Gaps Phase Iii: Incorporation of Capacity Based Weighting in the Global Assessment for Pediatric Surgery

Gaps Phase Iii: Incorporation of Capacity Based Weighting in the Global Assessment for Pediatric Surgery | 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 Gaps Phase Iii: Incorporation of Capacity Based Weighting in the Global Assessment for Pediatric Surgery Yasmine Yousef, Emmanuel Ameh, Luc Malemo Kalisya, Dan Poenaru This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4595115/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2024 Read the published version in Pediatric Surgery International → Version 1 posted 9 You are reading this latest preprint version Abstract Introduction: The Global Assessment for Pediatric Surgery (GAPS) tool was developed to enhance pediatric surgical care in Low- and Middle-Income Countries. This study presents the addition of a capacity-based weighting system to the GAPS tool. Methods: GAPS, developed through a multi-phase process including systematic review, international testing, item analysis, and refinement, assesses 64 items across five domains: human resources, material resources, education, accessibility, and outcomes. This new weighting system differentially weighs each domain. The GAPS Score was evaluated using pilot study data, focusing on hospital and country income levels, human development index, under-five mortality rate, neonatal mortality rate, deaths due to injury and deaths due to congenital anomalies. Analysis involved the Kruskal-Wallis test and linear regression. Benchmark values for the GAPS overall score and subsection scores were identified. Results: The GAPS score’s capacity-based weighting system effectively discriminated between levels of hospital (p = 0.0001) and country income level (p = 0.002). The GAPS scores showed significant associations with human development index (p < 0.001) and key health indicators such as under-five mortality rates (p < 0.001), neonatal mortality rate (p < 0.001), and deaths due to injury (p < 0.001). Benchmark scores for the GAPS overall score and the subsection scores included most institutions within their respective hospital level. Conclusions: The GAPS tool and score, enhanced with the capacity-based weighting system, marks progress in assessing pediatric surgical capacity in resource-limited settings. By mirroring the complex reality of hospital functionality in low-resource centers, it provides a refined mechanism for fostering effective partnerships and data-driven strategic interventions. pediatric surgery children’s surgery global surgery global health low-income country middle-income country capacity assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION In low- and middle-income countries, with children constituting up to half the population, up to 85% may require surgical treatment by age 15, significantly impacting the global disease burden ( 1 – 5 ). North-South partnerships, while aiming to enhance pediatric surgical resources in these countries, often face challenges due to undefined objectives, leading to suboptimal resource utilization and impact ( 6 – 8 ). In this context, our research team's systematic review in 2019 delineated the landscape of existing capacity assessment tools (CATs) for pediatric surgical services ( 1 ). The review revealed a need for a more encompassing tool that factors in outcomes, quality measures, and capacity-building elements. To address these gaps, we initiated the development of the Global Assessment for Pediatric Surgery (GAPS), a comprehensive, standardized instrument designed explicitly for low- and middle-income settings( 9 ). The project is comprised of three phases. Phase I was comprised of a systematic review to identify and generate a pool of items from existing relevant CATs ( 10 ). Phase II detailed initial tool development, international pilot testing, construct validation, and refinement to ensure relevance and discriminative capacity for low- and middle-income contexts ( 9 ) Phase III , the focus of this current work, introduces capacity-based weighting to GAPS, optimizing it as a metric for assessing and enhancing pediatric surgical capacity in low- and middle-income countries. 2. METHODS 2.1 Determining GAPS score weights Individual GAPS items each received a uniform weight of one point, since varying weights among similar items yield minimal benefit to large tools ( 11 , 12 ). However, given GAPS's composition of five distinct subsections, a specific weight was assigned to each subsection. Items with ordinal responses were scored by dividing one by the response count.. To yield an integer, the weight of each item was multiplied by a factor of 120, yielding ‘Q’ . The ‘ Q ’s for each item in each subsection were summed to ‘ E ’ for each subsection: ‘ E HR , E MR , E AC , E ED , E OU ’ . A multiple ordinal logistic regression was then run with level of hospital as the outcome, based on the predictor variables ‘ E Subsection ’ . Coefficients for each predictor were multiplied by the respective ‘ E Subsection ’ and summed to yield the GAPS score. Missing values were treated as a score of zero. The score was then rounded to the nearest whole integer. Following is a formulaic representation of the analyses: Score of each item * 120 = Q1 : Q64 Sum( Q1:Q9 ) = E HR ; Sum ( Q10:Q48 ) = E MR ; Sum( Q49: Q51 ) = E AC ; Sum( Q52:Q54 ) = E ED ; Sum( Q55:Q64 ) = E OU GAPS Score = ( E HR *Coeff HR ) + ( E MR *Coeff MR ) + ( E AC *Coeff AC ) + ( E ED *Coeff ED ) + ( E OU *Coeff OU ) 2.2 Correlation of GAPS score with capacity and outcomes Data for these analyses was retrieved from the pilot study phase ( 9 ). The tool was piloted in person between February 2017 and March 2018. Each survey was completed with the aid of a local senior medical representative. Countries were chosen pragmatically based on their geographical localization, ease of access, safety for travel, and number of accessible institutions. The primary outcome was level of hospital care (1st level, 2nd level, 3rd level and national children’s hospital). Level of hospital was predefined by the corresponding countries ministry of health. Secondary outcomes included country level of income as per World Bank 2018 and 2017 ( 13 ), human development index ( 14 ), under-five mortality rate (per 1,000 live births) (U5MR), and neonatal mortality rate (NNMR), deaths due to congenital anomalies (CA) per 1,000 births (0–27 days, 1–59 months, and 0–4 years), and deaths due to injuries (INJ) per 1,000 births (0–27 days, 1–59 months, and 0–4 years). Analyses consisted of the Kruskal-Wallis Test and linear regression, with p < 0.05 considered significant. All analyses were performed in STATA/MP 13.0 (StataCorp, College Station, TX). 2.3 GAPS Score Benchmarks Benchmark GAPS scores for each hospital level were derived from the 75th and 90th percentiles, then analyzed via dot plots to establish cut-offs maximizing data point inclusion. 3. RESULTS 3.1 Determining GAPS score weights The GAPS pilot encompassed 65 institutions in eight countries, including 28 1st level (43%), 23 2nd level (35%), 11 3rd level (17%), and three national children’s hospitals (5%). Most countries included in the pilot study were Low-Income Countries (LIC: Somaliland, Democratic Republic of Congo (DRC), Uganda). The DRC comprised 63% of institutions. Hospitals were most frequently public (45/65; 69%), followed by faith-based (15/65; 25%). (Table 1 ). Table 1 Descriptive Statistics of Pilot Data by Level of Hospital 1st Level (n = 28) 2nd Level (n = 23) 3rd Level (n = 11) National Children’s Hospital (n = 3) Level of Income LIC 26 14 6 0 LMIC 1 7 4 3 UMIC 1 2 1 0 Country Somaliland 0 0 1 0 Democratic Republic of Congo 26 12 3 0 Uganda 0 2 2 0 Mongolia 1 6 1 0 Algeria 1 2 0 0 Thailand 0 0 1 0 Vietnam 0 1 0 2 Egypt 0 0 3 1 Type of Hospital Public / Government 18 16 8 3 Non-Governmental Organization 0 1 1 0 Faith-based 9 6 1 0 Private 1 0 1 0 GAPS Score [median (IQR)] 3 ( 2 ) 6 ( 1 ) 11 ( 4 ) 16 ( 5 ) GAPS Subsection Scores [median (IQR)] Human Resources 135 (100) 290 (300) 480 (450) 1020 (400) Material Resources 1005 (770) 2380 (1040) 3180 (1000) 4260 (1360) Accessibility 78 (60) 78 (96) 138 (258) 360 (84) Education 0 (120) 120 (180) 660 (540) 900 (840) Outcomes 80 (80) 80 (40) 120 (120) 280 (80) LIC: Low-Income Country, LMIC: Low-Middle Income Country, UMIC: Upper-Middle Income Country, IQR: Inter-quartile range The GAPS Score equation ( Appendix A ) is detailed below: GAPS Score = ( E HR * 0.003297) + ( E MR * 0.0020106) + ( E AC * 0.0008398) + ( E ED * 0.0026368) + ( E OU * 0.0042668) The resultant possible range of values are 0.0453492–17.577264. The score is then rounded to the nearest integer, resulting in the final GAPS Score (range of values from 0–18). 