A retrospective cross-sectional study on the effectiveness of using ad-hoc staff in outpatient departments to increase tuberculosis case notification

A retrospective cross-sectional study on the effectiveness of using ad-hoc staff in outpatient departments to increase tuberculosis case notification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A retrospective cross-sectional study on the effectiveness of using ad-hoc staff in outpatient departments to increase tuberculosis case notification Joseph Kuye, Emmanuel Olashore, Otse Ogorry, Airaoje O. Karl, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7904791/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The use of ad-hoc staff to boost TB notification rates is becoming more significant, and its effect has been observed in certain settings. This study aimed to assess the impact of engaging temporary staff on TB notification at health facilities where intensified TB case-finding efforts were implemented in Kano State, Nigeria, from January 2022 to December 2023. Methods This observational, descriptive, cross-sectional study retrospectively examined data from the Outpatient Department (OPD) TB Screening program implemented in Kano State, Nigeria. Data collection and reporting followed the national TB program's data flow and management system. Quarterly TB care cascade and key efficiency metrics were displayed in numbers and proportions to identify gaps along the TB care continuum for the intervention. An unpaired t-test was used to determine whether the quarterly average TB case notification rates differed between intervention and non-intervention sites during the study period. The significance level was set at 5%. Results From January 2022 to December 2023, 832,658 people visited the OPD at the 40 health facilities selected for the PQE intervention, with a TB screening rate of 67%, a presumptive yield of 5.1%, a TB yield of 3.4%, NNS of 658, NNT of 30, and an enrolment rate of 99%. The quarterly number of TB cases notified increased from zero at the start of the intervention to 133 in the last quarter, with periods of rise and fall. The quarterly percentage contribution of the PQE intervention to TB notifications grew from 0% (January – March 2022) to 38% (October – December 2023). The intervention facilities had a mean TB case notification of 23.0, compared to 13.2 for non-intervention facilities, with a marginally significant p-value of approximately 0.063. Conclusions The study found that deploying ad-hoc staff to support TB service delivery in health facilities with high patient volumes can increase TB case notifications and improve overall TB services in Kano State, Nigeria. It underscores the importance of focusing on the design, implementation, and monitoring of the intervention. Tuberculosis ad-hoc staff Volunteers Kano Nigeria Figures Figure 1 Figure 2 Figure 3 Background Reducing the global incidence of tuberculosis (TB) remains a key goal of TB control efforts [ 1 ]. However, progress has been slower than expected. By 2022, only an 8.7% reduction in TB incidence was achieved, falling far short of the 50% target set by the WHO End TB Strategy 2025 milestones [ 2 ]. In 2022, approximately 10.6 million people had TB, and 1.3 million died from the disease, making it the second leading cause of death from a single infectious organism after COVID-19 that year [ 2 , 3 ]. As efforts to control TB worldwide begin to recover from disruptions caused by COVID-19, 7.5 million new TB cases were diagnosed in 2022, the highest number recorded since WHO started global TB monitoring in 1995 [ 2 ]. Continuing this progress is crucial for reducing TB incidence and reaching the WHO End TB Strategy 2025 milestones. The decrease in TB incidence is closely linked to more consistent TB case notification. Although the global gap between incident and notified cases narrowed to 3.1 million in 2022, low TB notifications continued to pose a challenge, especially in Nigeria [ 2 ], where treatment coverage increased from 24% in 2016 to 74% in 2023, demonstrating the country’s potential to reach the end TB milestone by 2030 [ 4 – 6 ]. The number of people diagnosed and started on TB treatment rose to 367,250 in 2023 from 135,800 in 2020 [ 6 ]. Nigeria’s TB control efforts have achieved notable results, but the notification gap persists, underscoring the need for more innovative strategies to increase TB notification. Currently, Nigeria’s health workforce size and capacity to deliver quality TB services are inadequate [ 7 – 9 ]. The gap in TB notifications could be bridged if temporary staff are effectively mobilized to support facility-level TB efforts. Scaling up the facility-based TB case finding (intensified case finding) strategy marked a significant shift in Nigeria’s surge in TB case notifications [ 6 , 10 ]. Temporary staff have effectively assisted with patient triaging, screening, identification [ 10 , 11 ], referral [ 12 ], and specimen transportation [ 13 ] in many settings. Understanding how engaging this cadre of health workers has contributed to case notification can provide Nigeria with insights on how to sustain its current progress toward achieving the WHO End TB Strategy 2025 milestones. Several studies have examined how trained temporary staff and volunteers can enhance TB service delivery at both facility and community levels, particularly among vulnerable groups like migrant slum dwellers [ 14 – 18 ]. However, only a few have specifically reported the benefits of using such support at the facility level [ 10 ], and there are no reports from northern Nigeria. Additionally, research on facility-based case-finding strategies in Northern Nigeria has primarily focused on their effects on patient yield and cost-effectiveness, with limited information on how involving ad-hoc staff in healthcare facilities impacts TB case notifications and the overall quality of TB care [ 19 , 20 ]. This study aims to fill the current knowledge gap regarding how ad-hoc staff contribute to TB case notification at the facility level, particularly in northern Nigeria. It aims to assess the impact of involving ad-hoc staff on TB notification rates in health facilities where intensified TB case-finding interventions were implemented in Kano State, Nigeria, from January 2022 to December 2023. Methods Study setting The study was conducted in 40 out of 1,296 health facilities providing TB services across 19 Local Government Areas (LGAs) of Kano State (Fig. 1 ), one of Nigeria’s six states in the northwest geopolitical zone. These facilities are in 19 of the 44 LGAs in the state, primarily inhabited by the Hausa and Fulani, who are predominantly Muslim, with an estimated population of 15,462,178 in 2022 [ 21 ]. Kano has 1,486 health facilities [ 22 ], of which 1,296 (87%) offer TB services, including prevention, diagnosis, treatment, and care. The 40 selected facilities, in addition to providing other healthcare services, focus on TB screening, diagnosis, treatment, and care for all ages; patients visiting these facilities often come from various parts of the state [ 23 ]. These facilities represent all three levels of Nigeria’s healthcare system (tertiary [ 2 ], secondary [ 13 ], and primary [ 24 ] ) and serve children, adults, indigenous people, and non-indigenous people. The selected 40 health facilities have a high OPD attendance rate. Health facilities with high OPD attendance rates have significantly contributed to Nigeria’s annual increase in TB notifications over the past three years [ 2 , 10 , 25 ]. Study population The study population includes all individuals who attended the 40 selected health facilities in Kano State, Nigeria, between January 2022 and December 2023. This included 832,658 OPD attendees who were the target for tuberculosis (TB) screening under the intervention involving ad-hoc staff. Study procedure The 40 health facilities were divided into two equal groups, each comprising 20 facilities: an intervention group and a control group. The intervention was integrated into routine cascade care and combined with quality improvement measures. The data collected spanned from January 2022 to December 2023. To ensure data quality, the purpose of data collection was clearly defined, as outlined in the objectives, and aligned with the research questions and planned analysis. Since this is a secondary data analysis, the credibility of the data source was carefully assessed. The data source was the quarterly reporting form of the Kano State Tuberculosis and Leprosy Control Program (KSTBLCP). Additionally, ethical procedures were followed, and the accuracy, completeness, and consistency of the data were verified, including cross-validation with multiple sources. Data cleaning techniques (such as handling missing values through imputation, correcting duplicates, and standardizing formats) were applied to ensure the data used for analysis were reliable. Data collection This study was a retrospective review of existing program data. The researchers did not collect new data; instead, they extracted and analyzed information already gathered during routine operations. Data were collected from facilities between January 2020 and December 2023, and from the OPD (intervention site) between January 2022 and December 2023. As part of their standard responsibilities, ad hoc and facility staff collected case-based data along the TB care cascade for every patient visiting the OPD. This information was recorded using the standardized Nigerian National Tuberculosis and Leprosy Control Programme (NTBLCP) OPD Screening Reporting tool. Individual patient data were compiled at each facility on a quarterly basis. These compiled reports were then submitted through the national TB program's official data flow, which includes reporting, management, and validation systems. The study gathered and examined variables mainly representing process indicators from the TB care cascade, along with outcome measures related to case notification. Since the data source consisted of aggregated program data, individual patient demographic or clinical characteristics were not available for analysis. Data was collected for the previous year (2021) before the intervention to demonstrate that the pre-intervention TB notification by the facilities was relatively similar. The primary outcome variable was TB case notification, defined as the number of tuberculosis cases diagnosed and officially reported to the national program. This was measured as the quarterly number of notified TB cases and used to compare intervention and non-intervention sites. Process and performance indicators were derived from the efficiency cascade of the TB screening intervention conducted by ad-hoc staff. These included OPD