Integrated Surveillance of Polio-negative AFP Cases for Tuberculosis: A Novel Approach in Rivers State, Nigeria

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With tuberculosis (TB) remaining a leading cause of childhood mortality and underdiagnosis persistent in Nigeria, integrating childhood TB testing into AFP case investigations offers a novel strategy for pediatric TB detection. This study assesses the yield of TB testing among polio-negative AFP cases under 15 years of age in Rivers State, Nigeria. Methods: A retrospective analysis was conducted using AFP surveillance line-list data from January 2022 to March 2025. All children under 15 years with polio-negative AFP who were tested for TB using the GeneXpert MTB RIF assay were included. Demographic and clinical variables were analyzed using descriptive statistics. Associations between TB positivity and sociodemographic factors were assessed using chi-square tests and logistic regression. Results: A total of 118 AFP cases were tested for TB. The mean age was 3.6 years (SD ± 3.25), with 76.3% of participants under the age of five and 52.5% male. Five cases (4.2%) tested positive for TB, with no significant differences in TB positivity by sex (p = 0.923) or age group (p = 1.000). Logistic regression revealed no significant predictors of TB positivity, though most TB cases were under five years and presented with limb weakness, suggesting possible extra-pulmonary TB. Conclusion: The integration of TB testing into AFP surveillance yielded a 4.2% TB detection rate among children under 15 with non-polio AFP, highlighting the potential of surveillance synergy. This innovative approach may help bridge the gap in pediatric TB detection. Policymakers should consider adopting integrated protocols to maximize surveillance platforms and strengthen early case detection in high-burden settings. AFP tuberculosis polio surveillance pediatric TB integrated disease surveillance Nigeria Rivers State case finding Figures Figure 1 Figure 2 Figure 3 Introduction Nigeria was declared free of wild poliovirus in 2020, marking a significant milestone in global public health after decades of intensive eradication efforts [ 1 ]. However, the country still faces significant challenges with other infectious diseases, notably tuberculosis (TB), which remains a leading cause of morbidity and mortality, particularly among children [ 2 ]. In 2022 alone, an estimated 1.25 million new TB cases were recorded in Nigeria, with children contributing a substantial but often underdiagnosed proportion of the burden [ 2 , 3 ]. Childhood TB in Nigeria is frequently missed due to non-specific symptoms, limited diagnostic capacity, and underreporting—challenges that persist across the health system [ 3 , 12 , 14 ]. In response, the World Health Organization (WHO) has emphasized the importance of integrating TB screening into existing public health platforms, particularly in resource-constrained settings where vertical programming may be unsustainable [ 6 , 11 ]. In Nigeria, integration has been explored through house-to-house polio vaccination campaigns, which have been leveraged to screen for TB symptoms among children under five, demonstrating feasibility and early success [ 4 ]. Such strategies align with the Global Polio Eradication Initiative’s (GPEI) 2022–2026 roadmap to maintain polio-free status while strengthening broader surveillance and health system capacity [ 8 ]. The Integrated Disease Surveillance and Response (IDSR) framework adopted by Nigeria provides an avenue for such synergy, emphasizing multi-disease surveillance through harmonized data collection and reporting [ 5 ]. Specifically, the use of Acute Flaccid Paralysis (AFP) surveillance —a cornerstone of polio eradication—to identify other conditions, such as TB, has not been systematically explored, despite the presence of overlapping clinical presentations in children [ 5 , 7 , 15 ]. Children with polio-negative AFP often present with limb weakness or neurological symptoms that may mimic extrapulmonary TB. Yet, the current surveillance system seldom pursues TB diagnostic pathways once poliovirus is excluded [ 7 , 16 ]. This represents a missed opportunity for early TB detection, especially among underserved and vulnerable populations such as children in urban slums or those with disabilities, who already face barriers to accessing care [ 9 , 10 ]. Given the persistent gaps in childhood TB detection and the high burden in subnational settings, such as Rivers State, Nigeria, a novel integrated surveillance model using polio-negative AFP case investigations to screen for TB was piloted [ 13 , 17 ]. Rivers State, with its dense population and diverse epidemiological landscape, presents an ideal setting for testing such an approach, consistent with national goals outlined in Nigeria’s TB Strategic Plan (2020–2025) and the Health Sector Performance Report (2023) [ 7 , 18 ]. This study, therefore, explores the feasibility, outcomes, and public health implications of integrating TB screening into AFP case investigations where polio has been ruled out. It offers insights into how leveraging existing surveillance infrastructure can contribute to TB control among children, in line with the WHO’s operational framework for primary health care and Nigeria’s push for integrated, people-centered services [ 19 , 20 ]. Methods Study Design and Setting This was a retrospective record review of AFP surveillance data in Rivers State, Nigeria, from January 2022 to March 2025. Rivers State is an oil-rich state in southern Nigeria with an estimated 2024 population of about 9.9 million. The state’s health system conducts routine AFP surveillance in accordance with national and WHO guidelines: any child under 15 years with a sudden onset of flaccid paralysis is reported and investigated. Two stool specimens are collected for poliovirus testing as the standard protocol; however, an additional stool sample was collected for TB testing. For this analysis, we considered only those AFP cases that ultimately tested negative for poliovirus (i.e., polio-negative cases). All such polio-negative AFP cases that were tested for TB (at the time of investigation) are included in this analysis. AFP case-finding in Nigeria involves community informants and health facility reporting. Once an AFP case is identified and stool samples are collected, the case investigators record basic demographics and clinical information on a line list. In parallel, TB screening was offered; children had one additional stool sample collected for testing by Genexpert MTB/RIF (the national diagnostic algorithm). The dataset (“TB LINE LIST-1.xlsx”) provided all AFP case investigation records for Rivers State, where TB testing was performed. Data Collection and Variables From the line-list, we extracted each case’s unique ID, Local Government Area (LGA), name (for identification only, later removed for analysis), sample type, year of onset, age (in months), sex, TB laboratory result (Positive/Negative), laboratory where tested, provisional clinical diagnosis (often describing