3.2 Correlation of GAPS score with capacity and outcomes 3.2.1 Capacity The range of values for the GAPS score in our pilot data for the 65 institutions was 1–16 with a median of 6 and an interquartile range (IQR) of 5. Median GAPS scores significantly increased with level of hospital (p = 0.0001) (Fig. 1 ) . Furthermore, GAPS score significantly increased with level of income (p = 0.0001, Fig. 1 ). When evaluating hospital type, median GAPS score was lowest in public / government hospital ( 6 ) and faith-based hospital ( 6 ), followed by private (7.5), and non-governmental organizations (10.5). All subsection scores progressively and significantly rose with each hospital level. Human resources and material resources subsection scores significantly increased with level of income (p = 0.0001, p = 0.0004, respectfully). Hospital type was only significant in the human resources subsection score (p = 0.0491). ( Appendix B ). Furthermore, a linear regression of GAPS Score with country income was significant (p < 0.001) between LIC and Low- and Middle-Income Countries (LMIC), and between 1st level and all other hospital levels. (Table 2 ). Table 2 Linear Regression Analysis of GAPS Score to Human development Index, Under 5 Mortality Rate, and Neonatal Mortality Rate. GAPS Score Regression Coefficient P value 95% Confidence Interval Human Development Index 0.02 < 0.001 0.01–0.02 Under 5 Mortality Rate (per 1,000 live births) − 4.48 < 0.001 (− 6.10) – (− 2.85) Neonatal Mortality Rate (per 1,000 live births) − 1.21 < 0.001 (− 1.66) – (− 0.75) Deaths due to Congenital Anomalies (per 1,000 live births) 0–27 days 0.02 0.015 0.00–0.03 1–59 months − 0.00 0.889 (-)0.03–0.03 0–4 years 0.01 0.385 (-) 0.02–0.05 Deaths due to Injury (per 1,000 live births) 0–27 days − 0.02 < 0.001 (-) 0.03 – (-) 0.15 1–59 months − 0.22 < 0.001 (-)0.30 – (-) 0.14 0–4 years − 0.24 < 0.001 (-)0.32 – (-) 0.15 3.2.2 Outcomes The GAPS score showed a significant association with HDI (p < 0.001), U5MR (p < 0.001) and NNMR (p < 0.001). (Table 2 , Appendix C ). Linear regression analysis of GAPS score to U5MR identified that for each unit increase in GAPS score the expected value of U5MR decreases by 4.5 units. Furthermore, a substantial proportion (33%) of the variability in U5MR is explained by the GAPS score. Similarly, for each unit increase in GAPS score the expected value of NNMR decreases by 1.2 units and explains a large proportion (31%) of the variability within NNMR. Regarding HDI, each unit increase in GAPS score is associated with an increase in HDI of 0.02 units. The GAPS score showed a significant increase as the number of deaths due to injuries declined across all age groups (0–27 days, p < 0.001; 1–59 months, p < 0.001; 0–4 years, p < 0.001) (Table 2 ). However, no significant relationship was found between the GAPS score and deaths due to congenital anomalies. While the p-value for deaths due to congenital anomalies in neonates (0–27 days) was statistically significant, the corresponding 95% confidence interval included zero, indicating that this finding does not reflect a statistically significant effect (Table 2 ). 3.3 GAPS Score Benchmarks Dot graphs (Fig. 2) show the number of institutions captured by the GAPS score benchmarks for each level of hospital (Table 3 ). The GAPS benchmarks correctly captured 78% (21/27) of hospital identified as 1st level, 78% (18/23) of 2nd level, (9/11) 82% of 3rd level, and (2/3) 67% of National Children’s Hospitals. Table 3 Benchmarks Values for GAPS Score by Level of Hospital GAPS Score Human Resources Material Resources Accessibility Education Outcomes 1st Level Hospital ≤ 4 ≤ 200 ≤ 2000 ≤ 105 ≤ 120 ≤ 80 2nd Level Hospital 5–8 201–560 2001–3560 106–200 121–400 81–150 3rd Level Hospital 9–14 561–800 3561–4090 201–359 401–799 151–300 National Children’s Hospital ≥ 15 ≥ 801 ≥ 4091 ≥ 360 ≥ 800 ≥ 301 In the 1st level hospital group, six hospitals scored above the benchmark (≤ 4) (Fig. 2): centers 12, 23, 40, 43, 54 and 64 ( Appendix D, Fig. 3 ) located in Mongolia, Algeria, and the DRC (Fig. 4). In the 2nd level hospital group, five hospitals scored outside the benchmark GAPS scores ( 5 – 8 ): three below and two above (Fig. 2). Despite identifying as 2nd level hospitals, centers 34, 35, and 36 located in the DRC scored below benchmark values (Fig. 3). Center 1, located in Vietnam and Center 55, located in the DRC scored within benchmarks for 3rd level hospitals (Figs. 2 and 3, Appendix D ). For 3rd level hospitals, two hospitals scored below the benchmark ( 14) (Fig. 2). Center 10, located in Mongolia, scored below benchmarks for 3rd level hospitals in all sections. Center 44, located in the DRC, scored above benchmark levels for all section except education. (Figs. 2 and 3, Appendix D ). For National Children’s Hospitals, one hospital scored below the benchmark (Fig. 2). Center 2, located in Vietnam, scored below benchmarks for all sections except human resources (Figs. 2 and 3, Appendix D ). DISCUSSION The GAPS tool, based on a 2019 systematic review of CATs and surgical guidelines, was developed through multiple iterations with a 37-person expert panel, international testing, and item refinement. It includes 64 questions across five domains: Human Resources, Material Resources, Outcomes, Accessibility, and Education ( 9 , 10 ). The introduction of a capacity-based weighting system to the GAPS tool represents a methodological advancement compared to previously published CATs ( 10 ). In a study of the Service Availability and Readiness Assessment across six countries, O'Neill et al highlight the importance of tailored weighting in health assessment tools to improve the accuracy of capacity evaluations ( 15 ). Assigning differential weights to the various domains of pediatric surgical capacity allows the GAPS tool to be more closely aligned with the multifaceted reality of hospital functionality. This approach of capacity-based weighting is consistent with the multi-dimensional assessment frameworks proposed by Cometto et al, highlighting the necessity of encapsulating the intricacies of health systems within evaluation instruments ( 16 ). The GAPS weighting system marks a significant departure from the equal weighting paradigm used in homogeneous item scales such as the Surgeons OverSeas Pediatric - Personnel, Infrastructure, Procedure, Equipment, and Supplies (Pedi-PIPES) ( 17 ). Unlike Pedi-PIPES, which applies equal weighting across all its items, GAPS employs a dual approach: it uses equal weighting for items within each subscale but adopts differential weighting for the subscales themselves. This method allows for a more accurate representation of hospital capacity, recognizing the differential impact of individual domains on overall surgical capability ( 12 , 18 ). This approach aligns with DeVellis' concept that differential weighting enhances the sensitivity of tools in heterogeneous settings ( 11 ). The resultant GAPS score revealed significant correlations with hospital levels and income categories, as well as delineating differences across different hospital types. This score serves not merely as a gauge of capacity, but as a mirror reflecting the intricate interplay between a facility's resources and the socioeconomic milieu in which it operates. The study's findings underscore a positive correlation between GAPS scores and hospital levels, a relationship consonant with the stratified nature of healthcare systems ( 15 ). Similarly, the association between GAPS scores and income levels reinforces the influence of economic status on healthcare quality and availability ( 19 ). In terms of outcomes, the GAPS score's significant association with health indicators such as U5MR and NNMR substantiates its validity as a performance measure ( 20 , 21 ). Furthermore, the score correlates with the number of child deaths due injury, accentuating its potential as an indicator of pediatric surgical outcomes. While we found no significant correlation between the GAPS score and deaths from congenital anomalies, we attribute this to the limited sample size of the participating institutions. The score therefore reinforces the connection between health system inputs and health outcomes( 22 – 25 ). The establishment of benchmark GAPS scores delineates a clear framework for assessing pediatric surgical capacity across hospital levels, reflecting a critical step towards standardized performance metrics in low- and middle-income countries. These benchmarks provide a tangible target for institutions striving to enhance their surgical services. In our analysis, the benchmarks effectively discriminated between different hospital levels, corroborating the tiers of healthcare delivery described by Hsia et al., who emphasize the importance of clear standards in evaluating service capacity ( 26 ). The performance analysis against these benchmarks revealed that most hospitals conformed to expected levels, yet the outliers offer valuable insights. Certain first-level hospitals, notably in Mongolia and Algeria, surpassed their benchmark scores, suggesting a potential overqualification for their designated level, or a misalignment in hospital categorization ( 19 ). Conversely, some second-level hospitals, particularly in the DRC, fell short of their benchmarks, highlighting areas for targeted capacity-building, in line with strategies advocated by Grimes et al. for strengthening surgical systems ( 27 ). These outliers underscore the variability within categories and suggest that while benchmarks are vital for goal setting and evaluation, they must be contextualized within the specific health ecosystem and resource availability of each hospital. The cross-sectional analysis of hospital performance using the GAPS score highlights notable disparities, with some institutions out- or under-performing relative to their designated levels. Hospitals that scored above their anticipated benchmark, such as those in Mongolia, may be indicative of successful policy implementations that have bolstered their surgical capacity.. Conversely, hospitals