attendance, representing the target population; screening rate, reflecting the proportion of OPD attendees screened for TB symptoms; presumptive TB yield, defined as the proportion of screened individuals identified with symptoms suggestive of TB; and evaluation rate, measuring the proportion of presumptive TB patients who underwent diagnostic testing. Additional indicators included TB yield, or the proportion of evaluated patients diagnosed with TB; treatment enrolment rate, indicating the proportion of diagnosed patients who started treatment; the number needed to screen (NNS), which quantifies how many people need to be screened to detect one TB case; and the number needed to test (NNT), which measures how many presumptive TB patients must be tested to confirm one case. The primary independent variable was the engagement of ad-hoc staff, measured as a binary variable. Facilities where ad-hoc staff supported TB screening and case finding in outpatient departments formed the intervention group, while those operating under routine conditions without such support served as the control group. Additionally, time served was used as a comparison variable, with quarterly data analyzed from the first quarter (Q1) of 2022 through the fourth quarter (Q4) of 2023 to evaluate trends during the intervention period. Independent variables such as age, sex, occupation, type of TB, and HIV status were neither collected nor analyzed, since these factors were not part of the national TB program’s aggregated reporting template. This represents a key limitation of the study. Data analysis Descriptive analysis focused on efficiency cascade indicators for the TB screening intervention, calculating and presenting totals and proportions for key performance metrics. These included the screening rate, presumptive yield, evaluation rate, TB yield, and enrolment rate, as well as the number needed to screen (NNS) and the number needed to test (NNT) to identify one TB case. Trend analysis was used to examine changes over time. The quarterly number of TB cases notified specifically through the outpatient department (OPD) intervention was plotted to visualize trends, while the percentage contribution of the ad-hoc staff intervention to the facilities’ total TB notifications was calculated for each quarter. The comparative analysis further explored differences by graphically comparing overall TB case notification trends between 20 intervention facilities and 20 matched non-intervention facilities within the same Local Government Areas (LGAs) over the period from 2019 to 2023. The non-intervention facilities were carefully chosen because their case notifications in the year before the intervention (2021) were within 10–15% of those reported by the intervention sites. An unpaired t-test was performed to assess whether the quarterly average number of TB cases notified by the intervention facilities significantly differed from that of the non-intervention facilities during the two-year intervention period, with a significance level set at 0.05. Ethical considerations This study was reviewed and approved by the Kano State Ministry of Health's Health Research Ethics Committee in Kano, Nigeria (reference number SHREC/2025/6071), with approval number NHREC/17/03/2018. Additionally, approval was obtained from the Kano State TB, Leprosy, and Buruli Ulcer Control Program. The design did not involve direct contact with human subjects, as no interviews were conducted and no biological specimens were collected. Data collection involved pooling de-identified program data gathered during routine standard care across all facilities; therefore, no consent was obtained for this study. Nonetheless, the study was conducted with careful consideration, ensuring compliance with all necessary research ethics. The design of this research adhered to the principles of the Helsinki Declaration, which safeguard the well-being of survey participants. Results A total of 640 facility-period observations from Kano State were analyzed, evenly divided between pre-intervention (n = 320) and intervention (n = 320) periods. Of these, 328 (51.3%) are from intervention facilities, and 312 (48.7%) are from non-intervention facilities (Table 1 ). The Mean presumptive TB cases were 206.5 (95% CI: 169.5–243.6), diagnosed 16.0 (95% CI: 12.5–19.5), and notified 15.7 (95% CI: 12.9–23.3) Table 1 Distribution of Observations by Intervention Status and Period Variable Category Frequency Percentage Period (January 2020 – December 2021) Pre-intervention 320 50.0 Period (January 2022 – December 2023) intervention 320 50.0 Facility period observations Intervention 328 51.3 Facility period observations Non-intervention 312 48.7 Table 2 shows the efficiency cascade across the intervention facilities. A total of 519,989 patients visited outpatient departments at the 20 intervention sites, with 347,787 individuals, or 66.9%, screened for TB. Among those screened, 18,190 (5.2%) were identified as presumptive TB, and 16,440 (90%) received further evaluation. This process led to 562 confirmed TB cases, representing 3.4% of all evaluated cases. Of these, 555 patients (98.8% of confirmed cases) were successfully enrolled in treatment. Throughout the study period, key efficiency indicators highlighted the significant effort involved: 67% of the target population was screened, resulting in a presumptive yield of 5.1%, an evaluation rate of 88%, a TB yield of 3.4%, an enrolment rate of 99%, a number needed to screen (NNS) of 658, and a number needed to test (NNT) of 30. Quarterly trends showed steady improvements in screening rates, which increased from 34% in the second quarter of 2022 (April – June 2022) to a peak of 84% in the first quarter of 2023 (January – March 2023), before declining to 65% in the last quarter of 2023 (October – December 2023). The presumptive yield was highest initially in the second quarter of 2022 (April – June 2022) at 18.3%, then fluctuated between 7.3% in the third quarter of 2022 (July – September 2022) and 4.7% in the last quarter of 2023 (October – December 2023). Evaluation rates dropped from 99% in the second quarter of 2022 (April – June 202) to 86% in the fourth quarter of 2023 (October – December 2023), while treatment enrolment mostly remained at 100%, slightly decreasing from 100% in the second quarter of 2022 (April – June 2022) to 99% in the last quarter of 2023 (October – December 2023). The NNS and NNT increased from 215 in the second quarter of 2022 (April – June 2022) to 742 in the fourth quarter of 2023 (October – December 2023), with the NNT decreasing from 39 in the second quarter of 2022 (April – June 2022) to 30 in the last quarter of 2023 (October – December 2023). Table 2 PQE intervention efficiency cascade Period Targeted population Persons screened n (screening rate - %) Presumed to have TB n (presumptive yield - %) Evaluated for TB n (evaluation rate - %) TB patients n (TB yield - %) TB patients on treatment n (TB yield - %) NNS NNT Apr - Jun (2022) 25035 8606 (34) 1575 (18.3) 1551 (99) 40 (2.6) 40 (100) 215 39 Jul - Sep (2022) 156074 74693 (48) 5424 (7.3) 3900 (72) 142 (3.6) 135 (95) 526 27 Oct - Dec (2022) 130731 83914 (64) 3213 (3.8) 3096 (96) 108 (3.5) 106 (98) 805 29 Jan - Mar (2023) 118170 99393 (84) 3594 (3.6) 3466 (96) 132 (3.8) 132 (100) 753 26 Apr - Jun (2023) 113830 84662 (74) 4084 (4.8) 3893 (95) 145 (3.7) 145 (100) 584 27 Jul - Sep (2023) 131631 100037 (76) 5499 (5.5) 4722 (85) 140 (3) 140 (100) 715 34 Oct - Dec (2023) 150803 97297 (65) 4593 (4.7) 3953 (86) 134 (3.4) 133 (99) 742 32 Total (2022–2023) 832658 553762 (67) 28327 (5.1) 24941 (88) 842 (3.4) 832 (99) 658 30 Restricting the analysis to the intervention period (January 2020 – December 2023), with 320 observations (160 intervention, 160 non-intervention), the intervention facilities had a mean TB case notification of 23.0, compared to 13.2 for non-intervention facilities. The mean difference was 9.81 with a t of 1.86, and a marginally significant p-value of approximately 0.063 (Table 3 ). Table 3 Difference in mean notified cases between intervention and non-intervention facilities (January 2022 – December 2023). Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] t p-value Intervention 160 23.0 4.7 59.6 13.7–32.3 1.9 0.063 Non-intervention 160 13.2 2.3 29.6 8.6–17.8 combined 320 18.1 2.6 47.2 12.9–23.3 Diff + 9.8 5.3 -0.5–20.2 Figure 2 shows the contribution of the OPD PQE intervention to TB case notifications. Overall, the quarterly trend of notified TB cases at the intervention facilities showed a 41% decrease, from 584 cases in the first quarter of the intervention year (January – March 2022) to 347 in the last quarter of the intervention (October – December 2023). In contrast, TB cases notified at the OPD (the intervention sites) increased from zero at the start of the intervention to 135 in the second quarter of 2022, then dropped to 106 in the following quarter. It remained higher during the first two quarters of 2023 before declining in the remaining two quarters of the intervention period (Fig. 2 ) . Additionally, the quarterly percentage contribution of the PQE intervention to TB notifications at these facilities increased from 7% (April - June 2022) to 39% (July – September 2023), then decreased slightly by only 1% to 38% in the final quarter of the intervention period (Fig. 2 ). Also, the contribution percentage dropped from 26% in the third quarter of 2022 to 22% between October and December, before rising again to 33% in the subsequent quarter. Figure 3 visually shows the trends in TB case notifications over three years, comparing overall TB notifications from the intervention and non-intervention sites. The graph clearly displays a consistent divergence between the two groups, with intervention facilities generally reporting more notifications than non-intervention sites throughout most of the specified period. Table 4 compares the "number needed to test" (NNT) to diagnose a single TB case among presumptive patients. The results show that the mean NNT was significantly higher at intervention facilities (Mean = 461.6, Std. Dev. = 93.0) than at non-intervention facilities (Mean = 264.1, Std. Dev. = 51.1). The extremely small p-value (0.0001) indicates that intervention sites needed to test more presumptive patients to diagnose one TB case. Table 4 The results of paired t-tests for the number needed to test to diagnose tuberculosis cases among presumptive cases. Variable Obs. Mean Std. Err. Std. Dev. P value [95% Conf. Interval] Intervention facilities 8 461.6 33.0 93.0 383.9–539.4 Non-intervention facilities 8 264.1 18.1 51.1 221.4–306.8 Combined 16 362.9 31.3 125.1 0.0001 296.2–429.5 Discussion This study found that deploying ad-hoc staff in the outpatient department