Predictive value of IHR surveillance (C.5) and laboratory (C.2) capacities for national measles incidence: A 2011–2023 panel analysis of 14 West African countries

Predictive value of IHR surveillance (C.5) and laboratory (C.2) capacities for national measles incidence: A 2011–2023 panel analysis of 14 West African countries | 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 Predictive value of IHR surveillance (C.5) and laboratory (C.2) capacities for national measles incidence: A 2011–2023 panel analysis of 14 West African countries John Kwame Duah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7000777/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: Despite sustained progress in immunization over the past decades, measles outbreaks persist in West Africa, suggesting gaps in disease detection and response systems. The International Health Regulations (IHR) State Party Self-Assessment Annual Reporting (SPAR) tool provides standardized indicators for laboratory capacity (C.2) and surveillance capacity (C.5). However, the extent to which these capacities, alongside vaccination coverage, influence measles incidence remains understudied. Methods: An ecological panel study was conducted using data from 14 West African countries (2011–2023). Measles case counts from the World Health Organization (WHO) Global Health Observatory were merged with laboratory (C.2) and surveillance (C.5) capacity indicators from the IHR SPAR tool. Measles-containing vaccine first-dose (MCV1) and second-dose (MCV2) coverage from WHO and the United Nations Children's Fund (UNICEF), and national-level covariates, including gross domestic product per capita, health expenditure, and population, were sourced from the World Bank. Descriptive analyses (means, standard deviations, correlations, scatterplots) were performed, followed by panel negative binomial and zero-inflated negative binomial regressions with interaction terms, adjusting for year fixed effects and population offset. Model diagnostics employed DHARMa simulations and variance inflation factor checks. Results: Higher SPAR surveillance capacity (C.5) was associated with increased reported measles incidence, moderated by first-dose vaccine coverage (MCV1; interaction p < 0.01). Laboratory capacity (C.2) was not significantly associated when considered jointly. GDP per capita and health expenditure were also not significant predictors. Zero-inflated models improved fit but did not alter substantive findings. Conclusions: Strengthening surveillance capacity enhances measles detection where vaccine coverage is suboptimal. Combining robust laboratory and surveillance systems with high vaccination uptake is vital for effective measles control in West Africa. Measles surveillance Ecological panel study Vaccination coverage West Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Measles remains a leading cause of under-five mortality in sub-Saharan Africa despite sustained progress toward elimination. In 2021 alone, an estimated 66,000 measles-related deaths occurred in the WHO African Region [ 1 ], the majority among children under five years of age. Between 2010 and 2024, West Africa experienced multiple epidemic waves, including severe outbreaks in 2010–2012, 2015–2016, and, more recently, 2020–2022 [ 2 ]. These periodic surges underscore persistent immunity gaps and the fragility of outbreak detection and response systems across the region [ 3 ]. Notably, in 2022, countries in the WHO African Region reported more than 125,000 suspected measles cases [ 4 ], with over 73,000 confirmed [ 4 ]. These numbers highlight the continued transmission of the virus and underscore declines in population immunity to measles. More crucially, routine immunization coverage has stalled in recent years [ 5 ], as the regional average for the first-dose measles-containing vaccine (MCV1) was 69%, and second-dose (MCV2) coverage reached only 45% [ 5 ]. Both figures fall well below the 95% threshold needed to stop measles from spreading and achieve elimination goals [ 4 , 5 ]. Robust surveillance and laboratory systems are essential for detecting measles outbreaks early and confirming cases accurately. These systems serve as early warning tools, enabling health authorities to respond quickly and limit the spread of disease, particularly in regions like West Africa, where measles population immunity remains low [ 1 ]. There are also health infrastructure challenges that continue to affect health service delivery in rural and other remote areas in the sub-region. Altogether, these systems form the foundation of outbreak preparedness and play a vital role in identifying potential epidemics before they escalate. To support countries in assessing and improving these systems, the World Health Organization developed the SPAR tool, which monitors national performance across 15 technical areas [ 6 ]. Two of the most vital domains assessed by SPAR are C.5, which focuses on disease surveillance, and C.2, which evaluates laboratory services. Both are critical for managing vaccine-preventable diseases such as measles. Under the 2005 IHR monitoring and evaluation framework, all WHO Member States are required to develop and maintain these core capacities to detect, assess, report, and respond to public health threats that may cross international borders [ 1 , 6 ]. Although the IHR framework and the SPAR tool have helped Member States strengthen their core public health capacities, systemic and structural gaps in West Africa have made it tough for countries to achieve and maintain these requirements [ 7 ]. These difficulties are often linked to limited domestic funding for health security efforts [ 8 ], a heavy reliance on external partners for technical and financial support [ 9 ], and ongoing shortages of trained personnel and infrastructure, especially in rural and underserved areas [ 10 ]. While SPAR offers a standardized approach for tracking annual progress, it relies solely on self-assessment, which raises concerns about the accuracy and consistency of the data reported [ 11 ]. Independent evaluations, such as the Joint External Evaluation (JEE), have revealed that no country in the WHO African Region has yet achieved full capacity across all the IHR domains [ 10 ]. In particular, some countries have scored lowest in areas related to preparedness, emergency response operations, and laboratory systems [ 10 ]. These enduring gaps call for sober reflections on reliable indicators that reflect how well national surveillance and laboratory systems perform in practice. Measles can serve as a practical proxy for the overall effectiveness of disease surveillance infrastructure [ 7 ], as its high transmissibility, short incubation period, and recognizable clinical symptoms make it particularly useful for evaluating how promptly and accurately cases are detected and reported [ 12 ]. Measles also serves as a key indicator of immunization program performance since sustained high coverage is required to interrupt transmission [ 13 ]. Moreover, measles case data are routinely compiled through the WHO/UNICEF Joint Reporting Form (JRF) [ 14 ], providing a standardized and relatively comprehensive dataset that supports comparisons across countries and over time. While prior studies have assessed links between SPAR scores and outcomes during Ebola and COVID-19 outbreaks, few have examined how these core capacities relate to endemic vaccine-preventable diseases such as measles. Much of the existing research has relied on cross-sectional designs or single-country case studies, limiting the ability to assess temporal trends and draw broader conclusions. More crucially, to date, few peer-reviewed studies have employed a multi-country ecological panel design to test whether year-to-year improvements in SPAR surveillance and laboratory scores are associated with reductions in measles incidence after accounting for immunization coverage and socioeconomic factors. To address this evidence gap, this study employs an ecological panel design using data from 14 West African countries between 2011 and 2023. It investigates whether improvements in IHR core capacities, specifically SPAR indicators C.5 (surveillance) and C.2 (laboratory), are associated with reductions in national measles incidence the following year after accounting for routine immunization coverage and key socioeconomic covariates such as population size, GDP per capita, and healthcare spending. The study aims to quantify the association between national IHR SPAR capacities (Surveillance C.5, Laboratory C.2) and measles incidence over time and settings in West Africa. The primary research question asks whether stronger disease surveillance in one year leads to fewer measles cases the following year after accounting for vaccination coverage, economic status, population size, and healthcare spending. The investigative questions examine whether a higher SPAR C.2 (Laboratory) score in one year predicts lower measles incidence the next year; whether it predicts a greater-than-expected decrease in measles cases the following year even after adjusting for key covariates; whether countries that improve both C.5 and C.2 simultaneously experience larger-than-additive decreases in measles incidence the following year; and how robust these associations are when using different lag structures, such as a two-year lag, or when excluding pandemic years (2020–2021). The study also tests four hypotheses: (1) each 10-percentage-point increase in SPAR C.5 in the previous year is associated with a greater than or equal to 15% reduction in measles incidence (cases per 100,000) in the current year, controlling for other covariates; (2) each 10-percentage-point increase in SPAR C.2 in the previous year is associated with a greater than or equal to 10% reduction in measles incidence in the current year after controlling for other covariates; (3) countries that improve both C.5 and C.2 by 10 percentage points or more in the same year exhibit a synergistic effect, resulting in a greater than or equal to 25% reduction in measles incidence the following year, compared to improvement in only one capacity; and (4) the association between SPAR scores and measles incidence remains statistically significant when the pandemic years 2020–2021 are excluded or when alternative lag structures, such as a two-year lag, are used. Methods Study design This study employed a retrospective ecological panel design to evaluate whether changes in IHR core public health capacities were associated with measles case counts in 14 West African countries from 2011 through 2023. The dataset included 182 country-year observations, each representing one country in one year. The main exposures were SPAR C.5 (Surveillance) and SPAR C.2 (Laboratory) scores, each lagged by one year to reflect the hypothesis that stronger public health capacities may contribute to lower disease burden in the following year. All data were publicly available and aggregated at the national level. Ethical considerations This study used publicly available, de-identified, aggregate data. In accordance with U.S. federal regulations (45 CFR 46.104d][ 4 ]), the research does not involve human subjects and did not require institutional review board (IRB) approval. Study setting and population The study included 14 West African countries: Benin, Burkina Faso, Côte d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo. Cabo Verde was excluded because it reported zero measles cases during the study period, which limited its comparability for regression analysis with other countries in the panel. These West African countries vary in population size, immunization coverage, and public health infrastructure, providing a diverse sample for regional analysis. Data sources and variable definitions The annual measles case counts were obtained from WHO/UNICEF Joint Reporting Form (JRF) datasets. SPAR C.5 (Surveillance) and SPAR C.2 (Laboratory) scores were sourced from WHO’s IHR Monitoring and Evaluation Framework. Coverage with the first and second doses of the measles-containing vaccine (MCV1 and MCV2) was obtained from WHO/UNICEF WUENIC estimates. Additional country-level indicators, including gross domestic product (GDP) per capita (constant 2010 USD), total population, and current health expenditure as a percentage of GDP, were also obtained from the World Bank’s World Development Indicators. The primary outcome was the number of measles cases reported annually by each country. Descriptive summaries and measles incidence per 100,000 population were also calculated. The main predictors were C.5 and C.2 scores from the previous year. All covariates were treated as continuous variables. Table 1 shows the variables, data sources, and coding procedures. Table 1 Operational definitions and measurement of country-year study variables (2011–2023) Variable Operational Definition Coding/Measurement Data Source Annual national measles cases Number of confirmed measles cases reported to World Health Organization (WHO) Continuous count (integer) WHO Global Health Observatory Annual IHR SPAR C.2 (laboratory capacity) score International Health Regulations (IHR) State Party Self-Assessment Annual Reporting (SPAR) score for laboratory capacity (Category C.2) Continuous score (0–100) WHO IHR SPAR annual reports Annual IHR SPAR C.5 (surveillance capacity) score International Health Regulations (IHR) State Party Self-Assessment Annual Reporting (SPAR) score for surveillance capacity (Category C.5) Continuous score (0–100) WHO IHR SPAR annual reports Annual national MCV1 coverage (%) Percentage of surviving infants in the birth cohort receiving first dose of measles-containing vaccine (MCV1) by age 12 months Continuous percentage (0–100) WHO/UNICEF immunization coverage estimates Annual national MCV2 coverage (%) Percentage of the same birth cohort receiving second dose of measles-containing vaccine (MCV2) by age 24 months (NA if not available) Continuous percentage (0–100); NA indicates not available WHO/UNICEF immunization coverage estimates GDP per capita (current US $ ) Market value of all final goods and services produced per person in current US dollars Continuous (USD) World Bank World Development Indicators Total population (persons) Total number of individuals residing in the country Continuous (persons) World Bank World Development Indicators Health expenditure (% GDP) Total national health spending as a percentage of gross domestic product (GDP) Continuous percentage (0–100) World Bank World Development Indicators Note: Countries included: Benin, Burkina Faso, Côte d'Ivoire, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Gambia, and Togo. Cabo Verde was excluded due to zero reported measles cases from 2011 to 2023. Population was included as a log-transformed offset in regression models. IHR = International Health Regulations; SPAR = State Party Self-Assessment Annual Reporting; MCV = measles-containing vaccine; WHO = World Health Organization; GDP = gross domestic product; NA = not available. Data preparation and management Data were processed using R version 4.4.2. Country names and formats were standardized across files, including renaming The Gambia to Gambia for consistency. Cabo Verde was excluded from the analytic sample because it consistently reported zero measles cases throughout the study period. Measles case counts were