scoring below their expected level, particularly 2nd level hospitals in the DRC, may be experiencing systemic challenges such as resource constraints, inadequate training, or governance issues ( 28 ). This mirrors previous findings of the DRC’s significant barriers to increasing surgical capacity ( 29 – 31 ). Understanding the underlying factors contributing to these performance variations is crucial, as it can inform policy changes, guide the optimization of international aid, and may, improve surgical outcomes. Compared to other CATs explored in our systematic review, GAPS uniquely integrates a more granular, multidimensional analysis of pediatric surgical services ( 10 ). Unlike prior tools focusing inventorying material resources, GAPS’s inclusive framework ensures that both tangible and previously intangible facets of surgical capacity are accounted for, providing a more holistic evaluation. GAPS’s capacity-based weighting system, not found in other CATs, reflects the complexity of health service delivery in low- and middle-income countries. It stand as a unique contribution to the field and a potential to guide to refined resource allocation, training initiatives, and policy reforms for pediatric surgery ( 32 ). The methodological rigor underpinning the development and validation of the GAPS tool constitutes a significant strength of this research. The systematic approach, informed by a comprehensive review and subsequent refinement phases, ensures that the tool is both inclusive and reflective of the multifaceted nature of surgical capacity in low-resource settings. The iterative process of development provided a robust construct validation, enhancing the tool's credibility and applicability ( 9 , 33 ). Nonetheless, the reliance on pilot data from limited geographical settings may impact the generalizability of the findings. However, any tool's responsiveness to changes over time remains to be evaluated ( 34 ). Future research could address these limitations by expanding the validation process to include a broader range of countries, potentially employing machine learning techniques to refine the weighting system for simplicity and efficacy ( 35 ). Furthermore, the reliance on surveys completed with the aid of local senior medical representatives a clear source of reporting bias. Moreover, our study relies on internal data and pilot testing for validation. External validation with a broader range of institutions in different settings would be necessary to ensure the tool’s applicability and reliability across diverse global health contexts. Additionally, the study does not encompass long-term follow-up data to evaluate temporal changes. Incorporating longitudinal studies would offer valuable perspectives on the tool's responsiveness to evolving surgical capacities, thereby enhancing its effectiveness in ongoing quality enhancement ( 36 – 38 ). Another important limitation is the lack of integration of surgical output in the GAPS tool. Stewart et al previously noted that assessments of surgical capacity have not adequately correlated with actual surgical output( 39 ). We concur that material-focused CATs are inadequate in predicting surgical output. To mitigate this, the GAPS tool incorporates additional dimensions (human resources, accessibility, outcomes, and education) moving beyond an inventory-based approach. While GAPS offers a more panoramic view, its effectiveness in directly quantifying surgical output has not been validated. A thorough evaluation of surgical procedures and outcomes is necessary for an accurate assessment of surgical output, an area beyond the scope of this study but vital for future research. We envision GAPS as a pivotal tool in future research, enhancing the evaluation of both surgical capacity and output. CONCLUSION GAPS quantifies pediatric surgical services in low-resource settings, offering a structured method for identifying healthcare disparities and prioritizing needs. The capacity-based weighting system offers a granular assessment of hospital capabilities, thus enabling interested parties to transcend traditional resource inventories and engage in more refined evaluation of service readiness. This specificity is crucial in guiding partnerships, ensuring collaborations are driven by precisely identified needs, thereby enhancing their efficiency and impact. Moreover, the GAPS tool facilitates targeted interventions by providing benchmarks reflective of service levels. The GAPS tool serves as an instrumental resource in the concerted effort to improve pediatric surgical outcomes through data-driven, context-specific strategies. Declarations FUNDING : Fonds de Recherche du Québec - Santé (FRQS) Canadian Institutes of Health Research CONFLICT OF INTEREST STATEMENT BY AUTHORS: Yasmine Yousef – NONE Emmanuel Ameh – NONE Luc Malemo Kalisya – NONE Dan Poenaru – NONE DATA AVAILABILITY STATEMENT The datasets generated and analyzed during the current study are not publicly available due to privacy of the institutions studied. Data are however available from the authors upon reasonable request and with permission of the Pediatric Research Ethics Board of the McGill University Health Centre Research Institute. ACKNOWLEDGEMENTS : Special thanks to the following: Kevyn Armand, Daniel Agbo-Panzo, Marilyn Butler, Beda Espineda, Kenzy Abdel-Hamid, Sonia Anchouche, Ai-Xuan Holterman, George W. Galiwango, Nathalie MacKinnon, Ritesh Shrestha, Desigen Reddy, William Harkness, Phyllis Kisa, Rashmi Kumar, Ibrahima Fall, Justin Onen, Ananda K. Lamahewage, Mike Ganey, Tessa Concepcion, Peter Donkor, Zaitun Bokhary, Anette S. Jacobsen, Reju Thomas, Brouh Yapo, Adiyasuren Jamiyanjav, Marcia Matias, Benno Ure, Vanda Amado, Bahati Robert, Lubna Samad and Roumanatou Bankolé, Sarah Cairo, Etienne St-Louis, Laura F. Goodman, Doulia M. Hamad, Robert Baird, Emily R. Smith, Sherif Emil, Jean-Martin Laberge, Robin Petroze, Mohamed Abdelmalak, Zipporah Gathuy, Faye Evans, Maryam Ghavami Adel, Ki K. 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Price R, Makasa E, Hollands M. World Health Assembly Resolution WHA68.15: "Strengthening Emergency and Essential Surgical Care and Anesthesia as a Component of Universal Health Coverage"-Addressing the Public Health Gaps Arising from Lack of Safe, Affordable and Accessible Surgical and Anesthetic Services. World J Surg. 2015;39(9):2115-25. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60(1):34-42. Fayers PM, Machin D. Quality of life : the assessment, analysis, and reporting of patient-reported outcomes. Third edition. ed. Chichester, West Sussex, UK ; Hoboken, NJ: John Wiley & Sons Inc.; 2016. p. p. Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;375(13):1216-9. Debas H, Alatise OI, Balch CM, Brennan M, Cusack J, Donkor P, et al. Academic Partnerships in Global Surgery: An Overview American Surgical Association Working Group on Academic Global Surgery. Ann Surg. 2020;271(3):460-9. Philipo GS, Nagraj S, Bokhary ZM, Lakhoo K. Lessons from developing, implementing and sustaining a participatory partnership for children's surgical care in Tanzania. BMJ Glob Health. 2020;5(3):e002118. Naluyimbazi R, Nimanya S, Kisa P. Anatomy and lessons of partnerships in global pediatric surgery. Semin Pediatr Surg. 2023;32(6):151353. Stewart BT, Gyedu A, Gaskill C, Boakye G, Quansah R, Donkor P, et al. Exploring the Relationship Between Surgical Capacity and Output in Ghana: Current Capacity Assessments May Not Tell the Whole Story. World J Surg. 2018;42(10):3065-74. Additional Declarations No competing interests reported. Supplementary Files AppendixABDEPSI.docx AppendixC600dpi.jpg Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2024 Read the published version in Pediatric Surgery International → Version 1 posted Editorial decision: Revision requested 17 Sep, 2024 Reviews received at journal 04 Sep, 2024 Reviewers agreed at journal 02 Sep, 2024 Reviews received at journal 11 Aug, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers invited by journal 17 Jul, 2024 Editor assigned by journal 18 Jun, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 17 Jun, 2024 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. 