of high-volume health facilities can increase the number of reported TB cases. Intervention facilities consistently reported higher notified counts than non-intervention facilities. However, key cascade efficiency indicators, such as screening rate, presumptive TB yield, and TB yield, remained low, while NNS and NNT were unacceptably high. The intervention, which involved engaging ad-hoc staff at the OPD, contributed, on average, 23% of the total TB cases reported from the participating facilities. Additionally, the quarterly average of notified TB cases increased significantly in facilities where ad-hoc staff were employed compared to those without such staff, with a marginally significant p-value, suggesting a likely positive effect of the intervention. However, TB notification steadily declined in intervention facilities starting from the third quarter of 2022; in contrast, it began to rise again in the second quarter of 2023 in non-intervention facilities. The study provides valuable insights into TB control efforts in Northern Nigeria, where such evidence has been scarce. Using data from approximately 832,658 outpatients across 20 health facilities, it offers a large, real-world sample that enhances the relevance of its findings for program implementation. The inclusion of a control group, though not perfect, improves the analysis by going beyond simple description. Its two-year longitudinal design allows for tracking performance trends over time, while the process evaluation offers detailed information on screening, presumptive yield, TB yield, and efficiency measures. Additionally, the use of standardized data from the Nigerian National TB program increases the credibility of its results. Despite these strengths, the study is limited by its retrospective and ecological design, the lack of adjustment in the analysis to control for potential confounders (such as facility size, staffing, and catchment population), and the non-random selection of facilities, including the introduction of newly established TB sites. These limitations introduce bias, reduce the generalizability of the findings, restrict causal inference, and prevent the analysis of individual-level factors. Additionally, the post-hoc identification of control sites and their baseline differences weakens the comparability of the groups. Furthermore, because the intervention was implemented as a package, the specific contribution of each activity cannot be distinguished. However, the study offers preliminary yet valuable evidence that deploying ad-hoc staff in high-volume facilities may help increase TB case notifications in resource-limited settings; a marginal p-value indicates results that are suggestive but not definitive at conventional thresholds. These limitations should be considered within the broader policy and programmatic landscape. Over the past decade, the Nigerian National and State TB Programs have prioritized increasing TB notifications through various strategies, including systematic screening at health facilities, as recommended by the World Health Organization [ 24 , 25 ]. One increasingly common approach has been to involve ad-hoc staff or volunteers to support facility staff with TB screening, identifying presumptive cases, and facilitating further examinations. Evidence from other settings suggests that this strategy has strong potential to boost TB notifications in high-volume health facilities at a relatively low cost [ 2 , 15 , 17 , 18 ]. Our findings reinforce this evidence, indicating that engaging ad-hoc or support staff in busy outpatient departments can significantly raise TB case notifications and reduce missed cases when well optimized [ 26 – 29 ]. The rise in case notifications likely resulted from deliberate efforts to address known bottlenecks at the facility level. Previous studies have shown that low motivation, work overload, limited TB suspicion, and the lack of coordinated systems for systematic screening hinder case detection [ 7 – 9 , 24 , 30 ]. By deploying trained ad-hoc staff to outpatient departments, assigning them specific tasks and tools, and ensuring their collaboration with facility staff, the intervention helped close some of these critical gaps. This approach builds on existing evidence that the effectiveness of ad-hoc or support staff depends on creating an environment that enables them to contribute effectively to TB service delivery [ 25 , 28 , 31 – 34 ]. However, the increase in TB cases linked to the intervention of ad-hoc staff is difficult to interpret due to potential shifts in reporting. These findings suggest caution in drawing conclusions from the results. They highlight the need for a more rigorous, prospective study. Ideally, a cluster-randomized trial with predefined controls, individual-level data, and enhanced monitoring systems should be used to establish causality and refine the intervention model for scale-up. However, unlike many other studies, the efficiency indicators in this intervention performed poorly, falling short of expected standards, suggesting weaknesses in monitoring and implementation fidelity. While TB notifications increased during the first two quarters, they gradually declined in the subsequent quarters (Fig. 2 ), highlighting challenges related to sustainability and quality assurance. If key efficiency indicators such as presumptive TB yield, TB yield, NNS, and NNT had been maintained at optimal levels, the number of reported cases could have been substantially higher. This finding underscores the need to strengthen monitoring systems for both ad-hoc and routine staff to ensure consistent TB service delivery [ 34 – 36 ]. Evidence from other settings, where efficiency cascade indicators performed strongly [ 10 , 26 , 27 , 31 ], further emphasizes their direct link to case notification and overall quality of TB care. Conducting a dedicated study to better understand this relationship would be valuable for guiding future interventions. The findings have important implications for TB policy and program implementation in Nigeria. To ensure sustainability, the National and State TB Programs should formally incorporate the ad-hoc staff model into strategic and operational plans, supported by dedicated government and donor funding. Creating national guidelines to standardize recruitment, training, deployment, supervision, and remuneration will be essential for ensuring and maintaining quality and equity across facilities. Programmatically, ad-hoc staff should be prioritized for high-volume facilities, where their contributions are most impactful, and they must receive comprehensive training and necessary tools to perform efficiently. Clearly defined roles and an emphasis on teamwork with permanent staff will further enhance their effectiveness. Secondly, strengthening monitoring and quality improvement is equally important. The national reporting system should be updated to include key efficiency indicators such as screening rates, presumptive yield, NNS, and NNT, allowing program managers to make decisions based on real-time data. Supportive supervision should be institutionalized, combining mentorship with data-driven problem-solving to address challenges, such as the declining yields observed later in the intervention. Additional operational research is needed to determine the optimal ratio of ad-hoc staff to patient load, assess cost-effectiveness within the Nigerian context, and explore broader health system benefits, such as reducing nurse workload and increasing patient satisfaction. Conclusion Our study provides essential, context-specific data from a high-burden setting in Nigeria. It demonstrated that deploying ad-hoc staff positively impacted TB notification rates and strengthened the care cascade. Our findings indicate that ad-hoc staff can play a crucial role in improving TB screening and notification in busy facilities. For this model to reach its full potential, it needs to shift from a project-based approach to a sustainably funded and strategically integrated component of Nigeria’s national TB control program, with an emphasis on training, role clarity, teamwork, and ongoing performance monitoring. Declarations Acknowledgments Professor Morenike Oluwatoyin Folayan from the Faculty of Dentistry at Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria, is especially recognized for her significant contribution in reviewing the draft of this work. The authors thank all participants who contributed to this study. We also appreciate the facility and ad-hoc staff who collected the data, the Kano State TBL Control Programme for implementing this intervention, the National TB and Leprosy Control Programme for overseeing and monitoring it, and the Global Fund for providing funding. Authors’ Contributions , JK, EO, IAU, KS, CS, ZS, SA, BA, ALY, and KAO contributed to the study's concept and design; JK, SA, KAO, IAU, KS, CS, ZS, SA, and EO wrote the main manuscript; JK, EO, CS, ZS, and ALY conducted data analysis; JK, EO, and KAO performed statistical analysis and data visualization; JK, EO, IAU, OO, AOK, BA, KS, CS, ZS, SA, BA, ALY, and KAO reviewed the initial draft; JK, EO, OO, AOK, BA, and KAO interpreted the results; and all authors critically reviewed the manuscript for significant intellectual content. Funding Nigeria received funding from the Global Fund through its grant to combat TB, supporting the PQE initiative, which incorporates quality improvement measures into OPD screening. However, the funders had no role in conducting this study, including its design, data collection and analysis, publication decisions, or manuscript preparation. Availability of data and materials All relevant data are included in the paper and the supporting information files. The datasets used for variables are available from the Kano State Ministry of Health, Tuberculosis and Leprosy Control Program, Kano, Nigeria, and from the corresponding author upon reasonable request. Additional details about the databases used for this study are available from Nigeria’s National TB and Leprosy Control Program website [37]. Ethics approval and consent to participate Ethical approval was granted by the Kano State Ministry of Health's Health Research Ethics Committee in Kano, Nigeria (reference number SHREC/2025/6071), with approval number NHREC/17/03/2018. No consent was obtained for this study because the design did not involve direct contact with human subjects, interviews, or collection of biological specimens, as approved by the Kano State Ministry of Health's Health Research Ethics Committee. Additionally, the research design adhered to the principles of the Helsinki Declaration, which safeguard the well-being of survey participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References World Health Organization. Implementing the End TB Strategy: The Essentials. 