Ecological comparison of tuberculosis and TB HIV coinfection in postconflict Liberia and Sierra Leone as markers of health security

Ecological comparison of tuberculosis and TB HIV coinfection in postconflict Liberia and Sierra Leone as markers of health security | 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 Ecological comparison of tuberculosis and TB HIV coinfection in postconflict Liberia and Sierra Leone as markers of health security John Kwame Duah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7510274/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tuberculosis (TB) remains a significant public health concern in fragile and post-conflict settings. Liberia and Sierra Leone, which are undergoing recovery from extended periods of civil unrest, continue to experience high TB rates, including TB-HIV co-infection. Examining temporal trends and testing coverage in these countries is critical for developing integrated surveillance and response strategies. Methods This retrospective comparative analysis used publicly available data for Liberia and Sierra Leone (2000-2022) from the World Health Organization, World Bank, and ACLED. The primary outcome was the TB incidence rate (per 100,000 population). Two-way fixed-effects panel regressions with country and year fixed effects were estimated, using Driscoll-Kraay standard errors (Bartlett kernel; plug-in bandwidth). Sensitivity analyses included a linear mixed-effects regression with a country random intercept and year fixed effects, and pooled ordinary least squares with country and year fixed effects and heteroskedasticity-robust (HC3) standard errors. Covariates were TB-HIV co-infection (percent), health expenditure per capita (US dollars), GDP per capita (2015 US dollars), and conflict events (count). A subset analysis (2016-2022) examined TB-HIV testing coverage, and a supplementary model evaluated TB mortality (excluding HIV-attributable deaths) from 2000 to 2022 using the same specification. Results Regression models consistently identified negative associations between GDP per capita and TB incidence across specifications. TB-HIV co-infection was inversely associated with TB incidence in most models, though not uniformly significant. In the subset analysis, TB-HIV testing coverage showed a positive coefficient but was not statistically significant ( b = 0.087, p = 0.160). Given two country clusters (G = 2), statistical inference was interpreted cautiously . Conclusions The findings suggest that economic conditions and TB-HIV dynamics may influence TB incidence in post-conflict settings. Although TB-HIV testing coverage was not significantly associated with TB incidence in this sample, its potential role warrants further investigation. Strengthening integrated TB-HIV surveillance and tailoring interventions to country-specific contexts may support more resilient health systems in fragile environments. Tuberculosis TB-HIV co-infection post-conflict health systems Liberia Sierra Leone health security comparative analysis Figures Figure 1 1. Introduction Tuberculosis (TB) poses a disproportionate threat to public health in fragile and post-conflict settings, where systemic instability undermines disease control efforts. It remains one of the most persistent and lethal infectious diseases worldwide, causing an estimated 10.8 million new cases and 1.25 million deaths in 2023, including 161,000 among people with human immunodeficiency virus (HIV) infection [ 1 ]. Despite international initiatives, the fight to control TB has faltered [ 2 ], and progress is inconsistent [ 3 ]. In fragile and post-conflict states, under-resourced health systems [ 2 ], fragmented surveillance infrastructure [ 2 ], and limited access to diagnostic and treatment services leave populations acutely vulnerable [ 4 ]. In these environments, TB-HIV co-infection forms a critical and growing syndemic, rapidly increasing risk at individual and population levels. This intersection complicates case management and places even heightened stress on already overburdened health systems [ 5 ]. The World Health Organization (WHO) warns that without urgent, integrated TB-HIV testing and treatment strategies, especially in high-burden settings where co-infection rates are high and health system resilience is weakened, the human toll will intensify [ 6 ]. Liberia and Sierra Leone, which are two West African nations recovering from prolonged civil conflict and the devastating 2014–2016 Ebola outbreak [ 7 ], illustrate the intersection of post-conflict recovery and infectious disease vulnerability [ 7 – 9 ]. These countries have experienced significant disruptions to health service delivery [ 8 ], including TB control programs that were adversely affected during the Ebola crisis and subsequently targeted for strategic recovery [ 10 ], and both countries report persistently high TB incidence and TB-HIV co-infection rates [ 11 ]. Despite regional proximity, similar conflict histories, and comparable challenges in rebuilding health infrastructure, the TB trajectories in these two countries over the past two decades have diverged noticeably. For instance, between 2000 and 2022, Liberia’s TB incidence rose from approximately 240 to over 300 cases per 100,000 population [ 11 ], plateauing in recent years, while Sierra Leone’s incidence declined from 318 to 283 per 100,000 [ 11 ]. These contrasting patterns raise vital questions about the need to examine the influence of economic conditions, health system recovery, and programmatic integration on TB outcomes in fragile settings. The rationale for this study is informed by the need to understand how two similarly situated post-conflict countries have managed dual TB-HIV burdens under resource constraints. Prior research has examined the influence of structural determinants, such as gross domestic product (GDP) per capita, health expenditure, and conflict exposure on TB outcomes [ 12 – 14 ]. Despite growing interest, few studies have examined these relationships across fragile states using comparative designs that incorporate temporal trend analysis. Moreover, while TB-HIV testing coverage has been identified as a key intervention point [ 15 ], empirical evidence on its association with TB incidence and mortality remains limited [16], especially in settings where surveillance systems are still being rebuilt [ 17 ]. This study employs a retrospective comparative design using publicly available WHO burden data from 2000 to 2022 to examine temporal trends in TB incidence, TB-HIV co-infection rates, and TB mortality, excluding HIV-attributable deaths in Liberia and Sierra Leone. Regression models assess the associations between TB outcomes and key covariates, including GDP per capita, health expenditure per capita, TB-HIV co-infection, and conflict events (count). A subset analysis for the period 2016–2022 evaluates whether TB-HIV testing coverage is associated with TB incidence. Integrating epidemiological data with contextual indicators, this study contributes to a growing body of evidence on TB control in fragile settings and provides insights for regional strategies to strengthen integrated disease surveillance and health system resilience. The analytical approach is anchored in three intersecting frameworks: health security and disease surveillance in fragile states, including core capacities outlined in the International Health Regulations (IHR); syndemic theory, which conceptualizes TB-HIV co-infection as a biosocial interaction that heightens vulnerability in post-conflict populations [ 5 ]; and health systems strengthening, with emphasis on intervention and system indicators such as TB-HIV testing coverage [16], health expenditure [ 14 ], and economic recovery [18]. The analysis is guided by the research question: How have TB incidence and TB-HIV co-infection dynamics evolved in Liberia and Sierra Leone between 2000 and 2022, and to what extent is TB-HIV testing coverage associated with changes in TB incidence during 2016–2022? This study evaluates three interrelated aims: (1) to describe and compare tuberculosis (TB) incidence trends in Liberia and Sierra Leone from 2000 to 2022; (2) to examine trajectories of TB-HIV co-infection rates and TB-HIV testing coverage over the study period; and (3) to assess the relationship between TB-HIV testing coverage and TB incidence during 2016–2022, and to evaluate associations between TB mortality (excluding HIV-attributable deaths) and co-infection, economic, and conflict indicators across 2000–2022. The study tests two hypotheses consistent with these aims: (H1) that TB incidence declined during 2000–2022 in both countries; and (H2) that higher TB-HIV testing coverage is associated with lower TB incidence in 2016–2022. Altogether, this approach aligns with broader calls for equity-focused, context-sensitive strategies for infectious disease control in post-conflict settings. 2. Methods 2.1 Study design and setting This retrospective comparative study uses a country-year panel dataset for Liberia and Sierra Leone from 2000 to 2022. The analytic unit is the country-year, with 46 observations for the full period and 14 for the 2016–2022 subset. 2.2 Data sources and variable definitions Tuberculosis indicators, including incidence rate, mortality excluding HIV, case notification rate, case detection rate, TB-HIV co-infection, and TB-HIV testing coverage, were obtained from the WHO Global Tuberculosis Database. GDP per capita in constant 2015 US dollars and health expenditure per capita in current US dollars were extracted from the World Bank World Development Indicators. Conflict events were aggregated to the country-year level using data from the Armed Conflict Location and Event Data Project (ACLED). The operational definitions, coding and measurement, variable type, and data sources for all study variables are presented in Table 1 . Table 1 Operational definitions, coding/measurement, variable type, and data sources Variable Operational definition Coding/Measurement Variable type Data source TB incidence rate Annual number of new and relapse TB cases per 100,000 population Numeric rate per 100,000; WHO modelled country-year estimate Continuous (rate) WHO Global Tuberculosis Report/ Global TB Database TB-HIV co-infection (%) Percentage of notified TB cases that are HIV-positive in a given year Percent (%); proportion of TB cases co-infected with HIV Continuous (percent) WHO TB-HIV Surveillance/ Global TB Database TB-HIV testing coverage (%) Percentage of notified TB cases with a documented HIV test result (known HIV status) in a given year. Percent (%); (TB cases with known HIV status ÷ notified TB cases) × 100. Continuous (percent) WHO Global TB Database (TB-HIV module). Available 2016–2022 TB mortality rate Annual TB deaths (excluding HIV-attributable) per 100,000 population Numeric rate per 100,000; WHO modelled mortality estimate Continuous (rate) WHO Global Health Estimates/ Global TB Database TB case notification rate Number of new and relapsed TB cases notified to national programs per 100,000 population Numeric rate per 100,000; derived from WHO notification data Continuous (rate) WHO TB Notification Database TB case detection rate (%) Proportion of estimated incident TB cases that were notified Percent (%); notified cases ÷ estimated incident cases × 100 Continuous (percent) WHO TB Estimates and Notification Data Year (centered) Derived year index used for descriptive summaries; not entered as a regresso r (models include year fixed effects) Calendar year minus 2000 Continuous (numeric) Derived from study period Health expenditure per capita Total current health expenditure per person US dollars per capita; annual current health spending ÷ mid-year population Continuous (currency) World Bank World Development Indicators (WDI): SH.XPD.CHEX.PC.CD) GDP per capita Gross domestic product per person Constant 2015 US dollars per capita Continuous (currency) World Bank WDI HIV prevalence (%) Prevalence of HIV among adults aged 15–49 Percent of adults aged 15–49 living with HIV Continuous (percent) World Bank WDI (WDI: SH.DYN.AIDS.ZS) Population density Number of people per square kilometer of land area People per km² of land area Continuous (rate) World Bank WDI WDI: EN.POP.DNST Urban population (%) Share of population living in urban areas Percent of total population residing in urban areas Continuous (percent) World Bank WDI SP.URB.TOTL.IN.ZS Hospital beds per 1,000 Hospital bed capacity Number of beds per 1,000 population Continuous (rate) World Bank WDI (WDI: SH.MED.BEDS.ZS) Conflict events Total number of political-violence events recorded in a country-year Integer count of all ACLED events recorded in the year Discrete (count) Armed Conflict Location and Event Data Project (ACLED); author’s countryyear aggregation Conflict fatalities Total number of deaths from political-violence events in a country-year Integer sum of ACLED fatalities in the year Discrete (count) ACLED; author’s countryyear aggregation Battles Number of ACLED events classified as Battles in a countryyear. Integer count where event_type = Battles Discrete (count) ACLED; author’s countryyear aggregation Violence against civilians Number of ACLED events classified as Violence against civilians” in a countryyear. Integer count where event_type = Violence against civilians Discrete (count) ACLED; author’s countryyear aggregation Protests Number of events classified as Protests in a country-year Integer count of events with type Protests Discrete (count) ACLED Note: All data sources cover Liberia and Sierra Leone for 2000–2022 unless otherwise specified. ACLED variables are aggregated from eventlevel records to the countryyear level for analysis. Where applicable, indicator codes from WDI are provided in parentheses for clarity. 