pattern of weakness), and any comments (including drug-resistance or trace results). “POSITIVE” in the data indicated MTB detected, “NEGATIVE” indicated not detected. Many results included notes such as “MTB DETECTED” or “MTB TRACE DETECTED, RIF RESISTANCE INDETERMINATE” in the comments. Data cleaning steps included removing duplicate or incomplete entries and entries with missing key information (e.g., cases without recorded LGA or age). After cleaning, 118 valid AFP cases with TB test results remained. Age in months was converted to years for analysis (rounded to one decimal). We categorized age into two groups: 0–4 years and 5–14 years. Statistical Analysis Descriptive statistics were computed for the cohort of AFP cases. We calculated counts and proportions for categorical variables (sex, age group, LGA, year, TB result) and means or medians for continuous variables (age). The primary outcome was TB positivity among the AFP cases. We calculated the overall TB positivity rate (yield) as the number of TB-positive cases divided by the total number of tests. We examined the associations between TB positivity (yes/no) and potential predictors, including sex, age group, and year of onset. For categorical comparisons (e.g., TB status by sex or by year), we used Pearson’s chi-square test or Fisher’s exact test as appropriate (with significance at p < 0.05). For age (continuous), we used t-tests or nonparametric tests as needed. Additionally, a multivariate logistic regression model was fitted to identify independent predictors of TB positivity. The model included sex and age (in years) as covariates. Given the small number of TB-positive cases, this model had limited power but was explored for completeness. The regression yielded adjusted odds ratios (aOR) with 95% confidence intervals (CI). Likelihood ratio tests assessed model fit. All analyses were done using R (version 4.2) and Epi Info. We generated tables and figures to summarize findings. In particular, we prepared: (1) Table 1 summarizing demographics and clinical characteristics of AFP cases by TB result; (2) Table 2 with logistic regression estimates; (3) Fig. 2 illustrating the number of AFP cases tested (and TB positives) by year; and (4) Fig. 3 showing the age distribution of cases. Rivers State’s location in Nigeria is shown in Fig. 1 for geographic context. As this study involved analysis of de-identified surveillance data collected as part of routine public health activities, it did not require individual informed consent. Local regulations for programmatic data analysis did not require ethical approval. Results Between January 2022 and March 2025, 118 children under 15 years with AFP (all polio-negative) were tested for tuberculosis in Rivers State. The sample included 56 females (47.5%) and 62 males (52.5%). The mean age was 3.6 years (SD 3.25), with a median of 2.3 years (IQR 1.3–4.9). Ages ranged from 0.3 to 14.5 years. Most (90/118; 76.3%) were in the 0–4 year age group, and 28 (23.7%) were 5–14 years. A majority of cases (64%) originated from rural or peri-urban local government areas (LGAs), with the remaining cases from urban LGAs (see Table 1 ). Bonny LGA had the most significant number of cases (31, 26%), followed by Khana (20, 17%) and Degema (10, 8%); 13 of the 23 LGAs reported at least one case. Overall, 5 out of the 118 AFP cases (4.2%; 95% CI 1.6–9.6%) tested positive for TB. The annual distribution of cases and TB positives is shown in Fig. 2 . The yield by year was: 2022 (0/5, 0%), 2023 (1/50, 2.0%), 2024 (1/12, 8.3%), and 2025 (3/51, 5.9%). (Because only 3 months of 2025 data were included, the 2025 figure is interim.) These small numbers precluded formal trend testing, but the crude yield was similar across years. The five TB-positive cases originated from four different Local Government Areas (LGAs): Bonny (2 cases), Khana (1 case), Tai (1 case), and Opobo-Nkoro (1 case). Three of the five TB-positive children were female (60%). Their ages (in months) were 11, 16, 51, 56, and 151 (equivalent to roughly 0.9, 1.3, 4.3, 4.7, and 12.6 years). Thus, four of the five were under 5 years, and one was a teenager. The provisional diagnoses recorded were all related to limb weakness/paralysis (e.g. “weakness of left upper and left lower limb”, “sudden weakness of the lower limbs”). In all five cases, the laboratory reported the detection of Mycobacterium tuberculosis (some with “trace detected” and indeterminate rifampicin resistance), as noted in the comments. Table 1 (below) compares characteristics of TB-positive vs. TB-negative AFP cases. The overall male-to-female ratio was balanced, and there were no statistically significant differences by sex: 5.3% (3/57) of females were TB-positive, versus 3.3% (2/61) of males (χ² p = 0.94). By age group, 0–4-year-olds had a positivity rate of 4.7% (4/86) and 5–14-year-olds had a rate of 3.4% (1/32); this difference was not significant (p = 1.00). The mean age among TB-positive cases (58.0 months) was higher than among TB-negative cases (41.8 months), but this difference did not reach significance (t-test, p = 0.50). In short, none of the demographic variables had a statistically significant association with TB outcome (Table 1 ). The logistic regression (Table 2 ) included female sex and age (years) as predictors. Neither factor was significantly associated with TB positivity (female sex aOR 1.67, 95% CI 0.25–11.2; age aOR 1.10 per year, 95% CI 0.87–1.40). The model had limited power due to the small number of events. No other variables (e.g., year or Local Government Area) were included in the model due to the sparse data per category. Table 1 Demographic and clinical characteristics of polio-negative AFP cases, by TB test result (Rivers State, 2022–2025). Characteristic TB-negative (n = 113) TB-positive (n = 5) Total (n = 118) p-value (χ² or t) Sex (female) 54 (47.8%) 3 (60.0%) 57 (48.3%) 0.94 Age, years (mean ± SD) 3.49 ± 3.20 4.83 ± 4.09 3.61 ± 3.25 0.50 (t-test) <5 years old 82 (72.6%) 4 (80.0%) 86 (72.9%) 1.00 5–14 years 31 (27.4%) 1 (20.0%) 32 (27.1%) LGA (highest counts) – Bonny 30 (26.5%) 1 (20.0%) 31 (26.3%) – Khana 20 (17.7%) 0 20 (16.9%) – Andoni 10 (8.8%) 0 10 (8.5%) – Emohua 10 (8.8%) 0 10 (8.5%) – Port Harcourt 7 (6.2%) 0 7 (5.9%) – Provisional diagnosis – Various limb weaknesses 113 (100%) 5 (100%) 118 (100%) – Characteristics of children with AFP by TB test result. No significant differences in sex or age distribution were observed between the TB-negative and TB-positive groups (the last column shows chi-square or t-test p-values). Table 2 Logistic regression for TB positivity (N = 118). Variable Adjusted OR (95% CI) p-value Female sex 1.67 (0.25–11.2) 0.58 Age (per year) 1.10 (0.87–1.40) 0.44 Predictors of TB-positive status among AFP cases. Neither sex nor age was statistically significant, likely due to the small sample size (only 5 TB-positive cases). Discussion This evaluation of AFP surveillance data in Rivers State reveals a modest but essential yield of TB detection. By testing all polio-negative AFP cases for TB, we identified five children with active TB