checked for missing values. Where incidence data were available but raw case counts were missing, case estimates were computed using population size and then rounded to the nearest whole number. Gross domestic product (GDP) per capita was rescaled by dividing by 1,000 to improve model interpretability. Lagged versions of SPAR C.5 and C.2 were created by shifting values backward one year to align with the study’s temporal framework. Country years with missing SPAR values were flagged. For countries missing more than three years of SPAR data, exclusion was considered. Otherwise, rows with incomplete data were dropped listwise. All numeric variables were reviewed and harmonized across sources. The final panel dataset included complete values for exposures, covariates, and outcomes, matched by country and year. Statistical analysis Descriptive statistics summarized the distribution of all study variables across the full panel. Country-level means and standard deviations were calculated for measles incidence, SPAR C.5 and C.2 scores, MCV1 and MCV2 coverage, GDP per capita, total population, and health expenditure. Line graphs and scatterplots were generated to visualize changes in SPAR scores and measles incidence over time (See Figs. 1 and 2 under Results). Negative binomial mixed-effects regression models were employed to examine the association between lagged SPAR scores and annual measles case counts. This approach accounts for overdispersion in count data and allows for between-country differences through random intercepts. All models included a log-transformed population offset. The primary analyses used a one-year lag structure to test the hypothesis that changes in C.5 and C.2 in a given year may be associated with measles burden the next year. The first model assessed the association between lagged C.5 and measles cases, controlling for MCV1 coverage, GDP per capita, total population, and health expenditure. The second model added lagged C.2. The final model introduced an interaction between C.5 and C.2 to assess whether improvements in both capacities jointly corresponded to further reductions. SPAR indicators were scaled in 10-percentage-point increments to aid interpretation. As documented in the code, GDP per capita was rescaled by dividing by 1,000 to improve the interpretability of regression coefficients. Model fit was evaluated using the Akaike Information Criterion (AIC), and simulation-based residual checks were performed using the DHARMa package. Overdispersion and zero inflation were assessed using the performance package. Zero-inflation tests indicated significant excess zeros only in the interaction model with SPAR C.5 × MCV1 (Model 4); accordingly, Models 1 through 3 were fit with standard negative binomial specifications, while Models 4 through 7 employed zero-inflated negative binomial families. Collinearity among the primary predictors was evaluated by calculating variance inflation factors (VIFs) from a linear model regressing measles case counts on SPAR laboratory capacity (C.2), SPAR surveillance capacity (C.5), MCV1 coverage, GDP per capita, and health expenditure. All VIFs were below commonly accepted thresholds (C.2 = 3.29, C.5 = 3.24, MCV1 = 1.07, GDP per capita = 1.27, health expenditure = 1.18), suggesting that multicollinearity was not a substantial concern in the models. Sensitivity analyses evaluated the robustness of results by excluding the pandemic years 2020–2021, applying a two-year lag structure for SPAR variables, and re-estimating models using fixed-effects Poisson regression. All analyses were conducted in R version 4.4.2, and the full code is available for submission as a supplementary file. Results Descriptive summary of annual measles case counts and incidence Table 2 summarizes country-level annual measles case counts and incidence rates per 100,000 population across 14 West African countries from 2011 to 2023. All countries were included for each of the 13 calendar years. The number of years with available MCV2 data ranged from 12 to 13, reflecting country-specific timelines for introducing the second measles dose. Mean annual case counts ranged from 444 in Gambia to 34,360,552 in Nigeria, while incidence varied from 18.2 to 17,274.0 per 100,000 population. Standard deviations also demonstrated wide variability. For Gambia, the SD for cases was 646, and for incidence was 26.8. In contrast, Nigeria had an SD of 23,933,625 for case counts and 12,726.0 for incidence. These descriptive metrics show marked variation in measles burden and immunization coverage across countries. For detailed descriptive summaries, see Table 2 . Table 2 Descriptive summary of annual measles cases and incidence per country (2011–2023) Country Panel years Years with MCV2 data Mean cases SD cases Mean incidence SD incidence Benin 13 13 39, 228 29, 596 330.0 252.0 Burkina Faso 13 12 277, 293 396, 658 1, 456.0 2, 206.0 Côte d’Ivoire 13 13 163, 727 192, 408 566.0 630.0 Gambia 13 12 444 646 18.2 26.8 Ghana 13 13 150, 921 236, 513 490.0 742.0 Guinea 13 13 93, 135 164, 006 723.0 1, 266.0 Guinea-Bissau 13 12 809 1, 403 41.1 69.8 Liberia 13 13 88, 758 131, 493 1,707.0 2, 442.0 Mali 13 13 96, 265 120, 642 452.0 530.0 Niger 13 13 976, 482 1, 059, 382 4, 234.0 4, 343.0 Nigeria 13 13 34, 360, 552 23, 933, 625 17, 274.0 12, 726.0 Senegal 13 13 27, 295 33, 790 161.0 188.0 Sierra Leone 13 13 49, 246 43, 068 686.0 624.0 Togo 13 13 21, 082 23, 770 256.0 274.0 Note: SD = standard deviation; incidence per 100 000 population; Panel years indicate the full 13-year span (2011–2023) for 14 West African countries (Cabo Verde excluded due to zero cases in all years). Years with MCV2 data indicate the number of years in which the measles second dose (MCV2) was introduced (12 years for Burkina Faso, Guinea-Bissau, and Gambia); missing values were handled via pairwise deletion. Correlation patterns among key predictors Figure 1 shows the pairwise Pearson correlations among the study’s primary predictors: SPAR laboratory capacity (C.2), SPAR surveillance capacity (C.5), measles vaccine coverage, first dose and second dose (MCV1 and MCV2), gross domestic product (GDP) per capita, total population, and health expenditure as a percentage of GDP, across 14 West African countries from 2011 to 2023. SPAR C.2 and C.5 reveal a strong positive correlation (r = 0.82), suggesting that improvements in laboratory and surveillance capacity tend to occur together. MCV1 and MCV2 coverage are also moderately correlated (r = 0.70), implying alignment in vaccine uptake or routine immunization practices. GDP per capita and population size also show a moderate positive correlation (r = 0.61). All other pairwise correlations had absolute values below 0.40, suggesting no problematic concerns regarding multicollinearity. For detailed correlation coefficients, see Fig. 1 To further assess the relationships between predictors and measles incidence before multivariable modeling, Fig. 2 presents scatter plots showing the bivariate associations. Bivariate relationships between measles incidence and study predictors Figure 2 shows scatterplots of annual measles case counts and each continuous predictor. The observed associations appear roughly linear, with higher SPAR laboratory (C.2) and surveillance (C.5) scores generally corresponding to lower measles case counts. Similarly, greater coverage with the first-dose (MCV1) and second-dose (MCV2) measles vaccine is associated with fewer reported cases. GDP per capita and total population are positively correlated with measles case counts, suggesting that larger and wealthier countries tend to report more cases. The scatterplots also reveal a few high-leverage observations, especially that of Nigeria, which underscores the importance of robust regression methods. The MCV2 plot includes only years following the introduction of the second dose of MCV. See Fig. 2 for bivariate plots across all predictors. Association between SPAR laboratory capacity and measles incidence A negative binomial regression model was used to examine the association between SPAR laboratory capacity (C.2) in the previous year and annual measles incidence, adjusting for first-dose measles vaccine coverage (MCV1), year effects, and population size (as a log-transformed offset). Each one-point increase in C.2 score was associated with a 1% increase in measles incidence (incidence rate ratio [IRR] = 1.01, 95% CI [1.00, 1.02], p = 0.007). In contrast, every one-percentage-point increase in MCV1 coverage corresponded to a 5% reduction in measles incidence (IRR = 0.95, 95% CI [0.92, 0.98], p < 0.001). GDP per capita, scaled per USD 1,000, and health expenditure as a percentage of GDP were not statistically significant predictors. The IRR for GDP per capita was 1.41 (95% CI [0.78, 2.55], p = 0.261), and for health expenditure it was 1.07 (95% CI [0.96, 1.20], p = 0.230). For detailed model estimates and confidence intervals, see Table 3 . Table 3 Incidence rate ratios (IRRs) for model 1 predicting annual measles cases (2011–2023) Predictor IRR 95% CI p -Value SPAR C.2 (laboratory capacity) 1.01 [1.00, 1.02] 0.007 MCV1 coverage (%) 0.95 [0.92, 0.98] < .001 GDP per capita (per $ 1,000 USD) 1.41 [0.78, 2.55] 0.261 Health expenditure (% GDP) 1.07 [0.96, 1.20] 0.230 Note: IRRs are exponentiated coefficients from a negative binomial regression of annual measles cases on SPAR C.2 (laboratory capacity), controlling for first-dose measles vaccine coverage (MCV1), GDP per capita (scaled per USD 1,000), and health expenditure (% GDP), with year fixed effects and offset by log(total population). CI = confidence interval; SPAR = State Party Self-Assessment Annual Reporting; MCV = measles-containing vaccine. Association between SPAR surveillance capacity and measles incidence A negative binomial regression model was employed to evaluate the relationship between SPAR surveillance capacity (C.5) in the previous year and annual measles incidence, adjusting for first-dose measles vaccine coverage (MCV1), year effects, and population size (as a log-transformed offset). Each one-point increase in SPAR C.5 corresponded to a 2% increase in the incidence rate of measles (incidence rate ratio [IRR] = 1.02, 95% CI [1.01, 1.03], p < 0.001). Every one-percentage-point increase in MCV1 coverage was associated with a 5% reduction in measles incidence (IRR = 0.95, 95% CI [0.92, 0.98], p < 0.001). GDP per capita, scaled per USD 1,000, and health expenditure as a percentage of GDP were not statistically significant predictors. The IRR for GDP per capita was 1.21 (95% CI [0.67, 2.18], p = 0.521), and for health expenditure, it was 1.05 (95% CI [0.94, 1.18], p = 0.351). For complete incidence rate ratio estimates and confidence intervals, see Table 4 . Also, detailed robustness and diagnostic checks are provided in Supplementary Tables S1–S3. Table 4 Incidence rate ratios (IRRs) for model 2 predicting annual measles cases (2011–2023) Predictor IRR 95% CI p -Value SPAR C.5 (surveillance capacity) 1.02 [1.01, 1.03] < 0.001 MCV1 coverage (%) 0.95 [0.92, 0.98] < 0.001 GDP per capita (per USD 1,000) 1.21 [0.67, 2.18] 0.521 Health expenditure (% GDP) 1.05 [0.94, 1.18] 0.351 Note: IRRs are exponentiated coefficients from a negative binomial regression of annual measles cases on SPAR C.5 (surveillance capacity), controlling for first-dose measles vaccine coverage (MCV1), GDP per capita (scaled per USD 1,000), and health expenditure (% GDP), with year fixed effects and offset by log(total population). CI = confidence interval; SPAR = State Party Self-Assessment Annual Reporting; MCV = measles-containing vaccine. Combined effects of SPAR laboratory and surveillance capacities on measles incidence A joint negative binomial regression model incorporating SPAR laboratory capacity (C.2) and SPAR surveillance capacity (C.5) was utilized to assess their relationship with annual measles incidence, adjusting for first-dose measles vaccine coverage (MCV1), GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and population size (as a log-transformed offset). In the joint model presented in Table 5 , SPAR surveillance capacity (C.5) remained a statistically significant predictor, with each one-point increase corresponding to a 3% increase in the incidence rate of measles (incidence rate ratio [IRR] = 1.03, 95% CI [1.01, 1.04], p = 0.006). SPAR laboratory capacity (C.2), however, was not statistically significant when both predictors were included (IRR = 0.99, 95% CI [0.97, 1.01], p = 0.325). First-dose measles vaccine coverage (MCV1) continued to demonstrate a protective effect, with each percentage-point increase associated with a 5% reduction in measles incidence (IRR = 0.95, 95% CI [0.92, 0.98], p < 0.001). GDP per capita and health expenditure as a percentage of GDP were not significantly associated with measles incidence (IRR = 1.18, 95% CI [0.66, 2.10], p = 0.573; IRR = 1.05, 95% CI [0.94, 1.17], p = 0.403). For the detailed incidence rate ratio estimates and confidence intervals, see Table 5 . Also, full robustness and diagnostic checks are provided in Supplementary Tables S1–S3. Table 5 Incidence rate ratios (IRRs) for model 3 (joint C.2 and C.5) Predictor IRR 95% CI p -Value SPAR C.2 (laboratory capacity) 0.99 [0.97, 1.01] 0.325 SPAR C.5 (surveillance capacity) 1.03 [1.01, 1.04] 0.006 MCV1 coverage (%) 0.95 [0.92, 0.98] < 0.001 GDP per capita (per USD 1,000) 1.18 [0.66, 2.10] 0.573 Health expenditure (% GDP) 1.05 [0.94, 1.17] 0.403 Note: IRRs are exponentiated coefficients from a negative binomial regression of annual measles cases on SPAR C.2 (laboratory capacity) and SPAR C.5 (surveillance capacity), controlling for MCV1 coverage, GDP per capita (per USD 1,000), and health expenditure (% GDP), with year fixed effects and offset by log(total population). Interaction between SPAR laboratory and surveillance capacities A zero-inflated negative binomial regression model was used to account for the high proportion of zero case counts. This model evaluated the associations of SPAR surveillance capacity (C.5), first-dose measles vaccine coverage (MCV1), and their interaction with annual measles incidence while adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled using an intercept-only zero-inflation component, improving model fit (ΔAIC = − 4.4). In the count component (Table 6 ), each one-point increase in SPAR C.5 was associated with a 9% increase in measles incidence (IRR = 1.09, 95% CI [1.04, 1.14], p < .001). The interaction between SPAR C.5 and MCV1 was statistically significant (IRR = 1.00, 95% CI [1.00, 1.00], p = .005), suggesting that vaccine coverage slightly moderates the association between surveillance capacity and measles incidence. The main effect of MCV1 coverage was not statistically significant in this interaction model (IRR = 1.00, 95% CI [0.96, 1.04], p = .864). Neither GDP per capita nor health expenditure as a percentage of GDP showed significant associations with measles incidence (IRR = 0.95, 95% CI [0.56, 1.62], p = .856; IRR = 1.08, 95% CI [0.97, 1.20], p = .174). Table 6 presents the detailed incidence rate ratio estimates and confidence intervals. Also, full robustness and diagnostic checks are provided in Supplementary Tables S1–S3. Table 6 Incidence rate ratios for zero-inflated negative binomial model 4 evaluating the interaction between SPAR C.5 and MCV1 in predicting annual measles cases (2011–2023) Predictor IRR 