2.3 Measures The primary outcome was the tuberculosis incidence rate, defined as the number of new and relapse cases per 100,000 population. Covariates in the incidence models included TB-HIV co-infection (percent), health expenditure per capita (US dollars), GDP per capita (2015 US dollars), and conflict events (count). TB-HIV testing coverage, defined as the percentage of notified TB cases with a documented HIV status, was available only for 2016–2022 and included in subset analyses. In addition to TB incidence, the TB mortality rate excluding HIV-attributable deaths (per 100,000 population) was analyzed over 2000–2022, using the same complete-case approach and harmonized country-year panel. 2.4 Data preparation and management Datasets were harmonized by ISO3 country code and calendar year, then merged to form a balanced two-country panel. TB-HIV testing coverage was derived as the proportion of notified TB cases with known HIV status and constrained to a valid range of 0 to 100 percent following quality checks. Complete-case analysis was applied within relevant time windows. No imputation was performed. 2.5 Statistical analysis Primary models employed two-way fixed effects with country and year indicators, estimated via the within estimator to adjust for fixed country-level attributes and year-specific shocks affecting both countries. Driscoll-Kraay heteroskedasticity- and autocorrelation-consistent standard errors (HC1; Bartlett kernel with plug-in bandwidth) were used to address serial and cross-sectional dependence. Because the panel comprises only two countries (G = 2), small-sample inference is fragile; effect sizes (coefficients) and 95% Driscoll-Kraay confidence intervals are emphasized. Where p-values are reported in the main text, they are computed using a conservative t reference distribution with degrees of freedom df = G − 1 = 1. Analyses incorporating TB-HIV testing coverage were limited to 2016–2022, and full-period models (2000–2022) excluded this variable. Sensitivity analyses included (1) a linear mixed-effects regression with a country random intercept and year fixed effects and (2) pooled ordinary least squares with country and year fixed effects and heteroskedasticity-robust (HC3) standard errors. Given the two-country panel, cluster-robust CR2 standard errors were evaluated but produced numerically unstable estimates; therefore, HC3 results are reported for the OLS robustness model. All analyses were conducted in R version 4.4.2 using plm (two-way fixed effects; Driscoll-Kraay via vcovSCC), lmtest (inference), sandwich (HC1/HC3), lme4, and lmerTest. In Supplementary Tables S1-S4, p -values follow the asymptotic normal reference; Driscoll-Kraay 95% confidence intervals can be computed as B ± 1.96 × SE from the reported standard errors. 2.6 Ethical considerations This study utilized publicly available, de-identified, aggregate data. In accordance with U.S. federal regulations (45 CFR 46.104(d)(4), the research does not involve human subjects and did not require institutional review board (IRB) approval. Clinical Trial Registration: Not applicable. This study does not involve a clinical trial. 3. Results 3.1 Descriptive statistics summary Table 2 reports means and standard deviations for Liberia (n = 23), Sierra Leone (n = 23), and the combined sample (N = 46). Liberia’s TB incidence rate averaged 287.6 per 100,000 ( SD = 23.9), compared with 307.9 ( SD = 9.9) in Sierra Leone; the overall mean was 297.7 ( SD = 20.8). TB-HIV co-infection averaged 22.1 percent ( SD = 7.9) in Liberia and 15.6 percent ( SD = 6.2) in Sierra Leone, with an overall mean of 18.9 percent ( SD = 7.8). The TB mortality rate was 84.1 per 100,000 ( SD = 12.3) in Liberia and 74.1 ( SD = 26.4) in Sierra Leone, with an overall mean of 79.1 ( SD = 21.0). Case notification rates were lower in Liberia (132.8 per 100,000; SD = 36.4) than in Sierra Leone (171.5; SD = 47.5). Case detection rates averaged 45.5 percent ( SD = 10.7) in Liberia and 56.0 percent ( SD = 16.5) in Sierra Leone. Full summary statistics for all variables appear in Table 2 . Table 2 Descriptive statistics (mean and standard deviation) of study variables for Liberia (n = 23), Sierra Leone (n = 23), and overall sample (N = 46), 2000–2022 Variable Liberia (n = 23) Sierra Leone (n = 23) Overall (n = 46) TB incidence rate (per 100,000) 287.6 (23.9) 307.9 (9.9) 297.7 (20.8) TB–HIV co-infection (%) 22.1 (7.9) 15.6 (6.2) 18.9 (7.8) TB mortality rate (per 100,000) 84.1 (12.3) 74.1 (26.4) 79.1 (21.0) TB case notification rate (per 100,000) 132.8 (36.4) 171.5 (47.5) 152.6 (46.3) TB case detection rate (%) 45.5 (10.7) 56.0 (16.5) 50.9 (14.8) Year (centered) 11.0 (6.8) 11.0 (6.8) 11.0 (6.7) Health expenditure per capita (US$) 45.2 (28.7) 48.6 (31.5) 46.9 (29.8) GDP per capita (2015 US$) 641.9 (70.4) 927.5 (124.4) 784.7 (175.6) HIV prevalence (%) 1.6 (0.4) 1.6 (0.1) 1.6 (0.3) Population density (people/km²) 43.0 (8.3) 88.8 (15.6) 65.9 (26.2) Urban population (%) 48.4 (2.7) 39.4 (2.6) 43.9 (5.2) Hospital beds per 1,000 1.1 (0.5) 0.4 (–) 1.0 (0.5) Conflict events (count) 66.0 (58.1) 57.8 (107.1) 61.9 (85.3) Conflict fatalities (count) 26.2 (66.6) 5.7 (7.7) 15.9 (48.0) Violence against civilians (count) 11.0 (10.2) 8.5 (15.4) 9.7 (12.9) Protests (count) 17.0 (17.7) 6.0 (7.9) 11.5 (14.6) TB-HIV testing coverage (%) 82.4 (10.7) 98.5 (1.1) 90.5 (11.1) Note: “Battles” is omitted because it recorded zero events in both countries from 2000 to 2022. TB-HIV testing coverage (%) is available only for 2016–2022 (n = 7 per country); all other variables use 2000–2022 (n = 23). A dash (–) indicates that the standard deviation could not be computed due to data being available for only one year. SD = standard deviation; TB = tuberculosis; HIV = human immunodeficiency virus To examine temporal trends in TB burden across the two countries, annual WHO-derived estimates of TB incidence for Liberia and Sierra Leone from 2000 to 2022 were plotted (see Fig. 1 ). As shown in Fig. 1 , Liberia’s estimated TB incidence rate increased steadily from 240.0 per 100,000 population in 2000 to a peak of 308.0 per 100,000 around 2013, followed by a plateau through 2022 ( M = 287.6, SD = 23.9). In comparison, Sierra Leone began with a higher incidence rate (approximately 305.0 per 100,000 in 2000), rose modestly to about 318.0 in 2008, and then declined to 283.0 by 2022 ( M = 307.9, SD = 9.9). The divergence in trends after 2008, with Liberia’s rates remaining relatively high and Sierra Leone’s showing a gradual decline, may reflect differences in epidemic dynamics or the timing and implementation of national TB control efforts. These temporal patterns indicate the potential advantages of customized, country-specific strategies for preventing and managing TB. 3.2 Primary regression results (2000–2022) A two-way fixed-effects model of TB incidence (per 100,000 population) was estimated with covariates TB-HIV co-infection (percent), health expenditure per capita (US dollars), GDP per capita (2015 US dollars), and conflict events (count). The specification included country and year fixed effects with Driscoll-Kraay (HC1) standard errors (Bartlett kernel with plug-in bandwidth). For 2000–2022, the estimated coefficients were: TB-HIV co-infection ( b = − 1.760, SE = 0.454; t = − 3.880; p = 0.161); health expenditure per capita ( b = 0.014, SE = 0.138; t = 0.105; p = 0.934); GDP per capita ( b = − 0.145, SE = 0.018; t = − 7.903; p = 0.080); and conflict events ( b = − 0.047, SE = 0.035; t = − 1.364; p = 0.403). None of the predictors reached statistical significance at p < 0.05 under the conservative t reference with df = 1; coefficient signs were negative for TB-HIV co-infection, GDP per capita, and conflict events, and near zero for health expenditure. Additional details are provided in Table 3 . Table 3 Two-way fixed-effects regression of TB incidence rate (per 100,000) in Liberia and Sierra Leone, 2000–2022 (Driscoll-Kraay SEs) Predictor B SE (DK) t p TB-HIV co-infection (%) −1.760 0.454 −3.880 0.161 Health expenditure per capita (US$) 0.014 0.138 0.105 0.934 GDP per capita (2015 US$) −0.145 0.018 −7.903 0.080 Conflict events (count) −0.047 0.035 −1.364 0.403 Note : Outcome is TB incidence (cases per 100,000 population). Models include country and year fixed effects. Standard errors are Driscoll-Kraay (HC1) with a Bartlett kernel and plug-in bandwidth. Sample: Liberia and Sierra Leone, 2000–2022 (N = 46 country-year observations). p values use a conservative t reference with df = 1; interpretation emphasizes coefficients and DK 95% confidence intervals, which can be computed as B ± 1.96 × SE from the reported standard errors. Supplementary Table S1 reports the same coefficients and Driscoll-Kraay (HC1) standard errors for the 2000–2022 specification; as specified in subsection 2.5 , p -values in the supplement use the asymptotic normal reference. 3.3 Subset re-estimation (2016–2022) Two-way fixed-effects models with Driscoll-Kraay standard errors were estimated for 2016–2022, with coefficients scaled per 10 percentage points for percentage variables and per $100 for monetary variables. A $100 higher GDP per capita was associated with 9.87 fewer TB cases per 100,000 population (95% CI: −11.98, − 7.76). A $100 increase in current health expenditure per capita corresponded to 8.87 more cases (95% CI: 6.68, 11.06). A 10-percentage-point increase in TB-HIV testing coverage was associated with 0.87 more cases (95% CI: 0.43, 1.31). Associations for TB-HIV co-infection (per 10 percentage points: 1.39; 95% CI: −1.81, 4.59) and conflict events (per 100 events: −0.15; 95% CI: −1.39, 1.09) were uncertain. Given G = 2, estimates are interpreted as descriptive effect sizes with Driscoll-Kraay confidence intervals; p -values are conservative and de-emphasized. Table 4 Two-way fixed-effects regression estimates for tuberculosis incidence (cases per 100,000), 2016–2022, with Driscoll-Kraay standard errors Predictor B SE (DK) t 95% CI (DK) p TB-HIV co-infection (per 10 pp) 1.390 1.632 0.852 [-1.808, 4.589] 0.551 Health expenditure per capita (per $100) 8.873 1.117 7.942 [ 6.683, 11.062] 0.080 GDP per capita, 2015 US$ (per $100) -9.870 1.076 -9.173 [-11.979, -7.762] 0.069 Conflict events (per 100 events) -0.150 0.631 -0.237 [-1.386, 1.087] 0.852 TB-HIV testing coverage (per 10 pp) 0.873 0.225 3.883 [ 0.432, 1.314] 0.160 Note: Two-way fixed-effects models with Driscoll-Kraay standard errors (HC1) were used; coefficients (B) indicate the change in TB incidence (per 100,000) for the stated unit change in each predictor (per 10 percentage points for percentage variables, per $100 for monetary variables, and per 100 events for conflict). Because the panel includes only two countries (G = 2), p -values are computed from a t reference distribution with df = 1; interpretation emphasizes effect magnitudes and Driscoll-Kraay 95% confidence intervals. For the same 2016–2022 window, a re-estimated model excluding TB-HIV testing coverage applied Driscoll-Kraay (HC1) standard errors with country and year fixed effects. Estimates were: TB-HIV co-infection (percent) b = − 0.377, SE = 0.335; health expenditure per capita (US dollars) b = 0.093, SE = 0.010; GDP per capita (2015 US dollars) b = − 0.085, SE = 0.020; and conflict events (count) b = − 0.014, SE = 0.013. Results were broadly consistent with the primary specification. A complete coefficient table for the model including TB-HIV testing coverage appears in Supplementary Table S2 ; the corresponding re-estimated model excluding testing coverage is provided in Supplementary Table S3. 