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Yousef","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYLCCBDjLxoaHgR1I8xCvJS2Nh4GZGC0IkHaYgaAWg+PHn254uMMGyOgx/vAh4bwMfzPzMYk3DHZyug04tJzJMbuReCYNyDhjJjkj4TaPxGG2NMk5DMnGZgdwaDmQw3Yjse0wg8GNHDNm3h+3eQyYecykeRgOJG7DpeX882dALf9BWow//0k4R4SWGwlAh7UdAGkxkGZIOEBYi+SNNyAtyTySZ46VSfYkJIP8kmw5xwC3X/jOpz+7+bPNTo7vePPmDz8S7Oz525sP3nhTYSeHS4sCVJxHAVWBAXblICDfgM4YBaNgFIyCUYAOABi6XOIdrmthAAAAAElFTkSuQmCC","orcid":"","institution":"McGill University","correspondingAuthor":true,"prefix":"","firstName":"Yasmine","middleName":"","lastName":"Yousef","suffix":""},{"id":319255253,"identity":"c25d845f-dbfd-4cec-b206-810d49aaa2dd","order_by":1,"name":"Emmanuel Ameh","email":"","orcid":"","institution":"National Hospital","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Ameh","suffix":""},{"id":319255254,"identity":"ac261bc7-f4a3-4cda-a928-4c2f1471c2d2","order_by":2,"name":"Luc Malemo Kalisya","email":"","orcid":"","institution":"Great Lakes University of Kisumu","correspondingAuthor":false,"prefix":"","firstName":"Luc","middleName":"Malemo","lastName":"Kalisya","suffix":""},{"id":319255255,"identity":"003f452f-0810-4f8d-84fb-47cb48bf61b6","order_by":3,"name":"Dan Poenaru","email":"","orcid":"","institution":"Montreal Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Poenaru","suffix":""}],"badges":[],"createdAt":"2024-06-17 15:32:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4595115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4595115/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00383-024-05870-2","type":"published","date":"2024-11-06T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60354335,"identity":"fc84e33b-6924-4925-a21d-2eeb4fcc73c7","added_by":"auto","created_at":"2024-07-15 23:52:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":359540,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-4595115/v1/d88c7eafadf91e8928d76112.png"},{"id":60355076,"identity":"4b4178cb-5959-4f46-8429-7d415a7253d5","added_by":"auto","created_at":"2024-07-16 00:00:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":384871,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure2600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-4595115/v1/5ced2aff193365e30b47b509.png"},{"id":60354339,"identity":"a7a027c4-2ea0-4262-872f-711efe81df94","added_by":"auto","created_at":"2024-07-15 23:52:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2271887,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure3600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-4595115/v1/ad1098d23b4af4e26c365563.png"},{"id":60355077,"identity":"a2a514a9-8cb7-483b-bd96-3e6785df1a47","added_by":"auto","created_at":"2024-07-16 00:00:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":937622,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure4600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-4595115/v1/94ed38428714b111f2a51f68.png"},{"id":68749828,"identity":"325ad832-6155-4ebd-87c7-01b4d39b846f","added_by":"auto","created_at":"2024-11-11 16:06:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4332725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4595115/v1/4e490c23-a75e-4e39-9a9c-c0c47f1431b4.pdf"},{"id":60354334,"identity":"4688e233-6566-4eaa-9035-fb433a39d5fe","added_by":"auto","created_at":"2024-07-15 23:52:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33997,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixABDEPSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-4595115/v1/8a569c39ccefee8f91360b9b.docx"},{"id":60354338,"identity":"ab6323f4-8147-49f6-ba8b-f7b6b3aac292","added_by":"auto","created_at":"2024-07-15 23:52:11","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":273164,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixC600dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4595115/v1/408cacac306421918adb869e.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGaps Phase Iii: Incorporation of Capacity Based Weighting in the Global Assessment for Pediatric Surgery\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn low- and middle-income countries, with children constituting up to half the population, up to 85% may require surgical treatment by age 15, significantly impacting the global disease burden (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNorth-South partnerships, while aiming to enhance pediatric surgical resources in these countries, often face challenges due to undefined objectives, leading to suboptimal resource utilization and impact (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, our research team's systematic review in 2019 delineated the landscape of existing capacity assessment tools (CATs) for pediatric surgical services (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The review revealed a need for a more encompassing tool that factors in outcomes, quality measures, and capacity-building elements. To address these gaps, we initiated the development of the Global Assessment for Pediatric Surgery (GAPS), a comprehensive, standardized instrument designed explicitly for low- and middle-income settings(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe project is comprised of three phases. \u003cem\u003ePhase I\u003c/em\u003e was comprised of a systematic review to identify and generate a pool of items from existing relevant CATs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). \u003cem\u003ePhase II\u003c/em\u003e detailed initial tool development, international pilot testing, construct validation, and refinement to ensure relevance and discriminative capacity for low- and middle-income contexts (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) \u003cem\u003ePhase III\u003c/em\u003e, the focus of this current work, introduces capacity-based weighting to GAPS, optimizing it as a metric for assessing and enhancing pediatric surgical capacity in low- and middle-income countries.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Determining GAPS score weights\u003c/h2\u003e \u003cp\u003eIndividual GAPS items each received a uniform weight of one point, since varying weights among similar items yield minimal benefit to large tools (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, given GAPS's composition of five distinct subsections, a specific weight was assigned to each subsection.\u003c/p\u003e \u003cp\u003eItems with ordinal responses were scored by dividing one by the response count.. To yield an integer, the weight of each item was multiplied by a factor of 120, yielding \u003cem\u003e\u0026lsquo;Q\u0026rsquo;\u003c/em\u003e. The \u0026lsquo;\u003cem\u003eQ\u003c/em\u003e\u0026rsquo;s for each item in each subsection were summed to \u0026lsquo;\u003cem\u003eE\u003c/em\u003e\u0026rsquo; for each subsection: \u0026lsquo;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eAC\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eED\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eOU\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026rsquo;\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA multiple ordinal logistic regression was then run with level of hospital as the outcome, based on the predictor variables \u0026lsquo;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eSubsection\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026rsquo;\u003c/em\u003e. Coefficients for each predictor were multiplied by the respective \u0026lsquo;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eSubsection\u003c/em\u003e\u003c/sub\u003e\u0026rsquo; and summed to yield the GAPS score. Missing values were treated as a score of zero. The score was then rounded to the nearest whole integer.\u003c/p\u003e \u003cp\u003eFollowing is a formulaic representation of the analyses:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eScore of each item * 120\u0026thinsp;=\u0026thinsp;\u003cem\u003eQ1 : Q64\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSum(\u003cem\u003eQ1:Q9\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/sub\u003e ; Sum (\u003cem\u003eQ10:Q48\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/sub\u003e ; Sum(\u003cem\u003eQ49: Q51\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eAC\u003c/em\u003e\u003c/sub\u003e ; Sum(\u003cem\u003eQ52:Q54\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eED\u003c/em\u003e\u003c/sub\u003e ; Sum(\u003cem\u003eQ55:Q64\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eOU\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e \u003cb\u003eGAPS Score\u003c/b\u003e = (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/sub\u003e*Coeff\u003csub\u003eHR\u003c/sub\u003e) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/sub\u003e*Coeff\u003csub\u003eMR\u003c/sub\u003e) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eAC\u003c/em\u003e\u003c/sub\u003e*Coeff\u003csub\u003eAC\u003c/sub\u003e) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eED\u003c/em\u003e\u003c/sub\u003e*Coeff\u003csub\u003eED\u003c/sub\u003e) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eOU\u003c/em\u003e\u003c/sub\u003e*Coeff\u003csub\u003eOU\u003c/sub\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Correlation of GAPS score with capacity and outcomes\u003c/h2\u003e \u003cp\u003eData for these analyses was retrieved from the pilot study phase (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The tool was piloted in person between February 2017 and March 2018. Each survey was completed with the aid of a local senior medical representative. Countries were chosen pragmatically based on their geographical localization, ease of access, safety for travel, and number of accessible institutions.\u003c/p\u003e \u003cp\u003eThe primary outcome was level of hospital care (1st level, 2nd level, 3rd level and national children\u0026rsquo;s hospital). Level of hospital was predefined by the corresponding countries ministry of health. Secondary outcomes included country level of income as per World Bank 2018 and 2017 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), human development index (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), under-five mortality rate (per 1,000 live births) (U5MR), and neonatal mortality rate (NNMR), deaths due to congenital anomalies (CA) per 1,000 births (0\u0026ndash;27 days, 1\u0026ndash;59 months, and 0\u0026ndash;4 years), and deaths due to injuries (INJ) per 1,000 births (0\u0026ndash;27 days, 1\u0026ndash;59 months, and 0\u0026ndash;4 years).\u003c/p\u003e \u003cp\u003eAnalyses consisted of the Kruskal-Wallis Test and linear regression, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. All analyses were performed in STATA/MP 13.0 (StataCorp, College Station, TX).