2015. https://doi.org/10.1017/CBO9781107415324.004. World Health Organization (WHO). Global Tuberculosis Report. Geneva; 2023. World Health Organization. Global Tuberculosis Report. Geneva; 2021. World Health Organization. Global Tuberculosis Report. Geneva; 2024. World Health Organization. Global Tuberculosis Report 2017. Geneva; 2017. World Health Organization. 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PLoS Med. 2020;17:e1003218. https://doi.org/10.1371/JOURNAL.PMED.1003218. National Population Commission (NPC). Nigeria Population Projections and Demographic Indicators. 2020. Federal Ministry of Health Nigeria. Nigeria Health Facility Registry. https://www.hfr.health.gov.ng/statistics/tables. Accessed 30 Oct 2024. Adamu AL, Gadanya MA, Abubakar IS, Jibo AM, Bello MM, Gajida AU, et al. High mortality among tuberculosis patients on treatment in Nigeria: A retrospective cohort study. BMC Infect Dis. 2017;17. https://doi.org/10.1186/S12879-017-2249-4. World Health Organization (WHO). WHO operational handbook on tuberculosis. Module 2: screening - systematic screening for tuberculosis disease. 2021. National TB and Leprosy Control Programme Nigeria. Compendium of Best Practices in Tuberculosis Case Finding in Nigeria 2018–2021. 2022. https://ntblcp.org.ng/resources/compendium-of-best-practices-in-tuberculosis-case-finding-in-nigeria-2018-2021/. Accessed 7 Sep 2024. Kagujje Id M, Chilukutu L, Somwe P, Mutale Id 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. 2020. https://doi.org/10.1371/journal.pone.0237931. Kakinda M, Matovu JKB. A yield and cost comparison of tuberculosis contact investigation and intensified case finding in Uganda. 2020. https://doi.org/10.1371/journal.pone.0234418. Okello D, Kisakye AN, Namakula J, Buryegyeya E. Implementation of intensified tuberculosis case finding among people living with HIV in Kampala, Uganda: a cross sectional study. Journal of Interventional Epidemiology and Public Health. 2021;4. https://doi.org/10.37432/jieph.2021.4.4.46. Osei E, Akweongo P, Binka F. Factors associated with DELAY in diagnosis among tuberculosis patients in Hohoe Municipality, Ghana. BMC Public Health. 2015;15. https://doi.org/10.1186/S12889-015-1922-Z. Oga-Omenka C, Zarowsky C, Agbaje A, Kuye J, Menzies D. Rates and timeliness of treatment initiation among drug-resistant tuberculosis patients in Nigeria- A retrospective cohort study. PLoS One. 2019;14:e0215542. https://doi.org/10.1371/JOURNAL.PONE.0215542. Vo LNQ, Codlin AJ, Forse RJ, Nguyen NT, Vu TN, Le GT, et al. Evaluating the yield of systematic screening for tuberculosis among three priority groups in Ho Chi Minh City, Viet Nam. Infect Dis Poverty. 2020;9. https://doi.org/10.1186/s40249-020-00766-4. Shete PB, Haguma P, Miller CR, Ochom E, Ayakaka I, Davis JL, et al. Pathways and costs of care for patients with tuberculosis symptoms in rural Uganda. Int J Tuberc Lung Dis. 2015;19:912–7. https://doi.org/10.5588/IJTLD.14.0166. Kazibwe A, Twinomugisha F, Musaazi J, Nakaggwa F, Lukanga D, Aleu P, et al. Comparative yield of different active TB case finding interventions in a large urban TB project in central Uganda: a descriptive study. Afr Health Sci. 2021;21:975–84. https://doi.org/10.4314/AHS.V21I3.3. Adejumo AO, Azuogu B, Okorie O, Lawal OM, Onazi OJ, Gidado M, et al. Community referral for presumptive TB in Nigeria: A comparison of four models of active case finding. BMC Public Health. 2016;16. https://doi.org/10.1186/S12889-016-2769-7. Babayi AP, Odume BB, Ogbudebe CL, Chukwuogo O, Nwokoye N, Dim CC, et al. Improving TB control: efficiencies of case-finding interventions in Nigeria. Public Health Action. 2023;13:90–6. https://doi.org/10.5588/PHA.23.0028. Broger T, Marx FM, Theron G, Marais BJ, Nicol MP, Kerkhoff AD, et al. Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests: rationale and guidance for future research. Lancet Glob Health. 2024;12:e1184–91. https://doi.org/10.1016/S2214-109X(24)00148-7/ATTACHMENT/712B732F-B5C5-44C0-A12D-49FB3C4EEAAF/MMC2.PDF. National Tuberculosis & Leprosy Control Programme. NTBLCP Website. https://ntblcp.org.ng/. Accessed 14 Sep 2025. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 18 Nov, 2025 Editor invited by journal 29 Oct, 2025 Submission checks completed at journal 28 Oct, 2025 First submitted to journal 28 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7904791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551621412,"identity":"c9fbf10b-0d61-4111-aa14-74fc57c9c551","order_by":0,"name":"Joseph Kuye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACCTDJYwMkGBsPkKIlDaSlgRQtDIfBJHFaJGfkGD74IHPebm37YaAtNTbRBLVIS+QYG87guZ287UwiUMuxtNwGQlrkJHLMpHmAWswOALUwNhwmSov5bx6ec8lm5x8SqQXoMDNmHp4DdmY3iLVFsudZseQMnuQEsxtAWxKI8YvE8eSNHz722NmbnU9/+OBDjQ1hLQwMHAYMjD0MiWCVCYSVgwD7AwaGHwz2xCkeBaNgFIyCEQkAd8ZD92v4W34AAAAASUVORK5CYII=","orcid":"","institution":"Palladium International LLC","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Kuye","suffix":""},{"id":551621414,"identity":"974d4a51-9b3f-4ab4-85ae-b4da57e09c35","order_by":1,"name":"Emmanuel Olashore","email":"","orcid":"","institution":"Palladium International LLC","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Olashore","suffix":""},{"id":551621415,"identity":"225ee107-b7cc-4e16-a029-ed5aebdd1666","order_by":2,"name":"Otse Ogorry","email":"","orcid":"","institution":"Palladium International LLC","correspondingAuthor":false,"prefix":"","firstName":"Otse","middleName":"","lastName":"Ogorry","suffix":""},{"id":551621416,"identity":"32d0cdd5-b426-4898-8d6a-e8d534e0bfa2","order_by":3,"name":"Airaoje O. 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08:34:22","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120613,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7904791/v1/8a851534351a17a5d1b81589.html"},{"id":97128339,"identity":"4e5cd85e-1877-4c92-8776-c96bb81f721f","added_by":"auto","created_at":"2025-12-01 08:34:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":603942,"visible":true,"origin":"","legend":"\u003cp\u003eSelected Local Government Areas (LGAs) of Kano State\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7904791/v1/861e1157ea23edc9e9b78cd3.png"},{"id":97142047,"identity":"f2e8079b-0660-4b2f-8f1f-a49bc514c4da","added_by":"auto","created_at":"2025-12-01 10:07:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContribution of TB notification from PQE intervention to the health facilities, 2022 -2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7904791/v1/f796d22e45a28d81b34d611b.png"},{"id":97128335,"identity":"7f1a66b2-db45-468a-acfd-b97f17c05e88","added_by":"auto","created_at":"2025-12-01 08:34:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparing TB case notification between intervention and non-intervention sites, 2021 -2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7904791/v1/f765514aeb483781b49d2c9c.png"},{"id":97145272,"identity":"ce1d2784-2704-446b-bbd4-628271717261","added_by":"auto","created_at":"2025-12-01 10:13:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1836965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7904791/v1/48cefd56-32a0-496e-8f67-929ecc57ea42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A retrospective cross-sectional study on the effectiveness of using ad-hoc staff in outpatient departments to increase tuberculosis case notification","fulltext":[{"header":"Background","content":"\u003cp\u003eReducing the global incidence of tuberculosis (TB) remains a key goal of TB control efforts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, progress has been slower than expected. By 2022, only an 8.7% reduction in TB incidence was achieved, falling far short of the 50% target set by the WHO End TB Strategy 2025 milestones [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2022, approximately 10.6\u0026nbsp;million people had TB, and 1.3\u0026nbsp;million died from the disease, making it the second leading cause of death from a single infectious organism after COVID-19 that year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As efforts to control TB worldwide begin to recover from disruptions caused by COVID-19, 7.5\u0026nbsp;million new TB cases were diagnosed in 2022, the highest number recorded since WHO started global TB monitoring in 1995 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Continuing this progress is crucial for reducing TB incidence and reaching the WHO End TB Strategy 2025 milestones.\u003c/p\u003e\u003cp\u003eThe decrease in TB incidence is closely linked to more consistent TB case notification. Although the global gap between incident and notified cases narrowed to 3.1\u0026nbsp;million in 2022, low TB notifications continued to pose a challenge, especially in Nigeria [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], where treatment coverage increased from 24% in 2016 to 74% in 2023, demonstrating the country\u0026rsquo;s potential to reach the end TB milestone by 2030 [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The number of people diagnosed and started on TB treatment rose to 367,250 in 2023 from 135,800 in 2020 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nigeria\u0026rsquo;s TB control efforts have achieved notable results, but the notification gap persists, underscoring the need for more innovative strategies to increase TB notification.\u003c/p\u003e\u003cp\u003eCurrently, Nigeria\u0026rsquo;s health workforce size and capacity to deliver quality TB services are inadequate [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The gap in TB notifications could be bridged if temporary staff are effectively mobilized to support facility-level TB efforts. Scaling up the facility-based TB case finding (intensified case finding) strategy marked a significant shift in Nigeria\u0026rsquo;s surge in TB case notifications [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Temporary staff have effectively assisted with patient triaging, screening, identification [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], referral [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and specimen transportation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] in many settings. Understanding how engaging this cadre of health workers has contributed to case notification can provide Nigeria with insights on how to sustain its current progress toward achieving the WHO End TB Strategy 2025 milestones.