3.4 Sensitivity analyses and robustness checks A linear mixed-effects model with year fixed effects and country-level random intercepts yielded patterns consistent with the primary specification. GDP per capita (2015 US dollars) was inversely associated with TB incidence ( b = − 0.140, SE = 0.030; t = − 4.695). TB-HIV co-infection (percent) was likewise negative ( b = − 1.831, SE = 0.639; t = − 2.867). Health expenditure per capita and conflict events showed effects near zero in magnitude. Pooled ordinary least squares with country and year fixed effects and heteroskedasticity-robust (HC3) standard errors also aligned with the primary model: TB-HIV co-infection (percent) was negative ( b = − 1.760, SE = 0.784; t = − 2.245) and GDP per capita (2015 US dollars) remained inversely associated ( b = − 0.145, SE = 0.051; t = − 2.865). Health expenditure per capita and conflict events were small and imprecise ( t = 0.057 and − 0.374, respectively). Because these robustness models use different error structures than the two-way fixed-effects estimator with Driscoll-Kraay standard errors, inference is treated as descriptive; primary conclusions rely on the fixed-effects estimates reported above (see Tables 3 – 4 ). For clarity, the OLS specification conditions on country and year indicators, so its coefficient estimates match those in Table 3 ; differences in standard errors, test statistics, and p -values reflect the use of HC3 here versus Driscoll-Kraay (HC1) with a conservative t (df = 1) reference in the main tables. Table 4 reports a distinct 2016–2022 specification that includes TB-HIV testing coverage and uses rescaled units (per 10 percentage points for percentages, per $100 for monetary measures, and per 100 events for conflict), and is therefore not directly comparable to the full-period robustness estimates. 3.5 Supplementary mortality model (2000–2022) Using the same two-way fixed-effects specification to examine associations between the TB mortality rate (excluding HIV attributable deaths), per 100,000 population, and selected covariates for Liberia and Sierra Leone from 2000 to 2022 (N = 46 country-year observations), TB-HIV co-infection (percent) was negatively associated with TB mortality ( b = − 2.138, SE = 0.377, t = − 5.665, p < 0.001). Health expenditure per capita (US dollars) was not statistically significant ( b = 0.101, SE = 0.128, t = 0.792, p = 0.438). GDP per capita (2015 US dollars) showed an inverse association ( b = − 0.075, SE = 0.014, t = − 5.437, p < 0.001). Conflict events (count) were negatively associated with TB mortality ( b = − 0.041, SE = 0.018, t = − 2.245, p = 0.038). Models included country and year fixed effects; inference employed Driscoll-Kraay (HC1) standard errors with a Bartlett kernel and plug-in bandwidth (see Table S4). 3.6 Synthesis of findings Across specifications, higher GDP per capita was consistently associated with lower TB incidence, and was also inversely associated with TB mortality (excluding HIV-attributable deaths). TB-HIV co-infection (percent) was negatively associated with TB incidence in the full-period model and in the 2016–2022 re-estimation without testing coverage, but was small, positive, and not statistically significant when testing coverage was included. Conflict events were inversely associated with TB mortality (excluding HIV-attributable deaths). The findings are broadly consistent across methods and time windows. Statistical significance is interpreted cautiously, given the small number of country clusters (G = 2). 4. Discussion This comparative analysis examined TB burden in Liberia and Sierra Leone from 2000 to 2022. Both countries showed divergent epidemiological trajectories despite common post-conflict challenges. For instance, Liberia experienced a sustained increase in TB incidence, while Sierra Leone showed a gradual decline. These patterns suggest that national recovery strategies and health system performance may have influenced TB outcomes. However, causality cannot be inferred from the current design. Across all specifications, GDP per capita was broadly consistent and inversely associated with TB incidence and mortality (excluding HIV-attributable deaths). This finding aligns with prior research, which indicates that economic recovery improves health outcomes by enhancing access to care, diagnostic capacity, and treatment adherence [ 2 ]. TB-HIV co-infection rates were also negatively associated with TB incidence and mortality in most models. While this may seem counterintuitive, it likely reflects enhanced surveillance and case detection among co-infected individuals, not a protective effect. This interpretation should be considered carefully, given the limited number of country clusters and possible measurement variability in co-infection estimates. 4.1 Testing coverage and surveillance limitations The subset analysis (2016–2022) identified no statistically significant association between TB-HIV testing coverage and TB incidence. Although the coefficient was positive, the lack of significance suggests that increased testing alone may not be enough to reduce TB incidence in the short term. Importantly, increases in testing can also elevate reported incidence via improved case ascertainment, even if true transmission is unchanged. This finding is consistent with past literature, which emphasizes that testing must be integrated with treatment and follow-up services to have population-level impact [ 15 ]. In fragile settings, surveillance systems often experience underreporting and incomplete data capture, which can weaken observed associations [ 8 ]. 4.2 Conflict exposure and health outcomes Conflict events were not significantly associated with TB incidence in most models. However, the supplementary analysis (see Table S4) found a negative association with TB mortality. This result should be interpreted cautiously. It may reflect reporting artifacts or shifts in mortality attribution during periods of instability. Prior studies have found that indirect effects of conflict, such as displacement and service disruption, can exacerbate TB burden, even if direct associations are difficult to quantify [ 7 ]. 4.3 Implications for health system strengthening The study findings highlight the value of customizing TB control strategies to country-specific contexts. While economic recovery appears to support reductions in TB burden, integrated TB-HIV surveillance and testing must be partnered with sustained investments in health infrastructure to achieve health gains. Additionally, the divergence in TB trends between Liberia and Sierra Leone suggests that programmatic implementation and system resilience may play a critical role. Future interventions should prioritize strengthening diagnostic and treatment linkages for the co-infected population. Furthermore, expanding surveillance capacity to improve data completeness and reliability must be supported. Importantly, embedding TB-HIV services within broader health system recovery frameworks must be adequately funded. Altogether, these priorities align with the IHR core capacities and syndemic theory, which emphasize the co-occurring health risks in post-conflict populations [ 5 ]. 4.4 Study limitations and future research This study has several limitations worth noting. First, it relies on publicly available surveillance datasets, which may be subject to measurement error, especially in conflict-affected environments where surveillance systems are often disrupted or incomplete. Second, although the analysis employs panel regression models with Driscoll-Kraay standard errors to address serial and cross-sectional dependence and heteroskedasticity across country-year observations, the small number of country clusters (G = 2) means that statistical inference should be interpreted cautiously. Third, the study does not claim causal attribution. Observed associations are interpreted within the study’s design and available data. These limitations do not compromise the internal consistency of the findings. However, they warrant caution in generalizing the results to other post-conflict settings. Future research could extend this framework to a broader set of countries to assess the robustness of observed patterns and examine context-specific mechanisms underlying TB-HIV dynamics. Such comparative work may help inform equity-focused strategies in controlling infectious diseases in fragile health systems. 5. Conclusion This study demonstrates the methodological utility of panel regression techniques for evaluating TB trends across fragile health systems. The divergent trajectories observed in Liberia and Sierra Leone reflect the complex interplay between economic indicators, co-infection dynamics, and surveillance capacity. While GDP per capita broadly correlated with reduced TB burden, the lack of a statistically significant association between TB-HIV testing coverage and incidence in the 2016–2022 subset suggests the need for more nuanced metrics of integration effectiveness. Moreover, the analytic framework presented in this study provides a replicable model for assessing infectious disease control in post-conflict environments. Future research should extend this approach to additional contexts, prioritizing equity, system resilience, and sustained investment in public health infrastructure. Abbreviations ACLED Armed Conflict Location and Event Data CFR Code of Federal Regulations CI Confidence interval CR2 Cluster-robust standard errors (small-sample correction), clustered by country DK Driscoll-Kraay (standard errors) df degrees of freedom GDP Gross Domestic Product HC1 Heteroskedasticity-consistent standard errors HC3 Heteroskedasticity-consistent standard errors (type 3) HIV Human Immunodeficiency Virus IHR International Health Regulations IRB Institutional Review Board M Mean OLS Ordinary Least Squares p p-value SD Standard deviation TB Tuberculosis t t-statistic TWFE Two-way fixed effects WHO World Health Organization Declarations Supplementary Information Analysis_TB_TBHIV_Liberia_SierraLeone_2000_2022.R Supplementary Tables S1-S4 Author contributions JKD conceptualized the study, developed the methodology, curated and analyzed the data, conducted validation and visualization, prepared the original draft, reviewed and edited the manuscript, and managed overall project administration . Funding This study was conducted independently, without any financial support from government agencies, commercial entities, or non-profit organizations. Data availability All data supporting the conclusions of this article are included in the supplementary materials Ethics approval and consent to participate This study used publicly available, de-identified, aggregate data. In accordance with U.S. federal regulations (45 CFR 46.104(d)(4)), the research does not involve human subjects and does not require institutional review board (IRB) approval. Consent for publication Not applicable. No individual patient data was included in this manuscript. Competing interests The author declares no competing interests. References World Health Organization. Tuberculosis [Internet], Geneva WHO. 2025 Mar 14 [cited 2025 Aug 17]. Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis Reid MJA, Arinaminpathy N, Bloom A, Bloom BR. Building a tuberculosis-free world: The Lancet Commission on tuberculosis. Lancet. 2019;393(10178):1331–84. https://doi.org/10.1016/S0140-6736(19)30024-8 . Lönnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: The role of risk factors and social determinants. Soc Sci Med. 2009;68(12):2240–6. https://doi.org/10.1016/j.socscimed.2009.03.041 . Sossauer L, Schindler M, Hurst S. Vulnerability identified in clinical practice: A qualitative analysis. BMC Medical Ethics [Internet]. 2019;20(1). Available from: https://bmcmedethics.biomedcentral.com/articles/ 10.1186/s12910-019-0416-4 Singer M, Bulled N, Ostrach B, Mendenhall E. Syndemics and the biosocial conception of health. Lancet. 2017;389(10072):941–50. https://doi.org/10.1016/S0140-6736(17)30003-X . World Health Organization. WHO consolidated guidelines on tuberculosis: Module 2: Screening – systematic screening for tuberculosis disease. Geneva: WHO. 2021. Available from: https://www.who.int/publications/i/item/9789240022676 Abramowitz SA, McLean KE, McKune SL, Bardosh KL, Fallah M, Monger J, et al. Community-centered responses to Ebola in urban Liberia: The view from below. PLoS Negl Trop Dis. 2015;9(4):e0003706. https://doi.org/10.1371/journal.pntd.0003706 . Elston JWT, Cartwright C, Ndumbi P, Wright J. The health impact of the 2014–15 Ebola outbreak. Public Health. 2017;143:60–70. https://doi.org/10.1016/j.puhe.2016.10.020 . Bolkan HA, Bash-Taqi DA, Samai M, Gerdin M, Wibe T, von Schreeb J. Ebola and indirect effects on health service function in Sierra Leone. PLoS Curr. 2014. https://doi.org/10.1371/currents.outbreaks.0307d588df619f9c9447f8ead5b72b2d . 6. Stop TB, Partnership, Project Services. Global Plan to End TB 2016–2020: The Paradigm Shift. Geneva: United Nations Office for ; 2015. Available from: https://www.stoptb.org/news/africa-region-ministers-health-approve-new-regional-framework-ending-tb-and-endorse-global World Health Organization. Global Health Observatory data repository: TB incidence. Geneva: WHO. 2023. Available from: https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/tuberculosis Dye C, Williams BG. The population dynamics and control of tuberculosis. Science. 2010;328(5980):856–61. https://doi.org/10.1126/science.1185449 . Cegielski JP, McMurray DN. The relationship between malnutrition and tuberculosis: evidence from studies in humans and experimental animals. Int J Tuberc Lung Dis. 2004;8(3):286–98. PMID: 15139466. World Bank. World Development Indicators, Washington DC. World Bank; 2023. Available from: https://databank.worldbank.org/source/world-development-indicators Salomon A, Law S, Johnson C, Baddeley A, Rangaraj A, Singh S Interventions to improve linkage along the HIV-tuberculosis care cascades in low- and middle-income countries: A systematic review and meta-analysis. Dida GO, WHO News Release, editors. PLOS ONE. 2022;17(5):e0267511. World Health Organization. WHO launches updated guidance on HIV-associated TB. 2024 May 16 .https://www.who.int/news/item/16-05-2024-who-launches-updated-guidance-on-hiv-associated-tb Hermans S, Horsburgh CR Jr., Wood R. A Century of Tuberculosis Epidemiology in the Northern and Southern Hemisphere: The Differential Impact of Control Interventions. PLoS ONE. 2015;10(8):e0135179. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0135179 . International Monetary Fund (IMF). Poorest Countries and Fragile States Are Increasingly Falling Behind. 2025 Jun 26. Available from: https://www.imf.org/en/Blogs/Articles/2025/06/26/poorest-countries-and-fragile-states-are-increasingly-falling-behind Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialsTablesS1S4.docx analysisTBTBHIVLiberiaSierraLeone20002022.R.docx SupplementaryMaterialsTablesS1S4.pdf Cite Share Download PDF Status: Posted Version 1 posted 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-7510274","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517989550,"identity":"c8c7c6dd-2c0f-474c-b6e8-2c95069756bc","order_by":0,"name":"John Kwame 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09:48:30","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122499,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7510274/v1/6154a4ee5a27d3d2779deabe.html"},{"id":91838600,"identity":"08ea3381-756b-4f70-b2b0-4308e8efd039","added_by":"auto","created_at":"2025-09-22 09:48:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157053,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual tuberculosis incidence rates in Liberia and Sierra Leone, 2000–2022. Estimates derived from the WHO Global Tuberculosis Database; 23 annual observations per country.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7510274/v1/b277b5aa879587c14d9ba8c2.jpeg"},{"id":92568797,"identity":"5f0b3966-62a4-4a87-a162-8b9eccc8bbda","added_by":"auto","created_at":"2025-10-01 07:17:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271170,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510274/v1/d5d58522-d952-40d5-aee4-ebe1a27c8709.pdf"},{"id":91838597,"identity":"9cc4b100-ea8a-418f-9da5-736357b099ba","added_by":"auto","created_at":"2025-09-22 09:48:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":34713,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsTablesS1S4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7510274/v1/d8841500b71e610a973834c0.docx"},{"id":91838602,"identity":"f41ccbfa-f12f-419b-ac15-73dbba88d3e8","added_by":"auto","created_at":"2025-09-22 09:48:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29752,"visible":true,"origin":"","legend":"","description":"","filename":"analysisTBTBHIVLiberiaSierraLeone20002022.R.docx","url":"https://assets-eu.researchsquare.com/files/rs-7510274/v1/3a1af6de63a1a0c792cc8572.docx"},{"id":91838603,"identity":"c77e9bc0-f1d9-4689-9883-bda1a87d4fb4","added_by":"auto","created_at":"2025-09-22 09:48:30","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":85466,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsTablesS1S4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510274/v1/129ac10065b55cd8de6fdcdf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ecological comparison of tuberculosis and TB HIV coinfection in postconflict Liberia and Sierra Leone as markers of health security","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB) poses a disproportionate threat to public health in fragile and post-conflict settings, where systemic instability undermines disease control efforts. It remains one of the most persistent and lethal infectious diseases worldwide, causing an estimated 10.8\u0026nbsp;million new cases and 1.25\u0026nbsp;million deaths in 2023, including 161,000 among people with human immunodeficiency virus (HIV) infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite international initiatives, the fight to control TB has faltered [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and progress is inconsistent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In fragile and post-conflict states, under-resourced health systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], fragmented surveillance infrastructure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and limited access to diagnostic and treatment services leave populations acutely vulnerable [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In these environments, TB-HIV co-infection forms a critical and growing syndemic, rapidly increasing risk at individual and population levels. This intersection complicates case management and places even heightened stress on already overburdened health systems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The World Health Organization (WHO) warns that without urgent, integrated TB-HIV testing and treatment strategies, especially in high-burden settings where co-infection rates are high and health system resilience is weakened, the human toll will intensify [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLiberia and Sierra Leone, which are two West African nations recovering from prolonged civil conflict and the devastating 2014\u0026ndash;2016 Ebola outbreak [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], illustrate the intersection of post-conflict recovery and infectious disease vulnerability [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These countries have experienced significant disruptions to health service delivery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], including TB control programs that were adversely affected during the Ebola crisis and subsequently targeted for strategic recovery [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and both countries report persistently high TB incidence and TB-HIV co-infection rates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite regional proximity, similar conflict histories, and comparable challenges in rebuilding health infrastructure, the TB trajectories in these two countries over the past two decades have diverged noticeably. For instance, between 2000 and 2022, Liberia\u0026rsquo;s TB incidence rose from approximately 240 to over 300 cases per 100,000 population [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], plateauing in recent years, while Sierra Leone\u0026rsquo;s incidence declined from 318 to 283 per 100,000 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These contrasting patterns raise vital questions about the need to examine the influence of economic conditions, health system recovery, and programmatic integration on TB outcomes in fragile settings.\u003c/p\u003e\u003cp\u003eThe rationale for this study is informed by the need to understand how two similarly situated post-conflict countries have managed dual TB-HIV burdens under resource constraints. Prior research has examined the influence of structural determinants, such as gross domestic product (GDP) per capita, health expenditure, and conflict exposure on TB outcomes [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite growing interest, few studies have examined these relationships across fragile states using comparative designs that incorporate temporal trend analysis. Moreover, while TB-HIV testing coverage has been identified as a key intervention point [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], empirical evidence on its association with TB incidence and mortality remains limited [16], especially in settings where surveillance systems are still being rebuilt [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study employs a retrospective comparative design using publicly available WHO burden data from 2000 to 2022 to examine temporal trends in TB incidence, TB-HIV co-infection rates, and TB mortality, excluding HIV-attributable deaths in Liberia and Sierra Leone. Regression models assess the associations between TB outcomes and key covariates, including GDP per capita, health expenditure per capita, TB-HIV co-infection, and conflict events (count). A subset analysis for the period 2016\u0026ndash;2022 evaluates whether TB-HIV testing coverage is associated with TB incidence.\u003c/p\u003e\u003cp\u003eIntegrating epidemiological data with contextual indicators, this study contributes to a growing body of evidence on TB control in fragile settings and provides insights for regional strategies to strengthen integrated disease surveillance and health system resilience. The analytical approach is anchored in three intersecting frameworks: health security and disease surveillance in fragile states, including core capacities outlined in the International Health Regulations (IHR); syndemic theory, which conceptualizes TB-HIV co-infection as a biosocial interaction that heightens vulnerability in post-conflict populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; and health systems strengthening, with emphasis on intervention and system indicators such as TB-HIV testing coverage [16], health expenditure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and economic recovery [18]. The analysis is guided by the research question: How have TB incidence and TB-HIV co-infection dynamics evolved in Liberia and Sierra Leone between 2000 and 2022, and to what extent is TB-HIV testing coverage associated with changes in TB incidence during 2016\u0026ndash;2022?