among 118 tested (4.2%). This demonstrates that the AFP surveillance platform, initially designed for polio surveillance, can also contribute to TB case-finding among children. Given that the polio program routinely reaches remote and underserved populations, its integration with TB activities can help close Nigeria’s pediatric TB detection gap [ 4 , 8 ]. The overall TB positivity of 4.2% in this cohort is notable. In contrast, passive TB programs rarely detect so many pediatric cases. For comparison, national TB notifications indicate that only ~ 7–9% of reported cases are in children, reflecting considerable underdiagnosis in that age group [ 3 ]. Our proactive and innovative approach found a higher proportion. Similarly, Ugwu et al. reported that integrating TB screening into polio campaigns yielded a positivity rate of ~ 11% among presumptive cases [ 4 ], affirming that combining efforts is fruitful. Although our sample was small, even a few additional pediatric TB diagnoses could justify the effort, given the high mortality of untreated TB in children [ 6 , 11 ]. No clear demographic risk factors were identified among the AFP cases with TB positivity. Our logistic analysis did not identify any significant predictors (female sex aOR 1.67, p = 0.58; older age aOR 1.10 per year, p = 0.44). The lack of strong associations likely reflects the limited statistical power, given the small number of events. Interestingly, both male and female children were affected, and TB-positives spanned an age range from infants to teenagers. The oldest child (12.6 years) had TB; this suggests older children with AFP may still be at risk. All TB cases had clinical presentations of limb weakness, underscoring that extrapulmonary TB (e.g., tuberculous meningitis or spinal TB) can mimic AFP [ 14 , 16 ]. Unfortunately, we lacked sufficient data to differentiate between pulmonary and extrapulmonary TB in these cases. However, the use of stool samples for children is nationally encouraged for detecting childhood pulmonary TB cases [ 6 ]. Our findings have practical implications. First, policy integration: National TB and polio programs should formalize collaboration. For instance, TB screening (including history, examination, and Xpert testing) could be added to the standard case investigation form for AFP cases. The fact that Nigerian IDSR guidelines already envision such synergy [ 5 ] supports this approach. Similarly, global polio strategy documents encourage the integration of polio assets into routine surveillance after eradication by extending AFP investigations to include TB and other health system gains [ 8 ]. Second, case management: TB-positive children identified through AFP surveillance should be linked to care promptly. The surveillance system should ensure that confirmed TB cases are treated under DOTS and that contact tracing is initiated. Conversely, pediatric TB cases with neurological symptoms should perhaps trigger AFP notification and polio testing to ensure comprehensive assessment [ 7 , 13 ]. Third, further research and monitoring: Although this study provides proof of concept, larger evaluations would be beneficial. Ongoing surveillance should track TB yields among AFP cases over time and possibly expand to neighboring states. It would be valuable to document outcomes (e.g., whether these children completed TB treatment, and follow-up of sequelae) to fully assess the impact [ 20 ]. Finally, our experience suggests broader applicability. Nigeria could consider integrated screening in other VPD surveillance (e.g., measles alerts, neonatal tetanus) or during vaccination campaigns, as demonstrated by Ugwu et al. [ 4 ]. Wherever the polio network reaches children, offering TB screening, especially with point-of-care tests, could identify otherwise hidden cases [ 9 , 10 ]. This aligns with Nigeria’s commitment to end TB by 2030: leveraging existing structures helps accelerate case finding and treatment [ 7 , 18 , 19 ]. Study Strengths and Limitations The primary strength of this study lies in its utilization of real-world surveillance data, which accurately reflects routine program conditions. It covers multiple years and includes essentially the entire cohort of interest in the state. However, limitations are noted. The sample size is small, particularly in terms of the number of TB cases, which limits statistical inference. The line list lacked some clinical details (e.g., contact history, BCG status) that might help interpret the findings. Also, AFP cases represent a specific subset of children (those with paralysis), so these results cannot be generalized to all children. Finally, as a descriptive study, we cannot assess cost-effectiveness or long-term outcomes. Conclusion In summary, screening for tuberculosis among children investigated for acute flaccid paralysis in Rivers State revealed a meaningful number of TB cases (4.2% positivity). This innovative use of the polio surveillance system illustrates how vertical programs can be synergized. We recommend that polio-negative AFP cases in Nigeria be routinely evaluated for TB, especially if TB symptoms are present. Such integrated surveillance can contribute to earlier TB diagnosis in children, strengthening both TB control and post-polio health systems. This model of cross-program integration may serve as an example for other regions seeking to maximize public health impact. Policy Recommendation National and state TB programs should adopt protocols to collect stool samples for GeneXpert testing from all polio-negative AFP children (under 15) in whom TB is not already ruled out. Training of surveillance officers to recognize TB signs (e.g., chronic cough, fever, weight loss) in AFP cases and fast-tracking those samples to TB labs could be implemented quickly. The polio and TB programs should coordinate data sharing so that any child identified with TB through AFP surveillance is linked to TB treatment services without delay. Further Research Continued monitoring of TB yield in AFP cases will determine if this approach consistently identifies cases. Additionally, qualitative studies could explore the feasibility and acceptability of this approach among healthcare workers. Ultimately, measuring the impact on TB case detection rates and child health outcomes will guide scale-up decisions. Abbreviations AFP Acute Flaccid Paralysis TB Tuberculosis IDSR Integrated Disease Surveillance and Response WHO World Health Organization LGA Local Government Area SD Standard Deviation IQR Interquartile Range MTB Mycobacterium tuberculosis RIF Rifampicin aOR Adjusted Odds Ratio CI Confidence Interval DOTS Directly Observed Treatment, Short-course FMoH Federal Ministry of Health NTBLCP National Tuberculosis and Leprosy Control Programme GPEI Global Polio Eradication Initiative NBS Nigeria Bureau of Statistics Declarations Ethics approval This study was conducted as part of routine public health surveillance activities and was approved by the Rivers State Ministry of Health. Ethics approval was obtained from the Rivers State Hospital Management Board Ethical Committee, with Reference Number RSHMB/RSHREC/2025/049. Verbal informed consent to participate in the study was obtained from the parents or legal guardians of all children at the time of filling out the IDSR 001 form for the suspected AFP case. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 revision) ( https://www.wma.net/policies-post/wma-declaration-of-helsinki/ ). Clinical Trial Not applicable. Consent for publication All authors consented to the publication. Competing interests The authors declare no competing interests. Conflicts of Interest The authors declare that they have no conflicts of interest. Funding No external funding supported this work. Author Contribution I.N. and V.O. conceptualized the study, coordinated data analysis, and led the manuscript writing.G.A., C.O., and B.E. contributed to the study design, surveillance coordination, and manuscript review.D.U. and P.E. assisted in data collection, validation, and interpretation of the findings.A.E. and K.O. provided technical support for laboratory data integration and contributed to the literature review.G.O. and A.O. contributed to the statistical analysis and critically revised the manuscript for intellectual content.All authors read and approved the final manuscript. Data Availability Data is provided within the manuscript or supplementary information files References World Health Organization. WHO and UNICEF congratulate Nigeria on ending wild poliovirus; call for strengthening of routine immunisation [Internet]. Geneva: WHO. 2020 [cited 2025 May 21]. 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Nwachukwu CE, Umeokonkwo CD, Ogbudebe CL, Uzochukwu BSC. Childhood tuberculosis detection and treatment in Nigeria: A call to action. Ann Trop Med Public Health. 2022;15:1–7. https://doi.org/10.36295/ASRO.2022.15116 . Musa BM, Ibrahim DA, Makarfi HS, Galadima HM, Aliyu I, Lawan AM. Outcomes of childhood tuberculosis in a tertiary centre in north-eastern Nigeria: A five-year review. Int J Mycobacteriol. 2020;9(4):408–14. https://doi.org/10.4103/ijmy.ijmy_194_20 . Nigeria Bureau of Statistics. Demographic Statistics Bulletin 2023. Abuja: NBS. 2023. Available from: https://www.nigerianstat.gov.ng Federal Ministry of Health. Nigeria Health Sector Performance Report 2023. Abuja: FMoH; 2024. World Health Organization. Operational framework for primary health care: transforming vision into action. Geneva: WHO; 2020. National Tuberculosis and Leprosy Control Programme. Annual TB Surveillance Report 2023. Abuja: NTBLCP/FMoH; 2024. Additional Declarations No competing interests reported. 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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-6864550","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492651841,"identity":"dc031989-5a4a-4f1e-bf55-6c1d0ec57874","order_by":0,"name":"Nwadiuto 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Organization","correspondingAuthor":false,"prefix":"","firstName":"Ekpoudom","middleName":"","lastName":"Ayakeme","suffix":""},{"id":492651853,"identity":"25acb78d-be89-4734-bf6b-92ebcce2bf2e","order_by":8,"name":"Olufunmilola Kolude","email":"","orcid":"","institution":"World Health Organization","correspondingAuthor":false,"prefix":"","firstName":"Olufunmilola","middleName":"","lastName":"Kolude","suffix":""},{"id":492651854,"identity":"37a8ac97-90b2-4595-b012-920b390cc83e","order_by":9,"name":"Owhonda Golden","email":"","orcid":"","institution":"Rivers State University","correspondingAuthor":false,"prefix":"","firstName":"Owhonda","middleName":"","lastName":"Golden","suffix":""},{"id":492651855,"identity":"a214d213-9851-49a7-90f9-9a6f7ddf6f7c","order_by":10,"name":"Oreh Adaeze","email":"","orcid":"","institution":"Rivers State Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Oreh","middleName":"","lastName":"Adaeze","suffix":""}],"badges":[],"createdAt":"2025-06-10 15:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6864550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6864550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88003980,"identity":"78ab2653-3b70-4bca-bd04-10e8ad6b6e04","added_by":"auto","created_at":"2025-07-31 10:34:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":291344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMap of Nigeria highlighting Rivers State (red)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6864550/v1/ca6f735cae5b2587e2e6c51a.jpeg"},{"id":88003984,"identity":"eef70c09-4c58-4881-ba43-f56cc5bd9d15","added_by":"auto","created_at":"2025-07-31 10:34:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAFP cases tested for TB by year\u003c/em\u003e. Bars show the total number of AFP cases tested each year, with the portion in green indicating TB-positive cases.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6864550/v1/52dded1fd0ca673c8dea677e.jpeg"},{"id":88005495,"identity":"4cdb00b1-5196-4bbd-8fee-2c7e3d966e8e","added_by":"auto","created_at":"2025-07-31 10:42:50","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":228981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAge distribution of AFP cases tested for TB\u003c/em\u003e. (A histogram showing the majority of cases under 5 years old, with a smaller tail into older ages.)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6864550/v1/96ffb724d6b1db38ffcd19ef.jpeg"},{"id":88006530,"identity":"a8fb847e-e455-4a92-833a-563758d81306","added_by":"auto","created_at":"2025-07-31 10:58:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1455788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6864550/v1/cd83d04f-9fbb-4cd9-8c70-b592a793ed01.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Surveillance of Polio-negative AFP Cases for Tuberculosis: A Novel Approach in Rivers State, Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNigeria was declared free of wild poliovirus in 2020, marking a significant milestone in global public health after decades of intensive eradication efforts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the country still faces significant challenges with other infectious diseases, notably tuberculosis (TB), which remains a leading cause of morbidity and mortality, particularly among children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2022 alone, an estimated 1.25\u0026nbsp;million new TB cases were recorded in Nigeria, with children contributing a substantial but often underdiagnosed proportion of the burden [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Childhood TB in Nigeria is frequently missed due to non-specific symptoms, limited diagnostic capacity, and underreporting—challenges that persist across the health system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn response, the World Health Organization (WHO) has emphasized the importance of integrating TB screening into existing public health platforms, particularly in resource-constrained settings where vertical programming may be unsustainable [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Nigeria, integration has been explored through house-to-house polio vaccination campaigns, which have been leveraged