The Impact of Hospital Accreditation on Health Utilization and Outcome in Rwanda: An Interrupted Time Series Analysis

The Impact of Hospital Accreditation on Health Utilization and Outcome in Rwanda: An Interrupted Time Series Analysis | 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 The Impact of Hospital Accreditation on Health Utilization and Outcome in Rwanda: An Interrupted Time Series Analysis Corneille Killy Ntihabose, Modeste Gashayija, Jean Baptiste Ntihumbya, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7379706/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Over the past 30 years, Rwanda has improved health outcomes and access to care, though progress has slowed in the last decade. In 2013, the government launched a hospital accreditation program to enhance efficiency and care quality. The program has 79 measurable standards evaluated at three levels. Level 1 consists of having policies, guidelines, and plans in place, and when there is evidence that health facilities implement those documents, level 2 is achieved. If data is used to guide clinical and quality improvement decisions, level 3 is also achieved. The level 1 program was implemented from 2013 to 2021, and in 2022, the country shifted to level 2. However, there is no evidence that this upgrade in the level of evaluation has resulted in improved clinical outcomes and service utilization. Methods: Using routine data from February 2020 to December 2024, we employed interrupted time series analysis to evaluate the impact of increasing the level of evaluation of the hospital accreditation program to 10 outcome indicators. The interruption period was March 2022, we took 24 monthly observations before and 33 monthly observations after the intervention period. Findings: The upgrading of the evaluation level was associated with a statistically significant immediate monthly increase in hospital admissions per 10,000 population (β = 2.08; 95% CI: 0.44 to 3.72) and a monthly reduction in post-surgical infection rates (β = -0.82; 95% CI: -1.30 to -0.34). Statistically significant monthly trends were also observed for admissions (β = -0.14; 95% CI: -0.26 to -0.02), neonatal asphyxia rate (β = 0.05; 95% CI: 0.03 to 0.06), and the proportion of newborns not breathing who were successfully resuscitated (β = -0.26; 95% CI: -0.41 to -0.10). No immediate or trend changes in outpatient visits per 10,000, peri-operative mortality, under-5 mortality, neonatal mortality, intrahospital mortality, and maternal mortality rates. Conclusion: We observed that upgrading the level of evaluation of Rwanda hospital's accreditation was associated with improvements in selected outcome indicators, others showing concerning trends and several other indicators showed limited or no response. We recommend Policy makers to continue investment in accreditation but also implementing targeted interventions to address persistent gaps in service quality and health outcomes. Accreditation Healthcare quality Interrupted time series analysis Figures Figure 1 Figure 2 BACKGROUND Over the last two decades, Rwanda has made notable progress in improving health outcomes and expanding healthcare access. The country has registered an 80% reduction in maternal and under-five mortalities [ 1 , 2 ]. This was achieved through a decentralized, equity-focused health system and a strong network of community health workers, who deliver essential health services at the community level. The country's achievement of universal health coverage (UHC) through a regular and consistent enrollment rate of over 90% in the community-based health insurance scheme, known as Mutuelle de Santé, has been the driver of this success [ 3 ]. These results reflect substantial investments in service delivery, health financing, human resources, and health infrastructure. Even though there have been significant improvements in access and equity, there are reports that show inconsistencies in clinical practices, inadequate infection prevention, and differences among hospitals in adhering to clinical standards [ 4 – 7 ]. Evidence supports that the hospital accreditation improves safety culture, process-related performance measures, efficiency, and length of stay [ 8 ]. To this note, Rwanda adopted the hospital accreditation program in 2013 to improve the persistent bottlenecks in the quality of health care. The accreditation was then linked to the performance-based financing, which was implemented since the early 2000s. There had been regular annual evaluations of hospitals against a set of standards, aiming at assessing hospital governance, human resources management, safety and hygiene, clinical care delivery, and quality improvements and overall the improved care. An independent organization has been established to coordinate these evaluations, aiming objectivity and institutionalization of process. The program have been designed to be implemented in three phases depending on levels of evaluation. Phase one ended in 2021 with level 1, while in March 2022, Rwanda moved to phase 2. However, during the entire implementation of this program, no study has evaluated its impact on health outcomes. Therefore, our interest is to assess whether the transition from level 1 to level 2 of evaluation of accreditation program has resulted in measurable improvements in health service quality, using robust statistical methods to analyze trends across a set of health indicators and utilizing routine health data. METHOD Study Intervention Recognizing that access and equity alone are insufficient, Rwanda introduced the hospital accreditation program in 2013, institutionalizing quality assurance in clinical care delivery and driving improvements in patient safety, clinical effectiveness, and hospital governance [4,8]. This program represents a strategic, cost-effective initiative designed to improve healthcare quality and accountability through structured evaluation. It is implemented through annual assessments against 79 measurable standards organized within five risk areas: leadership and governance, human resources, infection control and hygiene, clinical care, and quality improvement [8]. Rwanda has expanded the accreditation to private hospitals and health centers, which further institutionalized quality health care. This program has a three-point scale to assess the institutional performance: At level 1: The health facility must have key policies and develop relevant procedures. For example, among 79 standards, there is a standard on pain assessment, reassessment, and appropriate management. To get a full mark during the evaluation, the hospital must demonstrate that there is a policy, procedure, guideline, and tools (including assessment or pain score cards) approved by hospital management that are used by clinical staff in examining, re-examining, and managing patients' pain. At level 2: There is evidence of consistent implementation of the policies. Based on the same example of the standard in pain management, evaluators check medical records if there is documentation by clinical staff of the assessment, reassessment, and management of pain from admission to discharge. At level 3: There is clear and sustained monitoring of quality improvement using data. Again, on the same standard, if the health facility evaluates the patient pain management process for effectiveness and improvements. Assessments of compliance with accreditation standards are conducted by trained evaluators using Rwanda Hospital Accreditation Standards (supplementary material 1) , which promotes objectivity and consistency across facilities. The results are published, and hospitals are incentivized through Rwanda's performance-based Financing (PBF) based on scores from evaluations [4]. Initially, the national rollout emphasized documentation (Level 1). From March 2022 onward, the Ministry of Health prioritized the evaluation of Level 2, which focuses on operationalizing standards in day-to-day facility operations [4,8-9]. This includes demonstrable changes in infection prevention practices, patient safety culture, documentation and record-keeping, and use of clinical guidelines. Study Setting Rwanda, located in East Africa, has a population of over 14 million people and covers an area of 26,338 km². Administratively, the country is divided into four provinces and the City of Kigali. The annual per capita income remains modest, at approximately USD 999.7 [11]. In the health sector, services are provided by both public and private facilities, with public institutions constituting the majority. There are 1,863 public health facilities across the country, including 1,280 health posts, 518 health centers, eight medicalized health centers, and 57 hospitals [12]. This study focused on hospitals, as these are the institutions where the accreditation process initially began and where annual evaluations are routinely conducted. However, three major referral hospitals, the University Teaching Hospital of Kigali (CHUK), the University Teaching Hospital of Butare (CHUB), and Rwanda Military Hospital, were excluded due to their affiliation with international accreditation standards under the Council of Health Care Accreditation for Southern Africa (COHSASA). Five Specialized hospitals were also excluded, as they joined the national accreditation program at a later stage and are still being evaluated at Level 1. Ultimately, this study recruited 49 hospitals. Figure 1 summarizes the process of selection of hospitals included in the study. Data Source We used data from a single source, the Rwanda Health Management Information System (HMIS), which collects facility-level data every month across the country. Outcome measures To assess health system performance, we categorized indicators into five core domains of the accreditation program: leadership and governance, workforce competence, a safe environment and hygiene, patient clinical care, and quality improvement and safety. We selected indicators based on their relevance, availability, and alignment with global standards. For the leadership and governance domain, we used three measures that reflect the capacity of hospitals to manage service demand, maximize patient flow, and ensure there is effective leadership in care delivery: Outpatient visit rate : Outpatient consultations per 10,000 population; Admission rate : Number of admissions per 10,000 population; Intra-hospital mortality rate : Number of Intrahospital deaths per 100 hospital admissions. The competent workforce domain was assessed using: The proportion of newborns not breathing at birth who were successfully resuscitated upon birth . This measure indicates the capacity of staff to manage emergency newborn care. Under the safe environment and hygiene domain, we included: Post-surgical infection rate : the number of patients diagnosed with post-surgical infections divided by the total number of