\u003c/p\u003e\u003cp\u003eThis study evaluates three interrelated aims: (1) to describe and compare tuberculosis (TB) incidence trends in Liberia and Sierra Leone from 2000 to 2022; (2) to examine trajectories of TB-HIV co-infection rates and TB-HIV testing coverage over the study period; and (3) to assess the relationship between TB-HIV testing coverage and TB incidence during 2016\u0026ndash;2022, and to evaluate associations between TB mortality (excluding HIV-attributable deaths) and co-infection, economic, and conflict indicators across 2000\u0026ndash;2022. The study tests two hypotheses consistent with these aims: (H1) that TB incidence declined during 2000\u0026ndash;2022 in both countries; and (H2) that higher TB-HIV testing coverage is associated with lower TB incidence in 2016\u0026ndash;2022. Altogether, this approach aligns with broader calls for equity-focused, context-sensitive strategies for infectious disease control in post-conflict settings.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and setting\u003c/h2\u003e\u003cp\u003eThis retrospective comparative study uses a country-year panel dataset for Liberia and Sierra Leone from 2000 to 2022. The analytic unit is the country-year, with 46 observations for the full period and 14 for the 2016\u0026ndash;2022 subset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data sources and variable definitions\u003c/h2\u003e\u003cp\u003eTuberculosis indicators, including incidence rate, mortality excluding HIV, case notification rate, case detection rate, TB-HIV co-infection, and TB-HIV testing coverage, were obtained from the WHO Global Tuberculosis Database. GDP per capita in constant 2015 US dollars and health expenditure per capita in current US dollars were extracted from the World Bank World Development Indicators. Conflict events were aggregated to the country-year level using data from the Armed Conflict Location and Event Data Project (ACLED). The operational definitions, coding and measurement, variable type, and data sources for all study variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eOperational definitions, coding/measurement, variable type, and data sources\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOperational definition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoding/Measurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariable type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eData source\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB incidence rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual number of new and relapse TB cases per 100,000 population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumeric rate per 100,000; WHO modelled country-year estimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (rate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHO Global Tuberculosis Report/\u003c/p\u003e\u003cp\u003eGlobal TB Database\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB-HIV co-infection (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of notified TB cases that are HIV-positive in a given year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent (%); proportion of TB cases co-infected with HIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (percent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHO TB-HIV Surveillance/ Global TB Database\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB-HIV testing coverage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of notified TB cases with a documented HIV test result (known HIV status) in a given year.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent (%); (TB cases with known HIV status\u0026thinsp;\u0026divide;\u0026thinsp;notified TB cases) \u0026times; 100.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (percent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHO Global TB Database (TB-HIV module). Available 2016\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB mortality rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual TB deaths (excluding HIV-attributable) per 100,000 population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumeric rate per 100,000; WHO modelled mortality estimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (rate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHO Global Health Estimates/\u003c/p\u003e\u003cp\u003eGlobal TB Database\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB case notification rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of new and relapsed TB cases notified to national programs per 100,000 population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumeric rate per 100,000; derived from WHO notification data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (rate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHO TB Notification Database\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB case detection rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion of estimated incident TB cases that were notified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent (%); notified cases\u0026thinsp;\u0026divide;\u0026thinsp;estimated incident cases \u0026times; 100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (percent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHO TB Estimates and Notification Data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear (centered)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDerived year index used for descriptive summaries; not entered as a regresso\u003cb\u003er\u003c/b\u003e (models include year fixed effects)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCalendar year minus 2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (numeric)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDerived from study period\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth expenditure per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal current health expenditure per person\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUS dollars per capita; annual current health spending\u0026thinsp;\u0026divide;\u0026thinsp;mid-year population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (currency)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorld Bank World Development Indicators (WDI):\u003c/p\u003e\u003cp\u003eSH.XPD.CHEX.PC.CD)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGross domestic product per person\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConstant 2015 US dollars per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (currency)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorld Bank WDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV prevalence (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrevalence of HIV among adults aged 15\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent of adults aged 15\u0026ndash;49 living with HIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (percent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorld Bank WDI\u003c/p\u003e\u003cp\u003e(WDI: SH.DYN.AIDS.ZS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of people per square kilometer of land area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeople per km\u0026sup2; of land area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (rate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorld Bank WDI\u003c/p\u003e\u003cp\u003eWDI: EN.POP.DNST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban population (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShare of population living in urban areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent of total population residing in urban areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (percent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorld Bank WDI\u003c/p\u003e\u003cp\u003eSP.URB.TOTL.IN.ZS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital beds per 1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospital bed capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of beds per 1,000 population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContinuous (rate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorld Bank WDI (WDI: SH.MED.BEDS.ZS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConflict events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal number of political-violence events recorded in a country-year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInteger count of all ACLED events recorded in the year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiscrete (count)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eArmed Conflict Location and Event Data Project (ACLED); author\u0026rsquo;s countryyear aggregation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConflict fatalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal number of deaths from political-violence events in a country-year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInteger sum of ACLED fatalities in the year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiscrete (count)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eACLED; author\u0026rsquo;s countryyear aggregation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBattles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of ACLED events classified as Battles in a countryyear.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInteger count where event_type\u0026thinsp;=\u0026thinsp;Battles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiscrete (count)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eACLED; author\u0026rsquo;s countryyear aggregation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViolence against civilians\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of ACLED events classified as Violence against civilians\u0026rdquo; in a countryyear.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInteger count where event_type\u0026thinsp;=\u0026thinsp;Violence against civilians\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiscrete (count)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eACLED; author\u0026rsquo;s countryyear aggregation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of events classified as Protests in a country-year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInteger count of events with type Protests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiscrete (count)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eACLED\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: All data sources cover Liberia and Sierra Leone for 2000\u0026ndash;2022 unless otherwise specified. ACLED variables are aggregated from eventlevel records to the countryyear level for analysis. Where applicable, indicator codes from WDI are provided in parentheses for clarity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Measures\u003c/h2\u003e\u003cp\u003eThe primary outcome was the tuberculosis incidence rate, defined as the number of new and relapse cases per 100,000 population. Covariates in the incidence models included TB-HIV co-infection (percent), health expenditure per capita (US dollars), GDP per capita (2015 US dollars), and conflict events (count). TB-HIV testing coverage, defined as the percentage of notified TB cases with a documented HIV status, was available only for 2016\u0026ndash;2022 and included in subset analyses. In addition to TB incidence, the TB mortality rate excluding HIV-attributable deaths (per 100,000 population) was analyzed over 2000\u0026ndash;2022, using the same complete-case approach and harmonized country-year panel.