to screen for TB symptoms among children under five, demonstrating feasibility and early success [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Such strategies align with the Global Polio Eradication Initiative’s (GPEI) 2022–2026 roadmap to maintain polio-free status while strengthening broader surveillance and health system capacity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Integrated Disease Surveillance and Response (IDSR) framework adopted by Nigeria provides an avenue for such synergy, emphasizing multi-disease surveillance through harmonized data collection and reporting [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Specifically, the use of Acute Flaccid Paralysis (AFP) surveillance —a cornerstone of polio eradication—to identify other conditions, such as TB, has not been systematically explored, despite the presence of overlapping clinical presentations in children [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChildren with polio-negative AFP often present with limb weakness or neurological symptoms that may mimic extrapulmonary TB. Yet, the current surveillance system seldom pursues TB diagnostic pathways once poliovirus is excluded [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This represents a missed opportunity for early TB detection, especially among underserved and vulnerable populations such as children in urban slums or those with disabilities, who already face barriers to accessing care [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the persistent gaps in childhood TB detection and the high burden in subnational settings, such as Rivers State, Nigeria, a novel integrated surveillance model using polio-negative AFP case investigations to screen for TB was piloted [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Rivers State, with its dense population and diverse epidemiological landscape, presents an ideal setting for testing such an approach, consistent with national goals outlined in Nigeria’s TB Strategic Plan (2020–2025) and the Health Sector Performance Report (2023) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study, therefore, explores the feasibility, outcomes, and public health implications of integrating TB screening into AFP case investigations where polio has been ruled out. It offers insights into how leveraging existing surveillance infrastructure can contribute to TB control among children, in line with the WHO’s operational framework for primary health care and Nigeria’s push for integrated, people-centered services [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis was a retrospective record review of AFP surveillance data in Rivers State, Nigeria, from January 2022 to March 2025. Rivers State is an oil-rich state in southern Nigeria with an estimated 2024 population of about 9.9\u0026nbsp;million. The state’s health system conducts routine AFP surveillance in accordance with national and WHO guidelines: any child under 15 years with a sudden onset of flaccid paralysis is reported and investigated. Two stool specimens are collected for poliovirus testing as the standard protocol; however, an additional stool sample was collected for TB testing. For this analysis, we considered only those AFP cases that ultimately tested negative for poliovirus (i.e., polio-negative cases). All such polio-negative AFP cases that were tested for TB (at the time of investigation) are included in this analysis.\u003c/p\u003e\u003cp\u003eAFP case-finding in Nigeria involves community informants and health facility reporting. Once an AFP case is identified and stool samples are collected, the case investigators record basic demographics and clinical information on a line list. In parallel, TB screening was offered; children had one additional stool sample collected for testing by Genexpert MTB/RIF (the national diagnostic algorithm). The dataset (“TB LINE LIST-1.xlsx”) provided all AFP case investigation records for Rivers State, where TB testing was performed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection and Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e From the line-list, we extracted each case’s unique ID, Local Government Area (LGA), name (for identification only, later removed for analysis), sample type, year of onset, age (in months), sex, TB laboratory result (Positive/Negative), laboratory where tested, provisional clinical diagnosis (often describing pattern of weakness), and any comments (including drug-resistance or trace results). “POSITIVE” in the data indicated MTB detected, “NEGATIVE” indicated not detected. Many results included notes such as “MTB DETECTED” or “MTB TRACE DETECTED, RIF RESISTANCE INDETERMINATE” in the comments.\u003c/p\u003e\u003cp\u003eData cleaning steps included removing duplicate or incomplete entries and entries with missing key information (e.g., cases without recorded LGA or age). After cleaning, 118 valid AFP cases with TB test results remained. Age in months was converted to years for analysis (rounded to one decimal). We categorized age into two groups: 0–4 years and 5–14 years.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were computed for the cohort of AFP cases. We calculated counts and proportions for categorical variables (sex, age group, LGA, year, TB result) and means or medians for continuous variables (age). The primary outcome was TB positivity among the AFP cases. We calculated the overall TB positivity rate (yield) as the number of TB-positive cases divided by the total number of tests.\u003c/p\u003e\u003cp\u003eWe examined the associations between TB positivity (yes/no) and potential predictors, including sex, age group, and year of onset. For categorical comparisons (e.g., TB status by sex or by year), we used Pearson’s chi-square test or Fisher’s exact test as appropriate (with significance at p \u0026lt; 0.05). For age (continuous), we used t-tests or nonparametric tests as needed.\u003c/p\u003e\u003cp\u003eAdditionally, a multivariate logistic regression model was fitted to identify independent predictors of TB positivity. The model included sex and age (in years) as covariates. Given the small number of TB-positive cases, this model had limited power but was explored for completeness. The regression yielded adjusted odds ratios (aOR) with 95% confidence intervals (CI). Likelihood ratio tests assessed model fit.\u003c/p\u003e\u003cp\u003eAll analyses were done using R (version 4.2) and Epi Info. We generated tables and figures to summarize findings. In particular, we prepared: (1) Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizing demographics and clinical characteristics of AFP cases by TB result; (2) Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e with logistic regression estimates; (3) Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrating the number of AFP cases tested (and TB positives) by year; and (4) Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showing the age distribution of cases. Rivers State’s location in Nigeria is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for geographic context.