, et al. Point of care Xpert MTB/RIF versus smear microscopy for tuberculosis diagnosis in southern African primary care clinics: a multicentre economic evaluation. Lancet Glob Health. 2019;7(6):e798-e807. doi: 10.1016/S2214-109X(19)30164-0. PubMed PMID: 31097281; PubMed Central PMCID: PMCPMC7197817.

3. Laurence YV, Griffiths UK, Vassall A. Costs to Health Services and the Patient of Treating Tuberculosis: A Systematic Literature Review. Pharmacoeconomics. 2015;33(9):939-55. doi: 10.1007/s40273-015-0279-6. PubMed PMID: 25939501; PubMed Central PMCID: PMCPMC4559093.

4. Woods B, Revill P, Sculpher M, Claxton K. Country-Level Cost-Effectiveness Thresholds: Initial Estimates and the Need for Further Research. Value Health. 2016;19(8):929-35. doi: 10.1016/j.jval.2016.02.017. PubMed PMID: 27987642; PubMed Central PMCID: PMCPMC5193154.

5. Kagujje M, Chilukutu L, Somwe P, Mutale J, Chiyenu K, Lumpa M, et al. Active TB case finding in a high burden setting; comparison of community and facility-based strategies in Lusaka, Zambia. PLoS One. 2020;15(9):e0237931. doi: 10.1371/journal.pone.0237931. PubMed PMID: 32911494; PubMed Central PMCID: PMCPMC7482928.

6. Dobler CC. Screening strategies for active tuberculosis: focus on cost-effectiveness. Clinicoecon Outcomes Res. 2016;8:335-47. doi: 10.2147/CEOR.S92244. PubMed PMID: 27418848; PubMed Central PMCID: PMCPMC4934456.

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PLoS One. doi: 10.1371/journal.pone.0256531.r003

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10 Aug 2021

Costs and Cost-Effectiveness of a Comprehensive Tuberculosis Case Finding Strategy in Zambia

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25 Aug 2021

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Costs and Cost-Effectiveness of a Comprehensive Tuberculosis Case Finding Strategy in Zambia

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PERMALINK

Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia

Youngji Jo

Mary Kagujje

Karl Johnson

David Dowdy

Peter Hangoma

Lophina Chiliukutu

Monde Muyoyeta

Hojoon Sohn

Roles

Abstract

Introduction

Methods

Results

Conclusions

Introduction

Methods

Study setting and operations

Cost analysis

Cost-effectiveness analysis

Fig 1. Conceptual framework for the Markov model.

Table 1. Key model parameters.

Sensitivity analysis

Ethical statement

Results

Table 2. Service volumes and unit costs associated with an active case finding program for tuberculosis in Zambia.

Table 3. Five-year epidemiological and economic outcomes of 100,000 individuals exposed to status quo or active tuberculosis case finding.

Fig 2. One-way sensitivity analysis of the cost-effectiveness of active tuberculosis case finding over five years (2018–2022) in Zambia.

Fig 3. Three-way sensitivity analysis of the cost-effectiveness of active tuberculosis case finding over five years (2018–2022) in Zambia.

Fig 4. Cost effectiveness acceptability curve of an active tuberculosis case finding intervention compared to routine care, over one to five years (2018–2022) in Zambia.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Kevin Schwartzman

Roles

Author response to Decision Letter 0

Decision Letter 1

Kevin Schwartzman

Roles

Acceptance letter

Kevin Schwartzman

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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