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data preparation and management\u003c/h2\u003e\u003cp\u003eDatasets were harmonized by ISO3 country code and calendar year, then merged to form a balanced two-country panel. TB-HIV testing coverage was derived as the proportion of notified TB cases with known HIV status and constrained to a valid range of 0 to 100 percent following quality checks. Complete-case analysis was applied within relevant time windows. No imputation was performed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003ePrimary models employed two-way fixed effects with country and year indicators, estimated via the within estimator to adjust for fixed country-level attributes and year-specific shocks affecting both countries. Driscoll-Kraay heteroskedasticity- and autocorrelation-consistent standard errors (HC1; Bartlett kernel with plug-in bandwidth) were used to address serial and cross-sectional dependence. Because the panel comprises only two countries (G\u0026thinsp;=\u0026thinsp;2), small-sample inference is fragile; effect sizes (coefficients) and 95% Driscoll-Kraay confidence intervals are emphasized. Where p-values are reported in the main text, they are computed using a conservative t reference distribution with degrees of freedom df\u0026thinsp;=\u0026thinsp;G\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e\u003cp\u003eAnalyses incorporating TB-HIV testing coverage were limited to 2016\u0026ndash;2022, and full-period models (2000\u0026ndash;2022) excluded this variable. Sensitivity analyses included (1) a linear mixed-effects regression with a country random intercept and year fixed effects and (2) pooled ordinary least squares with country and year fixed effects and heteroskedasticity-robust (HC3) standard errors. Given the two-country panel, cluster-robust CR2 standard errors were evaluated but produced numerically unstable estimates; therefore, HC3 results are reported for the OLS robustness model. All analyses were conducted in R version 4.4.2 using plm (two-way fixed effects; Driscoll-Kraay via vcovSCC), lmtest (inference), sandwich (HC1/HC3), lme4, and lmerTest. In Supplementary Tables S1-S4, \u003cem\u003ep\u003c/em\u003e-values follow the asymptotic normal reference; Driscoll-Kraay 95% confidence intervals can be computed as B\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 \u0026times; SE from the reported standard errors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Ethical considerations\u003c/h2\u003e\u003cp\u003eThis study utilized publicly available, de-identified, aggregate data. In accordance with U.S. federal regulations (45 CFR 46.104(d)(4), the research does not involve human subjects and did not require institutional review board (IRB) approval.\u003c/p\u003e\u003cp\u003eClinical Trial Registration: Not applicable. This study does not involve a clinical trial.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Descriptive statistics summary\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reports means and standard deviations for Liberia (n\u0026thinsp;=\u0026thinsp;23), Sierra Leone (n\u0026thinsp;=\u0026thinsp;23), and the combined sample (N\u0026thinsp;=\u0026thinsp;46). Liberia\u0026rsquo;s TB incidence rate averaged 287.6 per 100,000 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.9), compared with 307.9 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.9) in Sierra Leone; the overall mean was 297.7 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.8). TB-HIV co-infection averaged 22.1 percent (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.9) in Liberia and 15.6 percent (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.2) in Sierra Leone, with an overall mean of 18.9 percent (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.8). The TB mortality rate was 84.1 per 100,000 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.3) in Liberia and 74.1 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26.4) in Sierra Leone, with an overall mean of 79.1 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21.0). Case notification rates were lower in Liberia (132.8 per 100,000; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36.4) than in Sierra Leone (171.5; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;47.5). Case detection rates averaged 45.5 percent (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.7) in Liberia and 56.0 percent (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.5) in Sierra Leone. Full summary statistics for all variables appear in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics (mean and standard deviation) of study variables for Liberia (n\u0026thinsp;=\u0026thinsp;23), Sierra Leone (n\u0026thinsp;=\u0026thinsp;23), and overall sample (N\u0026thinsp;=\u0026thinsp;46), 2000\u0026ndash;2022\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLiberia (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSierra Leone (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTB incidence rate (per 100,000)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e287.6 (23.9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e307.9 (9.9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e297.7 (20.8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTB\u0026ndash;HIV co-infection (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e22.1 (7.9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e15.6 (6.2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e18.9 (7.8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB mortality rate (per 100,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.1 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.1 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.1 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB case notification rate (per 100,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.8 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.5 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.6 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB case detection rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.5 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.0 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.9 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear (centered)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure per capita (US$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.2 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.9 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (2015 US$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641.9 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e927.5 (124.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e784.7 (175.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIV prevalence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation density (people/km\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.0 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.8 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.9 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban population (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.4 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.4 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.9 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital beds per 1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4 (\u0026ndash;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConflict events (count)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.0 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.8 (107.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.9 (85.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConflict fatalities (count)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.2 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eViolence against civilians (count)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtests (count)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.0 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB-HIV testing coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.4 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.5 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.5 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: \u0026ldquo;Battles\u0026rdquo; is omitted because it recorded zero events in both countries from 2000 to 2022. TB-HIV testing coverage (%) is available only for 2016\u0026ndash;2022 (n\u0026thinsp;=\u0026thinsp;7 per country); all other variables use 2000\u0026ndash;2022 (n\u0026thinsp;=\u0026thinsp;23). A dash (\u0026ndash;) indicates that the standard deviation could not be computed due to data being available for only one year. SD\u0026thinsp;=\u0026thinsp;standard deviation; TB\u0026thinsp;=\u0026thinsp;tuberculosis; HIV\u0026thinsp;=\u0026thinsp;human immunodeficiency virus\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo examine temporal trends in TB burden across the two countries, annual WHO-derived estimates of TB incidence for Liberia and Sierra Leone from 2000 to 2022 were plotted (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Liberia\u0026rsquo;s estimated TB incidence rate increased steadily from 240.0 per 100,000 population in 2000 to a peak of 308.0 per 100,000 around 2013, followed by a plateau through 2022 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;287.6, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.9). In comparison, Sierra Leone began with a higher incidence rate (approximately 305.0 per 100,000 in 2000), rose modestly to about 318.0 in 2008, and then declined to 283.0 by 2022 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;307.9, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.9). The divergence in trends after 2008, with Liberia\u0026rsquo;s rates remaining relatively high and Sierra Leone\u0026rsquo;s showing a gradual decline, may reflect differences in epidemic dynamics or the timing and implementation of national TB control efforts. These temporal patterns indicate the potential advantages of customized, country-specific strategies for preventing and managing TB.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Primary regression results (2000\u0026ndash;2022)\u003c/h2\u003e\n \u003cp\u003eA two-way fixed-effects model of TB incidence (per 100,000 population) was estimated with covariates TB-HIV co-infection (percent), health expenditure per capita (US dollars), GDP per capita (2015 US dollars), and conflict events (count). The specification included country and year fixed effects with Driscoll-Kraay (HC1) standard errors (Bartlett kernel with plug-in bandwidth). For 2000\u0026ndash;2022, the estimated coefficients were: TB-HIV co-infection (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.760, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.454; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.880; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.161); health expenditure per capita (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.138; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.105; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.934); GDP per capita (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.145, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;7.903; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080); and conflict events (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.047, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.364; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.403). None of the predictors reached statistical significance at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 under the conservative \u003cem\u003et\u003c/em\u003e reference with \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1; coefficient signs were negative for TB-HIV co-infection, GDP per capita, and conflict events, and near zero for health expenditure. Additional details are provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTwo-way fixed-effects regression of TB incidence rate (per 100,000) in Liberia and Sierra Leone, 2000\u0026ndash;2022 (Driscoll-Kraay SEs)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE (DK)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTB-HIV co-infection (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.760\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026minus;3.880\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure per capita (US$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (2015 US$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;7.