95% CI p -Value SPAR C.5 (surveillance capacity) 1.09 [1.04, 1.14] < 0.001 MCV1 coverage (%) 1.00 [0.96, 1.04] 0.864 GDP per capita (per USD 1,000) 0.95 [0.56, 1.62] 0.856 Health expenditure (% GDP) 1.08 [0.97, 1.20] 0.174 SPAR C.5 × MCV1 interaction 1.00 [1.00, 1.00] 0.005 Note: IRRs are exponentiated coefficients from a zero-inflated negative‐binomial regression of annual measles cases on SPAR C.5 (surveillance capacity), MCV1 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), and year fixed effects, with an offset for total population (log scale). The zero‐inflation component was modeled with an intercept only. SPAR C.5 × MCV1 interaction true IRR is 0.999 (95% CI [0.999, 0.999], p = .005), shown here as 1.00 due to two‐decimal rounding. CI = confidence interval; SPAR = State Party Self-Assessment Annual Reporting; MCV = measles-containing vaccine. Interaction between SPAR laboratory capacity and MCV1 coverage A zero-inflated negative binomial regression model was used to examine whether SPAR laboratory capacity (C.2) moderated the effect of first-dose measles vaccine coverage (MCV1) on annual measles incidence, adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled using an intercept-only zero-inflation component. In the count component (Table 7 ), SPAR C.2 was not a statistically significant predictor (IRR = 1.03, 95% CI [0.97, 1.09], p = .345). Each percentage-point increase in MCV1 coverage was associated with a 4% reduction in measles incidence (IRR = 0.96, 95% CI [0.92, 1.00], p = .047). The interaction between SPAR C.2 and MCV1 was not statistically significant (IRR = 1.00, 95% CI [1.00, 1.00], p = .620), suggesting no evidence that laboratory capacity altered the vaccine effect. Neither GDP per capita nor health expenditure as a percentage of GDP showed significant associations with measles incidence (IRR = 1.33, 95% CI [0.76, 2.34], p = .319; IRR = 1.07, 95% CI [0.96, 1.20], p = .194). See Table 7 for the detailed incidence-rate ratios and confidence intervals. Also, full robustness and diagnostic checks are provided in Supplementary Tables S1–S3. Table 7 Incidence rate ratios for zero-inflated negative binomial model 5 evaluating the interaction between SPAR C.2 and MCV1 in predicting annual measles cases (2011–2023) Predictor IRR 95% CI p -Value SPAR C.2 (laboratory capacity) 1.03 [0.97, 1.09] 0.345 MCV1 coverage (%) 0.96 [0.92, 1.00] 0.047 GDP per capita (per USD 1,000) 1.33 [0.76, 2.34] 0.319 Health expenditure (% GDP) 1.07 [0.96, 1.20] 0.194 SPAR C.2 × MCV1 interaction 1.00 [1.00, 1.00] 0.620 Note: IRRs are exponentiated coefficients from a zero-inflated negative binomial regression of annual measles cases on SPAR C.2 (laboratory capacity), MCV1 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), year fixed effects, and total population (offset on the log scale). The zero‐inflation component was intercept‐only. The SPAR C.2 × MCV1 interaction true IRR is 0.999 (95% CI [0.999, 0.999], p = .620), shown here as 1.00 due to two‐decimal rounding. CI = confidence interval; SPAR = State Party Self-Assessment Annual Reporting; MCV = measles-containing vaccine. Interaction between SPAR surveillance capacity and MCV2 coverage A zero-inflated negative binomial regression model was employed to evaluate whether SPAR surveillance capacity (C.5) and second-dose measles vaccine coverage (MCV2) interacted in predicting annual measles incidence, adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled with an intercept-only zero-inflation component. In the count component, as shown in Table 8 , SPAR C.5 was not a statistically significant predictor of measles incidence (IRR = 1.01, 95% CI [0.97, 1.05], p = 0.616). Each percentage-point increase in MCV2 coverage was associated with a 4% reduction in measles incidence (IRR = 0.96, 95% CI [0.91, 1.00], p = 0.056). However, this effect did not reach conventional statistical significance (α = 0.05). The SPAR C.5 × MCV2 interaction was non-significant (IRR = 1.00, 95% CI [1.00, 1.00], p = 0.928), indicating no evidence that surveillance capacity moderated the vaccine effect. Neither GDP per capita nor health expenditure (% GDP) were significant predictors (IRR = 0.82, 95% CI [0.44, 1.55], p = 0.547; IRR = 1.08, 95% CI [0.94, 1.24], p = 0.269). See Table 8 for the detailed incidence-rate ratios and confidence intervals. Table 8 Incidence rate ratios for zero-inflated negative binomial model 6 evaluating the interaction between SPAR C.5 and MCV2 in predicting annual measles cases (2011–2023) Predictor IRR 95% CI p -Value SPAR C.5 (surveillance capacity) 1.01 [0.97, 1.05] 0.616 MCV2 coverage (%) 0.96 [0.91, 1.00] 0.056 GDP per capita (per USD 1,000) 0.82 [0.44, 1.55] 0.547 Health expenditure (% GDP) 1.08 [0.94, 1.24] 0.269 SPAR C.5 × MCV2 interaction 1.00 [1.00, 1.00] 0.928 Note: IRRs are exponentiated coefficients from a zero-inflated negative binomial regression of annual measles cases on SPAR C.5 (surveillance capacity), MCV2 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), year fixed effects, and total population (offset on the log scale). The zero-inflation component was intercept-only. The true IRR for the SPAR C.5 × MCV2 interaction is 0.999 (95% CI [0.999, 0.999], p = .928), shown here as 1.00 due to two-decimal rounding. The effect of MCV2 coverage (IRR = 0.96, p = .056) did not reach conventional significance (p < .05). CI = confidence interval; SPAR = State Party Self-Assessment Annual Reporting; MCV = measles-containing vaccine. Interaction between SPAR laboratory capacity and MCV2 coverage A zero-inflated negative binomial regression model was used to assess whether SPAR laboratory capacity (C.2) and second-dose measles vaccine coverage (MCV2) interacted in predicting annual measles incidence, adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled using an intercept-only zero-inflation component. In the count component, as shown in Table 9 , SPAR C.2 was not a statistically significant predictor of measles incidence (IRR = 1.02, 95% CI [0.97, 1.06], p = 0.497). Each percentage-point increase in MCV2 coverage was associated with a 4% reduction in measles incidence (IRR = 0.96, 95% CI [0.92, 1.01], p = 0.133). However, this effect did not reach statistical significance (α = 0.05). The interaction between SPAR C.2 and MCV2 was not statistically significant (IRR = 1.00, 95% CI [1.00, 1.00], p = 0.707), indicating no evidence that laboratory capacity moderated the vaccine effect. Neither GDP per capita nor health expenditure as a percentage of GDP exhibited significant associations with measles incidence (IRR = 0.86, 95% CI [0.46, 1.61], p = 0.634; IRR = 1.10, 95% CI [0.96, 1.26], p = 0.178). Table 9 Incidence rate ratios for zero-inflated negative binomial model 7 evaluating the interaction between SPAR C.2 and MCV2 in predicting annual measles cases (2011–2023) Predictor IRR 95% CI p -Value SPAR C.2 (laboratory capacity) 1.02 [0.97, 1.06] 0.497 MCV2 coverage (%) 0.96 [0.92, 1.01] 0.133 GDP per capita (per USD 1,000) 0.86 [0.46, 1.61] 0.634 Health expenditure (% GDP) 1.10 [0.96, 1.26] 0.178 SPAR C.2 × MCV2 interaction 1.00 [1.00, 1.00] 0.707 Note: IRRs are exponentiated coefficients from a zero‑inflated negative‑binomial regression of annual measles cases on SPAR C.2 (laboratory capacity), MCV2 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), year fixed effects, and total population (offset on the log scale). The zero‑inflation component was intercept‑only. The SPAR C.2 × MCV2 interaction true IRR is 0.999 (95% CI [0.999, 0.999], p = 0.707), shown here as 1.00 due to two‑decimal rounding. CI = confidence interval; SPAR = State Party Self‑Assessment Annual Reporting; MCV = measles‑containing vaccine. Robustness checks To assess whether the core findings were influenced by model specification or unobserved country-level factors, three sets of sensitivity analyses were employed. First, Models 1 through 3 were re-estimated with the inclusion of country fixed effects (Supplementary Table S1 ). Second, one-year and two-year lag specifications were compared (Supplementary Table S2 ). Third, DHARMa diagnostic tests were applied to all interaction models (Supplementary Table S3 ) to evaluate dispersion and zero-inflation. Across all checks, incidence-rate ratio estimates remained substantively unchanged, supporting the robustness of the main results. To graphically contextualize the joint effects of surveillance capacity and measles vaccine coverage, Fig. 3 depicts the predicted annual measles case counts across the range of SPAR surveillance capacity (C.5) scores for three levels of first-dose measles vaccine coverage (25th, 50th, and 75th percentiles), holding total population at its median value (log-offset). The plot pinpoints how measles incidence varies based on surveillance capacity and vaccine coverage, with steeper increases in predicted cases at lower MCV1 coverage and more gradual changes at higher coverage levels. This observed pattern suggests that higher surveillance capacity may be associated with increased detection of measles cases, but its relationship with incidence depends on vaccine uptake levels and immunization practices. To evaluate whether a similar interaction pattern holds for laboratory capacity, Fig. 4 shows the predicted annual measles case counts across the range of SPAR laboratory capacity (C.2) scores for three levels of first-dose vaccine coverage (25th, 50th, and 75th percentiles), with total population held at its median value (log-offset). Unlike SPAR surveillance capacity (C.5), the interaction between laboratory capacity and vaccine coverage appears less pronounced, as the predicted case curves for the three coverage levels largely overlap. This pattern suggests that laboratory capacity alone may have less influence on measles incidence when vaccine coverage varies, compared to the moderating role observed with surveillance capacity (C.5). Discussion Summary of key findings This ecological panel study employed negative binomial and zero-inflated negative binomial regression models to evaluate whether changes in national-level surveillance (C.5) and laboratory (C.2) capacities, as measured by the IHR SPAR tool, were associated with changes in measles incidence across 14 West African countries from 2011 to 2023. After adjusting for first-dose vaccine coverage (MCV1), population size, and socioeconomic covariates, the analysis found that higher SPAR C.5 (surveillance capacity) scores were significantly associated with increased reported measles incidence, and SPAR C.2 (laboratory capacity) showed no consistent association with case counts. A statistically significant interaction between SPAR C.5 and MCV1 coverage was also reported, but the effect size was small. Model fit was assessed using the Akaike Information Criterion (AIC), and residual diagnostics were performed with the DHARMa package in R. Relevance of findings within broader surveillance and immunization systems The positive association between surveillance capacity and measles incidence likely reflects improved detection rather than a genuine increase in transmission. Countries reporting higher SPAR C.5 scores may demonstrate an increased ability to detect and report cases that might have previously gone unrecognized. This interpretation is consistent with prior research indicating that enhanced surveillance systems can initially uncover higher case counts as underreporting declines [ 15 , 16 ]. Considering the self-reported nature of SPAR indicators, it is essential to interpret improvements as reflections of stronger reporting infrastructure rather than as direct causal drivers of disease outcomes [ 6 , 11 , 17 ]. Furthermore, the absence of a consistent association between laboratory capacity (C.2) and measles incidence may underscore the indirect and often capacity-limited role that laboratories fulfill in routine surveillance across many low-resource settings, as well as the structural orientation of the C.2 indicator. For instance, during outbreaks, countries may shift from laboratory-confirmed case detection to syndromic or clinical diagnosis as a pragmatic response to limited testing resources and surge-related constraints. In such situations, laboratory-based diagnostics may play a supporting role rather than serving as the primary confirmation method [ 12 , 18 ]. Additionally, SPAR laboratory scores emphasize structural capabilities and may not wholly capture functional readiness, diagnostic availability, or quality control processes [ 10 , 19 ]. The interaction between SPAR C.5 and MCV1 coverage suggests that vaccine uptake may modify the influence of surveillance capacity on reported incidence. In settings with lower coverage, increased surveillance may reveal a larger burden of disease, while in higher coverage contexts, such gains may yield fewer additional detections. This pattern aligns with ecological and systems-level models in public health, where the effectiveness of detection systems is influenced by immunization coverage and underlying susceptibility within the population [ 20 , 21 ]. Operational implications for disease monitoring and health security The study findings suggest that investments in surveillance systems may initially reveal larger-than-expected measles burdens, especially in regions with suboptimal vaccination coverage. However, this should not be assumed to be a programmatic failure. Instead, it should be a signal that detection capacities are functioning more effectively. As such, surveillance and immunization efforts must be enhanced in tandem to prevent the misinterpretation of surveillance-based case data [ 4 , 5 , 22 ]. Similarly, the consistently non-significant effects of GDP per capita and health expenditure on measles incidence emphasize that national income alone may not predict disease control success. Health system performance depends on how well resources are allocated and operationalized within surveillance and immunization systems [ 19 , 23 ]. Therefore, strengthening integrated public health infrastructure remains vital for achieving regional measles elimination goals, particularly in resource-challenged settings like West Africa. Strengths and Limitations This study provides new insights into the predictive value of IHR surveillance and laboratory capacities utilizing an ecological panel design. The analytic framework applies negative binomial models to account for overdispersion and population offsets, with additional evaluation of zero-inflated structures where appropriate. Model fit was assessed using the Akaike Information Criterion (AIC), and residual diagnostics were conducted using the DHARMa package, with additional checks for overdispersion and zero inflation via the performance package. Sensitivity analyses yielded consistent results across specifications, supporting the stability of the observed associations. One of the study’s key strengths lies in its use of multi-year, country-level data across 14 West African countries, integrating standardized SPAR progress indicators with independently sourced measles incidence data. The ecological panel design supports cross-national, time-series analysis through repeated annual measures, allowing for the evaluation of temporal associations while controlling for first-dose and second-dose measles vaccine coverage (MCV1 and MCV2), GDP per capita, health expenditure as a percentage of GDP, total population (via