surgeries performed, multiplying by 100. The measure reflects the level of adherence to infection prevention and control policies and protocols. Three outcome indicators represented the clinical care of the patients' domain: Maternal mortality ratio (MMR): the number of maternal deaths per 100,000 total live births; Under-5 mortality rate : the number of under-5 deaths per 1,000; Neonatal mortality rate (NMR): the number of deaths of newborns per 1,000 live births. Lastly, the quality improvement and safety domain were assessed using: Perioperative mortality rate : the number of deaths occurring within 24 hours after surgery, dividing by the total number of surgeries, and multiplying by 100. Neonatal asphyxia birth rate : the number of newborns diagnosed with birth asphyxia per 1,000 live births. The indicator serves as a marker for both intrapartum care quality and newborn resuscitation capacity. Statistical Analysis We excluded six hospitals from the analysis due to insufficient reporting, defined as submitting less than 80% of their monthly data over the entire study period ( Figure 1 ). Missing data were minimal across the dataset. All variables had less than 1% missing values, except for two indicators: post-surgical infection rate (3.7%) and perioperative mortality rate (3.1%). Missing values were addressed using an interpolation method. All facility-level data were aggregated to the monthly level. We screened for extreme values using the median absolute deviation (MAD) method, applying a threshold of ±2.5 MAD from the median. Values identified as outliers were trimmed and treated as missing prior to imputation. We employed interrupted time series analysis. The interruption period was March 2022, we took 24 monthly observations before and 33 monthly observations after the intervention period. Linearity assumptions were assessed using residual plots versus fitted values. Evidence of nonlinearity was observed in the models for post-surgical infection rates and perioperative mortality rates. To address this, quadratic terms were included in the respective models. We assessed seasonality through visual inspection of time series plots and time series decomposition. Evidence of seasonal patterns was identified in the models for outpatient visits per 10,000, admissions per 10,000, and post-surgical infection rate. To account for these seasonal effects, Fourier terms were added to the models. We also examined autocorrelation, which is a common feature in time series data, using the Durbin-Watson (DW) test. We detected autocorrelations in the models for admissions per 10,000, post-surgical infection rate, perioperative mortality rate, and neonatal mortality rate, and we employed generalized least squares (GLS) regression to account for autocorrelated error structures. COVID-19 was considered as a potential confounder. In conceptual understanding, COVID-19 had impacted healthcare utilization and service delivery during the study period. To identify the best-fitting models, we performed likelihood ratio tests by comparing nested models. These tests evaluated whether the inclusion of additional terms, such as seasonal or nonlinear components, significantly improved model fit. We conducted all analyses using R version 4.4.3, and we set the statistical significance at a p-value < 0.05. RESULTS Table 1 summarizes monthly averages of 10 outcome indicators over the 57-month observation period. Outpatient visits per 10,000 population were 93.45 (79.73 -105.83), while hospital admissions per 10,000 were 26.78 (range: 21.63–31.42). For surgical outcomes, the post-surgical infection rate was 0.96 per 100 surgeries, ranging from 0.36 to 2.34. The peri-operative mortality rate was relatively low, 0.89 per 100 procedures (range: 0.44–1.63). Regarding child and neonatal health, the neonatal mortality rate was 24.87 per 1,000 live births, with monthly rates ranging from 17.43 to 34.46, while the under-5 mortality rate was higher at 73.02 per 1,000 live births (range: 57.53–94.53). The neonatal asphyxia rate was 3.57 per 100 births, with a range of 2.66 to 5.11. The intrahospital mortality rate was 2.36 per 100 admissions, with a range of 1.72 to 3.04. The institutional maternal mortality rate was 65.51 per 100,000 live births, but showed wide monthly variability, ranging from 13.69 to 149.93. Lastly, the proportion of newborns not breathing at birth who were successfully resuscitated was 75.30 per 100, with a range of 69.34 to 81.58. Table 1. Descriptive analysis of outcome indicators Indicators Mean Minimum Maximum Outpatient visits per 10,000 93.45 79.73 105.83 Admissions per 10,000 26.78 21.63 31.42 Post-surgical infection rate (per 100) 0.96 0.36 2.34 Peri-operative mortality rate (per 100) 0.89 0.44 1.63 Neonatal mortality rate (per 1,000 live births) 24.87 17.43 34.46 Under-5 mortality rate (per 1,000 live births) 73.02 57.53 94.53 Neonatal asphyxia rate (per 100) 3.57 2.66 5.11 Intrahospital mortality rate (per 100) 2.36 1.72 3.04 Institutional maternal mortality rate (per 100,000 live births) 65.51 13.69 149.93 Proportion of newborn not breathing successfully resuscitated (per 100) 75.30 69.34 81.58 On the impact of the intervention, we examined monthly changes across ten outcome indicators. For each indicator, we report both the immediate change and the post-intervention trend. Table 2 summarizes the results, and Figure 2 (a-j) illustrates immediate post-intervention changes and trends for each outcome indicator. Table 2. Change in indicators after upgrading the level of evaluation of accreditation Indicators Immediate change Post-intervention trend Estimate 95% CI P-value* Estimate 95% CI P- value* Outpatient visits per 10,000 0.12 -4.17, 4.40 0.956 -0.13 -0.42, 0.14 0.346 Admissions per 10,000 2.08 0.44, 3.72 0.016 -0.14 0.021 Post-surgical infection rate -0.82 -1.30, -0.34 0.001 -0.01 -0.03, 0.02 0.716 Peri-operative mortality rate -0.18 -0.51, 0.15 0.281 -0.11 -0.23, 0.02 0.087 Neonatal mortality rate -3.03 -7.10, 1.04 0.150 0.25 -0.01, 0.51 0.072 Under-5 mortality rate -5.12 -12.1,1.80 0.144 0.001 -0.42, 0.42 0.998 Neonatal asphyxia rate -0.16 -0.43, 0.11 0.239 0.05 0.03, 0.06 <0.001 Intrahospital mortality rate 0.14 -0.03, 0.32 0.101 0.005 -0.01, 0.02 0.393 Institutional maternal mortality rate -7.38 -34.8, 20.0 0.599 -1.11 -2.94,0.72 0.240 Proportion of newborn not breathing successfully resuscitated 2.39 -0.18, 4.95 0.067 -0.26 -0.41, -0.10 0.001 95%CI= 95% Confidence Interval, *Significance level: p-value= 0.05 Outpatient visits per 10,000 Table 2 and Figure 1A show that the intervention was associated with a nonsignificant immediate change in outpatient visits per 10,000 population (β = 0.12; 95% CI: -4.17 to 4.40, P-value = 0.956) and a nonsignificant change in trend (β = -0.13; 95% CI: -0.42 to 0.14, P-value = 0.346). Hospital admissions per 10,000 Following the intervention, there was a statistically significant immediate increase in admissions per 10,000 population (β = 2.08; 95% CI: 0.44 to 3.72, P-value=0.016), accompanied by a modest but significant decline in trend (β = -0.14; 95% CI: -0.26 to -0.02, P-value=0.021) ( Table 2 and Figure 1B) . Post-surgical infection Rate The intervention was associated with a statistically significant overall immediate reduction in the post-surgical infection rate (β = -0.82; 95% CI: -1.30 to -0.34, P-value=0.001), while the post-intervention trend remained stable (β = -0.01; 95% CI: -0.03 to 0.02, P-value=0.716) ( Table 2 and Figure 1C) . Perioperative Mortality rate The intervention was associated with a nonsignificant overall immediate reduction in the perioperative mortality rate (β = -0.18; 95% CI: -0.51 to 0.15, P-value= 0.281), and a nonsignificant downward trend (β = -0.11; 95% CI: -0.23 to 0.02, P-value=0.087) ( Table 2 and Figure 1D) . Neonatal Mortality Rate Overall, the intervention was associated with a nonsignificant immediate reduction in the neonatal mortality rate (β = -3.03; 95% CI: -7.10 to 1.04, P-value=0.150) and a nonsignificant upward trend (β = 0.25; 95% CI: -0.01 to 0.51, P-value=0.072) ( Table 2 and Figure 1E) . Neonatal Asphyxia Rate Overall, the intervention was associated with a nonsignificant immediate level reduction in neonatal asphyxia rate (β = -0.16; 95% CI: -0.43 to 0.11, P-value=0.239), but a statistically significant positive trend post-intervention (β = 0.05; 95% CI: 0.03 to 0.06, P-value= <0.001 ), suggesting a gradual increase in rates over time ( Table 2 and Figure 1F) . Under-5 Mortality Rate The intervention was associated with a nonsignificant overall immediate reduction in under-5 mortality rate (β = -5.12; 95% CI: -12.1 to 1.80, P-value=0.144), and no meaningful change in trend (β = 0.001; 95% CI: -0.42 to 0.42, P-value=0.998) ( Table 2 and Figure 1G) . Maternal Mortality Ratio Overall, the intervention was associated with a nonsignificant immediate reduction in institutional maternal mortality rate (β = -7.38; 95% CI: -34.8 to 20.0, P-value=0.599) and a nonsignificant decreasing trend post-intervention (β = -1.11; 95% CI: -2.94 to 0.72, P-value=0.240) ( Table 2 and Figure 1H) . Intrahospital Mortality Rate intervention was associated with a nonsignificant immediate increase in intrahospital mortality rate overall (β = 0.14; 95% CI: -0.03 to 0.32, P-value=0.101), and a stable trend over time (β = 0.005; 95% CI: -0.01 to 0.02, P-value=0.393) ( Table 2 and Figure 1I) . Proportion of Successful Resuscitation of Newborns Not Breathing at Birth The intervention was associated with a nonsignificant overall immediate increase in the proportion of newborns successfully resuscitated (β = 2.39; 95% CI: -0.18 to 4.95, P-value=0.067), and a significant downward trend over time (β = -0.26; 95% CI: -0.41 to -0.10, P-value=0.001) ( Table 2 and Figure 1J) . DISCUSSIONS This study assessed the overall impact of increasing the level of evaluation of accreditation on 10 health utilization and outcome indicators. Some statistically significant effects were observed, particularly in post-surgical infection rates, hospital admissions, neonatal asphyxia, and the proportion of newborns not breathing who were successfully resuscitated. Following the intervention, we observed a statistically significant immediate increase in hospital admissions per 10,000 population, accompanied by a modest but sustained decline in trend. The immediate increase suggests that the intervention, expanded service capacity, or enhanced patient referral pathways, may have improved access to or demand for inpatient care. This is consistent with prior evidence showing that targeted health system reforms often stimulate service uptake, particularly in previously underserved settings [13-14]. The subsequent decline in the monthly trend, however, may reflect a stabilization of care-seeking behavior after the initial surge. It is also possible that as hospitals implemented stricter clinical protocols or improved triage