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConflict events (count)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Outcome is TB incidence (cases per 100,000 population). Models include country and year fixed effects. Standard errors are Driscoll-Kraay (HC1) with a Bartlett kernel and plug-in bandwidth. Sample: Liberia and Sierra Leone, 2000\u0026ndash;2022 (N\u0026thinsp;=\u0026thinsp;46 country-year observations). p values use a conservative t reference with df\u0026thinsp;=\u0026thinsp;1; interpretation emphasizes coefficients and DK 95% confidence intervals, which can be computed as B\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 \u0026times; SE from the reported standard errors.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e reports the same coefficients and Driscoll-Kraay (HC1) standard errors for the 2000\u0026ndash;2022 specification; as specified in subsection \u003cspan class=\"InternalRef\"\u003e2.5\u003c/span\u003e, \u003cem\u003ep\u003c/em\u003e-values in the supplement use the asymptotic normal reference.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Subset re-estimation (2016\u0026ndash;2022)\u003c/h2\u003e\n \u003cp\u003eTwo-way fixed-effects models with Driscoll-Kraay standard errors were estimated for 2016\u0026ndash;2022, with coefficients scaled per 10 percentage points for percentage variables and per $100 for monetary variables. A $100 higher GDP per capita was associated with 9.87 fewer TB cases per 100,000 population (95% CI: \u0026minus;11.98, \u0026minus;\u0026thinsp;7.76). A $100 increase in current health expenditure per capita corresponded to 8.87 more cases (95% CI: 6.68, 11.06). A 10-percentage-point increase in TB-HIV testing coverage was associated with 0.87 more cases (95% CI: 0.43, 1.31). Associations for TB-HIV co-infection (per 10 percentage points: 1.39; 95% CI: \u0026minus;1.81, 4.59) and conflict events (per 100 events: \u0026minus;0.15; 95% CI: \u0026minus;1.39, 1.09) were uncertain. Given G\u0026thinsp;\u003cstrong\u003e=\u003c/strong\u003e\u0026thinsp;2, estimates are interpreted as descriptive effect sizes with Driscoll-Kraay confidence intervals; \u003cem\u003ep\u003c/em\u003e-values are conservative and de-emphasized.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTwo-way fixed-effects regression estimates for tuberculosis incidence (cases per 100,000), 2016\u0026ndash;2022, with Driscoll-Kraay standard errors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE (DK)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI (DK)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB-HIV co-infection (per 10 pp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.808, 4.589]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure per capita (per $100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[ 6.683, 11.062]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita, 2015 US$ (per $100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-11.979, -7.762]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConflict events (per 100 events)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.386, 1.087]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB-HIV testing coverage (per 10 pp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[ 0.432, 1.314]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: Two-way fixed-effects models with Driscoll-Kraay standard errors (HC1) were used; coefficients (B) indicate the change in TB incidence (per 100,000) for the stated unit change in each predictor (per 10 percentage points for percentage variables, per $100 for monetary variables, and per 100 events for conflict). Because the panel includes only two countries (G\u0026thinsp;=\u0026thinsp;2), \u003cem\u003ep\u003c/em\u003e-values are computed from a \u003cem\u003et\u003c/em\u003e reference distribution with \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1; interpretation emphasizes effect magnitudes and Driscoll-Kraay 95% confidence intervals.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eFor the same 2016\u0026ndash;2022 window, a re-estimated model excluding TB-HIV testing coverage applied Driscoll-Kraay (HC1) standard errors with country and year fixed effects. Estimates were: TB-HIV co-infection (percent) \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.377, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.335; health expenditure per capita (US dollars) \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.093, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010; GDP per capita (2015 US dollars) \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.085, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; and conflict events (count) \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.014, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013. Results were broadly consistent with the primary specification. A complete coefficient table for the model including TB-HIV testing coverage appears in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e; the corresponding re-estimated model excluding testing coverage is provided in Supplementary Table S3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Sensitivity analyses and robustness checks\u003c/h2\u003e\n \u003cp\u003eA linear mixed-effects model with year fixed effects and country-level random intercepts yielded patterns consistent with the primary specification. GDP per capita (2015 US dollars) was inversely associated with TB incidence (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.140, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.695). TB-HIV co-infection (percent) was likewise negative (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.831, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.639; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.867). Health expenditure per capita and conflict events showed effects near zero in magnitude. Pooled ordinary least squares with country and year fixed effects and heteroskedasticity-robust (HC3) standard errors also aligned with the primary model: TB-HIV co-infection (percent) was negative (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.760, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.784; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.245) and GDP per capita (2015 US dollars) remained inversely associated (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.145, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.865). Health expenditure per capita and conflict events were small and imprecise (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057 and \u0026minus;\u0026thinsp;0.374, respectively). Because these robustness models use different error structures than the two-way fixed-effects estimator with Driscoll-Kraay standard errors, inference is treated as descriptive; primary conclusions rely on the fixed-effects estimates reported above (see Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor clarity, the OLS specification conditions on country and year indicators, so its coefficient estimates match those in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; differences in standard errors, test statistics, and \u003cem\u003ep\u003c/em\u003e-values reflect the use of HC3 here versus Driscoll-Kraay (HC1) with a conservative \u003cem\u003et\u003c/em\u003e(df\u0026thinsp;=\u0026thinsp;1) reference in the main tables. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reports a distinct 2016\u0026ndash;2022 specification that includes TB-HIV testing coverage and uses rescaled units (per 10 percentage points for percentages, per $100 for monetary measures, and per 100 events for conflict), and is therefore not directly comparable to the full-period robustness estimates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Supplementary mortality model (2000\u0026ndash;2022)\u003c/h2\u003e\n \u003cp\u003eUsing the same two-way fixed-effects specification to examine associations between the TB mortality rate (excluding HIV attributable deaths), per 100,000 population, and selected covariates for Liberia and Sierra Leone from 2000 to 2022 (N\u0026thinsp;=\u0026thinsp;46 country-year observations), TB-HIV co-infection (percent) was negatively associated with TB mortality (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.138, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.377, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.665, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Health expenditure per capita (US dollars) was not statistically significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.101, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.128, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.792, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.438). GDP per capita (2015 US dollars) showed an inverse association (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.075, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.437, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conflict events (count) were negatively associated with TB mortality (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.041, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.245, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). Models included country and year fixed effects; inference employed Driscoll-Kraay (HC1) standard errors with a Bartlett kernel and plug-in bandwidth (see Table S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Synthesis of findings\u003c/h2\u003e\n \u003cp\u003eAcross specifications, higher GDP per capita was consistently associated with lower TB incidence, and was also inversely associated with TB mortality (excluding HIV-attributable deaths). TB-HIV co-infection (percent) was negatively associated with TB incidence in the full-period model and in the 2016\u0026ndash;2022 re-estimation without testing coverage, but was small, positive, and not statistically significant when testing coverage was included. Conflict events were inversely associated with TB mortality (excluding HIV-attributable deaths). The findings are broadly consistent across methods and time windows. Statistical significance is interpreted cautiously, given the small number of country clusters (G\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis comparative analysis examined TB burden in Liberia and Sierra Leone from 2000 to 2022. Both countries showed divergent epidemiological trajectories despite common post-conflict challenges. For instance, Liberia experienced a sustained increase in TB incidence, while Sierra Leone showed a gradual decline. These patterns suggest that national recovery strategies and health system performance may have influenced TB outcomes. However, causality cannot be inferred from the current design.\u003c/p\u003e\u003cp\u003eAcross all specifications, GDP per capita was broadly consistent and inversely associated with TB incidence and mortality (excluding HIV-attributable deaths). This finding aligns with prior research, which indicates that economic recovery improves health outcomes by enhancing access to care, diagnostic capacity, and treatment adherence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. TB-HIV co-infection rates were also negatively associated with TB incidence and mortality in most models. While this may seem counterintuitive, it likely reflects enhanced surveillance and case detection among co-infected individuals, not a protective effect. This interpretation should be considered carefully, given the limited number of country clusters and possible measurement variability in co-infection estimates.