an offset), and year fixed effects. Moreover, the assessment of interactions between surveillance capacity and measles vaccine coverage highlights systems-level dynamics that are often not examined in similar studies. However, several limitations should be noted. First, SPAR indicators are based on self-assessment and may vary in reliability across countries and reporting periods. As self-reported metrics, SPAR scores may reflect differences in reporting effort or institutional engagement rather than objective public health capacity. This could introduce measurement bias and potentially inflate associations between higher SPAR scores and measles case detection. Second, the ecological nature of the data restricts conclusions about subnational or individual-level determinants of disease. Third, some countries may have made operational improvements not captured in SPAR scores, while others may report high scores without corresponding functional readiness. Conclusions This ecological panel analysis found that higher SPAR surveillance (C.5) scores were moderately associated with increased reported measles incidence across 14 West African countries from 2011 to 2023, and laboratory capacity (C.2) showed no independent association in joint or interaction models. These patterns may reflect the role of enhanced surveillance systems in improving case detection and reporting. SPAR indicators provide a potential proxy for tracking national surveillance performance over time. Future work should examine subnational disparities in immunization and case detection capacity and consider integrating SPAR-type assessments with locally disaggregated data to identify implementation gaps. Enhancing IHR capacity metrics to capture timeliness and completeness of reporting more robustly could improve their effectiveness as critical tools for measles control and preparedness monitoring. Abbreviations AIC Akaike Information Criterion C.2 SPAR laboratory capacity indicator C.5 SPAR surveillance capacity indicator CI Confidence interval DHARMa Diagnostics for HierArchical Regression Models GDP Gross domestic product IHR International Health Regulations IRR Incidence-rate ratio JIE Joint External Evaluation JRF Joint Reporting Form MCV1 First-dose measles-containing vaccine MCV2 Second-dose measles-containing vaccine NB Negative binomial S1 Supplementary Table S1 S2 Supplementary Table S2 SD Standard deviation SPAR State Party Self-Assessment Annual Reporting USD United States dollar VIF Variance inflation factor WUENIC WHO/UNICEF Estimates of National Immunization Coverage WHO World Health Organization ZINB Zero-inflated negative binomial Declarations 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. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References World Health Organization Regional Office for Africa. Measles . Brazzaville: WHO; 2024. 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Available from: WHO JRF Portal Maude RJ, Sayeed AA, Rahman MW, et al. Enhanced surveillance as a tool for health systems strengthening: lessons from measles and dengue monitoring in Asia. BMJ Glob Health . 2022;7(9):e009876. doi:10.1136/bmjgh-2022-009876 Nsubuga P, White ME, Thacker SB, et al. Public health surveillance: a tool for targeting and monitoring interventions. In: Jamison DT, Breman JG, Measham AR, et al., editors. Disease Control Priorities in Developing Countries . 2nd ed. Washington (DC): World Bank; 2006. Razavi A, Erondu NA, Khowaja AR, et al. Self-reported IHR core capacities: assessing reliability and relevance through external evaluations. Lancet Glob Health . 2020;8(1):e80–e82. doi:10.1016/S2214-109X(19)30404-5 Nandy R, Handzel T, Zaneidou M, et al. Case-based surveillance of measles in the African Region: experience and challenges. J Infect Dis . 2011;204(Suppl 1):S421–S425. doi:10.1093/infdis/jir097 Kandel N, Chungong S, Omaar A, Xing J. Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations Annual Report Data. Lancet . 2020;395(10229):1047–1053. doi:10.1016/S0140-6736(20)30553-5 Tatem AJ, Rogers DJ, Hay SI. Global transport networks and infectious disease spread: a meta-population approach. Int J Infect Dis . 2006;10(6):308–316. doi:10.1016/j.ijid.2005.09.008 Anderson RM, May RM. Infectious diseases of humans: dynamics and control. Oxford: Oxford University Press; 1992. Patel MK, Gacic-Dobo M, Strebel PM, et al. Progress toward regional measles elimination—worldwide, 2000–2020. MMWR Morb Mortal Wkly Rep . 2021;70(45):1563–1569. doi:10.15585/mmwr.mm7045a1 Ataguba JE. Health care financing in sub-Saharan Africa: from theory to practice. Glob Health Action . 2021;14(1):1861690. doi:10.1080/16549716.2020.1861690 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1DHARMaDiagnostics.pdf analysismain.R.docx SupplementaryTablesS1S3.docx analysisdatasetupdated.csv 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-7000777","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490999581,"identity":"41b0c4e3-f4d8-4321-aba5-a5c2ed7693a1","order_by":0,"name":"John Kwame Duah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxh4GgwMJPDZyDAw8UKEDhLUYPvggk2ZMvBagQmPDGTaHEhuI1sLcc3ibNE/OgfT+GbnHpCt3MMjx3Ugg4LDetjJpnjN3cmfcyEuTPHuGwViSoJZ+HjNp3p5nuQ03cswkG9sYEjcQp+Xf4XR5qJZ6wlp6e4De5zmcYADVAmQQ0tJzrPDBB540w41n3iVbNrZJGM488wC/FsOe5A2gqJSXO5578GZjm40833ECthg2oPIl8CsHAXnCSkbBKBgFo2DEAwCkH0nIEoUHAgAAAABJRU5ErkJggg==","orcid":"","institution":"Auburn University","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"Kwame","lastName":"Duah","suffix":""}],"badges":[],"createdAt":"2025-06-29 05:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7000777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7000777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87813364,"identity":"6cc8d432-339b-49ba-b086-8c1e1c2afcf2","added_by":"auto","created_at":"2025-07-29 09:40:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81273,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of key predictors\u003c/p\u003e\n\u003cp\u003eNote: Data are for 14 West African countries (Benin, Burkina Faso, Côte d’Ivoire, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Gambia, and Togo); Cabo Verde is excluded (zero measles cases, 2011–2023). Abbreviations: IHR = International Health Regulations; SPAR = State Party Self-Assessment Annual Reporting; MCV = measles-containing vaccine; GDP = gross domestic product.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/44d235e255d331d4216c5246.png"},{"id":87813365,"identity":"a5a696a5-b6c1-4c53-9098-74e06fc4e915","added_by":"auto","created_at":"2025-07-29 09:40:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115770,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of annual measles cases vs. key predictors\u003c/p\u003e\n\u003cp\u003eNote: Data are for the same 14 countries (Cabo Verde excluded). The MCV2 panel shows only the years when the second dose was introduced. Abbreviations: MCV1 = first dose, MCV2 = second dose; other abbreviations as in Fig. \u0026nbsp;1\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/a74a7ae8bac754daa3061d6e.png"},{"id":87813371,"identity":"1dc8e1a9-4d96-48f1-b4f8-8d716842a410","added_by":"auto","created_at":"2025-07-29 09:40:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59469,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effects of SPAR C.5 x MCV1 on measles cases\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/d0dfa78fbd47d2b0799b1f9c.png"},{"id":87813378,"identity":"0d350346-f793-4810-b064-6b9282511695","added_by":"auto","created_at":"2025-07-29 09:40:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68400,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effects of SPAR C.2 x MCV1 on measles cases\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/747a659ddc542c64d5c1c721.png"},{"id":93048661,"identity":"1811bf5d-e2cf-4769-b646-db0caf347c4a","added_by":"auto","created_at":"2025-10-08 13:53:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1641764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/b8dfe40e-3fb8-485b-af5d-fa63a65f75ff.pdf"},{"id":87813367,"identity":"c90edda8-9d77-43c0-bc71-fd849e6758c2","added_by":"auto","created_at":"2025-07-29 09:40:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":227298,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1DHARMaDiagnostics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/f7076867a1f8b3436141f33d.pdf"},{"id":87813369,"identity":"70e1f94c-5ce0-4a45-809e-835bd77ef200","added_by":"auto","created_at":"2025-07-29 09:40:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24370,"visible":true,"origin":"","legend":"","description":"","filename":"analysismain.R.docx","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/bfc76d2cdbda07a97a1cd4c5.docx"},{"id":87813373,"identity":"f0e760c5-a510-4221-93b2-f813148b1bd7","added_by":"auto","created_at":"2025-07-29 09:40:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34644,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS1S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/16d14198e36a17f640410bdf.docx"},{"id":87814044,"identity":"6e65da6d-4f18-4c19-8518-a87c9fbc31f4","added_by":"auto","created_at":"2025-07-29 09:48:18","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12508,"visible":true,"origin":"","legend":"","description":"","filename":"analysisdatasetupdated.csv","url":"https://assets-eu.researchsquare.com/files/rs-7000777/v1/5879a25002098598124f39ec.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePredictive value of IHR surveillance (C.5) and laboratory (C.2) capacities for national measles incidence: A 2011–2023 panel analysis of 14 West African countries\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMeasles remains a leading cause of under-five mortality in sub-Saharan Africa despite sustained progress toward elimination. In 2021 alone, an estimated 66,000 measles-related deaths occurred in the WHO African Region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the majority among children under five years of age. Between 2010 and 2024, West Africa experienced multiple epidemic waves, including severe outbreaks in 2010\u0026ndash;2012, 2015\u0026ndash;2016, and, more recently, 2020\u0026ndash;2022 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These periodic surges underscore persistent immunity gaps and the fragility of outbreak detection and response systems across the region [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, in 2022, countries in the WHO African Region reported more than 125,000 suspected measles cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with over 73,000 confirmed [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These numbers highlight the continued transmission of the virus and underscore declines in population immunity to measles. More crucially, routine immunization coverage has stalled in recent years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], as the regional average for the first-dose measles-containing vaccine (MCV1) was 69%, and second-dose (MCV2) coverage reached only 45% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Both figures fall well below the 95% threshold needed to stop measles from spreading and achieve elimination goals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRobust surveillance and laboratory systems are essential for detecting measles outbreaks early and confirming cases accurately. These systems serve as early warning tools, enabling health authorities to respond quickly and limit the spread of disease, particularly in regions like West Africa, where measles population immunity remains low [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There are also health infrastructure challenges that continue to affect health service delivery in rural and other remote areas in the sub-region. Altogether, these systems form the foundation of outbreak preparedness and play a vital role in identifying potential epidemics before they escalate. To support countries in assessing and improving these systems, the World Health Organization developed the SPAR tool, which monitors national performance across 15 technical areas [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Two of the most vital domains assessed by SPAR are C.5, which focuses on disease surveillance, and C.2, which evaluates laboratory services. Both are critical for managing vaccine-preventable diseases such as measles. Under the 2005 IHR monitoring and evaluation framework, all WHO Member States are required to develop and maintain these core capacities to detect, assess, report, and respond to public health threats that may cross international borders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the IHR framework and the SPAR tool have helped Member States strengthen their core public health capacities, systemic and structural gaps in West Africa have made it tough for countries to achieve and maintain these requirements [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These difficulties are often linked to limited domestic funding for health security efforts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], a heavy reliance on external partners for technical and financial support [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and ongoing shortages of trained personnel and infrastructure, especially in rural and underserved areas [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While SPAR offers a standardized approach for tracking annual progress, it relies solely on self-assessment, which raises concerns about the accuracy and consistency of the data reported [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Independent evaluations, such as the Joint External Evaluation (JEE), have revealed that no country in the WHO African Region has yet achieved full capacity across all the IHR domains [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In particular, some countries have scored lowest in areas related to preparedness, emergency response operations, and laboratory systems [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These enduring gaps call for sober reflections on reliable indicators that reflect how well national surveillance and laboratory systems perform in practice.\u003c/p\u003e\u003cp\u003eMeasles can serve as a practical proxy for the overall effectiveness of disease surveillance infrastructure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], as its high transmissibility, short incubation period, and recognizable clinical symptoms make it particularly useful for evaluating how promptly and accurately cases are detected and reported [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Measles also serves as a key indicator of immunization program performance since sustained high coverage is required to interrupt transmission [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, measles case data are routinely compiled through the WHO/UNICEF Joint Reporting Form (JRF) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], providing a standardized and relatively comprehensive dataset that supports comparisons across countries and over time. While prior studies have assessed links between SPAR scores and outcomes during Ebola and COVID-19 outbreaks, few have examined how these core capacities relate to endemic vaccine-preventable diseases such as measles. Much of the existing research has relied on cross-sectional designs or single-country case studies, limiting the ability to assess temporal trends and draw broader conclusions. More crucially, to date, few peer-reviewed studies have employed a multi-country ecological panel design to test whether year-to-year improvements in SPAR surveillance and laboratory scores are associated with reductions in measles incidence after accounting for immunization coverage and socioeconomic factors.