\u003c/p\u003e\u003cp\u003eSeveral studies have examined how trained temporary staff and volunteers can enhance TB service delivery at both facility and community levels, particularly among vulnerable groups like migrant slum dwellers [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, only a few have specifically reported the benefits of using such support at the facility level [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and there are no reports from northern Nigeria. Additionally, research on facility-based case-finding strategies in Northern Nigeria has primarily focused on their effects on patient yield and cost-effectiveness, with limited information on how involving ad-hoc staff in healthcare facilities impacts TB case notifications and the overall quality of TB care [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aims to fill the current knowledge gap regarding how ad-hoc staff contribute to TB case notification at the facility level, particularly in northern Nigeria. It aims to assess the impact of involving ad-hoc staff on TB notification rates in health facilities where intensified TB case-finding interventions were implemented in Kano State, Nigeria, from January 2022 to December 2023.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy setting\u003c/h2\u003e\u003cp\u003eThe study was conducted in 40 out of 1,296 health facilities providing TB services across 19 Local Government Areas (LGAs) of Kano State (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), one of Nigeria\u0026rsquo;s six states in the northwest geopolitical zone. These facilities are in 19 of the 44 LGAs in the state, primarily inhabited by the Hausa and Fulani, who are predominantly Muslim, with an estimated population of 15,462,178 in 2022 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Kano has 1,486 health facilities [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], of which 1,296 (87%) offer TB services, including prevention, diagnosis, treatment, and care. The 40 selected facilities, in addition to providing other healthcare services, focus on TB screening, diagnosis, treatment, and care for all ages; patients visiting these facilities often come from various parts of the state [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These facilities represent all three levels of Nigeria\u0026rsquo;s healthcare system (tertiary [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], secondary [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and primary [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] ) and serve children, adults, indigenous people, and non-indigenous people.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe selected 40 health facilities have a high OPD attendance rate. Health facilities with high OPD attendance rates have significantly contributed to Nigeria\u0026rsquo;s annual increase in TB notifications over the past three years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population includes all individuals who attended the 40 selected health facilities in Kano State, Nigeria, between January 2022 and December 2023. This included 832,658 OPD attendees who were the target for tuberculosis (TB) screening under the intervention involving ad-hoc staff.\u003c/p\u003e\n\u003ch3\u003eStudy procedure\u003c/h3\u003e\n\u003cp\u003eThe 40 health facilities were divided into two equal groups, each comprising 20 facilities: an intervention group and a control group. The intervention was integrated into routine cascade care and combined with quality improvement measures. The data collected spanned from January 2022 to December 2023. To ensure data quality, the purpose of data collection was clearly defined, as outlined in the objectives, and aligned with the research questions and planned analysis. Since this is a secondary data analysis, the credibility of the data source was carefully assessed. The data source was the quarterly reporting form of the Kano State Tuberculosis and Leprosy Control Program (KSTBLCP). Additionally, ethical procedures were followed, and the accuracy, completeness, and consistency of the data were verified, including cross-validation with multiple sources. Data cleaning techniques (such as handling missing values through imputation, correcting duplicates, and standardizing formats) were applied to ensure the data used for analysis were reliable.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThis study was a retrospective review of existing program data. The researchers did not collect new data; instead, they extracted and analyzed information already gathered during routine operations. Data were collected from facilities between January 2020 and December 2023, and from the OPD (intervention site) between January 2022 and December 2023. As part of their standard responsibilities, ad hoc and facility staff collected case-based data along the TB care cascade for every patient visiting the OPD. This information was recorded using the standardized Nigerian National Tuberculosis and Leprosy Control Programme (NTBLCP) OPD Screening Reporting tool. Individual patient data were compiled at each facility on a quarterly basis. These compiled reports were then submitted through the national TB program's official data flow, which includes reporting, management, and validation systems.\u003c/p\u003e\u003cp\u003eThe study gathered and examined variables mainly representing process indicators from the TB care cascade, along with outcome measures related to case notification. Since the data source consisted of aggregated program data, individual patient demographic or clinical characteristics were not available for analysis. Data was collected for the previous year (2021) before the intervention to demonstrate that the pre-intervention TB notification by the facilities was relatively similar.\u003c/p\u003e\u003cp\u003eThe primary outcome variable was TB case notification, defined as the number of tuberculosis cases diagnosed and officially reported to the national program. This was measured as the quarterly number of notified TB cases and used to compare intervention and non-intervention sites.\u003c/p\u003e\u003cp\u003eProcess and performance indicators were derived from the efficiency cascade of the TB screening intervention conducted by ad-hoc staff. These included OPD attendance, representing the target population; screening rate, reflecting the proportion of OPD attendees screened for TB symptoms; presumptive TB yield, defined as the proportion of screened individuals identified with symptoms suggestive of TB; and evaluation rate, measuring the proportion of presumptive TB patients who underwent diagnostic testing. Additional indicators included TB yield, or the proportion of evaluated patients diagnosed with TB; treatment enrolment rate, indicating the proportion of diagnosed patients who started treatment; the number needed to screen (NNS), which quantifies how many people need to be screened to detect one TB case; and the number needed to test (NNT), which measures how many presumptive TB patients must be tested to confirm one case.\u003c/p\u003e\u003cp\u003eThe primary independent variable was the engagement of ad-hoc staff, measured as a binary variable. Facilities where ad-hoc staff supported TB screening and case finding in outpatient departments formed the intervention group, while those operating under routine conditions without such support served as the control group. Additionally, time served was used as a comparison variable, with quarterly data analyzed from the first quarter (Q1) of 2022 through the fourth quarter (Q4) of 2023 to evaluate trends during the intervention period. Independent variables such as age, sex, occupation, type of TB, and HIV status were neither collected nor analyzed, since these factors were not part of the national TB program\u0026rsquo;s aggregated reporting template. This represents a key limitation of the study.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eDescriptive analysis focused on efficiency cascade indicators for the TB screening intervention, calculating and presenting totals and proportions for key performance metrics. These included the screening rate, presumptive yield, evaluation rate, TB yield, and enrolment rate, as well as the number needed to screen (NNS) and the number needed to test (NNT) to identify one TB case.\u003c/p\u003e\u003cp\u003eTrend analysis was used to examine changes over time. The quarterly number of TB cases notified specifically through the outpatient department (OPD) intervention was plotted to visualize trends, while the percentage contribution of the ad-hoc staff intervention to the facilities\u0026rsquo; total TB notifications was calculated for each quarter.\u003c/p\u003e\u003cp\u003eThe comparative analysis further explored differences by graphically comparing overall TB case notification trends between 20 intervention facilities and 20 matched non-intervention facilities within the same Local Government Areas (LGAs) over the period from 2019 to 2023. The non-intervention facilities were carefully chosen because their case notifications in the year before the intervention (2021) were within 10\u0026ndash;15% of those reported by the intervention sites. An unpaired t-test was performed to assess whether the quarterly average number of TB cases notified by the intervention facilities significantly differed from that of the non-intervention facilities during the two-year intervention period, with a significance level set at 0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEthical considerations\u003c/h2\u003e\u003cp\u003e This study was reviewed and approved by the Kano State Ministry of Health's Health Research Ethics Committee in Kano, Nigeria (reference number SHREC/2025/6071), with approval number NHREC/17/03/2018. Additionally, approval was obtained from the Kano State TB, Leprosy, and Buruli Ulcer Control Program. The design did not involve direct contact with human subjects, as no interviews were conducted and no biological specimens were collected. Data collection involved pooling de-identified program data gathered during routine standard care across all facilities; therefore, no consent was obtained for this study. Nonetheless, the study was conducted with careful consideration, ensuring compliance with all necessary research ethics. The design of this research adhered to the principles of the Helsinki Declaration, which safeguard the well-being of survey participants.