\u003c/p\u003e\u003cp\u003eAs this study involved analysis of de-identified surveillance data collected as part of routine public health activities, it did not require individual informed consent. Local regulations for programmatic data analysis did not require ethical approval.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween January 2022 and March 2025, 118 children under 15 years with AFP (all polio-negative) were tested for tuberculosis in Rivers State. The sample included 56 females (47.5%) and 62 males (52.5%). The mean age was 3.6 years (SD 3.25), with a median of 2.3 years (IQR 1.3\u0026ndash;4.9). Ages ranged from 0.3 to 14.5 years. Most (90/118; 76.3%) were in the 0\u0026ndash;4 year age group, and 28 (23.7%) were 5\u0026ndash;14 years. A majority of cases (64%) originated from rural or peri-urban local government areas (LGAs), with the remaining cases from urban LGAs (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Bonny LGA had the most significant number of cases (31, 26%), followed by Khana (20, 17%) and Degema (10, 8%); 13 of the 23 LGAs reported at least one case.\u003c/p\u003e\u003cp\u003eOverall, 5 out of the 118 AFP cases (4.2%; 95% CI 1.6\u0026ndash;9.6%) tested positive for TB. The annual distribution of cases and TB positives is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The yield by year was: 2022 (0/5, 0%), 2023 (1/50, 2.0%), 2024 (1/12, 8.3%), and 2025 (3/51, 5.9%). (Because only 3 months of 2025 data were included, the 2025 figure is interim.) These small numbers precluded formal trend testing, but the crude yield was similar across years.\u003c/p\u003e\u003cp\u003eThe five TB-positive cases originated from four different Local Government Areas (LGAs): Bonny (2 cases), Khana (1 case), Tai (1 case), and Opobo-Nkoro (1 case). Three of the five TB-positive children were female (60%). Their ages (in months) were 11, 16, 51, 56, and 151 (equivalent to roughly 0.9, 1.3, 4.3, 4.7, and 12.6 years). Thus, four of the five were under 5 years, and one was a teenager. The provisional diagnoses recorded were all related to limb weakness/paralysis (e.g. \u0026ldquo;weakness of left upper and left lower limb\u0026rdquo;, \u0026ldquo;sudden weakness of the lower limbs\u0026rdquo;). In all five cases, the laboratory reported the detection of Mycobacterium tuberculosis (some with \u0026ldquo;trace detected\u0026rdquo; and indeterminate rifampicin resistance), as noted in the comments.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (below) compares characteristics of TB-positive vs. TB-negative AFP cases. The overall male-to-female ratio was balanced, and there were no statistically significant differences by sex: 5.3% (3/57) of females were TB-positive, versus 3.3% (2/61) of males (χ\u0026sup2; p\u0026thinsp;=\u0026thinsp;0.94). By age group, 0\u0026ndash;4-year-olds had a positivity rate of 4.7% (4/86) and 5\u0026ndash;14-year-olds had a rate of 3.4% (1/32); this difference was not significant (p\u0026thinsp;=\u0026thinsp;1.00). The mean age among TB-positive cases (58.0 months) was higher than among TB-negative cases (41.8 months), but this difference did not reach significance (t-test, p\u0026thinsp;=\u0026thinsp;0.50). In short, none of the demographic variables had a statistically significant association with TB outcome (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) included female sex and age (years) as predictors. Neither factor was significantly associated with TB positivity (female sex aOR 1.67, 95% CI 0.25\u0026ndash;11.2; age aOR 1.10 per year, 95% CI 0.87\u0026ndash;1.40). The model had limited power due to the small number of events. No other variables (e.g., year or Local Government Area) were included in the model due to the sparse data per category.\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\u003eDemographic and clinical characteristics of polio-negative AFP cases, by TB test result (Rivers State, 2022\u0026ndash;2025).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTB-negative (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTB-positive (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;118)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value (χ\u0026sup2; or t)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50 (t-test)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;5 years old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (72.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (80.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (72.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;14 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (27.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLGA (highest counts)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBonny\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndoni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmohua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePort Harcourt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProvisional diagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVarious limb weaknesses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\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\u003eCharacteristics of children with AFP by TB test result. No significant differences in sex or age distribution were observed between the TB-negative and TB-positive groups (the last column shows chi-square or t-test p-values).\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\u003eLogistic regression for TB positivity (N\u0026thinsp;=\u0026thinsp;118).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\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\u003eAdjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\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\u003eFemale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.67 (0.25\u0026ndash;11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (per year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10 (0.87\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\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\u003ePredictors of TB-positive status among AFP cases. Neither sex nor age was statistically significant, likely due to the small sample size (only 5 TB-positive cases).