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Testing coverage and surveillance limitations\u003c/h2\u003e\u003cp\u003eThe subset analysis (2016\u0026ndash;2022) identified no statistically significant association between TB-HIV testing coverage and TB incidence. Although the coefficient was positive, the lack of significance suggests that increased testing alone may not be enough to reduce TB incidence in the short term. Importantly, increases in testing can also elevate reported incidence via improved case ascertainment, even if true transmission is unchanged. This finding is consistent with past literature, which emphasizes that testing must be integrated with treatment and follow-up services to have population-level impact [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In fragile settings, surveillance systems often experience underreporting and incomplete data capture, which can weaken observed associations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Conflict exposure and health outcomes\u003c/h2\u003e\u003cp\u003eConflict events were not significantly associated with TB incidence in most models. However, the supplementary analysis (see Table S4) found a negative association with TB mortality. This result should be interpreted cautiously. It may reflect reporting artifacts or shifts in mortality attribution during periods of instability. Prior studies have found that indirect effects of conflict, such as displacement and service disruption, can exacerbate TB burden, even if direct associations are difficult to quantify [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Implications for health system strengthening\u003c/h2\u003e\u003cp\u003eThe study findings highlight the value of customizing TB control strategies to country-specific contexts. While economic recovery appears to support reductions in TB burden, integrated TB-HIV surveillance and testing must be partnered with sustained investments in health infrastructure to achieve health gains. Additionally, the divergence in TB trends between Liberia and Sierra Leone suggests that programmatic implementation and system resilience may play a critical role. Future interventions should prioritize strengthening diagnostic and treatment linkages for the co-infected population. Furthermore, expanding surveillance capacity to improve data completeness and reliability must be supported. Importantly, embedding TB-HIV services within broader health system recovery frameworks must be adequately funded. Altogether, these priorities align with the IHR core capacities and syndemic theory, which emphasize the co-occurring health risks in post-conflict populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Study limitations and future research\u003c/h2\u003e\u003cp\u003eThis study has several limitations worth noting. First, it relies on publicly available surveillance datasets, which may be subject to measurement error, especially in conflict-affected environments where surveillance systems are often disrupted or incomplete. Second, although the analysis employs panel regression models with Driscoll-Kraay standard errors to address serial and cross-sectional dependence and heteroskedasticity across country-year observations, the small number of country clusters (G\u0026thinsp;=\u0026thinsp;2) means that statistical inference should be interpreted cautiously.\u003c/p\u003e\u003cp\u003eThird, the study does not claim causal attribution. Observed associations are interpreted within the study\u0026rsquo;s design and available data. These limitations do not compromise the internal consistency of the findings. However, they warrant caution in generalizing the results to other post-conflict settings. Future research could extend this framework to a broader set of countries to assess the robustness of observed patterns and examine context-specific mechanisms underlying TB-HIV dynamics. Such comparative work may help inform equity-focused strategies in controlling infectious diseases in fragile health systems.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates the methodological utility of panel regression techniques for evaluating TB trends across fragile health systems. The divergent trajectories observed in Liberia and Sierra Leone reflect the complex interplay between economic indicators, co-infection dynamics, and surveillance capacity. While GDP per capita broadly correlated with reduced TB burden, the lack of a statistically significant association between TB-HIV testing coverage and incidence in the 2016\u0026ndash;2022 subset suggests the need for more nuanced metrics of integration effectiveness. Moreover, the analytic framework presented in this study provides a replicable model for assessing infectious disease control in post-conflict environments. Future research should extend this approach to additional contexts, prioritizing equity, system resilience, and sustained investment in public health infrastructure.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACLED \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Armed Conflict Location and Event Data\u003c/p\u003e\n\u003cp\u003eCFR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Code of Federal Regulations\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eCR2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cluster-robust standard errors (small-sample correction), clustered by country\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDK \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Driscoll-Kraay (standard errors)\u003c/p\u003e\n\u003cp\u003edf \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; degrees of freedom\u003c/p\u003e\n\u003cp\u003eGDP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gross Domestic Product\u003c/p\u003e\n\u003cp\u003eHC1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Heteroskedasticity-consistent standard errors\u003c/p\u003e\n\u003cp\u003eHC3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Heteroskedasticity-consistent standard errors (type 3)\u003c/p\u003e\n\u003cp\u003eHIV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eIHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; International Health Regulations\u003c/p\u003e\n\u003cp\u003eIRB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Institutional Review Board\u003c/p\u003e\n\u003cp\u003eM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Mean\u003c/p\u003e\n\u003cp\u003eOLS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Ordinary Least Squares\u003c/p\u003e\n\u003cp\u003ep \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; p-value\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard deviation\u003c/p\u003e\n\u003cp\u003eTB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tuberculosis\u003c/p\u003e\n\u003cp\u003et \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;t-statistic\u003c/p\u003e\n\u003cp\u003eTWFE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Two-way fixed effects\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis_TB_TBHIV_Liberia_SierraLeone_2000_2022.R\u003c/p\u003e\n\u003cp\u003eSupplementary Tables S1-S4\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJKD conceptualized the study, developed the methodology, curated and analyzed the data, conducted validation and visualization, prepared the original draft, reviewed and edited the manuscript, and managed overall project administration\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted independently, without any financial support from government agencies, commercial entities, or non-profit organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the conclusions of this article are included in the supplementary materials\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available, de-identified, aggregate data. In accordance with U.S. federal regulations (45 CFR 46.104(d)(4)), the research does not involve human subjects and does not require institutional review board (IRB) approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual patient data was included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Tuberculosis [Internet], Geneva WHO. 2025 Mar 14 [cited 2025 Aug 17]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.imf.org/en/Blogs/Articles/2025/06/26/poorest-countries-and-fragile-states-are-increasingly-falling-behind\u003c/span\u003e\u003cspan address=\"https://www.imf.org/en/Blogs/Articles/2025/06/26/poorest-countries-and-fragile-states-are-increasingly-falling-behind\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, TB-HIV co-infection, post-conflict health systems, Liberia, Sierra Leone, health security, comparative analysis","lastPublishedDoi":"10.21203/rs.3.rs-7510274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7510274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTuberculosis (TB) remains a significant public health concern in fragile and post-conflict settings. Liberia and Sierra Leone, which are undergoing recovery from extended periods of civil unrest, continue to experience high TB rates, including TB-HIV co-infection. Examining temporal trends and testing coverage in these countries is critical for developing integrated surveillance and response strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective comparative analysis used publicly available data for Liberia and Sierra Leone (2000-2022) from the World Health Organization, World Bank, and ACLED. The primary outcome was the TB incidence rate (per 100,000 population). Two-way fixed-effects panel regressions with country and year fixed effects were estimated, using Driscoll-Kraay standard errors (Bartlett kernel; plug-in bandwidth). Sensitivity analyses included a linear mixed-effects regression with a country random intercept and year fixed effects, and pooled ordinary least squares with country and year fixed effects and heteroskedasticity-robust (HC3) standard errors. Covariates were TB-HIV co-infection (percent), health expenditure per capita (US dollars), GDP per capita (2015 US dollars), and conflict events (count). A subset analysis (2016-2022) examined TB-HIV testing coverage, and a supplementary model evaluated TB mortality (excluding HIV-attributable deaths) from 2000 to 2022 using the same specification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegression models consistently identified negative associations between GDP per capita and TB incidence across specifications. TB-HIV co-infection was inversely associated with TB incidence in most models, though not uniformly significant. In the subset analysis, TB-HIV testing coverage showed a positive coefficient but was not statistically significant (\u003cem\u003eb\u003c/em\u003e = 0.087, \u003cem\u003ep\u003c/em\u003e = 0.160). Given two country clusters (G = 2), statistical inference was interpreted cautiously\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings suggest that economic conditions and TB-HIV dynamics may influence TB incidence in post-conflict settings. Although TB-HIV testing coverage was not significantly associated with TB incidence in this sample, its potential role warrants further investigation. Strengthening integrated TB-HIV surveillance and tailoring interventions to country-specific contexts may support more resilient health systems in fragile environments.\u003c/p\u003e","manuscriptTitle":"Ecological comparison of tuberculosis and TB HIV coinfection in postconflict Liberia and Sierra Leone as markers of health security","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:48:25","doi":"10.21203/rs.3.rs-7510274/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f975d3e5-6685-4867-9616-20d31693d0f6","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T07:09:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 09:48:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7510274","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7510274","identity":"rs-7510274","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}