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 640 facility-period observations from Kano State were analyzed, evenly divided between pre-intervention (n\u0026thinsp;=\u0026thinsp;320) and intervention (n\u0026thinsp;=\u0026thinsp;320) periods. Of these, 328 (51.3%) are from intervention facilities, and 312 (48.7%) are from non-intervention facilities (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Mean presumptive TB cases were 206.5 (95% CI: 169.5\u0026ndash;243.6), diagnosed 16.0 (95% CI: 12.5\u0026ndash;19.5), and notified 15.7 (95% CI: 12.9\u0026ndash;23.3)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Observations by Intervention Status and Period\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod (January 2020 \u0026ndash; December 2021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod (January 2022 \u0026ndash; December 2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eintervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFacility period observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFacility period observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the efficiency cascade across the intervention facilities. A total of 519,989 patients visited outpatient departments at the 20 intervention sites, with 347,787 individuals, or 66.9%, screened for TB. Among those screened, 18,190 (5.2%) were identified as presumptive TB, and 16,440 (90%) received further evaluation. This process led to 562 confirmed TB cases, representing 3.4% of all evaluated cases. Of these, 555 patients (98.8% of confirmed cases) were successfully enrolled in treatment.\u003c/p\u003e\u003cp\u003eThroughout the study period, key efficiency indicators highlighted the significant effort involved: 67% of the target population was screened, resulting in a presumptive yield of 5.1%, an evaluation rate of 88%, a TB yield of 3.4%, an enrolment rate of 99%, a number needed to screen (NNS) of 658, and a number needed to test (NNT) of 30. Quarterly trends showed steady improvements in screening rates, which increased from 34% in the second quarter of 2022 (April \u0026ndash; June 2022) to a peak of 84% in the first quarter of 2023 (January \u0026ndash; March 2023), before declining to 65% in the last quarter of 2023 (October \u0026ndash; December 2023). The presumptive yield was highest initially in the second quarter of 2022 (April \u0026ndash; June 2022) at 18.3%, then fluctuated between 7.3% in the third quarter of 2022 (July \u0026ndash; September 2022) and 4.7% in the last quarter of 2023 (October \u0026ndash; December 2023). Evaluation rates dropped from 99% in the second quarter of 2022 (April \u0026ndash; June 202) to 86% in the fourth quarter of 2023 (October \u0026ndash; December 2023), while treatment enrolment mostly remained at 100%, slightly decreasing from 100% in the second quarter of 2022 (April \u0026ndash; June 2022) to 99% in the last quarter of 2023 (October \u0026ndash; December 2023). The NNS and NNT increased from 215 in the second quarter of 2022 (April \u0026ndash; June 2022) to 742 in the fourth quarter of 2023 (October \u0026ndash; December 2023), with the NNT decreasing from 39 in the second quarter of 2022 (April \u0026ndash; June 2022) to 30 in the last quarter of 2023 (October \u0026ndash; December 2023).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePQE intervention efficiency cascade\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTargeted population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePersons screened\u003c/p\u003e\u003cp\u003en (screening rate - %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresumed \u003c/p\u003e\u003cp\u003eto have TB\u003c/p\u003e\u003cp\u003en (presumptive yield - %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEvaluated for TB \u003c/p\u003e\u003cp\u003en (evaluation rate - %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTB patients \u003c/p\u003e\u003cp\u003en (TB yield - %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTB patients on treatment\u003c/p\u003e\u003cp\u003en (TB yield - %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNNS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNNT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApr - Jun (2022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8606 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1575 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1551 (99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJul - Sep (2022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74693 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5424 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3900 (72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e142 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e135 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOct - Dec (2022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e130731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83914 (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3213 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3096 (96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e108 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e106 (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJan - Mar (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e118170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99393 (84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3594 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3466 (96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e132 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e132 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApr - Jun (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84662 (74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4084 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3893 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e145 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e145 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJul - Sep (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100037 (76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5499 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4722 (85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e140 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOct - Dec (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97297 (65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4593 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3953 (86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e134 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e133 (99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal (2022\u0026ndash;2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e832658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e553762 (67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28327 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24941 (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e842 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e832 (99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRestricting the analysis to the intervention period (January 2020 \u0026ndash; December 2023), with 320 observations (160 intervention, 160 non-intervention), the intervention facilities had a mean TB case notification of 23.0, compared to 13.2 for non-intervention facilities. The mean difference was 9.81 with a t of 1.86, and a marginally significant p-value of approximately 0.063 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifference in mean notified cases between intervention and non-intervention facilities (January 2022 \u0026ndash; December 2023).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[95% Conf. Interval]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.7\u0026ndash;32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.6\u0026ndash;17.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.9\u0026ndash;23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.5\u0026ndash;20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the contribution of the OPD PQE intervention to TB case notifications. Overall, the quarterly trend of notified TB cases at the intervention facilities showed a 41% decrease, from 584 cases in the first quarter of the intervention year (January \u0026ndash; March 2022) to 347 in the last quarter of the intervention (October \u0026ndash; December 2023). In contrast, TB cases notified at the OPD (the intervention sites) increased from zero at the start of the intervention to 135 in the second quarter of 2022, then dropped to 106 in the following quarter. It remained higher during the first two quarters of 2023 before declining in the remaining two quarters of the intervention period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Additionally, the quarterly percentage contribution of the PQE intervention to TB notifications at these facilities increased from 7% (April - June 2022) to 39% (July \u0026ndash; September 2023), then decreased slightly by only 1% to 38% in the final quarter of the intervention period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Also, the contribution percentage dropped from 26% in the third quarter of 2022 to 22% between October and December, before rising again to 33% in the subsequent quarter.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e visually shows the trends in TB case notifications over three years, comparing overall TB notifications from the intervention and non-intervention sites. The graph clearly displays a consistent divergence between the two groups, with intervention facilities generally reporting more notifications than non-intervention sites throughout most of the specified period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e compares the \"number needed to test\" (NNT) to diagnose a single TB case among presumptive patients. The results show that the mean NNT was significantly higher at intervention facilities (Mean\u0026thinsp;=\u0026thinsp;461.6, Std. Dev. = 93.0) than at non-intervention facilities (Mean\u0026thinsp;=\u0026thinsp;264.1, Std. Dev. = 51.1). The extremely small p-value (0.0001) indicates that intervention sites needed to test more presumptive patients to diagnose one TB case.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of paired t-tests for the number needed to test to diagnose tuberculosis cases among presumptive cases.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[95% Conf. Interval]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention facilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e461.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e383.9\u0026ndash;539.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-intervention facilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e264.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e221.4\u0026ndash;306.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e362.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e125.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e296.2\u0026ndash;429.