systems, standard features of accreditation programs, there was a reduction in unnecessary or inappropriate admissions, improving efficiency without reducing access. Similar patterns have been observed in other contexts where initial access improvements were followed by rationalization of service use as health systems matured [14,15]. The monthly reduction in the immediate post-surgical infection rate may reflect a reinforcement of infection prevention and control (IPC) protocols, including adherence to surgical safety checklists, improved hand hygiene compliance, sterile technique training, equipment sterilization standards, and regular audit practices. By enforcing these protocols, the program likely standardized surgical procedures, minimizing variation in care and reducing avoidable infections. While the accreditation upgrade may have prompted a rapid improvement in infection prevention practices, it did not lead to continuous monthly improvements thereafter. In the absence of continuous follow-up, supervision, or refresher training, the initial improvements may have plateaued, resulting in no further decline over time. Furthermore, facilities may have implemented most of the feasible infection control improvements immediately following the upgrade. Once these high-impact changes were in place, additional monthly gains became increasingly challenging to achieve, especially without further innovations or investments, leading to a flattening of the trend. The increasing trend in neonatal asphyxia may reflect a gradual diminution in clinical vigilance or competence in managing labor and delivery. Without sustained mentorship, on-the-job training, or clinical audits post-intervention, staff may have struggled to maintain high standards, leading to an accumulation of suboptimal birth outcomes such as asphyxia. This aligns with evidence showing that one-off interventions often fail to generate long-term behavioral change unless reinforced [16]. The significant negative trend in the proportion of newborns not breathing who were successfully resuscitated indicates a month-to-month decline in resuscitation success over the study period following the intervention. This is concerning and suggests a gradual deterioration in either clinical performance, system support, or quality of intrapartum and neonatal care. If no additional training or clinical reinforcement was provided post-accreditation, providers may have struggled to maintain proficiency, leading to poorer resuscitation outcomes over time. A gradual decline in resuscitation success could also reflect increased workload, staff shortages, or burnout, particularly in facilities under pressure to meet accreditation standards without receiving additional clinical support. This may result in delays in initiating resuscitation, missed steps in the algorithm, or suboptimal team coordination, all of which reduce survival among asphyxiated newborns. The absence of immediate or trend changes in outpatient visits, peri-operative, under-5, neonatal, intrahospital, and maternal mortality rates may be the result of the nature of these indicators, which are complex and depend on many factors to change positively. Many of these outcomes are influenced by factors beyond the hospital's control. Additionally, accreditation upgrades alone may not address structural gaps in staffing, emergency response, or continuity of care. Mortality outcomes may require stronger interventions and sustained system-wide improvements to yield measurable changes. INTERPRETATION, STRENGTHS, AND LIMITATIONS The findings demonstrate the potential of Rwanda’s hospital accreditation program, notably the transition from Level 1 (policy development) to Level 2 (policy implementation), to improve specific domains of healthcare quality. The positive results were observed in post-surgical infections and hospital admissions, both of which are strongly emphasized in Level 2 standards. However, the results also highlight areas where accreditation led to poor outcomes, such as neonatal asphyxia, and the proportion of newborns not breathing who were successfully resuscitated. Furthermore, no changes were observed in other indicators. Indicators such as maternal mortality and intrahospital deaths likely require more comprehensive system-wide approaches, including referral network strengthening, workforce capacity building, and availability of essential supplies. Moreover, some positive changes may take longer to manifest as facilities fully internalize new practices and staff competencies are reinforced over time. These mixed results suggest that upgrading the level of evaluation of accreditation should be viewed as a foundational step in a broader quality improvement journey. In the quest for maximum impact, accreditation efforts should be integrated with supportive supervision, performance-based financing, data feedback mechanisms, and patient-centered care reforms. The use of an interrupted time series (ITS) design, one of the robust quasi-experimental methods for evaluating the effects of upgrading the level of accreditation evaluation, was a key strength of this study. The ITS approach allowed us to account for underlying pre-intervention trends and evaluate both immediate and gradual impacts of accreditation upgrading. The use of a national health management information system ensured national representativeness. However, the study also has important limitations. First, as with all observational designs, causality cannot be definitively established. Although ITS improves causal inference, unmeasured confounders and concurrent health system initiatives may have influenced the outcomes. Second, we evaluated only the shift from Level 1 to Level 2 accreditation; future studies should examine the impacts of achieving Level 3 and consider qualitative assessments to better understand the mechanisms of change. CONCLUSION We observed that upgrading the level of evaluation of Rwanda hospital's accreditation program from level 1 to level 2 was associated with improvements in selected outcome indicators, especially those related to infection prevention and hospital admissions. However, neonatal indicators showed concerning trends, and several other indicators showed limited or no response, highlighting the need for complementary strategies. The shifting to level 3 where we shall observe the use of data in decisions can have more impacts that other preceding levels. These findings support continued investment in accreditation as a tool for strengthening health systems, while also underscoring the importance of implementing targeted interventions to address persistent gaps in service quality and health outcomes. Abbreviations CHUK: University Teaching Hospital of Kigali CHUB: University Teaching Hospital of Butare COHSASA: Council of Health Care Accreditation for Southern Africa DW: Durbin-Watson GLS: Generalized least squares HMIS: Health Management Information System IPC: Infection prevention and control ITS: Interrupted time series MAD: Median absolute deviation MMR: Maternal mortality ratio NMR: Neonatal mortality rate PBF: Performance-based Financing USD: United States Dollar UHC: Universal health coverage Declarations Contribution : CKN conceived, designed the study, acquired, analysed the data, and wrote the first draft of the manuscript. MG, JBN, BN, ET, KLN, ER interpreted the results and critically revised the manuscript. All authors had final responsibility for the decision to submit for publication. Competing interests : We declare no competing interests. Ethics approval and consent to participate : This study used aggregated, anonymized data from Rwanda's HMIS. We obtained ethical approval from Rwanda's National Health Research Committee under reference NHRC/2022/PROT/038. All methods were carried out in accordance with relevant guidelines and regulations of Declaration of Helsinki. Consent for publication: Not applicable Funding : No funding was received for this study. Author Contribution CKN conceived, designed the study, acquired, analysed the data, and wrote the first draft of the manuscript. MG, JBN, BN, ET, KLN, ER interpreted the results and critically revised the manuscript. All authors had final responsibility for the decision to submit for publication. Acknowledgement Not applicable Data Availability Our ethical approval only permitted access to the data by the research team, however, the data is available for any reseacher upon reasonable request. References National Institute of Statistics of Rwanda (NISR) [Rwanda], Ministry of Health (MOH) [Rwanda], and ICF. 2021. Rwanda Demographic and Health Survey 2019-20 Final Report . Kigali, Rwanda, and Rockville, Maryland, USA: NISR and IC Patrick M, Zaman MSU, Afzal G, Mahsud M, Hanifatu MN. Factors That Affect Maternal Mortality in Rwanda: A Comparative Study with India and Bangladesh. Comput Math Methods Med. 2022 Apr 9;2022:1940188. doi: 10.1155/2022/1940188. Retraction in: Comput Math Methods Med. 2023 Oct 4;2023:9846079. doi: 10.1155/2023/9846079. Koohpayehzadeh J, Azami-Aghdash S, Derakhshani N, Rezapour A, Alaei Kalajahi R, Sajjadi Khasraghi J, Nikoomanesh M, Sabetrohani H, Soleimanpour S. Best Practices in Achieving Universal Health Coverage: A Scoping Review. Med J Islam Repub Iran. 2021 Dec 30;35:191. doi: 10.47176/mjiri.35.191. Binagwaho A, Scott KW, Dushime T, Uwaliraye P, Kamuhangire E, Akishuri D, Wanyana D, Eagan A, Kakana L, Atwine J. Creating a pathway for public hospital accreditation in Rwanda: progress, challenges and lessons learned. Int J Qual Health Care. 2020 Apr 21;32(1):76-79. doi: 10.1093/intqhc/mzz063. Lewis M. Addressing efficiency and quality in Rwanda’s health system. Aceso Global Report to IGC and RSSB S‑19041‑RWA‑1A; 2019. International Growth Centre. Ingabire W, Reine PM, Hedt-Gauthier BL, Hirschhorn LR, Kirk CM, Nahimana E, et al. Roadmap to an effective quality improvement and patient safety program implementation in a rural hospital setting. Healthcare [Internet]. 2015;3(4):277–82. Available from: http://dx.doi.org/10.1016/j.hjdsi.2015.10.010 Celestin Hategeka, Catherine Arsenault, Margaret E Kruk - Temporal trends in coverage, quality and equity of maternal and child health services in Rwanda, 2000–2015: BMJ Global Health 2020;5:e002768. Hussein M, Pavlova M, Ghalwash M, Groot W. The impact of hospital accreditation on the quality of healthcare: a systematic literature review. BMC Health Serv Res. 2021 Oct 6;21(1):1057. doi: 10.1186/s12913-021-07097-6. PMID: 34610823; PMCID: PMC8493726. Rwanda Ministry of Health. Rwanda hospital accreditation standards. 