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis evaluation of AFP surveillance data in Rivers State reveals a modest but essential yield of TB detection. By testing all polio-negative AFP cases for TB, we identified five children with active TB among 118 tested (4.2%). This demonstrates that the AFP surveillance platform, initially designed for polio surveillance, can also contribute to TB case-finding among children. Given that the polio program routinely reaches remote and underserved populations, its integration with TB activities can help close Nigeria\u0026rsquo;s pediatric TB detection gap [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe overall TB positivity of 4.2% in this cohort is notable. In contrast, passive TB programs rarely detect so many pediatric cases. For comparison, national TB notifications indicate that only\u0026thinsp;~\u0026thinsp;7\u0026ndash;9% of reported cases are in children, reflecting considerable underdiagnosis in that age group [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Our proactive and innovative approach found a higher proportion. Similarly, Ugwu et al. reported that integrating TB screening into polio campaigns yielded a positivity rate of ~\u0026thinsp;11% among presumptive cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], affirming that combining efforts is fruitful. Although our sample was small, even a few additional pediatric TB diagnoses could justify the effort, given the high mortality of untreated TB in children [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNo clear demographic risk factors were identified among the AFP cases with TB positivity. Our logistic analysis did not identify any significant predictors (female sex aOR 1.67, p\u0026thinsp;=\u0026thinsp;0.58; older age aOR 1.10 per year, p\u0026thinsp;=\u0026thinsp;0.44). The lack of strong associations likely reflects the limited statistical power, given the small number of events. Interestingly, both male and female children were affected, and TB-positives spanned an age range from infants to teenagers. The oldest child (12.6 years) had TB; this suggests older children with AFP may still be at risk. All TB cases had clinical presentations of limb weakness, underscoring that extrapulmonary TB (e.g., tuberculous meningitis or spinal TB) can mimic AFP [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Unfortunately, we lacked sufficient data to differentiate between pulmonary and extrapulmonary TB in these cases. However, the use of stool samples for children is nationally encouraged for detecting childhood pulmonary TB cases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur findings have practical implications. First, policy integration: National TB and polio programs should formalize collaboration. For instance, TB screening (including history, examination, and Xpert testing) could be added to the standard case investigation form for AFP cases. The fact that Nigerian IDSR guidelines already envision such synergy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] supports this approach. Similarly, global polio strategy documents encourage the integration of polio assets into routine surveillance after eradication by extending AFP investigations to include TB and other health system gains [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, case management: TB-positive children identified through AFP surveillance should be linked to care promptly. The surveillance system should ensure that confirmed TB cases are treated under DOTS and that contact tracing is initiated. Conversely, pediatric TB cases with neurological symptoms should perhaps trigger AFP notification and polio testing to ensure comprehensive assessment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThird, further research and monitoring: Although this study provides proof of concept, larger evaluations would be beneficial. Ongoing surveillance should track TB yields among AFP cases over time and possibly expand to neighboring states. It would be valuable to document outcomes (e.g., whether these children completed TB treatment, and follow-up of sequelae) to fully assess the impact [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, our experience suggests broader applicability. Nigeria could consider integrated screening in other VPD surveillance (e.g., measles alerts, neonatal tetanus) or during vaccination campaigns, as demonstrated by Ugwu et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Wherever the polio network reaches children, offering TB screening, especially with point-of-care tests, could identify otherwise hidden cases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This aligns with Nigeria\u0026rsquo;s commitment to end TB by 2030: leveraging existing structures helps accelerate case finding and treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Strengths and Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary strength of this study lies in its utilization of real-world surveillance data, which accurately reflects routine program conditions. It covers multiple years and includes essentially the entire cohort of interest in the state. However, limitations are noted. The sample size is small, particularly in terms of the number of TB cases, which limits statistical inference. The line list lacked some clinical details (e.g., contact history, BCG status) that might help interpret the findings. Also, AFP cases represent a specific subset of children (those with paralysis), so these results cannot be generalized to all children. Finally, as a descriptive study, we cannot assess cost-effectiveness or long-term outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, screening for tuberculosis among children investigated for acute flaccid paralysis in Rivers State revealed a meaningful number of TB cases (4.2% positivity). This innovative use of the polio surveillance system illustrates how vertical programs can be synergized. We recommend that polio-negative AFP cases in Nigeria be routinely evaluated for TB, especially if TB symptoms are present. Such integrated surveillance can contribute to earlier TB diagnosis in children, strengthening both TB control and post-polio health systems. This model of cross-program integration may serve as an example for other regions seeking to maximize public health impact.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePolicy Recommendation\u003c/strong\u003e\u003cp\u003eNational and state TB programs should adopt protocols to collect stool samples for GeneXpert testing from all polio-negative AFP children (under 15) in whom TB is not already ruled out. Training of surveillance officers to recognize TB signs (e.g., chronic cough, fever, weight loss) in AFP cases and fast-tracking those samples to TB labs could be implemented quickly. The polio and TB programs should coordinate data sharing so that any child identified with TB through AFP surveillance is linked to TB treatment services without delay.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFurther Research\u003c/strong\u003e\u003cp\u003eContinued monitoring of TB yield in AFP cases will determine if this approach consistently identifies cases. Additionally, qualitative studies could explore the feasibility and acceptability of this approach among healthcare workers. Ultimately, measuring the impact on TB case detection rates and child health outcomes will guide scale-up decisions.