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that deploying ad-hoc staff in the outpatient department of high-volume health facilities can increase the number of reported TB cases. Intervention facilities consistently reported higher notified counts than non-intervention facilities. However, key cascade efficiency indicators, such as screening rate, presumptive TB yield, and TB yield, remained low, while NNS and NNT were unacceptably high. The intervention, which involved engaging ad-hoc staff at the OPD, contributed, on average, 23% of the total TB cases reported from the participating facilities. Additionally, the quarterly average of notified TB cases increased significantly in facilities where ad-hoc staff were employed compared to those without such staff, with a marginally significant p-value, suggesting a likely positive effect of the intervention. However, TB notification steadily declined in intervention facilities starting from the third quarter of 2022; in contrast, it began to rise again in the second quarter of 2023 in non-intervention facilities.\u003c/p\u003e\u003cp\u003eThe study provides valuable insights into TB control efforts in Northern Nigeria, where such evidence has been scarce. Using data from approximately 832,658 outpatients across 20 health facilities, it offers a large, real-world sample that enhances the relevance of its findings for program implementation. The inclusion of a control group, though not perfect, improves the analysis by going beyond simple description. Its two-year longitudinal design allows for tracking performance trends over time, while the process evaluation offers detailed information on screening, presumptive yield, TB yield, and efficiency measures. Additionally, the use of standardized data from the Nigerian National TB program increases the credibility of its results.\u003c/p\u003e\u003cp\u003eDespite these strengths, the study is limited by its retrospective and ecological design, the lack of adjustment in the analysis to control for potential confounders (such as facility size, staffing, and catchment population), and the non-random selection of facilities, including the introduction of newly established TB sites. These limitations introduce bias, reduce the generalizability of the findings, restrict causal inference, and prevent the analysis of individual-level factors. Additionally, the post-hoc identification of control sites and their baseline differences weakens the comparability of the groups. Furthermore, because the intervention was implemented as a package, the specific contribution of each activity cannot be distinguished. However, the study offers preliminary yet valuable evidence that deploying ad-hoc staff in high-volume facilities may help increase TB case notifications in resource-limited settings; a marginal p-value indicates results that are suggestive but not definitive at conventional thresholds.\u003c/p\u003e\u003cp\u003eThese limitations should be considered within the broader policy and programmatic landscape. Over the past decade, the Nigerian National and State TB Programs have prioritized increasing TB notifications through various strategies, including systematic screening at health facilities, as recommended by the World Health Organization [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. One increasingly common approach has been to involve ad-hoc staff or volunteers to support facility staff with TB screening, identifying presumptive cases, and facilitating further examinations. Evidence from other settings suggests that this strategy has strong potential to boost TB notifications in high-volume health facilities at a relatively low cost [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our findings reinforce this evidence, indicating that engaging ad-hoc or support staff in busy outpatient departments can significantly raise TB case notifications and reduce missed cases when well optimized [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe rise in case notifications likely resulted from deliberate efforts to address known bottlenecks at the facility level. Previous studies have shown that low motivation, work overload, limited TB suspicion, and the lack of coordinated systems for systematic screening hinder case detection [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. By deploying trained ad-hoc staff to outpatient departments, assigning them specific tasks and tools, and ensuring their collaboration with facility staff, the intervention helped close some of these critical gaps. This approach builds on existing evidence that the effectiveness of ad-hoc or support staff depends on creating an environment that enables them to contribute effectively to TB service delivery [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, the increase in TB cases linked to the intervention of ad-hoc staff is difficult to interpret due to potential shifts in reporting. These findings suggest caution in drawing conclusions from the results. They highlight the need for a more rigorous, prospective study. Ideally, a cluster-randomized trial with predefined controls, individual-level data, and enhanced monitoring systems should be used to establish causality and refine the intervention model for scale-up.\u003c/p\u003e\u003cp\u003eHowever, unlike many other studies, the efficiency indicators in this intervention performed poorly, falling short of expected standards, suggesting weaknesses in monitoring and implementation fidelity. While TB notifications increased during the first two quarters, they gradually declined in the subsequent quarters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), highlighting challenges related to sustainability and quality assurance. If key efficiency indicators such as presumptive TB yield, TB yield, NNS, and NNT had been maintained at optimal levels, the number of reported cases could have been substantially higher. This finding underscores the need to strengthen monitoring systems for both ad-hoc and routine staff to ensure consistent TB service delivery [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Evidence from other settings, where efficiency cascade indicators performed strongly [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], further emphasizes their direct link to case notification and overall quality of TB care. Conducting a dedicated study to better understand this relationship would be valuable for guiding future interventions.\u003c/p\u003e\u003cp\u003eThe findings have important implications for TB policy and program implementation in Nigeria. To ensure sustainability, the National and State TB Programs should formally incorporate the ad-hoc staff model into strategic and operational plans, supported by dedicated government and donor funding. Creating national guidelines to standardize recruitment, training, deployment, supervision, and remuneration will be essential for ensuring and maintaining quality and equity across facilities. Programmatically, ad-hoc staff should be prioritized for high-volume facilities, where their contributions are most impactful, and they must receive comprehensive training and necessary tools to perform efficiently. Clearly defined roles and an emphasis on teamwork with permanent staff will further enhance their effectiveness.\u003c/p\u003e\u003cp\u003eSecondly, strengthening monitoring and quality improvement is equally important. The national reporting system should be updated to include key efficiency indicators such as screening rates, presumptive yield, NNS, and NNT, allowing program managers to make decisions based on real-time data. Supportive supervision should be institutionalized, combining mentorship with data-driven problem-solving to address challenges, such as the declining yields observed later in the intervention. Additional operational research is needed to determine the optimal ratio of ad-hoc staff to patient load, assess cost-effectiveness within the Nigerian context, and explore broader health system benefits, such as reducing nurse workload and increasing patient satisfaction.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides essential, context-specific data from a high-burden setting in Nigeria. It demonstrated that deploying ad-hoc staff positively impacted TB notification rates and strengthened the care cascade. Our findings indicate that ad-hoc staff can play a crucial role in improving TB screening and notification in busy facilities. For this model to reach its full potential, it needs to shift from a project-based approach to a sustainably funded and strategically integrated component of Nigeria\u0026rsquo;s national TB control program, with an emphasis on training, role clarity, teamwork, and ongoing performance monitoring.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProfessor Morenike Oluwatoyin Folayan from the Faculty of Dentistry at Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria, is especially recognized for her significant contribution in reviewing the draft of this work. The authors thank all participants who contributed to this study. We also appreciate the facility and ad-hoc staff who collected the data, the Kano State TBL Control Programme for implementing this intervention, the National TB and Leprosy Control Programme for overseeing and monitoring it, and the Global Fund for providing funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e,\u003c/p\u003e\n\u003cp\u003eJK, EO, IAU, KS, CS, ZS, SA, BA, ALY, and KAO contributed to the study\u0026apos;s concept and design; JK, SA, KAO, IAU, KS, CS, ZS, SA, and EO wrote the main manuscript; JK, EO, CS, ZS, and ALY conducted data analysis; JK, EO, and KAO performed statistical analysis and data visualization; JK, EO, IAU, OO, AOK, BA, KS, CS, ZS, SA, BA, ALY, and KAO reviewed the initial draft; JK, EO, OO, AOK, BA, and KAO interpreted the results; and all authors critically reviewed the manuscript for significant intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNigeria received funding from the Global Fund through its grant to combat TB, supporting the PQE initiative, which incorporates quality improvement measures into OPD screening. However, the funders had no role in conducting this study, including its design, data collection and analysis, publication decisions, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are included in the paper and the supporting information files. The datasets used for variables are available from the Kano State Ministry of Health, Tuberculosis and Leprosy Control Program, Kano, Nigeria, and from the corresponding author upon reasonable request. Additional details about the databases used for this study are available from Nigeria\u0026rsquo;s National TB and Leprosy Control Program website [37].