3rd ed. Washington, DC: USAID – Wash in HCF Initiative; 2022. Available from: https://www.washinhcf.org/wp-content/uploads/2023/02/Signed-Rwanda-Hospital-Accreditation-Standards-3rd-Ed-2022.pdf World Health Organization. Country cooperation strategy: Rwanda 2021–2024 [Internet]. Brazzaville: WHO Regional Office for Africa; 2025 Apr [cited 2025 Jul 16]. Available from: https://www.afro.who.int/sites/default/files/2025-04/WHO%20Rwanda%20Country%20Cooperation%20Strategy%202021%E2%80%932024.pdf World Bank. GDP per capita (current US$): Rwanda [Internet]. [cited 2025 Jul 16].Available from: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=RW Rwanda Ministry of Health. Annual Health Sector Performance Report, Fiscal yeat 2021-2022 . Kigali: Ministry of Health; 2020.[cited 2025 Jul 16]. Available from: https://newmoh.prod.risa.rw/index.php?eID=dumpFile&t=f&f=95169&token=258bbe141678e0554f0113345848c0c6e0987e05 Kruk ME, Gage AD, Arsenault C, Jordan K, Leslie HH, Roder-DeWan S, Adeyi O, Barker P, Daelmans B, Doubova SV, English M, García-Elorrio E, Guanais F, Gureje O, Hirschhorn LR, Jiang L, Kelley E, Lemango ET, Liljestrand J, Malata A, Marchant T, Matsoso MP, Meara JG, Mohanan M, Ndiaye Y, Norheim OF, Reddy KS, Rowe AK, Salomon JA, Thapa G, Twum-Danso NAY, Pate M. High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob Health. 2018 Nov;6(11):e1196-e1252. doi: 10.1016/S2214-109X(18)30386-3. Epub 2018 Sep 5. Erratum in: Lancet Glob Health. 2018 Nov;6(11):e1162. doi: 10.1016/S2214-109X(18)30438-8. Erratum in: Lancet Glob Health. 2018 Nov;6(11):e1162. doi: 10.1016/S2214-109X(18)30456-X. Erratum in: Lancet Glob Health. 2021 Aug;9(8):e1067. doi: 10.1016/S2214-109X(21)00250-3. Devkaran S, O'Farrell PN. The impact of hospital accreditation on clinical documentation compliance: a life cycle explanation using interrupted time series analysis. BMJ Open. 2014 Aug 5;4(8):e005240. doi: 10.1136/bmjopen-2014-005240. Leslie HH, Spiegelman D, Zhou X, Kruk ME. Service readiness of health facilities in Bangladesh, Haiti, Kenya, Malawi, Namibia, Nepal, Rwanda, Senegal, Uganda and the United Republic of Tanzania. Bull World Health Organ. 2017 Nov 1;95(11):738-748. doi: 10.2471/BLT.17.191916. Epub 2017 Sep 5. Moxon SG, Ruysen H, Kerber KJ, Amouzou A, Fournier S, Grove J, Moran AC, Vaz LM, Blencowe H, Conroy N, Gülmezoglu A, Vogel JP, Rawlins B, Sayed R, Hill K, Vivio D, Qazi SA, Sitrin D, Seale AC, Wall S, Jacobs T, Ruiz Peláez J, Guenther T, Coffey PS, Dawson P, Marchant T, Waiswa P, Deorari A, Enweronu-Laryea C, Arifeen S, Lee AC, Mathai M, Lawn JE. Count every newborn; a measurement improvement roadmap for coverage data. BMC Pregnancy Childbirth. 2015;15 Suppl 2(Suppl 2):S8. doi: 10.1186/1471-2393-15-S2-S8. Epub 2015 Sep 11. Additional Declarations No competing interests reported. Supplementary Files RwandaHospitalaccreditationstandards3rdEdition.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviews received at journal 20 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 10 Sep, 2025 Editor invited by journal 21 Aug, 2025 Submission checks completed at journal 21 Aug, 2025 First submitted to journal 21 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7379706","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518364414,"identity":"6aec04e1-9715-46ce-8460-4bab5356e69c","order_by":0,"name":"Corneille Killy Ntihabose","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACHiBObGBIAFKMD0B8PiK0MDZAtTAbgPhsRGlhhGhhkwAJENTC33P2+IOHO+zy+PkPH6v8mmMnw8bA/PDRDTxaJM72JTYknkkulpyRlnZbdlsy0GFsxsY5eLQY8PMYNiS2MSduuMFjdltyGzNQCw+bNBFa6hM3nD//rVhyWz0RWnh7QFoOJ244kMPG+HHbYcJaJM6cMZyR2HYc5BdjacZtx3nYmAn4hb8nx+Djz7ZqUIg9/PhzW7U9P3vzw8f4tKAAZh4wSaxyEGD8QYrqUTAKRsEoGDEAAJzUR9PpN5PRAAAAAElFTkSuQmCC","orcid":"","institution":"University of British Columbia","correspondingAuthor":true,"prefix":"","firstName":"Corneille","middleName":"Killy","lastName":"Ntihabose","suffix":""},{"id":518364416,"identity":"608815bf-ffe5-4a8e-a425-9bc16446ec2d","order_by":1,"name":"Modeste Gashayija","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Modeste","middleName":"","lastName":"Gashayija","suffix":""},{"id":518364418,"identity":"9d568042-7d48-4720-bf2f-b0acf29e3e67","order_by":2,"name":"Jean Baptiste Ntihumbya","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"Baptiste","lastName":"Ntihumbya","suffix":""},{"id":518364420,"identity":"68027520-8e8f-47da-ae71-fcea4f403ca3","order_by":3,"name":"Bienvenu Niyongabo","email":"","orcid":"","institution":"Management Sciences for Health","correspondingAuthor":false,"prefix":"","firstName":"Bienvenu","middleName":"","lastName":"Niyongabo","suffix":""},{"id":518364421,"identity":"446e9dfa-df59-4804-ac2d-5a7c9ed1a4dd","order_by":4,"name":"Emile Twagirumukiza","email":"","orcid":"","institution":"King Faisal Hospital Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Emile","middleName":"","lastName":"Twagirumukiza","suffix":""},{"id":518364424,"identity":"511fd27f-f13f-4734-96ba-6683c511d954","order_by":5,"name":"Kara L. Neil","email":"","orcid":"","institution":"King Faisal Hospital Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Kara","middleName":"L.","lastName":"Neil","suffix":""},{"id":518364425,"identity":"31dbcb84-a6ca-4b79-88f7-8c18c19e5869","order_by":6,"name":"Erigene Rutayisire","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Erigene","middleName":"","lastName":"Rutayisire","suffix":""}],"badges":[],"createdAt":"2025-08-15 08:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7379706/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7379706/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91960557,"identity":"1592c4a3-3502-4692-b51d-2a4e886e41ae","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":469189,"visible":true,"origin":"","legend":"","description":"","filename":"TheImpactofHospitalAccreditationonHealthFinaldraft21082025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/7392e16a15691ef2c829d3d3.docx"},{"id":91960556,"identity":"47a33def-dd1f-4807-8be1-72af6d55f4ae","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9061,"visible":true,"origin":"","legend":"","description":"","filename":"a4ea96bfec0a4918b4bd8dadad0777a5.json","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/1fa9aa2b7e23deefd6f5fb66.json"},{"id":91962513,"identity":"5f35aa97-9dde-4dbf-9b12-52a4dee9bbca","added_by":"auto","created_at":"2025-09-23 07:55:24","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":700087,"visible":true,"origin":"","legend":"","description":"","filename":"RwandaHospitalaccreditationstandards3rdEdition.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/2d1f4b175229abcf08d14655.pdf"},{"id":91963344,"identity":"b508d337-a16b-4dbb-b327-65bd877593bb","added_by":"auto","created_at":"2025-09-23 08:03:24","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91889,"visible":true,"origin":"","legend":"","description":"","filename":"a4ea96bfec0a4918b4bd8dadad0777a51enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/0cfb8538fcaa3d29a24780fb.xml"},{"id":91962516,"identity":"eac19c89-e037-4f35-96c3-91da16f41fc0","added_by":"auto","created_at":"2025-09-23 07:55:24","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":81742,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/dba0bce2dd19b0d823853141.png"},{"id":91963345,"identity":"3fe70088-0682-4897-884c-b0c443b02265","added_by":"auto","created_at":"2025-09-23 08:03:24","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":271662,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/9670beb0d738d37cc155e7d9.jpeg"},{"id":91960561,"identity":"68f3fe87-2d1e-45fe-82b7-be13fb8958ff","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267527,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/23bae097a80a8f63a6f98aee.jpeg"},{"id":91960566,"identity":"bc0eb91e-dda1-4a38-8a6f-8fd460dbfd8a","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":266347,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/275c4861da502754567c4092.jpeg"},{"id":91962520,"identity":"a2690a6a-5651-47ed-a2fc-aa4dfcbe3309","added_by":"auto","created_at":"2025-09-23 07:55:24","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":281694,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/3695b29e7277d57020daeee5.jpeg"},{"id":91963341,"identity":"13f3a21a-73de-4a11-b5e3-214bcffcfaf6","added_by":"auto","created_at":"2025-09-23 08:03:24","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":282286,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/0a0f8f73eafda597eaabadf8.jpeg"},{"id":91962510,"identity":"35bceda5-8df1-440e-9960-e7c74fc50f9d","added_by":"auto","created_at":"2025-09-23 07:55:24","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36444,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/8dbf1c7819d034588da5cbae.png"},{"id":91960575,"identity":"1af1f936-83ee-4b7d-8d8e-19ece9aacea9","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48581,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/b850332224c0617ccd7d63f6.png"},{"id":91964347,"identity":"e922cf86-0167-45d5-849e-0e9ebbece5c2","added_by":"auto","created_at":"2025-09-23 08:11:46","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69549,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/d703cf49453234c9b01a1542.png"},{"id":91960570,"identity":"6de8e151-fd94-4ca1-9a9d-0bb83933d041","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72562,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/589e3458a6843927eb8d101d.png"},{"id":91962517,"identity":"2d295409-6452-4032-b101-b52024f7aa70","added_by":"auto","created_at":"2025-09-23 07:55:24","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74368,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/1073cfbbc57e93e01069ae34.png"},{"id":91960572,"identity":"02319579-95b0-4a08-b73f-23561ce8d4c9","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77282,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/f003d723ee542c3ab334df85.png"},{"id":91960576,"identity":"0d60d724-a093-46a6-b217-59cd83c3662b","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88033,"visible":true,"origin":"","legend":"","description":"","filename":"a4ea96bfec0a4918b4bd8dadad0777a51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/cbcecf391ff4135f2cbff6bb.xml"},{"id":91960574,"identity":"b5340349-702f-464c-b2f9-4acff73f48d7","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99900,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/30fcfef77a4413916f2e9420.html"},{"id":91960555,"identity":"446f62e4-a68c-4e4a-8ef1-1e35d280415d","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55460,"visible":true,"origin":"","legend":"\u003cp\u003eSelection process of hospitals included in the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/d67b49c46d5832bd6f3bacce.png"},{"id":91960558,"identity":"16979e05-0ee6-44cc-b24d-ef1864bca225","added_by":"auto","created_at":"2025-09-23 07:47:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":189696,"visible":true,"origin":"","legend":"\u003cp\u003eInterrupted Times Series analysis of 10 indicators after upgrading level of evaluation of accreditation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/2119de776da69bc583ea6895.png"},{"id":91965030,"identity":"4d26c954-3c1b-42a9-9fc0-04d848af6922","added_by":"auto","created_at":"2025-09-23 08:19:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/26a17839-3530-4df6-8d42-167fee6c3b41.pdf"},{"id":91964365,"identity":"fad6d477-d560-4f8c-bdab-e41960568acb","added_by":"auto","created_at":"2025-09-23 08:11:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":700087,"visible":true,"origin":"","legend":"","description":"","filename":"RwandaHospitalaccreditationstandards3rdEdition.