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 GAPS Score Benchmarks\u003c/h2\u003e \u003cp\u003eBenchmark GAPS scores for each hospital level were derived from the 75th and 90th percentiles, then analyzed via dot plots to establish cut-offs maximizing data point inclusion.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Determining GAPS score weights\u003c/h2\u003e \u003cp\u003eThe GAPS pilot encompassed 65 institutions in eight countries, including 28 1st level (43%), 23 2nd level (35%), 11 3rd level (17%), and three national children’s hospitals (5%). Most countries included in the pilot study were Low-Income Countries (LIC: Somaliland, Democratic Republic of Congo (DRC), Uganda). The DRC comprised 63% of institutions. Hospitals were most frequently public (45/65; 69%), followed by faith-based (15/65; 25%). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\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\u003eDescriptive Statistics of Pilot Data by Level of Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Level \u003c/p\u003e \u003cp\u003e(n = 28)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2nd Level \u003c/p\u003e \u003cp\u003e(n = 23)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3rd Level \u003c/p\u003e \u003cp\u003e(n = 11)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNational Children’s Hospital\u003c/p\u003e \u003cp\u003e(n = 3)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLevel of Income\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMIC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUMIC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomaliland\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemocratic Republic of Congo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMongolia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlgeria\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVietnam\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eType of Hospital\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic / Government\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Governmental Organization\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaith-based\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPS Score [median (IQR)]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eGAPS Subsection Scores [median (IQR)]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Resources\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290 (300)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e480 (450)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1020 (400)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial Resources\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1005 (770)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2380 (1040)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3180 (1000)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4260 (1360)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (96)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (258)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360 (84)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (120)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (180)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e660 (540)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e900 (840)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (80)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (120)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e280 (80)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLIC: Low-Income Country, LMIC: Low-Middle Income Country, UMIC: Upper-Middle Income Country, IQR: Inter-quartile range\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe GAPS Score equation (\u003cb\u003eAppendix A\u003c/b\u003e) is detailed below:\u003c/p\u003e \u003cp\u003e \u003cb\u003eGAPS Score\u003c/b\u003e = (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/sub\u003e * 0.003297) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/sub\u003e * 0.0020106) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eAC\u003c/em\u003e\u003c/sub\u003e * 0.0008398) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eED\u003c/em\u003e\u003c/sub\u003e * 0.0026368) + (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eOU\u003c/em\u003e\u003c/sub\u003e * 0.0042668)\u003c/p\u003e \u003cp\u003eThe resultant possible range of values are 0.0453492–17.577264. The score is then rounded to the nearest integer, resulting in the final \u003cb\u003eGAPS Score\u003c/b\u003e (range of values from 0–18).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation of GAPS score with capacity and outcomes\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Capacity\u003c/h2\u003e \u003cp\u003eThe range of values for the GAPS score in our pilot data for the 65 institutions was 1–16 with a median of 6 and an interquartile range (IQR) of 5. Median GAPS scores significantly increased with level of hospital (p = 0.0001) (Fig.\u0026nbsp;1\u003cb\u003e)\u003c/b\u003e. Furthermore, GAPS score significantly increased with level of income (p = 0.0001, \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e). When evaluating hospital type, median GAPS score was lowest in public / government hospital (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and faith-based hospital (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), followed by private (7.5), and non-governmental organizations (10.5).\u003c/p\u003e \u003cp\u003eAll subsection scores progressively and significantly rose with each hospital level. Human resources and material resources subsection scores significantly increased with level of income (p = 0.0001, p = 0.0004, respectfully). Hospital type was only significant in the human resources subsection score (p = 0.0491). (\u003cb\u003eAppendix B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, a linear regression of GAPS Score with country income was significant (p \u0026lt; 0.001) between LIC and Low- and Middle-Income Countries (LMIC), and between 1st level and all other hospital levels. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear Regression Analysis of GAPS Score to Human development Index, Under 5 Mortality Rate, and Neonatal Mortality Rate.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGAPS Score\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression Coefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Development Index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01–0.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder 5 Mortality Rate (per 1,000 live births)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e− 4.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(− 6.10) – (− 2.85)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal Mortality Rate (per 1,000 live births)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e− 1.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(− 1.66) – (− 0.75)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths due to Congenital Anomalies (per 1,000 live births)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0–27 days\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00–0.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–59 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e− 0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-)0.03–0.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0–4 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-) 0.02–0.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths due to Injury (per 1,000 live births)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0–27 days\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e− 0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-) 0.03 – (-) 0.15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–59 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e− 0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-)0.30 – (-) 0.14\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0–4 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e− 0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-)0.32 – (-) 0.15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Outcomes\u003c/h2\u003e \u003cp\u003eThe GAPS score showed a significant association with HDI (p \u0026lt; 0.001), U5MR (p \u0026lt; 0.001) and NNMR (p \u0026lt; 0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eAppendix C\u003c/b\u003e). Linear regression analysis of GAPS score to U5MR identified that for each unit increase in GAPS score the expected value of U5MR decreases by 4.5 units. Furthermore, a substantial proportion (33%) of the variability in U5MR is explained by the GAPS score. Similarly, for each unit increase in GAPS score the expected value of NNMR decreases by 1.2 units and explains a large proportion (31%) of the variability within NNMR. Regarding HDI, each unit increase in GAPS score is associated with an increase in HDI of 0.02 units.\u003c/p\u003e \u003cp\u003eThe GAPS score showed a significant increase as the number of deaths due to injuries declined across all age groups (0–27 days, p \u0026lt; 0.001; 1–59 months, p \u0026lt; 0.001; 0–4 years, p \u0026lt; 0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, no significant relationship was found between the GAPS score and deaths due to congenital anomalies. While the p-value for deaths due to congenital anomalies in neonates (0–27 days) was statistically significant, the corresponding 95% confidence interval included zero, indicating that this finding does not reflect a statistically significant effect (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 GAPS Score Benchmarks\u003c/h2\u003e \u003cp\u003eDot graphs (Fig.