\u003c/p\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcute Flaccid Paralysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTuberculosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntegrated Disease Surveillance and Response\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal Government Area\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIQR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterquartile Range\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMTB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMycobacterium tuberculosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRifampicin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDOTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDirectly Observed Treatment, Short-course\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMoH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFederal Ministry of Health\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNTBLCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNational Tuberculosis and Leprosy Control Programme\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal Polio Eradication Initiative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNBS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNigeria Bureau of Statistics\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":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003e This study was conducted as part of routine public health surveillance activities and was approved by the Rivers State Ministry of Health. Ethics approval was obtained from the Rivers State Hospital Management Board Ethical Committee, with Reference Number RSHMB/RSHREC/2025/049. Verbal informed consent to participate in the study was obtained from the parents or legal guardians of all children at the time of filling out the IDSR 001 form for the suspected AFP case. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 revision) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wma.net/policies-post/wma-declaration-of-helsinki/\u003c/span\u003e\u003cspan address=\"https://www.wma.net/policies-post/wma-declaration-of-helsinki/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eClinical Trial\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003e All authors consented to the publication.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo external funding supported this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.N. and V.O. conceptualized the study, coordinated data analysis, and led the manuscript writing.G.A., C.O., and B.E. contributed to the study design, surveillance coordination, and manuscript review.D.U. and P.E. assisted in data collection, validation, and interpretation of the findings.A.E. and K.O. provided technical support for laboratory data integration and contributed to the literature review.G.O. and A.O. contributed to the statistical analysis and critically revised the manuscript for intellectual content.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Abuja: FMoH; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Operational framework for primary health care: transforming vision into action. Geneva: WHO; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Tuberculosis and Leprosy Control Programme. Annual TB Surveillance Report 2023. Abuja: NTBLCP/FMoH; 2024.\u003c/span\u003e\u003c/li\u003e\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-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AFP, tuberculosis, polio surveillance, pediatric TB, integrated disease surveillance, Nigeria, Rivers State, case finding","lastPublishedDoi":"10.21203/rs.3.rs-6864550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6864550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eNigeria, certified free of wild poliovirus in 2020, continues nationwide acute flaccid paralysis (AFP) surveillance to detect potential poliovirus re-emergence. With tuberculosis (TB) remaining a leading cause of childhood mortality and underdiagnosis persistent in Nigeria, integrating childhood TB testing into AFP case investigations offers a novel strategy for pediatric TB detection. This study assesses the yield of TB testing among polio-negative AFP cases under 15 years of age in Rivers State, Nigeria.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA retrospective analysis was conducted using AFP surveillance line-list data from January 2022 to March 2025. All children under 15 years with polio-negative AFP who were tested for TB using the GeneXpert MTB RIF assay were included. Demographic and clinical variables were analyzed using descriptive statistics. Associations between TB positivity and sociodemographic factors were assessed using chi-square tests and logistic regression.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eA total of 118 AFP cases were tested for TB. The mean age was 3.6 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25), with 76.3% of participants under the age of five and 52.5% male. Five cases (4.2%) tested positive for TB, with no significant differences in TB positivity by sex (p\u0026thinsp;=\u0026thinsp;0.923) or age group (p\u0026thinsp;=\u0026thinsp;1.000). Logistic regression revealed no significant predictors of TB positivity, though most TB cases were under five years and presented with limb weakness, suggesting possible extra-pulmonary TB.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eThe integration of TB testing into AFP surveillance yielded a 4.2% TB detection rate among children under 15 with non-polio AFP, highlighting the potential of surveillance synergy. This innovative approach may help bridge the gap in pediatric TB detection. Policymakers should consider adopting integrated protocols to maximize surveillance platforms and strengthen early case detection in high-burden settings.\u003c/p\u003e","manuscriptTitle":"Integrated Surveillance of Polio-negative AFP Cases for Tuberculosis: A Novel Approach in Rivers State, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 10:34:45","doi":"10.21203/rs.3.rs-6864550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-30T13:42:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T18:02:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T00:15:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291289827637164170132912895140731879666","date":"2025-08-13T13:29:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T18:51:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312776584412095675009296527024557376040","date":"2025-08-09T14:57:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337042709852535190516268445997726481068","date":"2025-08-05T17:13:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206718403297510610120076061786797128546","date":"2025-08-03T07:18:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106864399419890944848479613047030335012","date":"2025-07-29T11:08:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T06:23:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T11:03:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-03T17:24:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-02T16:17:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-07-02T15:20:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce123d9c-4765-420a-abc1-b055c4e52a01","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T10:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 10:34:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6864550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6864550","identity":"rs-6864550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}