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7379706/v1/aad4be503a1e87910e352c30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Hospital Accreditation on Health Utilization and Outcome in Rwanda: An Interrupted Time Series Analysis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eOver the last two decades, Rwanda has made notable progress in improving health outcomes and expanding healthcare access. The country has registered an 80% reduction in maternal and under-five mortalities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This was achieved through a decentralized, equity-focused health system and a strong network of community health workers, who deliver essential health services at the community level. The country's achievement of universal health coverage (UHC) through a regular and consistent enrollment rate of over 90% in the community-based health insurance scheme, known as Mutuelle de Sant\u0026eacute;, has been the driver of this success [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These results reflect substantial investments in service delivery, health financing, human resources, and health infrastructure. Even though there have been significant improvements in access and equity, there are reports that show inconsistencies in clinical practices, inadequate infection prevention, and differences among hospitals in adhering to clinical standards [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEvidence supports that the hospital accreditation improves safety culture, process-related performance measures, efficiency, and length of stay [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To this note, Rwanda adopted the hospital accreditation program in 2013 to improve the persistent bottlenecks in the quality of health care. The accreditation was then linked to the performance-based financing, which was implemented since the early 2000s. There had been regular annual evaluations of hospitals against a set of standards, aiming at assessing hospital governance, human resources management, safety and hygiene, clinical care delivery, and quality improvements and overall the improved care. An independent organization has been established to coordinate these evaluations, aiming objectivity and institutionalization of process. The program have been designed to be implemented in three phases depending on levels of evaluation. Phase one ended in 2021 with level 1, while in March 2022, Rwanda moved to phase 2. However, during the entire implementation of this program, no study has evaluated its impact on health outcomes. Therefore, our interest is to assess whether the transition from level 1 to level 2 of evaluation of accreditation program has resulted in measurable improvements in health service quality, using robust statistical methods to analyze trends across a set of health indicators and utilizing routine health data.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003e\u003cstrong\u003eStudy Intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecognizing that access and equity alone are insufficient, Rwanda introduced the hospital accreditation program in 2013, institutionalizing quality assurance in clinical care delivery and driving improvements in patient safety, clinical effectiveness, and hospital governance [4,8]. This program represents a strategic, cost-effective initiative designed to improve healthcare quality and accountability through structured evaluation. It is implemented through annual assessments against 79 measurable standards organized within five risk areas: leadership and governance, human resources, infection control and hygiene, clinical care, and quality improvement [8]. Rwanda has expanded the accreditation to private hospitals and health centers, which further institutionalized quality health care.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis program has a three-point scale to assess the institutional performance:\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAt level 1: The\u0026nbsp;health facility must have key policies and develop relevant procedures. For example, among 79 standards, there is a standard on pain assessment, reassessment, and appropriate management. To get a full mark during the evaluation, the hospital must demonstrate that there is a policy, procedure, guideline, and tools (including assessment or pain score cards) approved by hospital management that are used by clinical staff in examining, re-examining, and managing patients\u0026apos; pain.\u003c/li\u003e\n \u003cli\u003eAt level 2: There is evidence of consistent implementation of the policies. Based on the same example of the standard in pain management, evaluators check medical records\u0026nbsp;if\u0026nbsp;there is documentation by clinical staff of the assessment, reassessment, and management of pain from admission to discharge.\u003c/li\u003e\n \u003cli\u003eAt level 3: There is clear and sustained monitoring of quality improvement using data. Again, on the same standard, if the health facility evaluates the patient pain management process for effectiveness and improvements.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAssessments of compliance with accreditation standards are conducted by trained evaluators using Rwanda Hospital Accreditation Standards\u0026nbsp;\u003cstrong\u003e(supplementary material 1)\u003c/strong\u003e, which promotes objectivity and consistency across facilities. The results are published, and hospitals are incentivized through Rwanda\u0026apos;s performance-based Financing (PBF) based on scores from evaluations [4]. \u0026nbsp;Initially, the national rollout emphasized documentation (Level 1). From March 2022 onward, the Ministry of Health prioritized the evaluation of Level 2, which focuses on operationalizing standards in day-to-day facility operations [4,8-9]. This includes demonstrable changes in infection prevention practices, patient safety culture, documentation and record-keeping, and use of clinical guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Setting \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRwanda, located in East Africa, has a population of over 14 million people and covers an area of 26,338 km\u0026sup2;. Administratively, the country is divided into four provinces and the City of Kigali. The annual per capita income remains modest, at approximately USD 999.7 [11]. In the health sector, services are provided by both public and private facilities, with public institutions constituting the majority. There are 1,863 public health facilities across the country, including 1,280 health posts, 518 health centers, eight medicalized health centers, and 57 hospitals [12]. \u0026nbsp;This study focused on hospitals, as these are the institutions where the accreditation process initially began and where annual evaluations are routinely conducted. However, three major referral hospitals, the University Teaching Hospital of Kigali (CHUK), the University Teaching Hospital of Butare (CHUB), and Rwanda Military Hospital, were excluded due to their affiliation with international accreditation standards under the Council of Health Care Accreditation for Southern Africa (COHSASA). Five Specialized hospitals were also excluded, as they joined the national accreditation program at a later stage and are still being evaluated at Level 1. Ultimately, this study recruited 49 hospitals. \u003cstrong\u003eFigure 1\u003c/strong\u003e summarizes the process of selection of hospitals included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Source\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used data from a single source, the Rwanda Health Management Information System (HMIS), which collects facility-level data every month across the country.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measures\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess health system performance, we categorized indicators into five core domains of the accreditation program: leadership and governance, workforce competence, a safe environment and hygiene, patient clinical care, and quality improvement and safety. We selected indicators based on their relevance, availability, and alignment with global standards.\u003c/p\u003e\n\u003cp\u003eFor the leadership and governance domain, we used three measures that reflect the capacity of hospitals to manage service demand, maximize patient flow, and ensure there is effective leadership in care delivery: \u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cem\u003eOutpatient visit rate\u003c/em\u003e : Outpatient consultations per 10,000 population;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eAdmission rate\u003c/em\u003e : Number of admissions per 10,000 population;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eIntra-hospital mortality rate\u003c/em\u003e : Number of Intrahospital deaths per 100 hospital admissions.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe competent workforce domain was assessed using:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cem\u003eThe proportion of newborns not breathing at birth who were successfully resuscitated upon birth\u003c/em\u003e. This measure indicates the capacity of staff to manage emergency newborn care.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUnder the safe environment and hygiene domain, we included: \u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cem\u003ePost-surgical infection rate\u003c/em\u003e : the number of patients diagnosed with post-surgical infections divided by the total number of surgeries performed, multiplying by 100. The measure reflects the level of adherence to infection prevention and control policies and protocols.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThree outcome indicators represented the clinical care of the patients\u0026apos; domain: \u0026nbsp;\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cem\u003eMaternal mortality ratio (MMR):\u0026nbsp;\u003c/em\u003ethe number of maternal deaths per 100,000 total live births;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eUnder-5 mortality rate\u003c/em\u003e: the number of under-5 deaths per 1,000;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eNeonatal mortality rate (NMR):\u0026nbsp;\u003c/em\u003ethe number of deaths of newborns per 1,000 live births. \u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eLastly, the quality improvement and safety domain were assessed using:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cem\u003ePerioperative mortality rate\u003c/em\u003e: the number of deaths occurring within 24 hours after surgery, dividing by the total number of surgeries, and multiplying by 100.