\u003c/p\u003e\u003cp\u003eTo address this evidence gap, this study employs an ecological panel design using data from 14 West African countries between 2011 and 2023. It investigates whether improvements in IHR core capacities, specifically SPAR indicators C.5 (surveillance) and C.2 (laboratory), are associated with reductions in national measles incidence the following year after accounting for routine immunization coverage and key socioeconomic covariates such as population size, GDP per capita, and healthcare spending. The study aims to quantify the association between national IHR SPAR capacities (Surveillance C.5, Laboratory C.2) and measles incidence over time and settings in West Africa. The primary research question asks whether stronger disease surveillance in one year leads to fewer measles cases the following year after accounting for vaccination coverage, economic status, population size, and healthcare spending.\u003c/p\u003e\u003cp\u003eThe investigative questions examine whether a higher SPAR C.2 (Laboratory) score in one year predicts lower measles incidence the next year; whether it predicts a greater-than-expected decrease in measles cases the following year even after adjusting for key covariates; whether countries that improve both C.5 and C.2 simultaneously experience larger-than-additive decreases in measles incidence the following year; and how robust these associations are when using different lag structures, such as a two-year lag, or when excluding pandemic years (2020\u0026ndash;2021). The study also tests four hypotheses: (1) each 10-percentage-point increase in SPAR C.5 in the previous year is associated with a greater than or equal to 15% reduction in measles incidence (cases per 100,000) in the current year, controlling for other covariates; (2) each 10-percentage-point increase in SPAR C.2 in the previous year is associated with a greater than or equal to 10% reduction in measles incidence in the current year after controlling for other covariates; (3) countries that improve both C.5 and C.2 by 10 percentage points or more in the same year exhibit a synergistic effect, resulting in a greater than or equal to 25% reduction in measles incidence the following year, compared to improvement in only one capacity; and (4) the association between SPAR scores and measles incidence remains statistically significant when the pandemic years 2020\u0026ndash;2021 are excluded or when alternative lag structures, such as a two-year lag, are used.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis study employed a retrospective ecological panel design to evaluate whether changes in IHR core public health capacities were associated with measles case counts in 14 West African countries from 2011 through 2023. The dataset included 182 country-year observations, each representing one country in one year. The main exposures were SPAR C.5 (Surveillance) and SPAR C.2 (Laboratory) scores, each lagged by one year to reflect the hypothesis that stronger public health capacities may contribute to lower disease burden in the following year. All data were publicly available and aggregated at the national level.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThis study used publicly available, de-identified, aggregate data. In accordance with U.S. federal regulations (45 CFR 46.104d][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]), the research does not involve human subjects and did not require institutional review board (IRB) approval.\u003c/p\u003e\n\u003ch3\u003eStudy setting and population\u003c/h3\u003e\n\u003cp\u003eThe study included 14 West African countries: Benin, Burkina Faso, C\u0026ocirc;te d\u0026rsquo;Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo. Cabo Verde was excluded because it reported zero measles cases during the study period, which limited its comparability for regression analysis with other countries in the panel. These West African countries vary in population size, immunization coverage, and public health infrastructure, providing a diverse sample for regional analysis.\u003c/p\u003e\n\u003ch3\u003eData sources and variable definitions\u003c/h3\u003e\n\u003cp\u003eThe annual measles case counts were obtained from WHO/UNICEF Joint Reporting Form (JRF) datasets. SPAR C.5 (Surveillance) and SPAR C.2 (Laboratory) scores were sourced from WHO\u0026rsquo;s IHR Monitoring and Evaluation Framework. Coverage with the first and second doses of the measles-containing vaccine (MCV1 and MCV2) was obtained from WHO/UNICEF WUENIC estimates. Additional country-level indicators, including gross domestic product (GDP) per capita (constant 2010 USD), total population, and current health expenditure as a percentage of GDP, were also obtained from the World Bank\u0026rsquo;s World Development Indicators. The primary outcome was the number of measles cases reported annually by each country. Descriptive summaries and measles incidence per 100,000 population were also calculated. The main predictors were C.5 and C.2 scores from the previous year. All covariates were treated as continuous variables. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the variables, data sources, and coding procedures.\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 and measurement of country-year study variables (2011\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\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\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\u003eAnnual national measles cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of confirmed measles cases reported to World Health Organization (WHO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous count (integer)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHO Global Health Observatory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual IHR SPAR C.2 (laboratory capacity) score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternational Health Regulations (IHR) State Party Self-Assessment Annual Reporting (SPAR) score for laboratory capacity (Category C.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous score (0\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHO IHR SPAR annual reports\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual IHR SPAR C.5 (surveillance capacity) score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternational Health Regulations (IHR) State Party Self-Assessment Annual Reporting (SPAR) score for surveillance capacity (Category C.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous score (0\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHO IHR SPAR annual reports\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual national MCV1 coverage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of surviving infants in the birth cohort receiving first dose of measles-containing vaccine (MCV1) by age 12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous percentage (0\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHO/UNICEF immunization coverage estimates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual national MCV2 coverage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of the same birth cohort receiving second dose of measles-containing vaccine (MCV2) by age 24 months (NA if not available)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous percentage (0\u0026ndash;100); NA indicates not available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHO/UNICEF immunization coverage estimates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP per capita (current US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarket value of all final goods and services produced per person in current US dollars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank World Development Indicators\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal population (persons)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal number of individuals residing in the country\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (persons)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank World Development Indicators\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal national health spending as a percentage of gross domestic product (GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous percentage (0\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank World Development Indicators\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Countries included: Benin, Burkina Faso, C\u0026ocirc;te d'Ivoire, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Gambia, and Togo. Cabo Verde was excluded due to zero reported measles cases from 2011 to 2023. Population was included as a log-transformed offset in regression models. IHR\u0026thinsp;=\u0026thinsp;International Health Regulations; SPAR\u0026thinsp;=\u0026thinsp;State Party Self-Assessment Annual Reporting; MCV\u0026thinsp;=\u0026thinsp;measles-containing vaccine; WHO\u0026thinsp;=\u0026thinsp;World Health Organization; GDP\u0026thinsp;=\u0026thinsp;gross domestic product; NA\u0026thinsp;=\u0026thinsp;not available.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eData preparation and management\u003c/h3\u003e\n\u003cp\u003eData were processed using R version 4.4.2. Country names and formats were standardized across files, including renaming \u003cem\u003eThe Gambia\u003c/em\u003e to Gambia for consistency. Cabo Verde was excluded from the analytic sample because it consistently reported zero measles cases throughout the study period. Measles case counts were checked for missing values. Where incidence data were available but raw case counts were missing, case estimates were computed using population size and then rounded to the nearest whole number. Gross domestic product (GDP) per capita was rescaled by dividing by 1,000 to improve model interpretability. Lagged versions of SPAR C.5 and C.2 were created by shifting values backward one year to align with the study\u0026rsquo;s temporal framework. Country years with missing SPAR values were flagged. For countries missing more than three years of SPAR data, exclusion was considered. Otherwise, rows with incomplete data were dropped listwise. All numeric variables were reviewed and harmonized across sources. The final panel dataset included complete values for exposures, covariates, and outcomes, matched by country and year.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics summarized the distribution of all study variables across the full panel. Country-level means and standard deviations were calculated for measles incidence, SPAR C.5 and C.2 scores, MCV1 and MCV2 coverage, GDP per capita, total population, and health expenditure. Line graphs and scatterplots were generated to visualize changes in SPAR scores and measles incidence over time (See Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e under Results). Negative binomial mixed-effects regression models were employed to examine the association between lagged SPAR scores and annual measles case counts. This approach accounts for overdispersion in count data and allows for between-country differences through random intercepts. All models included a log-transformed population offset.\u003c/p\u003e\u003cp\u003eThe primary analyses used a one-year lag structure to test the hypothesis that changes in C.5 and C.2 in a given year may be associated with measles burden the next year. The first model assessed the association between lagged C.5 and measles cases, controlling for MCV1 coverage, GDP per capita, total population, and health expenditure. The second model added lagged C.2. The final model introduced an interaction between C.5 and C.2 to assess whether improvements in both capacities jointly corresponded to further reductions. SPAR indicators were scaled in 10-percentage-point increments to aid interpretation. As documented in the code, GDP per capita was rescaled by dividing by 1,000 to improve the interpretability of regression coefficients.\u003c/p\u003e\u003cp\u003eModel fit was evaluated using the Akaike Information Criterion (AIC), and simulation-based residual checks were performed using the \u003cem\u003eDHARMa\u003c/em\u003e package. Overdispersion and zero inflation were assessed using the \u003cem\u003eperformance\u003c/em\u003e package. Zero-inflation tests indicated significant excess zeros only in the interaction model with SPAR C.5 \u0026times; MCV1 (Model 4); accordingly, Models 1 through 3 were fit with standard negative binomial specifications, while Models 4 through 7 employed zero-inflated negative binomial families. Collinearity among the primary predictors was evaluated by calculating variance inflation factors (VIFs) from a linear model regressing measles case counts on SPAR laboratory capacity (C.2), SPAR surveillance capacity (C.5), MCV1 coverage, GDP per capita, and health expenditure. All VIFs were below commonly accepted thresholds (C.2\u0026thinsp;=\u0026thinsp;3.29, C.5\u0026thinsp;=\u0026thinsp;3.24, MCV1\u0026thinsp;=\u0026thinsp;1.07, GDP per capita\u0026thinsp;=\u0026thinsp;1.27, health expenditure\u0026thinsp;=\u0026thinsp;1.18), suggesting that multicollinearity was not a substantial concern in the models. Sensitivity analyses evaluated the robustness of results by excluding the pandemic years 2020\u0026ndash;2021, applying a two-year lag structure for SPAR variables, and re-estimating models using fixed-effects Poisson regression. All analyses were conducted in R version 4.4.2, and the full code is available for submission as a supplementary file.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive summary of annual measles case counts and incidence\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes country-level annual measles case counts and incidence rates per 100,000 population across 14 West African countries from 2011 to 2023. All countries were included for each of the 13 calendar years. The number of years with available MCV2 data ranged from 12 to 13, reflecting country-specific timelines for introducing the second measles dose. Mean annual case counts ranged from 444 in Gambia to 34,360,552 in Nigeria, while incidence varied from 18.2 to 17,274.0 per 100,000 population. Standard deviations also demonstrated wide variability. For Gambia, the SD for cases was 646, and for incidence was 26.8. In contrast, Nigeria had an SD of 23,933,625 for case counts and 12,726.0 for incidence. These descriptive metrics show marked variation in measles burden and immunization coverage across countries. For detailed descriptive summaries, see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive summary of annual measles cases and incidence per country (2011\u0026ndash;2023)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePanel years\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYears with MCV2 data\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean incidence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD incidence\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\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39, 228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29, 596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurkina Faso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e277, 293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396, 658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1, 456.