\u0026nbsp;2) show the number of institutions captured by the GAPS score benchmarks for each level of hospital (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The GAPS benchmarks correctly captured 78% (21/27) of hospital identified as 1st level, 78% (18/23) of 2nd level, (9/11) 82% of 3rd level, and (2/3) 67% of National Children’s Hospitals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBenchmarks Values for GAPS Score by Level of Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAPS Score\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman Resources\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaterial Resources\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Level Hospital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e≤ 4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e≤ 200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e≤ 2000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e≤ 105\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e≤ 120\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e≤ 80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd Level Hospital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5–8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201–560\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2001–3560\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106–200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121–400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81–150\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd Level Hospital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9–14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e561–800\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3561–4090\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201–359\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e401–799\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e151–300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Children’s Hospital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e≥\u003c/em\u003e 15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e≥\u003c/em\u003e 801\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e≥\u003c/em\u003e 4091\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e≥\u003c/em\u003e 360\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e≥\u003c/em\u003e 800\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e≥\u003c/em\u003e 301\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn the 1st level hospital group, six hospitals scored above the benchmark (≤ 4) (Fig.\u0026nbsp;2): centers 12, 23, 40, 43, 54 and 64 (\u003cb\u003eAppendix D, Fig.\u0026nbsp;3\u003c/b\u003e) located in Mongolia, Algeria, and the DRC (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eIn the 2nd level hospital group, five hospitals scored outside the benchmark GAPS scores (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e): three below and two above (Fig.\u0026nbsp;2). Despite identifying as 2nd level hospitals, centers 34, 35, and 36 located in the DRC scored below benchmark values (Fig.\u0026nbsp;3). Center 1, located in Vietnam and Center 55, located in the DRC scored within benchmarks for 3rd level hospitals (Figs.\u0026nbsp;2 and 3, \u003cb\u003eAppendix D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFor 3rd level hospitals, two hospitals scored below the benchmark (\u0026lt; 9) and none above (\u0026gt; 14) (Fig.\u0026nbsp;2). Center 10, located in Mongolia, scored below benchmarks for 3rd level hospitals in all sections. Center 44, located in the DRC, scored above benchmark levels for all section except education. (Figs.\u0026nbsp;2 and 3, \u003cb\u003eAppendix D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFor National Children’s Hospitals, one hospital scored below the benchmark (Fig.\u0026nbsp;2). Center 2, located in Vietnam, scored below benchmarks for all sections except human resources (Figs.\u0026nbsp;2 and 3, \u003cb\u003eAppendix D\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe GAPS tool, based on a 2019 systematic review of CATs and surgical guidelines, was developed through multiple iterations with a 37-person expert panel, international testing, and item refinement. It includes 64 questions across five domains: Human Resources, Material Resources, Outcomes, Accessibility, and Education (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe introduction of a capacity-based weighting system to the GAPS tool represents a methodological advancement compared to previously published CATs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In a study of the \u003cem\u003eService Availability and Readiness Assessment\u003c/em\u003e across six countries, O'Neill et al highlight the importance of tailored weighting in health assessment tools to improve the accuracy of capacity evaluations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Assigning differential weights to the various domains of pediatric surgical capacity allows the GAPS tool to be more closely aligned with the multifaceted reality of hospital functionality. This approach of capacity-based weighting is consistent with the multi-dimensional assessment frameworks proposed by Cometto et al, highlighting the necessity of encapsulating the intricacies of health systems within evaluation instruments (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe GAPS weighting system marks a significant departure from the equal weighting paradigm used in homogeneous item scales such as the \u003cem\u003eSurgeons OverSeas Pediatric - Personnel, Infrastructure, Procedure, Equipment, and Supplies (Pedi-PIPES)\u003c/em\u003e (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Unlike Pedi-PIPES, which applies equal weighting across all its items, GAPS employs a dual approach: it uses equal weighting for items within each subscale but adopts differential weighting for the subscales themselves. This method allows for a more accurate representation of hospital capacity, recognizing the differential impact of individual domains on overall surgical capability (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This approach aligns with DeVellis' concept that differential weighting enhances the sensitivity of tools in heterogeneous settings (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe resultant GAPS score revealed significant correlations with hospital levels and income categories, as well as delineating differences across different hospital types. This score serves not merely as a gauge of capacity, but as a mirror reflecting the intricate interplay between a facility's resources and the socioeconomic milieu in which it operates. The study's findings underscore a positive correlation between GAPS scores and hospital levels, a relationship consonant with the stratified nature of healthcare systems (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Similarly, the association between GAPS scores and income levels reinforces the influence of economic status on healthcare quality and availability (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn terms of outcomes, the GAPS score's significant association with health indicators such as U5MR and NNMR substantiates its validity as a performance measure (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, the score correlates with the number of child deaths due injury, accentuating its potential as an indicator of pediatric surgical outcomes. While we found no significant correlation between the GAPS score and deaths from congenital anomalies, we attribute this to the limited sample size of the participating institutions. The score therefore reinforces the connection between health system inputs and health outcomes(\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe establishment of benchmark GAPS scores delineates a clear framework for assessing pediatric surgical capacity across hospital levels, reflecting a critical step towards standardized performance metrics in low- and middle-income countries. These benchmarks provide a tangible target for institutions striving to enhance their surgical services. In our analysis, the benchmarks effectively discriminated between different hospital levels, corroborating the tiers of healthcare delivery described by Hsia et al., who emphasize the importance of clear standards in evaluating service capacity (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The performance analysis against these benchmarks revealed that most hospitals conformed to expected levels, yet the outliers offer valuable insights. Certain first-level hospitals, notably in Mongolia and Algeria, surpassed their benchmark scores, suggesting a potential overqualification for their designated level, or a misalignment in hospital categorization (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Conversely, some second-level hospitals, particularly in the DRC, fell short of their benchmarks, highlighting areas for targeted capacity-building, in line with strategies advocated by Grimes et al. for strengthening surgical systems (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). These outliers underscore the variability within categories and suggest that while benchmarks are vital for goal setting and evaluation, they must be contextualized within the specific health ecosystem and resource availability of each hospital.