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eNeonatal asphyxia birth rate\u003c/em\u003e: the number of newborns diagnosed with birth asphyxia per 1,000 live births. The indicator serves as a marker for both intrapartum care quality and newborn resuscitation capacity.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe excluded six hospitals from the analysis due to insufficient reporting, defined as submitting less than 80% of their monthly data over the entire study period (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Missing data were minimal across the dataset. All variables had less than 1% missing values, except for two indicators: post-surgical infection rate (3.7%) and perioperative mortality rate (3.1%). Missing values were addressed using an interpolation method. All facility-level data were aggregated to the monthly level. We screened for extreme values using the median absolute deviation (MAD) method, applying a threshold of \u0026plusmn;2.5 MAD from the median. Values identified as outliers were trimmed and treated as missing prior to imputation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe employed interrupted time series analysis. The interruption period was March 2022, we took 24 monthly observations before and 33 monthly observations after the intervention period. Linearity assumptions were assessed using residual plots versus fitted values. Evidence of nonlinearity was observed in the models for post-surgical infection rates and perioperative mortality rates. To address this, quadratic terms were included in the respective models. We assessed seasonality through visual inspection of time series plots and time series decomposition. Evidence of seasonal patterns was identified in the models for outpatient visits per 10,000, admissions per 10,000, and post-surgical infection rate. To account for these seasonal effects, Fourier terms were added to the models. We also examined autocorrelation, which is a common feature in time series data, using the Durbin-Watson (DW) test. We detected autocorrelations in the models for admissions per 10,000, post-surgical infection rate, perioperative mortality rate, and neonatal mortality rate, and we employed generalized least squares (GLS) regression to account for autocorrelated error structures. COVID-19 was considered as a potential confounder. In conceptual understanding, COVID-19 had impacted healthcare utilization and service delivery during the study period. To identify the best-fitting models, we performed likelihood ratio tests by comparing nested models. These tests evaluated whether the inclusion of additional terms, such as seasonal or nonlinear components, significantly improved model fit. We conducted all analyses using R version 4.4.3, and we set the statistical significance at a p-value \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable 1 summarizes monthly averages of 10 outcome indicators over the 57-month observation period. Outpatient visits per 10,000 population were 93.45 (79.73 -105.83), while hospital admissions per 10,000 were 26.78 (range: 21.63\u0026ndash;31.42). For surgical outcomes, the post-surgical infection rate was 0.96 per 100 surgeries, ranging from 0.36 to 2.34. The peri-operative mortality rate was relatively low, 0.89 per 100 procedures (range: 0.44\u0026ndash;1.63). Regarding child and neonatal health, the neonatal mortality rate was 24.87 per 1,000 live births, with monthly rates ranging from 17.43 to 34.46, while the under-5 mortality rate was higher at 73.02 per 1,000 live births (range: 57.53\u0026ndash;94.53). The neonatal asphyxia rate was 3.57 per 100 births, with a range of 2.66 to 5.11. The intrahospital mortality rate was 2.36 per 100 admissions, with a range of 1.72 to 3.04. The institutional maternal mortality rate was 65.51 per 100,000 live births, but showed wide monthly variability, ranging from 13.69 to 149.93. Lastly, the proportion of newborns not breathing at birth who were successfully resuscitated was 75.30 per 100, with a range of 69.34 to 81.58.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Descriptive analysis of outcome indicators\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOutpatient visits per 10,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e93.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e79.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e105.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAdmissions per 10,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e26.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e21.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;31.42 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePost-surgical infection rate (per 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePeri-operative \u0026nbsp;mortality rate (per 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNeonatal mortality rate (per 1,000 live births)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e24.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e34.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eUnder-5 mortality rate (per 1,000 live births)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e73.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e57.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e94.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNeonatal asphyxia rate (per 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eIntrahospital mortality rate (per 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eInstitutional maternal mortality rate (per 100,000 live births)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e65.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e13.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e149.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eProportion of newborn not breathing successfully resuscitated (per 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e75.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e69.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e81.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOn the impact of the intervention, we examined monthly changes across ten outcome indicators. For each indicator, we report both the immediate change and the post-intervention trend. \u003cstrong\u003eTable 2\u003c/strong\u003e summarizes the results, and \u003cstrong\u003eFigure 2 (a-j)\u003c/strong\u003e illustrates immediate post-intervention changes and trends for each outcome indicator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Change in indicators after upgrading the level of evaluation of accreditation\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmediate change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-intervention trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;P-value*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- value*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eOutpatient visits per 10,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-4.17, 4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.42, 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAdmissions per 10,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.44, 3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePost-surgical infection rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.30, -0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.03, 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePeri-operative \u0026nbsp;\u003c/p\u003e\n \u003cp\u003emortality rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.51, 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.23, 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNeonatal mortality rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-7.10, 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.01, 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUnder-5 mortality rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-12.1,1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.42, 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNeonatal asphyxia rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.43, 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.03,\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eIntrahospital mortality rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.03, 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.01, 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eInstitutional maternal mortality rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-34.8, 20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-2.94,0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eProportion of newborn not breathing successfully resuscitated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.18, 4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.41,\u003c/p\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003e95%CI= 95% Confidence Interval, *Significance level: p-value= 0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eOutpatient visits\u0026nbsp;per 10,000\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1A\u003c/strong\u003e show that the intervention was associated with a nonsignificant immediate change in outpatient visits per 10,000 population (\u0026beta; = 0.12; 95% CI: -4.17 to 4.40, P-value = 0.956) and a nonsignificant change in trend (\u0026beta; = -0.13; 95% CI: -0.42 to 0.14, P-value = 0.346).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHospital admissions per 10,000\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the intervention, there was a statistically significant immediate increase in admissions per 10,000 population (\u0026beta; = 2.08; 95% CI: 0.44 to 3.72, P-value=0.016), accompanied by a modest but significant decline in trend (\u0026beta; = -0.14; 95% CI: -0.26 to -0.02, P-value=0.021) (\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1B)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost-surgical infection Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intervention was associated with a statistically significant overall immediate reduction in the post-surgical infection rate (\u0026beta; = -0.82; 95% CI: -1.30 to -0.34, P-value=0.001), while the post-intervention trend remained stable (\u0026beta; = -0.01; 95% CI: -0.03 to 0.02, P-value=0.716)\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1C)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerioperative Mortality rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intervention was associated with a nonsignificant overall immediate reduction in the perioperative mortality rate (\u0026beta; = -0.18; 95% CI: -0.51 to 0.15, P-value= 0.281), and a nonsignificant downward trend (\u0026beta; = -0.11; 95% CI: -0.23 to 0.02, P-value=0.087)\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1D)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeonatal Mortality Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the intervention was associated with a nonsignificant immediate reduction in the neonatal mortality rate (\u0026beta; = -3.03; 95% CI: -7.10 to 1.04, P-value=0.150) and a nonsignificant upward trend (\u0026beta; = 0.25; 95% CI: -0.01 to 0.51, P-value=0.072)\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1E)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeonatal Asphyxia Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the intervention was associated with a nonsignificant immediate level reduction in neonatal asphyxia rate (\u0026beta; = -0.16; 95% CI: -0.43 to 0.11, P-value=0.239), but a statistically significant positive trend post-intervention (\u0026beta; = 0.05; 95% CI: 0.03 to 0.06, P-value= \u0026lt;0.001 ), suggesting a gradual increase in rates over time\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1F)\u003c/strong\u003e .