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2, 206.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u0026ocirc;te d\u0026rsquo;Ivoire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163, 727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192, 408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e566.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e630.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150, 921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236, 513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e742.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93, 135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164, 006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e723.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1, 266.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuinea-Bissau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiberia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88, 758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131, 493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,707.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2, 442.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96, 265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120, 642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e452.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e530.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNiger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e976, 482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 059, 382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4, 234.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4, 343.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34, 360, 552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23, 933, 625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17, 274.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12, 726.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27, 295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33, 790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49, 246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43, 068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e686.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e624.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTogo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21, 082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23, 770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e274.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: SD\u0026thinsp;=\u0026thinsp;standard deviation; incidence per 100 000 population; Panel years indicate the full 13-year span (2011\u0026ndash;2023) for 14 West African countries (Cabo Verde excluded due to zero cases in all years). Years with MCV2 data indicate the number of years in which the measles second dose (MCV2) was introduced (12 years for Burkina Faso, Guinea-Bissau, and Gambia); missing values were handled via pairwise deletion.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation patterns among key predictors\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the pairwise Pearson correlations among the study\u0026rsquo;s primary predictors: SPAR laboratory capacity (C.2), SPAR surveillance capacity (C.5), measles vaccine coverage, first dose and second dose (MCV1 and MCV2), gross domestic product (GDP) per capita, total population, and health expenditure as a percentage of GDP, across 14 West African countries from 2011 to 2023. SPAR C.2 and C.5 reveal a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.82), suggesting that improvements in laboratory and surveillance capacity tend to occur together. MCV1 and MCV2 coverage are also moderately correlated (r\u0026thinsp;=\u0026thinsp;0.70), implying alignment in vaccine uptake or routine immunization practices. GDP per capita and population size also show a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.61). All other pairwise correlations had absolute values below 0.40, suggesting no problematic concerns regarding multicollinearity. For detailed correlation coefficients, see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eTo further assess the relationships between predictors and measles incidence before multivariable modeling, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents scatter plots showing the bivariate associations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eBivariate relationships between measles incidence and study predictors\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows scatterplots of annual measles case counts and each continuous predictor. The observed associations appear roughly linear, with higher SPAR laboratory (C.2) and surveillance (C.5) scores generally corresponding to lower measles case counts. Similarly, greater coverage with the first-dose (MCV1) and second-dose (MCV2) measles vaccine is associated with fewer reported cases. GDP per capita and total population are positively correlated with measles case counts, suggesting that larger and wealthier countries tend to report more cases. The scatterplots also reveal a few high-leverage observations, especially that of Nigeria, which underscores the importance of robust regression methods. The MCV2 plot includes only years following the introduction of the second dose of MCV. See Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for bivariate plots across all predictors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation between SPAR laboratory capacity and measles incidence\u003c/h2\u003e\n \u003cp\u003eA negative binomial regression model was used to examine the association between SPAR laboratory capacity (C.2) in the previous year and annual measles incidence, adjusting for first-dose measles vaccine coverage (MCV1), year effects, and population size (as a log-transformed offset). Each one-point increase in C.2 score was associated with a 1% increase in measles incidence (incidence rate ratio [IRR]\u0026thinsp;=\u0026thinsp;1.01, 95% CI [1.00, 1.02], p\u0026thinsp;=\u0026thinsp;0.007). In contrast, every one-percentage-point increase in MCV1 coverage corresponded to a 5% reduction in measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI [0.92, 0.98], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). GDP per capita, scaled per USD 1,000, and health expenditure as a percentage of GDP were not statistically significant predictors. The IRR for GDP per capita was 1.41 (95% CI [0.78, 2.55], p\u0026thinsp;=\u0026thinsp;0.261), and for health expenditure it was 1.07 (95% CI [0.96, 1.20], p\u0026thinsp;=\u0026thinsp;0.230). For detailed model estimates and confidence intervals, see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence rate ratios (IRRs) for model 1 predicting annual measles cases (2011\u0026ndash;2023)\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\u003eIRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\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\u003eSPAR C.2 (laboratory capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00, 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV1 coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.92, 0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (per \u003cspan\u003e$\u003c/span\u003e1,000 USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.78, 2.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.96, 1.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.230\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: IRRs are exponentiated coefficients from a negative binomial regression of annual measles cases on SPAR C.2 (laboratory capacity), controlling for first-dose measles vaccine coverage (MCV1), GDP per capita (scaled per USD 1,000), and health expenditure (% GDP), with year fixed effects and offset by log(total population). CI\u0026thinsp;=\u0026thinsp;confidence interval; SPAR\u0026thinsp;=\u0026thinsp;State Party Self-Assessment Annual Reporting; MCV\u0026thinsp;=\u0026thinsp;measles-containing vaccine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation between SPAR surveillance capacity and measles incidence\u003c/h2\u003e\n \u003cp\u003eA negative binomial regression model was employed to evaluate the relationship between SPAR surveillance capacity (C.5) in the previous year and annual measles incidence, adjusting for first-dose measles vaccine coverage (MCV1), year effects, and population size (as a log-transformed offset). Each one-point increase in SPAR C.5 corresponded to a 2% increase in the incidence rate of measles (incidence rate ratio [IRR]\u0026thinsp;=\u0026thinsp;1.02, 95% CI [1.01, 1.03], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Every one-percentage-point increase in MCV1 coverage was associated with a 5% reduction in measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI [0.92, 0.98], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). GDP per capita, scaled per USD 1,000, and health expenditure as a percentage of GDP were not statistically significant predictors. The IRR for GDP per capita was 1.21 (95% CI [0.67, 2.18], p\u0026thinsp;=\u0026thinsp;0.521), and for health expenditure, it was 1.05 (95% CI [0.94, 1.18], p\u0026thinsp;=\u0026thinsp;0.351). For complete incidence rate ratio estimates and confidence intervals, see Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Also, detailed robustness and diagnostic checks are provided in Supplementary Tables S1\u0026ndash;S3.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence rate ratios (IRRs) for model 2 predicting annual measles cases (2011\u0026ndash;2023)\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\u003eIRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\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\u003eSPAR C.5 (surveillance capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.01, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV1 coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.92, 0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (per USD 1,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.67, 2.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.94, 1.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.351\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: IRRs are exponentiated coefficients from a negative binomial regression of annual measles cases on SPAR C.5 (surveillance capacity), controlling for first-dose measles vaccine coverage (MCV1), GDP per capita (scaled per USD 1,000), and health expenditure (% GDP), with year fixed effects and offset by log(total population). CI\u0026thinsp;=\u0026thinsp;confidence interval; SPAR\u0026thinsp;=\u0026thinsp;State Party Self-Assessment Annual Reporting; MCV\u0026thinsp;=\u0026thinsp;measles-containing vaccine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCombined effects of SPAR laboratory and surveillance capacities on measles incidence\u003c/h2\u003e\n \u003cp\u003eA joint negative binomial regression model incorporating SPAR laboratory capacity (C.2) and SPAR surveillance capacity (C.5) was utilized to assess their relationship with annual measles incidence, adjusting for first-dose measles vaccine coverage (MCV1), GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and population size (as a log-transformed offset). In the joint model presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, SPAR surveillance capacity (C.5) remained a statistically significant predictor, with each one-point increase corresponding to a 3% increase in the incidence rate of measles (incidence rate ratio [IRR]\u0026thinsp;=\u0026thinsp;1.03, 95% CI [1.01, 1.04], p\u0026thinsp;=\u0026thinsp;0.006). SPAR laboratory capacity (C.2), however, was not statistically significant when both predictors were included (IRR\u0026thinsp;=\u0026thinsp;0.99, 95% CI [0.97, 1.01], p\u0026thinsp;=\u0026thinsp;0.325). First-dose measles vaccine coverage (MCV1) continued to demonstrate a protective effect, with each percentage-point increase associated with a 5% reduction in measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI [0.92, 0.98], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). GDP per capita and health expenditure as a percentage of GDP were not significantly associated with measles incidence (IRR\u0026thinsp;=\u0026thinsp;1.18, 95% CI [0.66, 2.10], p\u0026thinsp;=\u0026thinsp;0.573; IRR\u0026thinsp;=\u0026thinsp;1.05, 95% CI [0.94, 1.17], p\u0026thinsp;=\u0026thinsp;0.403). For the detailed incidence rate ratio estimates and confidence intervals, see Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Also, full robustness and diagnostic checks are provided in Supplementary Tables S1\u0026ndash;S3.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence rate ratios (IRRs) for model 3 (joint C.2 and C.5)\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\u003eIRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\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\u003eSPAR C.2 (laboratory capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.97, 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAR C.5 (surveillance capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.01, 1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV1 coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.92, 0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (per USD 1,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.66, 2.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.94, 1.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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=\"4\"\u003eNote: IRRs are exponentiated coefficients from a negative binomial regression of annual measles cases on SPAR C.2 (laboratory capacity) and SPAR C.5 (surveillance capacity), controlling for MCV1 coverage, GDP per capita (per USD 1,000), and health expenditure (% GDP), with year fixed effects and offset by log(total population).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction between SPAR laboratory and surveillance capacities\u003c/h2\u003e\n \u003cp\u003eA zero-inflated negative binomial regression model was used to account for the high proportion of zero case counts. This model evaluated the associations of SPAR surveillance capacity (C.5), first-dose measles vaccine coverage (MCV1), and their interaction with annual measles incidence while adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled using an intercept-only zero-inflation component, improving model fit (\u0026Delta;AIC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.4). In the count component (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), each one-point increase in SPAR C.5 was associated with a 9% increase in measles incidence (IRR\u0026thinsp;=\u0026thinsp;1.09, 95% CI [1.04, 1.14], p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The interaction between SPAR C.5 and MCV1 was statistically significant (IRR\u0026thinsp;=\u0026thinsp;1.00, 95% CI [1.00, 1.00], p\u0026thinsp;=\u0026thinsp;.005), suggesting that vaccine coverage slightly moderates the association between surveillance capacity and measles incidence. The main effect of MCV1 coverage was not statistically significant in this interaction model (IRR\u0026thinsp;=\u0026thinsp;1.00, 95% CI [0.96, 1.04], p\u0026thinsp;=\u0026thinsp;.864). Neither GDP per capita nor health expenditure as a percentage of GDP showed significant associations with measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI [0.56, 1.62], p\u0026thinsp;=\u0026thinsp;.856; IRR\u0026thinsp;=\u0026thinsp;1.08, 95% CI [0.97, 1.20], p\u0026thinsp;=\u0026thinsp;.174). Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the detailed incidence rate ratio estimates and confidence intervals. Also, full robustness and diagnostic checks are provided in Supplementary Tables S1\u0026ndash;S3.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence rate ratios for zero-inflated negative binomial model 4 evaluating the interaction between SPAR C.5 and MCV1 in predicting annual measles cases (2011\u0026ndash;2023)\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\u003eIRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\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\u003eSPAR C.5 (surveillance capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.04, 1.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV1 coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.96, 1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (per USD 1,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.56, 1.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.97, 1.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAR C.5 \u0026times; MCV1 interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\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: IRRs are exponentiated coefficients from a zero-inflated negative‐binomial regression of annual measles cases on SPAR C.5 (surveillance capacity), MCV1 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), and year fixed effects, with an offset for total population (log scale). The zero‐inflation component was modeled with an intercept only. SPAR C.5 \u0026times; MCV1 interaction true IRR is 0.999 (95% CI [0.999, 0.999], p\u0026thinsp;=\u0026thinsp;.005), shown here as 1.00 due to two‐decimal rounding. CI\u0026thinsp;=\u0026thinsp;confidence interval; SPAR\u0026thinsp;=\u0026thinsp;State Party Self-Assessment Annual Reporting; MCV\u0026thinsp;=\u0026thinsp;measles-containing vaccine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction between SPAR laboratory capacity and MCV1 coverage\u003c/h2\u003e\n \u003cp\u003eA zero-inflated negative binomial regression model was used to examine whether SPAR laboratory capacity (C.2) moderated the effect of first-dose measles vaccine coverage (MCV1) on annual measles incidence, adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled using an intercept-only zero-inflation component. In the count component (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e), SPAR C.2 was not a statistically significant predictor (IRR\u0026thinsp;=\u0026thinsp;1.03, 95% CI [0.97, 1.09], p\u0026thinsp;=\u0026thinsp;.345). Each percentage-point increase in MCV1 coverage was associated with a 4% reduction in measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.96, 95% CI [0.92, 1.00], p\u0026thinsp;=\u0026thinsp;.047). The interaction between SPAR C.2 and MCV1 was not statistically significant (IRR\u0026thinsp;=\u0026thinsp;1.00, 95% CI [1.00, 1.00], p\u0026thinsp;=\u0026thinsp;.620), suggesting no evidence that laboratory capacity altered the vaccine effect. Neither GDP per capita nor health expenditure as a percentage of GDP showed significant associations with measles incidence (IRR\u0026thinsp;=\u0026thinsp;1.33, 95% CI [0.76, 2.34], p\u0026thinsp;=\u0026thinsp;.319; IRR\u0026thinsp;=\u0026thinsp;1.07, 95% CI [0.96, 1.20], p\u0026thinsp;=\u0026thinsp;.194). See Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e for the detailed incidence-rate ratios and confidence intervals. Also, full robustness and diagnostic checks are provided in Supplementary Tables S1\u0026ndash;S3.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence rate ratios for zero-inflated negative binomial model 5 evaluating the interaction between SPAR C.2 and MCV1 in predicting annual measles cases (2011\u0026ndash;2023)\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\u003eIRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\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\u003eSPAR C.2 (laboratory capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.97, 1.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV1 coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.92, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (per USD 1,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.76, 2.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.96, 1.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAR C.2 \u0026times; MCV1 interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.620\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: IRRs are exponentiated coefficients from a zero-inflated negative binomial regression of annual measles cases on SPAR C.2 (laboratory capacity), MCV1 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), year fixed effects, and total population (offset on the log scale). The zero‐inflation component was intercept‐only. The SPAR C.2 \u0026times; MCV1 interaction true IRR is 0.999 (95% CI [0.999, 0.999], p\u0026thinsp;=\u0026thinsp;.620), shown here as 1.00 due to two‐decimal rounding. CI\u0026thinsp;=\u0026thinsp;confidence interval; SPAR\u0026thinsp;=\u0026thinsp;State Party Self-Assessment Annual Reporting; MCV\u0026thinsp;=\u0026thinsp;measles-containing vaccine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction between SPAR surveillance capacity and MCV2 coverage\u003c/h2\u003e\n \u003cp\u003eA zero-inflated negative binomial regression model was employed to evaluate whether SPAR surveillance capacity (C.5) and second-dose measles vaccine coverage (MCV2) interacted in predicting annual measles incidence, adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled with an intercept-only zero-inflation component. In the count component, as shown in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, SPAR C.5 was not a statistically significant predictor of measles incidence (IRR\u0026thinsp;=\u0026thinsp;1.01, 95% CI [0.97, 1.05], p\u0026thinsp;=\u0026thinsp;0.616). Each percentage-point increase in MCV2 coverage was associated with a 4% reduction in measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.96, 95% CI [0.91, 1.00], p\u0026thinsp;=\u0026thinsp;0.056). However, this effect did not reach conventional statistical significance (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05). The SPAR C.5 \u0026times; MCV2 interaction was non-significant (IRR\u0026thinsp;=\u0026thinsp;1.00, 95% CI [1.00, 1.00], p\u0026thinsp;=\u0026thinsp;0.928), indicating no evidence that surveillance capacity moderated the vaccine effect. Neither GDP per capita nor health expenditure (% GDP) were significant predictors (IRR\u0026thinsp;=\u0026thinsp;0.82, 95% CI [0.44, 1.55], p\u0026thinsp;=\u0026thinsp;0.547; IRR\u0026thinsp;=\u0026thinsp;1.08, 95% CI [0.94, 1.24], p\u0026thinsp;=\u0026thinsp;0.269). See Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e for the detailed incidence-rate ratios and confidence intervals.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence rate ratios for zero-inflated negative binomial model 6 evaluating the interaction between SPAR C.5 and MCV2 in predicting annual measles cases (2011\u0026ndash;2023)\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\u003eIRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\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\u003eSPAR C.5 (surveillance capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.97, 1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV2 coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.91, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (per USD 1,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.44, 1.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.94, 1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAR C.5 \u0026times; MCV2 interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.928\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: IRRs are exponentiated coefficients from a zero-inflated negative binomial regression of annual measles cases on SPAR C.5 (surveillance capacity), MCV2 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), year fixed effects, and total population (offset on the log scale). The zero-inflation component was intercept-only. The true IRR for the SPAR C.5 \u0026times; MCV2 interaction is 0.999 (95% CI [0.999, 0.999], p\u0026thinsp;=\u0026thinsp;.928), shown here as 1.00 due to two-decimal rounding. The effect of MCV2 coverage (IRR\u0026thinsp;=\u0026thinsp;0.96, p\u0026thinsp;=\u0026thinsp;.056) did not reach conventional significance (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). CI\u0026thinsp;=\u0026thinsp;confidence interval; SPAR\u0026thinsp;=\u0026thinsp;State Party Self-Assessment Annual Reporting; MCV\u0026thinsp;=\u0026thinsp;measles-containing vaccine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction between SPAR laboratory capacity and MCV2 coverage\u003c/h2\u003e\n \u003cp\u003eA zero-inflated negative binomial regression model was used to assess whether SPAR laboratory capacity (C.2) and second-dose measles vaccine coverage (MCV2) interacted in predicting annual measles incidence, adjusting for GDP per capita (scaled per USD 1,000), health expenditure as a percentage of GDP, year fixed effects, and total population (offset on the log scale). Excess zeros were modeled using an intercept-only zero-inflation component. In the count component, as shown in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, SPAR C.2 was not a statistically significant predictor of measles incidence (IRR\u0026thinsp;=\u0026thinsp;1.02, 95% CI [0.97, 1.06], p\u0026thinsp;=\u0026thinsp;0.497). Each percentage-point increase in MCV2 coverage was associated with a 4% reduction in measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.96, 95% CI [0.92, 1.01], p\u0026thinsp;=\u0026thinsp;0.133). However, this effect did not reach statistical significance (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05). The interaction between SPAR C.2 and MCV2 was not statistically significant (IRR\u0026thinsp;=\u0026thinsp;1.00, 95% CI [1.00, 1.00], p\u0026thinsp;=\u0026thinsp;0.707), indicating no evidence that laboratory capacity moderated the vaccine effect. Neither GDP per capita nor health expenditure as a percentage of GDP exhibited significant associations with measles incidence (IRR\u0026thinsp;=\u0026thinsp;0.86, 95% CI [0.46, 1.61], p\u0026thinsp;=\u0026thinsp;0.634; IRR\u0026thinsp;=\u0026thinsp;1.10, 95% CI [0.96, 1.26], p\u0026thinsp;=\u0026thinsp;0.178).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence rate ratios for zero-inflated negative binomial model 7 evaluating the interaction between SPAR C.2 and MCV2 in predicting annual measles cases (2011\u0026ndash;2023)\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\u003eIRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\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\u003eSPAR C.2 (laboratory capacity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.97, 1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV2 coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.92, 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (per USD 1,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.46, 1.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth expenditure (% GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.96, 1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAR C.2 \u0026times; MCV2 interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.707\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: IRRs are exponentiated coefficients from a zero‑inflated negative‑binomial regression of annual measles cases on SPAR C.2 (laboratory capacity), MCV2 coverage, and their interaction, controlling for GDP per capita (per USD 1,000), health expenditure (% GDP), year fixed effects, and total population (offset on the log scale). The zero‑inflation component was intercept‑only. The SPAR C.2 \u0026times; MCV2 interaction true IRR is 0.999 (95% CI [0.999, 0.999], p\u0026thinsp;=\u0026thinsp;0.707), shown here as 1.00 due to two‑decimal rounding. CI\u0026thinsp;=\u0026thinsp;confidence interval; SPAR\u0026thinsp;=\u0026thinsp;State Party Self‑Assessment Annual Reporting; MCV\u0026thinsp;=\u0026thinsp;measles‑containing vaccine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eRobustness checks\u003c/h2\u003e\n \u003cp\u003eTo assess whether the core findings were influenced by model specification or unobserved country-level factors, three sets of sensitivity analyses were employed. First, Models 1 through 3 were re-estimated with the inclusion of country fixed effects (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Second, one-year and two-year lag specifications were compared (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). Third, DHARMa diagnostic tests were applied to all interaction models (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e) to evaluate dispersion and zero-inflation. Across all checks, incidence-rate ratio estimates remained substantively unchanged, supporting the robustness of the main results.\u003c/p\u003e\n \u003cp\u003eTo graphically contextualize the joint effects of surveillance capacity and measles vaccine coverage, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the predicted annual measles case counts across the range of SPAR surveillance capacity (C.5) scores for three levels of first-dose measles vaccine coverage (25th, 50th, and 75th percentiles), holding total population at its median value (log-offset). The plot pinpoints how measles incidence varies based on surveillance capacity and vaccine coverage, with steeper increases in predicted cases at lower MCV1 coverage and more gradual changes at higher coverage levels. This observed pattern suggests that higher surveillance capacity may be associated with increased detection of measles cases, but its relationship with incidence depends on vaccine uptake levels and immunization practices.\u003c/p\u003e\n \u003cp\u003eTo evaluate whether a similar interaction pattern holds for laboratory capacity, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the predicted annual measles case counts across the range of SPAR laboratory capacity (C.2) scores for three levels of first-dose vaccine coverage (25th, 50th, and 75th percentiles), with total population held at its median value (log-offset). Unlike SPAR surveillance capacity (C.5), the interaction between laboratory capacity and vaccine coverage appears less pronounced, as the predicted case curves for the three coverage levels largely overlap. This pattern suggests that laboratory capacity alone may have less influence on measles incidence when vaccine coverage varies, compared to the moderating role observed with surveillance capacity (C.5).