\u003c/p\u003e\u003cp\u003eThe cross-sectional analysis of hospital performance using the GAPS score highlights notable disparities, with some institutions out- or under-performing relative to their designated levels. Hospitals that scored above their anticipated benchmark, such as those in Mongolia, may be indicative of successful policy implementations that have bolstered their surgical capacity.. Conversely, hospitals scoring below their expected level, particularly 2nd level hospitals in the DRC, may be experiencing systemic challenges such as resource constraints, inadequate training, or governance issues (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This mirrors previous findings of the DRC’s significant barriers to increasing surgical capacity (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e–\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Understanding the underlying factors contributing to these performance variations is crucial, as it can inform policy changes, guide the optimization of international aid, and may, improve surgical outcomes.\u003c/p\u003e\u003cp\u003eCompared to other CATs explored in our systematic review, GAPS uniquely integrates a more granular, multidimensional analysis of pediatric surgical services (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Unlike prior tools focusing inventorying material resources, GAPS’s inclusive framework ensures that both tangible and previously intangible facets of surgical capacity are accounted for, providing a more holistic evaluation.\u003c/p\u003e\u003cp\u003eGAPS’s capacity-based weighting system, not found in other CATs, reflects the complexity of health service delivery in low- and middle-income countries. It stand as a unique contribution to the field and a potential to guide to refined resource allocation, training initiatives, and policy reforms for pediatric surgery (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe methodological rigor underpinning the development and validation of the GAPS tool constitutes a significant strength of this research. The systematic approach, informed by a comprehensive review and subsequent refinement phases, ensures that the tool is both inclusive and reflective of the multifaceted nature of surgical capacity in low-resource settings. The iterative process of development provided a robust construct validation, enhancing the tool's credibility and applicability (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Nonetheless, the reliance on pilot data from limited geographical settings may impact the generalizability of the findings. However, any tool's responsiveness to changes over time remains to be evaluated (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Future research could address these limitations by expanding the validation process to include a broader range of countries, potentially employing machine learning techniques to refine the weighting system for simplicity and efficacy (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Furthermore, the reliance on surveys completed with the aid of local senior medical representatives a clear source of reporting bias. Moreover, our study relies on internal data and pilot testing for validation. External validation with a broader range of institutions in different settings would be necessary to ensure the tool’s applicability and reliability across diverse global health contexts. Additionally, the study does not encompass long-term follow-up data to evaluate temporal changes. Incorporating longitudinal studies would offer valuable perspectives on the tool's responsiveness to evolving surgical capacities, thereby enhancing its effectiveness in ongoing quality enhancement (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e–\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Another important limitation is the lack of integration of surgical output in the GAPS tool. Stewart et al previously noted that assessments of surgical capacity have not adequately correlated with actual surgical output(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). We concur that material-focused CATs are inadequate in predicting surgical output. To mitigate this, the GAPS tool incorporates additional dimensions (human resources, accessibility, outcomes, and education) moving beyond an inventory-based approach. While GAPS offers a more panoramic view, its effectiveness in directly quantifying surgical output has not been validated. A thorough evaluation of surgical procedures and outcomes is necessary for an accurate assessment of surgical output, an area beyond the scope of this study but vital for future research. We envision GAPS as a pivotal tool in future research, enhancing the evaluation of both surgical capacity and output.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eGAPS quantifies pediatric surgical services in low-resource settings, offering a structured method for identifying healthcare disparities and prioritizing needs. The capacity-based weighting system offers a granular assessment of hospital capabilities, thus enabling interested parties to transcend traditional resource inventories and engage in more refined evaluation of service readiness. This specificity is crucial in guiding partnerships, ensuring collaborations are driven by precisely identified needs, thereby enhancing their efficiency and impact. Moreover, the GAPS tool facilitates targeted interventions by providing benchmarks reflective of service levels. The GAPS tool serves as an instrumental resource in the concerted effort to improve pediatric surgical outcomes through data-driven, context-specific strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFUNDING\u003c/u\u003e\u003c/strong\u003e :\u003c/p\u003e\n\u003cp\u003eFonds de Recherche du Qu\u0026eacute;bec - Sant\u0026eacute; (FRQS)\u003c/p\u003e\n\u003cp\u003eCanadian Institutes of Health Research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eCONFLICT OF INTEREST STATEMENT BY AUTHORS:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYasmine Yousef\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026ndash; NONE\u003c/p\u003e\n\u003cp\u003eEmmanuel Ameh \u0026ndash; NONE\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLuc Malemo Kalisya\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026ndash; NONE\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDan Poenaru \u0026ndash; NONE\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDATA AVAILABILITY STATEMENT\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy of the institutions studied. Data are however available from the authors upon reasonable request and with permission of the Pediatric Research Ethics Board of the McGill University Health Centre Research Institute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eACKNOWLEDGEMENTS\u003c/u\u003e\u003c/strong\u003e :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecial thanks to the following: Kevyn Armand, Daniel Agbo-Panzo, Marilyn Butler, Beda Espineda, Kenzy Abdel-Hamid, Sonia Anchouche, Ai-Xuan Holterman, George W. Galiwango, Nathalie MacKinnon,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRitesh Shrestha, Desigen Reddy, William Harkness, Phyllis Kisa, Rashmi Kumar, Ibrahima Fall, Justin Onen, Ananda K. Lamahewage, Mike Ganey, Tessa Concepcion, Peter Donkor, Zaitun Bokhary, Anette S. Jacobsen, Reju Thomas, Brouh Yapo, Adiyasuren Jamiyanjav, Marcia Matias, Benno Ure, Vanda Amado, Bahati Robert, Lubna Samad and Roumanatou Bankol\u0026eacute;, Sarah Cairo, Etienne St-Louis, Laura F. Goodman, Doulia M. Hamad, Robert Baird, Emily R. Smith, Sherif Emil, Jean-Martin Laberge, Robin Petroze, Mohamed Abdelmalak, Zipporah Gathuy, Faye Evans, Maryam Ghavami Adel, Ki K. Bertille, Milind Chitnis, Leecarlo Millano, Peter Nthumba, Sergio d\u0026apos;Agostino, Bruno Cigliano, Luis Zea-Salazar, Doruk Ozgediz, and Elena Guadagno.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYousef Y, Lee A, Ayele F, Poenaru D. Delayed access to care and unmet burden of pediatric surgical disease in resource-constrained African countries. J Pediatr Surg. 2019;54(4):845-53.\u003c/li\u003e\n\u003cli\u003eSmith ER, Concepcion TL, Shrime M, Niemeier K, Mohamed M, Dahir S, et al. Waiting Too Long: The Contribution of Delayed Surgical Access to Pediatric Disease Burden in Somaliland. World J Surg. 2020;44(3):656-64.\u003c/li\u003e\n\u003cli\u003eBickler SW, Rode H. Surgical services for children in developing countries. 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Lancet Glob Health. 2018;6(12):e1397-e404.\u003c/li\u003e\n\u003cli\u003eOkoye MT, Ameh EA, Kushner AL, Nwomeh BC. A pilot survey of pediatric surgical capacity in West Africa. World J Surg. 2015;39(3):669-76.\u003c/li\u003e\n\u003cli\u003eStreiner DL. Starting at the beginning: an introduction to coefficient alpha and internal consistency. J Pers Assess. 2003;80(1):99-103.\u003c/li\u003e\n\u003cli\u003eAnand S, Barnighausen T. Human resources and health outcomes: cross-country econometric study. Lancet. 2004;364(9445):1603-9.