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the Kano State Ministry of Health\u0026apos;s Health Research Ethics Committee in Kano, Nigeria (reference number SHREC/2025/6071), with approval number NHREC/17/03/2018. No consent was obtained for this study because the design did not involve direct contact with human subjects, interviews, or collection of biological specimens, as approved by the Kano State Ministry of Health\u0026apos;s Health Research Ethics Committee. Additionally, the research design adhered to the principles of the Helsinki Declaration, which safeguard the well-being of survey participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Implementing the End TB Strategy: The Essentials. 2015. https://doi.org/10.1017/CBO9781107415324.004.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). Global Tuberculosis Report. Geneva; 2023.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Tuberculosis Report. Geneva; 2021.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Tuberculosis Report. 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BMC Global and Public Health 2025 3:1. 2025;3:1\u0026ndash;14. https://doi.org/10.1186/S44263-025-00175-5.\u003c/li\u003e\n\u003cli\u003eHassane-Harouna S, Gils T, Decroo T, Ortu\u0026ntilde;o-Guti\u0026eacute;rrez N, Delamou A, Cherif GF, et al. Community-supported self-administered tuberculosis treatment combined with active tuberculosis screening: a pilot experience in Conakry, Guinea. Glob Health Action. 2023;16. https://doi.org/10.1080/16549716.2023.2262134.\u003c/li\u003e\n\u003cli\u003eAdamou Mana Z, Beaudou CN, Hilaire KFJ, Konso J, Ndahbove C, Waindim Y, et al. Impact of intensified tuberculosis case finding at health facilities on case notifications in Cameroon: A controlled interrupted time series analysis. PLOS Global Public Health. 2022;2:e0000301. https://doi.org/10.1371/journal.pgph.0000301.\u003c/li\u003e\n\u003cli\u003eDutta A, Pattanaik S, Choudhury R, Nanda P, Sahu S, Panigrahi R, et al. 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Am J Trop Med Hyg. 2015;92:625. https://doi.org/10.4269/AJTMH.14-0527.\u003c/li\u003e\n\u003cli\u003eOgbuabor DC. Through service providers\u0026rsquo; eyes: health systems factors affecting implementation of tuberculosis control in Enugu State, South-Eastern Nigeria. BMC Infect Dis. 2020;20:206. https://doi.org/10.1186/S12879-020-4944-9.\u003c/li\u003e\n\u003cli\u003eJohn S, Gidado M, Dahiru T, Fanning A, Codlin AJ, Creswell J. Tuberculosis among nomads in Adamawa, Nigeria: outcomes from two years of active case finding. International Journal of Tuberculosis and Lung Disease. 2015;19:463\u0026ndash;8. https://doi.org/10.5588/IJTLD.14.0679.\u003c/li\u003e\n\u003cli\u003eAbdullahi SA, Smelyanskaya M, John S, Adamu HI, Ubochioma E, Kennedy I, et al. Providing TB and HIV outreach services to internally displaced populations in Northeast Nigeria: Results of a controlled intervention study. PLoS Med. 2020;17:e1003218. https://doi.org/10.1371/JOURNAL.PMED.1003218.\u003c/li\u003e\n\u003cli\u003eNational Population Commission (NPC). Nigeria Population Projections and Demographic Indicators. 2020.\u003c/li\u003e\n\u003cli\u003eFederal Ministry of Health Nigeria. Nigeria Health Facility Registry. https://www.hfr.health.gov.ng/statistics/tables. Accessed 30 Oct 2024.\u003c/li\u003e\n\u003cli\u003eAdamu AL, Gadanya MA, Abubakar IS, Jibo AM, Bello MM, Gajida AU, et al. High mortality among tuberculosis patients on treatment in Nigeria: A retrospective cohort study. BMC Infect Dis. 2017;17. https://doi.org/10.1186/S12879-017-2249-4.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). WHO operational handbook on tuberculosis. Module 2: screening - systematic screening for tuberculosis disease. 2021.\u003c/li\u003e\n\u003cli\u003eNational TB and Leprosy Control Programme Nigeria. Compendium of Best Practices in Tuberculosis Case Finding in Nigeria 2018\u0026ndash;2021. 2022. https://ntblcp.org.ng/resources/compendium-of-best-practices-in-tuberculosis-case-finding-in-nigeria-2018-2021/. Accessed 7 Sep 2024.\u003c/li\u003e\n\u003cli\u003eKagujje Id M, Chilukutu L, Somwe P, Mutale Id 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. 2020. https://doi.org/10.1371/journal.pone.0237931.\u003c/li\u003e\n\u003cli\u003eKakinda M, Matovu JKB. A yield and cost comparison of tuberculosis contact investigation and intensified case finding in Uganda. 2020. https://doi.org/10.1371/journal.pone.0234418.\u003c/li\u003e\n\u003cli\u003eOkello D, Kisakye AN, Namakula J, Buryegyeya E. Implementation of intensified tuberculosis case finding among people living with HIV in Kampala, Uganda: a cross sectional study. Journal of Interventional Epidemiology and Public Health. 2021;4. https://doi.org/10.37432/jieph.2021.4.4.46.\u003c/li\u003e\n\u003cli\u003eOsei E, Akweongo P, Binka F. Factors associated with DELAY in diagnosis among tuberculosis patients in Hohoe Municipality, Ghana. BMC Public Health. 2015;15. https://doi.org/10.1186/S12889-015-1922-Z.\u003c/li\u003e\n\u003cli\u003eOga-Omenka C, Zarowsky C, Agbaje A, Kuye J, Menzies D. Rates and timeliness of treatment initiation among drug-resistant tuberculosis patients in Nigeria- A retrospective cohort study. PLoS One. 2019;14:e0215542. https://doi.org/10.1371/JOURNAL.PONE.0215542.\u003c/li\u003e\n\u003cli\u003eVo LNQ, Codlin AJ, Forse RJ, Nguyen NT, Vu TN, Le GT, et al. Evaluating the yield of systematic screening for tuberculosis among three priority groups in Ho Chi Minh City, Viet Nam. Infect Dis Poverty. 2020;9. https://doi.org/10.1186/s40249-020-00766-4.\u003c/li\u003e\n\u003cli\u003eShete PB, Haguma P, Miller CR, Ochom E, Ayakaka I, Davis JL, et al. Pathways and costs of care for patients with tuberculosis symptoms in rural Uganda. Int J Tuberc Lung Dis. 2015;19:912\u0026ndash;7. https://doi.org/10.5588/IJTLD.14.0166.\u003c/li\u003e\n\u003cli\u003eKazibwe A, Twinomugisha F, Musaazi J, Nakaggwa F, Lukanga D, Aleu P, et al. Comparative yield of different active TB case finding interventions in a large urban TB project in central Uganda: a descriptive study. Afr Health Sci. 2021;21:975\u0026ndash;84. https://doi.org/10.4314/AHS.V21I3.3.\u003c/li\u003e\n\u003cli\u003eAdejumo AO, Azuogu B, Okorie O, Lawal OM, Onazi OJ, Gidado M, et al. Community referral for presumptive TB in Nigeria: A comparison of four models of active case finding. BMC Public Health. 2016;16. https://doi.org/10.1186/S12889-016-2769-7.\u003c/li\u003e\n\u003cli\u003eBabayi AP, Odume BB, Ogbudebe CL, Chukwuogo O, Nwokoye N, Dim CC, et al. Improving TB control: efficiencies of case-finding interventions in Nigeria. Public Health Action. 2023;13:90\u0026ndash;6. https://doi.org/10.5588/PHA.23.0028.\u003c/li\u003e\n\u003cli\u003eBroger T, Marx FM, Theron G, Marais BJ, Nicol MP, Kerkhoff AD, et al. Diagnostic yield as an important metric for the evaluation of novel tuberculosis tests: rationale and guidance for future research. Lancet Glob Health. 2024;12:e1184\u0026ndash;91. https://doi.org/10.1016/S2214-109X(24)00148-7/ATTACHMENT/712B732F-B5C5-44C0-A12D-49FB3C4EEAAF/MMC2.PDF.\u003c/li\u003e\n\u003cli\u003eNational Tuberculosis \u0026amp; Leprosy Control Programme. NTBLCP Website. https://ntblcp.org.ng/. Accessed 14 Sep 2025.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, ad-hoc staff, Volunteers, Kano, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-7904791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7904791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe use of ad-hoc staff to boost TB notification rates is becoming more significant, and its effect has been observed in certain settings. This study aimed to assess the impact of engaging temporary staff on TB notification at health facilities where intensified TB case-finding efforts were implemented in Kano State, Nigeria, from January 2022 to December 2023.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis observational, descriptive, cross-sectional study retrospectively examined data from the Outpatient Department (OPD) TB Screening program implemented in Kano State, Nigeria. Data collection and reporting followed the national TB program's data flow and management system. Quarterly TB care cascade and key efficiency metrics were displayed in numbers and proportions to identify gaps along the TB care continuum for the intervention. An unpaired t-test was used to determine whether the quarterly average TB case notification rates differed between intervention and non-intervention sites during the study period. The significance level was set at 5%.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom January 2022 to December 2023, 832,658 people visited the OPD at the 40 health facilities selected for the PQE intervention, with a TB screening rate of 67%, a presumptive yield of 5.1%, a TB yield of 3.4%, NNS of 658, NNT of 30, and an enrolment rate of 99%. The quarterly number of TB cases notified increased from zero at the start of the intervention to 133 in the last quarter, with periods of rise and fall. The quarterly percentage contribution of the PQE intervention to TB notifications grew from 0% (January \u0026ndash; March 2022) to 38% (October \u0026ndash; December 2023). The intervention facilities had a mean TB case notification of 23.0, compared to 13.2 for non-intervention facilities, with a marginally significant p-value of approximately 0.063.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe study found that deploying ad-hoc staff to support TB service delivery in health facilities with high patient volumes can increase TB case notifications and improve overall TB services in Kano State, Nigeria. It underscores the importance of focusing on the design, implementation, and monitoring of the intervention.\u003c/p\u003e","manuscriptTitle":"A retrospective cross-sectional study on the effectiveness of using ad-hoc staff in outpatient departments to increase tuberculosis case notification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:34:17","doi":"10.21203/rs.3.rs-7904791/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-11T06:42:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241705224017816266048089422068672835334","date":"2025-12-02T19:29:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T09:49:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T12:56:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T07:19:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T02:31:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-10-29T02:28:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a82e30f-b98e-4c61-a509-1fe6484984cd","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T08:34:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:34:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7904791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7904791","identity":"rs-7904791","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}