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eSummary of key findings\u003c/h2\u003e\u003cp\u003eThis ecological panel study employed negative binomial and zero-inflated negative binomial regression models to evaluate whether changes in national-level surveillance (C.5) and laboratory (C.2) capacities, as measured by the IHR SPAR tool, were associated with changes in measles incidence across 14 West African countries from 2011 to 2023. After adjusting for first-dose vaccine coverage (MCV1), population size, and socioeconomic covariates, the analysis found that higher SPAR C.5 (surveillance capacity) scores were significantly associated with increased reported measles incidence, and SPAR C.2 (laboratory capacity) showed no consistent association with case counts. A statistically significant interaction between SPAR C.5 and MCV1 coverage was also reported, but the effect size was small. Model fit was assessed using the Akaike Information Criterion (AIC), and residual diagnostics were performed with the DHARMa package in R.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eRelevance of findings within broader surveillance and immunization systems\u003c/h2\u003e\u003cp\u003eThe positive association between surveillance capacity and measles incidence likely reflects improved detection rather than a genuine increase in transmission. Countries reporting higher SPAR C.5 scores may demonstrate an increased ability to detect and report cases that might have previously gone unrecognized. This interpretation is consistent with prior research indicating that enhanced surveillance systems can initially uncover higher case counts as underreporting declines [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Considering the self-reported nature of SPAR indicators, it is essential to interpret improvements as reflections of stronger reporting infrastructure rather than as direct causal drivers of disease outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, the absence of a consistent association between laboratory capacity (C.2) and measles incidence may underscore the indirect and often capacity-limited role that laboratories fulfill in routine surveillance across many low-resource settings, as well as the structural orientation of the C.2 indicator. For instance, during outbreaks, countries may shift from laboratory-confirmed case detection to syndromic or clinical diagnosis as a pragmatic response to limited testing resources and surge-related constraints. In such situations, laboratory-based diagnostics may play a supporting role rather than serving as the primary confirmation method [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, SPAR laboratory scores emphasize structural capabilities and may not wholly capture functional readiness, diagnostic availability, or quality control processes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The interaction between SPAR C.5 and MCV1 coverage suggests that vaccine uptake may modify the influence of surveillance capacity on reported incidence. In settings with lower coverage, increased surveillance may reveal a larger burden of disease, while in higher coverage contexts, such gains may yield fewer additional detections. This pattern aligns with ecological and systems-level models in public health, where the effectiveness of detection systems is influenced by immunization coverage and underlying susceptibility within the population [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eOperational implications for disease monitoring and health security\u003c/h2\u003e\u003cp\u003eThe study findings suggest that investments in surveillance systems may initially reveal larger-than-expected measles burdens, especially in regions with suboptimal vaccination coverage. However, this should not be assumed to be a programmatic failure. Instead, it should be a signal that detection capacities are functioning more effectively. As such, surveillance and immunization efforts must be enhanced in tandem to prevent the misinterpretation of surveillance-based case data [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, the consistently non-significant effects of GDP per capita and health expenditure on measles incidence emphasize that national income alone may not predict disease control success. Health system performance depends on how well resources are allocated and operationalized within surveillance and immunization systems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, strengthening integrated public health infrastructure remains vital for achieving regional measles elimination goals, particularly in resource-challenged settings like West Africa.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThis study provides new insights into the predictive value of IHR surveillance and laboratory capacities utilizing an ecological panel design. The analytic framework applies negative binomial models to account for overdispersion and population offsets, with additional evaluation of zero-inflated structures where appropriate. Model fit was assessed using the Akaike Information Criterion (AIC), and residual diagnostics were conducted using the DHARMa package, with additional checks for overdispersion and zero inflation via the performance package. Sensitivity analyses yielded consistent results across specifications, supporting the stability of the observed associations.\u003c/p\u003e\u003cp\u003eOne of the study\u0026rsquo;s key strengths lies in its use of multi-year, country-level data across 14 West African countries, integrating standardized SPAR progress indicators with independently sourced measles incidence data. The ecological panel design supports cross-national, time-series analysis through repeated annual measures, allowing for the evaluation of temporal associations while controlling for first-dose and second-dose measles vaccine coverage (MCV1 and MCV2), GDP per capita, health expenditure as a percentage of GDP, total population (via an offset), and year fixed effects.\u003c/p\u003e\u003cp\u003eMoreover, the assessment of interactions between surveillance capacity and measles vaccine coverage highlights systems-level dynamics that are often not examined in similar studies. However, several limitations should be noted. First, SPAR indicators are based on self-assessment and may vary in reliability across countries and reporting periods. As self-reported metrics, SPAR scores may reflect differences in reporting effort or institutional engagement rather than objective public health capacity. This could introduce measurement bias and potentially inflate associations between higher SPAR scores and measles case detection. Second, the ecological nature of the data restricts conclusions about subnational or individual-level determinants of disease. Third, some countries may have made operational improvements not captured in SPAR scores, while others may report high scores without corresponding functional readiness.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis ecological panel analysis found that higher SPAR surveillance (C.5) scores were moderately associated with increased reported measles incidence across 14 West African countries from 2011 to 2023, and laboratory capacity (C.2) showed no independent association in joint or interaction models. These patterns may reflect the role of enhanced surveillance systems in improving case detection and reporting. SPAR indicators provide a potential proxy for tracking national surveillance performance over time. Future work should examine subnational disparities in immunization and case detection capacity and consider integrating SPAR-type assessments with locally disaggregated data to identify implementation gaps. Enhancing IHR capacity metrics to capture timeliness and completeness of reporting more robustly could improve their effectiveness as critical tools for measles control and preparedness monitoring.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Akaike Information Criterion\u003c/p\u003e\n\u003cp\u003eC.2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;SPAR laboratory capacity indicator\u003c/p\u003e\n\u003cp\u003eC.5 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;SPAR surveillance capacity indicator\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eDHARMa Diagnostics for HierArchical Regression Models\u003c/p\u003e\n\u003cp\u003eGDP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gross domestic product\u003c/p\u003e\n\u003cp\u003eIHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; International Health Regulations\u003c/p\u003e\n\u003cp\u003eIRR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Incidence-rate ratio\u003c/p\u003e\n\u003cp\u003eJIE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Joint External Evaluation\u003c/p\u003e\n\u003cp\u003eJRF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Joint Reporting Form\u003c/p\u003e\n\u003cp\u003eMCV1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;First-dose measles-containing vaccine\u003c/p\u003e\n\u003cp\u003eMCV2 \u0026nbsp; \u0026nbsp; \u0026nbsp; Second-dose measles-containing vaccine\u003c/p\u003e\n\u003cp\u003eNB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative binomial\u003c/p\u003e\n\u003cp\u003eS1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Supplementary Table S1\u003c/p\u003e\n\u003cp\u003eS2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Supplementary Table S2\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Standard deviation\u003c/p\u003e\n\u003cp\u003eSPAR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;State Party Self-Assessment Annual Reporting\u003c/p\u003e\n\u003cp\u003eUSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;United States dollar\u003c/p\u003e\n\u003cp\u003eVIF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Variance inflation factor\u003c/p\u003e\n\u003cp\u003eWUENIC \u0026nbsp;WHO/UNICEF Estimates of National Immunization Coverage\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e\n\u003cp\u003eZINB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Zero-inflated negative binomial\u003c/p\u003e"},{"header":"Declarations","content":"\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\n\u003cp\u003e\u003cstrong\u003ePublisher’s note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization Regional Office for Africa. \u003cem\u003eMeasles\u003c/em\u003e. Brazzaville: WHO; 2024. Available from: https://www.afro.who.int/health-topics/measles\u003c/li\u003e\n \u003cli\u003eMinta AA, Ferrari M, Antoni S, et al. Progress Toward Measles Elimination \u0026mdash; Worldwide, 2000\u0026ndash;2023. \u003cem\u003eMMWR Morb Mortal Wkly Rep\u003c/em\u003e. 2024;73(45):1036\u0026ndash;1042. doi:10.15585/mmwr.mm7345a4\u003c/li\u003e\n \u003cli\u003eDateline Health Africa. \u003cem\u003eBasic Epidemiology of Measles in Sub-Saharan Africa\u003c/em\u003e. 2025. https://www.datelinehealthafrica.org/basic-epidemiology-of-measles-in-sub-sahara-africa\u003c/li\u003e\n \u003cli\u003eMasresha BG, Wiysonge CS, Reggis Katsande, O\u0026rsquo;Connor PM, Lebo E, Perry RT. Tracking Measles and Rubella Elimination Progress\u0026mdash;World Health Organization African Region, 2022\u0026ndash;2023. Vaccines. 2024 Aug 22;12(8):949\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization Regional Office for Africa. \u003cem\u003eStatus of immunization coverage in Africa as of the end of 2022\u003c/em\u003e. Brazzaville: WHO; 2023. Available from: https://www.afro.who.int/sites/default/files/2023-10/Status%20of%20immunization%20coverage_final-compressed_compressed.pdf\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. \u003cem\u003eInternational Health Regulations (2005): State Party Self-Assessment Annual Reporting Tool. Second edition\u003c/em\u003e. Geneva: WHO; 2021. Available from: https://apps.who.int/iris/handle/10665/350218\u003c/li\u003e\n \u003cli\u003eCham D, Barrow A, Shah-Rohlfs R, Standley CJ. Can global health security frameworks measure One Health implementation in West Africa? 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Health care financing in sub-Saharan Africa: from theory to practice. \u003cem\u003eGlob Health Action\u003c/em\u003e. 2021;14(1):1861690. doi:10.1080/16549716.2020.1861690\u003c/li\u003e\n\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":"Measles surveillance, Ecological panel study, Vaccination coverage, West Africa","lastPublishedDoi":"10.21203/rs.3.rs-7000777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7000777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDespite sustained progress in immunization over the past decades, measles outbreaks persist in West Africa, suggesting gaps in disease detection and response systems. The International Health Regulations (IHR) State Party Self-Assessment Annual Reporting (SPAR) tool provides standardized indicators for laboratory capacity (C.2) and surveillance capacity (C.5). However, the extent to which these capacities, alongside vaccination coverage, influence measles incidence remains understudied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eAn ecological panel study was conducted using data from 14 West African countries (2011–2023). Measles case counts from the World Health Organization (WHO) Global Health Observatory were merged with laboratory (C.2) and surveillance (C.5) capacity indicators from the IHR SPAR tool. Measles-containing vaccine first-dose (MCV1) and second-dose (MCV2) coverage from WHO and the United Nations Children's Fund (UNICEF), and national-level covariates, including gross domestic product per capita, health expenditure, and population, were sourced from the World Bank. Descriptive analyses (means, standard deviations, correlations, scatterplots) were performed, followed by panel negative binomial and zero-inflated negative binomial regressions with interaction terms, adjusting for year fixed effects and population offset. Model diagnostics employed DHARMa simulations and variance inflation factor checks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eHigher SPAR surveillance capacity (C.5) was associated with increased reported measles incidence, moderated by first-dose vaccine coverage (MCV1; interaction \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01). Laboratory capacity (C.2) was not significantly associated when considered jointly. GDP per capita and health expenditure were also not significant predictors. Zero-inflated models improved fit but did not alter substantive findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eStrengthening surveillance capacity enhances measles detection where vaccine coverage is suboptimal. Combining robust laboratory and surveillance systems with high vaccination uptake is vital for effective measles control in West Africa.\u003c/p\u003e","manuscriptTitle":"Predictive value of IHR surveillance (C.5) and laboratory (C.2) capacities for national measles incidence: A 2011–2023 panel analysis of 14 West African countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 09:40:13","doi":"10.21203/rs.3.rs-7000777/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":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T13:42:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 09:40:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7000777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7000777","identity":"rs-7000777","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}