\u003c/li\u003e\n\u003cli\u003eRoa L, Jumbam DT, Makasa E, Meara JG. Global surgery and the sustainable development goals. Br J Surg. 2019;106(2):e44-e52.\u003c/li\u003e\n\u003cli\u003eTruche P, Smith ER, Ademuyiwa A, Buda A, Nabukenya MT, Kaseje N, et al. Defining Surgical Workforce Density Targets to Meet Child and Neonatal Mortality Rate Targets in the Age of the Sustainable Development Goals: A Global Cross-Sectional Study. World J Surg. 2022;46(9):2262-9.\u003c/li\u003e\n\u003cli\u003eLandrum K, Cotache-Condor CF, Liu Y, Truche P, Robinson J, Thompson N, et al. Global and regional overview of the inclusion of paediatric surgery in the national health plans of 124 countries: an ecological study. BMJ Open. 2021;11(6):e045981.\u003c/li\u003e\n\u003cli\u003eSaxton AT, Poenaru D, Ozgediz D, Ameh EA, Farmer D, Smith ER, et al. Economic Analysis of Children\u0026apos;s Surgical Care in Low- and Middle-Income Countries: A Systematic Review and Analysis. PLoS One. 2016;11(10):e0165480.\u003c/li\u003e\n\u003cli\u003eSeyi-Olajide JO, Anderson JE, Kaseje N, Ozgediz D, Gathuya Z, Poenaru D, et al. Inclusion of Children\u0026apos;s Surgery in National Surgical Plans and Child Health Programmes: the need and roadmap from Global Initiative for Children\u0026apos;s Surgery. Pediatr Surg Int. 2021;37(5):529-37.\u003c/li\u003e\n\u003cli\u003eSmith ER, Concepcion TL, Niemeier KJ, Ademuyiwa AO. Is Global Pediatric Surgery a Good Investment? World J Surg. 2019;43(6):1450-5.\u003c/li\u003e\n\u003cli\u003eHsia RY, Mbembati NA, Macfarlane S, Kruk ME. Access to emergency and surgical care in sub-Saharan Africa: the infrastructure gap. Health Policy Plan. 2012;27(3):234-44.\u003c/li\u003e\n\u003cli\u003eGrimes CE, Law RS, Borgstein ES, Mkandawire NC, Lavy CB. Systematic review of met and unmet need of surgical disease in rural sub-Saharan Africa. World J Surg. 2012;36(1):8-23.\u003c/li\u003e\n\u003cli\u003eFarmer PE, Kim JY. Surgery and global health: a view from beyond the OR. World J Surg. 2008;32(4):533-6.\u003c/li\u003e\n\u003cli\u003eCairo SB, Kalisya LM, Bigabwa R, Rothstein DH. Characterizing pediatric surgical capacity in the Eastern Democratic Republic of Congo: results of a pilot study. Pediatr Surg Int. 2018;34(3):343-51.\u003c/li\u003e\n\u003cli\u003eCairo SB, Pu Q, Malemo Kalisya L, Fadhili Bake J, Zaidi R, Poenaru D, et al. Geospatial Mapping of Pediatric Surgical Capacity in North Kivu, Democratic Republic of Congo. World J Surg. 2020;44(11):3620-8.\u003c/li\u003e\n\u003cli\u003eKalisya LM, Yap A, Mitume B, Salmon C, Karafuli K, Poenaru D, et al. Determinants of Access to Essential Surgery in the Democratic Republic of Congo. J Surg Res. 2023;291:480-7.\u003c/li\u003e\n\u003cli\u003ePrice R, Makasa E, Hollands M. World Health Assembly Resolution WHA68.15: \u0026quot;Strengthening Emergency and Essential Surgical Care and Anesthesia as a Component of Universal Health Coverage\u0026quot;-Addressing the Public Health Gaps Arising from Lack of Safe, Affordable and Accessible Surgical and Anesthetic Services. World J Surg. 2015;39(9):2115-25.\u003c/li\u003e\n\u003cli\u003eTerwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60(1):34-42.\u003c/li\u003e\n\u003cli\u003eFayers PM, Machin D. Quality of life : the assessment, analysis, and reporting of patient-reported outcomes. Third edition. ed. Chichester, West Sussex, UK ; Hoboken, NJ: John Wiley \u0026amp; Sons Inc.; 2016. p. p.\u003c/li\u003e\n\u003cli\u003eObermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;375(13):1216-9.\u003c/li\u003e\n\u003cli\u003eDebas H, Alatise OI, Balch CM, Brennan M, Cusack J, Donkor P, et al. Academic Partnerships in Global Surgery: An Overview American Surgical Association Working Group on Academic Global Surgery. Ann Surg. 2020;271(3):460-9.\u003c/li\u003e\n\u003cli\u003ePhilipo GS, Nagraj S, Bokhary ZM, Lakhoo K. Lessons from developing, implementing and sustaining a participatory partnership for children\u0026apos;s surgical care in Tanzania. BMJ Glob Health. 2020;5(3):e002118.\u003c/li\u003e\n\u003cli\u003eNaluyimbazi R, Nimanya S, Kisa P. Anatomy and lessons of partnerships in global pediatric surgery. Semin Pediatr Surg. 2023;32(6):151353.\u003c/li\u003e\n\u003cli\u003eStewart BT, Gyedu A, Gaskill C, Boakye G, Quansah R, Donkor P, et al. Exploring the Relationship Between Surgical Capacity and Output in Ghana: Current Capacity Assessments May Not Tell the Whole Story. World J Surg. 2018;42(10):3065-74.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pediatric-surgery-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pesi","sideBox":"Learn more about [Pediatric Surgery International](http://link.springer.com/journal/383)","snPcode":"383","submissionUrl":"https://submission.nature.com/new-submission/383/3","title":"Pediatric Surgery International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"pediatric surgery, children’s surgery, global surgery, global health, low-income country, middle-income country, capacity assessment","lastPublishedDoi":"10.21203/rs.3.rs-4595115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4595115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eThe Global Assessment for Pediatric Surgery (GAPS) tool was developed to enhance pediatric surgical care in Low- and Middle-Income Countries. This study presents the addition of a capacity-based weighting system to the GAPS tool.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eGAPS, developed through a multi-phase process including systematic review, international testing, item analysis, and refinement, assesses 64 items across five domains: human resources, material resources, education, accessibility, and outcomes. This new weighting system differentially weighs each domain. The GAPS Score was evaluated using pilot study data, focusing on hospital and country income levels, human development index, under-five mortality rate, neonatal mortality rate, deaths due to injury and deaths due to congenital anomalies. Analysis involved the Kruskal-Wallis test and linear regression. Benchmark values for the GAPS overall score and subsection scores were identified.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe GAPS score\u0026rsquo;s capacity-based weighting system effectively discriminated between levels of hospital (p\u0026thinsp;=\u0026thinsp;0.0001) and country income level (p\u0026thinsp;=\u0026thinsp;0.002). The GAPS scores showed significant associations with human development index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and key health indicators such as under-five mortality rates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), neonatal mortality rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and deaths due to injury (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Benchmark scores for the GAPS overall score and the subsection scores included most institutions within their respective hospital level.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThe GAPS tool and score, enhanced with the capacity-based weighting system, marks progress in assessing pediatric surgical capacity in resource-limited settings. By mirroring the complex reality of hospital functionality in low-resource centers, it provides a refined mechanism for fostering effective partnerships and data-driven strategic interventions.\u003c/p\u003e","manuscriptTitle":"Gaps Phase Iii: Incorporation of Capacity Based Weighting in the Global Assessment for Pediatric Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 23:52:06","doi":"10.21203/rs.3.rs-4595115/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-17T21:25:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-05T01:15:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334204933667243211695660140820911479366","date":"2024-09-03T00:02:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-11T23:49:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97525812175679837473235994504322690979","date":"2024-07-22T12:15:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-17T19:30:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-18T10:09:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-18T06:56:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Surgery International","date":"2024-06-17T15:30:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pediatric-surgery-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pesi","sideBox":"Learn more about [Pediatric Surgery International](http://link.springer.com/journal/383)","snPcode":"383","submissionUrl":"https://submission.nature.com/new-submission/383/3","title":"Pediatric Surgery International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"351f0cc5-ba47-44c8-83ea-d71d226d4126","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T15:59:35+00:00","versionOfRecord":{"articleIdentity":"rs-4595115","link":"https://doi.org/10.1007/s00383-024-05870-2","journal":{"identity":"pediatric-surgery-international","isVorOnly":false,"title":"Pediatric Surgery International"},"publishedOn":"2024-11-06 15:57:10","publishedOnDateReadable":"November 6th, 2024"},"versionCreatedAt":"2024-07-15 23:52:06","video":"","vorDoi":"10.1007/s00383-024-05870-2","vorDoiUrl":"https://doi.org/10.1007/s00383-024-05870-2","workflowStages":[]},"version":"v1","identity":"rs-4595115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4595115","identity":"rs-4595115","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}