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnder-5 Mortality Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intervention was associated with a nonsignificant overall immediate reduction in under-5 mortality rate (\u0026beta; = -5.12; 95% CI: -12.1 to 1.80, P-value=0.144), and no meaningful change in trend (\u0026beta; = 0.001; 95% CI: -0.42 to 0.42, P-value=0.998)\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1G)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal Mortality Ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the intervention was associated with a nonsignificant immediate reduction in institutional maternal mortality rate (\u0026beta; = -7.38; 95% CI: -34.8 to 20.0, P-value=0.599) and a nonsignificant decreasing trend post-intervention (\u0026beta; = -1.11; 95% CI: -2.94 to 0.72, P-value=0.240)\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1H)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntrahospital Mortality Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eintervention was associated with a nonsignificant immediate increase in intrahospital mortality rate overall (\u0026beta; = 0.14; 95% CI: -0.03 to 0.32, P-value=0.101), and a stable trend over time (\u0026beta; = 0.005; 95% CI: -0.01 to 0.02, P-value=0.393)\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 1I)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProportion of Successful Resuscitation of Newborns Not Breathing at Birth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intervention was associated with a nonsignificant overall immediate increase in the proportion of newborns successfully resuscitated (\u0026beta; = 2.39; 95% CI: -0.18 to 4.95, P-value=0.067), and a significant downward trend over time (\u0026beta; = -0.26; 95% CI: -0.41 to -0.10, P-value=0.001)\u0026nbsp;(\u003cstrong\u003eTable 2\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eFigure 1J)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSIONS","content":"\u003cp\u003eThis study assessed the overall impact of increasing the level of evaluation of accreditation on 10 health utilization and outcome indicators. Some statistically significant effects were observed, particularly in post-surgical infection rates, hospital admissions, neonatal asphyxia, and the proportion of newborns not breathing who were successfully resuscitated. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the intervention, we observed a\u0026nbsp;statistically significant immediate increase in hospital admissions per 10,000 population, accompanied by a\u0026nbsp;modest but sustained decline in trend. The immediate increase suggests that the intervention, expanded service capacity, or enhanced patient referral pathways, may have improved access to or demand for inpatient care. This is consistent with prior evidence showing that targeted health system reforms often stimulate service uptake, particularly in previously underserved settings [13-14]. The subsequent\u0026nbsp;decline in the monthly trend, however, may reflect a\u0026nbsp;stabilization of care-seeking behavior\u0026nbsp;after the initial surge. It is also possible that as hospitals implemented stricter clinical protocols or improved triage systems, standard features of accreditation programs, there was a\u0026nbsp;reduction in unnecessary or inappropriate admissions, improving efficiency without reducing access.\u0026nbsp;Similar patterns have been observed in other contexts\u0026nbsp;where initial access improvements were followed by\u0026nbsp;rationalization of service use\u0026nbsp;as health systems matured [14,15].\u003c/p\u003e\n\u003cp\u003eThe monthly reduction in the immediate post-surgical infection rate may reflect a reinforcement of infection prevention and control (IPC) protocols, including adherence to surgical safety checklists, improved hand hygiene compliance, sterile technique training, equipment sterilization standards, and regular audit practices. By enforcing these protocols, the program likely standardized surgical procedures, minimizing variation in care and reducing avoidable infections. While the accreditation upgrade may have prompted a\u0026nbsp;rapid improvement in infection prevention practices, it\u0026nbsp;did not lead to continuous monthly improvements thereafter. In the absence of\u0026nbsp;continuous follow-up, supervision, or refresher training, the initial improvements may have\u0026nbsp;plateaued, resulting in no further decline over time. Furthermore, facilities may have implemented most of the feasible infection control improvements immediately following the upgrade. Once these high-impact changes were in place,\u0026nbsp;additional monthly gains became increasingly challenging to achieve, especially without further innovations or investments, leading to a\u0026nbsp;flattening of the trend.\u003c/p\u003e\n\u003cp\u003eThe increasing trend in neonatal asphyxia may reflect a\u0026nbsp;gradual diminution in clinical vigilance or competence\u0026nbsp;in managing labor and delivery. Without sustained mentorship, on-the-job training, or clinical audits post-intervention, staff may have\u0026nbsp;struggled to maintain high standards, leading to an\u0026nbsp;accumulation of suboptimal birth outcomes\u0026nbsp;such as asphyxia. This aligns with evidence showing that one-off interventions often fail to generate long-term behavioral change unless reinforced [16].\u003c/p\u003e\n\u003cp\u003eThe significant\u0026nbsp;negative trend\u0026nbsp;in the\u0026nbsp;proportion of newborns not breathing who were successfully resuscitated indicates a\u0026nbsp;month-to-month decline\u0026nbsp;in resuscitation success over the study period following the intervention. This is concerning and suggests\u0026nbsp;a gradual deterioration in either clinical performance, system support, or quality of intrapartum and neonatal care. If no additional training or clinical reinforcement was provided post-accreditation, providers may have struggled to maintain proficiency, leading to poorer resuscitation outcomes over time. A gradual decline in resuscitation success could also reflect\u0026nbsp;increased workload,\u0026nbsp;staff shortages, or\u0026nbsp;burnout, particularly in facilities under pressure to meet accreditation standards without receiving additional clinical support. This may result in\u0026nbsp;delays in initiating resuscitation, missed steps in the algorithm, or suboptimal team coordination, all of which reduce survival among asphyxiated newborns.\u003c/p\u003e\n\u003cp\u003eThe absence of immediate or trend changes in outpatient visits, peri-operative, under-5, neonatal, intrahospital, and maternal mortality rates may be the result of the nature of these indicators, which are complex and depend on many factors to change positively. Many of these outcomes are influenced by factors beyond the hospital's control. Additionally, accreditation upgrades alone may not address structural gaps in staffing, emergency response, or continuity of care. Mortality outcomes may require stronger interventions and sustained system-wide improvements to yield measurable changes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINTERPRETATION, STRENGTHS, AND LIMITATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings demonstrate the potential of Rwanda’s hospital accreditation program, notably the transition from Level 1 (policy development) to Level 2 (policy implementation), to improve specific domains of healthcare quality. The positive results were observed in post-surgical infections and hospital admissions, both of which are strongly emphasized in Level 2 standards. However, the results also highlight areas where accreditation led to poor outcomes, such as neonatal asphyxia, and the proportion of newborns not breathing who were successfully resuscitated. Furthermore, no changes were observed in other indicators. Indicators such as maternal mortality and intrahospital deaths likely require more comprehensive system-wide approaches, including referral network strengthening, workforce capacity building, and availability of essential supplies. Moreover, some positive changes may take longer to manifest as facilities fully internalize new practices and staff competencies are reinforced over time. These mixed results suggest that upgrading the level of evaluation of accreditation should be viewed as a foundational step in a broader quality improvement journey. In the quest for maximum impact, accreditation efforts should be integrated with supportive supervision, performance-based financing, data feedback mechanisms, and patient-centered care reforms. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe use of an interrupted time series (ITS) design, one of the robust quasi-experimental methods for evaluating the effects of upgrading the level of accreditation evaluation, was a key strength of this study. The ITS approach allowed us to account for underlying pre-intervention trends and evaluate both immediate and gradual impacts of accreditation upgrading. The use of a national health management information system ensured national representativeness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, the study also has important limitations. First, as with all observational designs, causality cannot be definitively established. Although ITS improves causal inference, unmeasured confounders and concurrent health system initiatives may have influenced the outcomes. Second, we evaluated only the shift from Level 1 to Level 2 accreditation; future studies should examine the impacts of achieving Level 3 and consider qualitative assessments to better understand the mechanisms of change. \u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe observed that upgrading the level of evaluation of Rwanda hospital's accreditation program from level 1 to level 2 was associated with improvements in selected outcome indicators, especially those related to infection prevention and hospital admissions. However, neonatal indicators showed concerning trends, and several other indicators showed limited or no response, highlighting the need for complementary strategies. The shifting to level 3 where we shall observe the use of data in decisions can have more impacts that other preceding levels. These findings support continued investment in accreditation as a tool for strengthening health systems, while also underscoring the importance of implementing targeted interventions to address persistent gaps in service quality and health outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHUK: University Teaching Hospital of Kigali\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHUB: University Teaching Hospital of Butare\u003c/p\u003e\n\u003cp\u003eCOHSASA: Council of Health Care Accreditation for Southern Africa\u003c/p\u003e\n\u003cp\u003eDW: Durbin-Watson\u003c/p\u003e\n\u003cp\u003eGLS: Generalized least squares\u003c/p\u003e\n\u003cp\u003eHMIS: Health Management Information System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIPC: Infection prevention and control\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eITS: Interrupted time series\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAD: Median absolute deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMMR: Maternal mortality ratio\u003c/p\u003e\n\u003cp\u003eNMR: Neonatal mortality rate\u003c/p\u003e\n\u003cp\u003ePBF: Performance-based Financing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUSD: United States Dollar\u003c/p\u003e\n\u003cp\u003eUHC: Universal health coverage\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e :\u003c/p\u003e\n\u003cp\u003eCKN conceived, designed the study, acquired, analysed the data, and wrote the first draft of the manuscript. MG, JBN, BN, ET, KLN, ER interpreted the results and critically revised the manuscript. All authors had final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used aggregated, anonymized data from Rwanda\u0026apos;s HMIS. We obtained ethical approval from Rwanda\u0026apos;s National Health Research Committee under reference NHRC/2022/PROT/038. All methods were carried out in accordance with relevant guidelines and regulations of Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eConsent for publication:\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eFunding :\u003c/h2\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eCKN conceived, designed the study, acquired, analysed the data, and wrote the first draft of the manuscript. MG, JBN, BN, ET, KLN, ER interpreted the results and critically revised the manuscript. All authors had final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eOur ethical approval only permitted access to the data by the research team, however, the data is available for any reseacher upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNational Institute of Statistics of Rwanda (NISR) [Rwanda], Ministry of Health (MOH) [Rwanda], and ICF. 2021. \u003cem\u003eRwanda Demographic and Health Survey 2019-20 Final Report\u003c/em\u003e. Kigali, Rwanda, and Rockville, Maryland, USA: NISR and IC \u003c/li\u003e\n\u003cli\u003ePatrick M, Zaman MSU, Afzal G, Mahsud M, Hanifatu MN. Factors That Affect Maternal Mortality in Rwanda: A Comparative Study with India and Bangladesh. Comput Math Methods Med. 2022 Apr 9;2022:1940188. doi: 10.1155/2022/1940188. Retraction in: Comput Math Methods Med. 2023 Oct 4;2023:9846079. doi: 10.1155/2023/9846079. \u003c/li\u003e\n\u003cli\u003eKoohpayehzadeh J, Azami-Aghdash S, Derakhshani N, Rezapour A, Alaei Kalajahi R, Sajjadi Khasraghi J, Nikoomanesh M, Sabetrohani H, Soleimanpour S. Best Practices in Achieving Universal Health Coverage: A Scoping Review. Med J Islam Repub Iran. 2021 Dec 30;35:191. doi: 10.47176/mjiri.35.191. \u003c/li\u003e\n\u003cli\u003eBinagwaho A, Scott KW, Dushime T, Uwaliraye P, Kamuhangire E, Akishuri D, Wanyana D, Eagan A, Kakana L, Atwine J. Creating a pathway for public hospital accreditation in Rwanda: progress, challenges and lessons learned. Int J Qual Health Care. 2020 Apr 21;32(1):76-79. doi: 10.1093/intqhc/mzz063. \u003c/li\u003e\n\u003cli\u003eLewis M. Addressing efficiency and quality in Rwanda\u0026rsquo;s health system. Aceso Global Report to IGC and RSSB S‑19041‑RWA‑1A; 2019. International Growth Centre.\u003c/li\u003e\n\u003cli\u003eIngabire W, Reine PM, Hedt-Gauthier BL, Hirschhorn LR, Kirk CM, Nahimana E, et al. Roadmap to an effective quality improvement and patient safety program implementation in a rural hospital setting. Healthcare [Internet]. 2015;3(4):277\u0026ndash;82. Available from: http://dx.doi.org/10.1016/j.hjdsi.2015.10.010\u003c/li\u003e\n\u003cli\u003eCelestin Hategeka, Catherine Arsenault, Margaret E Kruk - Temporal trends in coverage, quality and equity of maternal and child health services in Rwanda, 2000\u0026ndash;2015: BMJ Global Health 2020;5:e002768.\u003c/li\u003e\n\u003cli\u003eHussein M, Pavlova M, Ghalwash M, Groot W. The impact of hospital accreditation on the quality of healthcare: a systematic literature review. BMC Health Serv Res. 2021 Oct 6;21(1):1057. doi: 10.1186/s12913-021-07097-6. PMID: 34610823; PMCID: PMC8493726.\u003c/li\u003e\n\u003cli\u003eRwanda Ministry of Health. Rwanda hospital accreditation standards. 3rd ed. Washington, DC: USAID \u0026ndash; Wash in HCF Initiative; 2022. Available from: https://www.washinhcf.org/wp-content/uploads/2023/02/Signed-Rwanda-Hospital-Accreditation-Standards-3rd-Ed-2022.pdf\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. \u003cem\u003eCountry cooperation strategy: Rwanda 2021\u0026ndash;2024\u003c/em\u003e [Internet]. Brazzaville: WHO Regional Office for Africa; 2025 Apr [cited 2025 Jul 16]. Available from: https://www.afro.who.int/sites/default/files/2025-04/WHO%20Rwanda%20Country%20Cooperation%20Strategy%202021%E2%80%932024.pdf\u003c/li\u003e\n\u003cli\u003eWorld Bank. GDP per capita (current US$): Rwanda [Internet]. [cited 2025 Jul 16].Available from: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=RW\u003c/li\u003e\n\u003cli\u003eRwanda Ministry of Health. Annual Health Sector Performance Report, Fiscal yeat 2021-2022 . Kigali: Ministry of Health; 2020.[cited 2025 Jul 16]. Available from: https://newmoh.prod.risa.rw/index.php?eID=dumpFile\u0026amp;t=f\u0026amp;f=95169\u0026amp;token=258bbe141678e0554f0113345848c0c6e0987e05 \u003c/li\u003e\n\u003cli\u003eKruk ME, Gage AD, Arsenault C, Jordan K, Leslie HH, Roder-DeWan S, Adeyi O, Barker P, Daelmans B, Doubova SV, English M, Garc\u0026iacute;a-Elorrio E, Guanais F, Gureje O, Hirschhorn LR, Jiang L, Kelley E, Lemango ET, Liljestrand J, Malata A, Marchant T, Matsoso MP, Meara JG, Mohanan M, Ndiaye Y, Norheim OF, Reddy KS, Rowe AK, Salomon JA, Thapa G, Twum-Danso NAY, Pate M. High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob Health. 2018 Nov;6(11):e1196-e1252. doi: 10.1016/S2214-109X(18)30386-3. Epub 2018 Sep 5. Erratum in: Lancet Glob Health. 2018 Nov;6(11):e1162. doi: 10.1016/S2214-109X(18)30438-8. Erratum in: Lancet Glob Health. 2018 Nov;6(11):e1162. doi: 10.1016/S2214-109X(18)30456-X. Erratum in: Lancet Glob Health. 2021 Aug;9(8):e1067. doi: 10.1016/S2214-109X(21)00250-3. \u003c/li\u003e\n\u003cli\u003eDevkaran S, O\u0026apos;Farrell PN. The impact of hospital accreditation on clinical documentation compliance: a life cycle explanation using interrupted time series analysis. BMJ Open. 2014 Aug 5;4(8):e005240. doi: 10.1136/bmjopen-2014-005240. \u003c/li\u003e\n\u003cli\u003eLeslie HH, Spiegelman D, Zhou X, Kruk ME. Service readiness of health facilities in Bangladesh, Haiti, Kenya, Malawi, Namibia, Nepal, Rwanda, Senegal, Uganda and the United Republic of Tanzania. Bull World Health Organ. 2017 Nov 1;95(11):738-748. doi: 10.2471/BLT.17.191916. Epub 2017 Sep 5. \u003c/li\u003e\n\u003cli\u003eMoxon SG, Ruysen H, Kerber KJ, Amouzou A, Fournier S, Grove J, Moran AC, Vaz LM, Blencowe H, Conroy N, G\u0026uuml;lmezoglu A, Vogel JP, Rawlins B, Sayed R, Hill K, Vivio D, Qazi SA, Sitrin D, Seale AC, Wall S, Jacobs T, Ruiz Pel\u0026aacute;ez J, Guenther T, Coffey PS, Dawson P, Marchant T, Waiswa P, Deorari A, Enweronu-Laryea C, Arifeen S, Lee AC, Mathai M, Lawn JE. Count every newborn; a measurement improvement roadmap for coverage data. BMC Pregnancy Childbirth. 2015;15 Suppl 2(Suppl 2):S8. doi: 10.1186/1471-2393-15-S2-S8. Epub 2015 Sep 11. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Accreditation, Healthcare quality, Interrupted time series analysis","lastPublishedDoi":"10.21203/rs.3.rs-7379706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7379706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Over the past 30 years, Rwanda has improved health outcomes and access to care, though progress has slowed in the last decade. In 2013, the government launched a hospital accreditation program to enhance efficiency and care quality. The program has 79 measurable standards evaluated at three levels. Level 1 consists of having policies, guidelines, and plans in place, and when there is evidence that health facilities implement those documents, level 2 is achieved. If data is used to guide clinical and quality improvement decisions, level 3 is also achieved. The level 1 program was implemented from 2013 to 2021, and in 2022, the country shifted to level 2. However, there is no evidence that this upgrade in the level of evaluation has resulted in improved clinical outcomes and service utilization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Using routine data from February 2020 to December 2024, we employed interrupted time series analysis to evaluate the impact of increasing the level of evaluation of the hospital accreditation program to 10 outcome indicators. The interruption period was March 2022, we took 24 monthly observations before and 33 monthly observations after the intervention period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e The upgrading of the evaluation level was associated with a statistically significant immediate monthly increase in hospital admissions per 10,000 population (β = 2.08; 95% CI: 0.44 to 3.72) and a monthly reduction in post-surgical infection rates (β = -0.82; 95% CI: -1.30 to -0.34). Statistically significant monthly trends were also observed for admissions (β = -0.14; 95% CI: -0.26 to -0.02), neonatal asphyxia rate (β = 0.05; 95% CI: 0.03 to 0.06), and the proportion of newborns not breathing who were successfully resuscitated (β = -0.26; 95% CI: -0.41 to -0.10). No immediate or trend changes in outpatient visits per 10,000, peri-operative mortality, under-5 mortality, neonatal mortality, intrahospital mortality, and maternal mortality rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e We observed that upgrading the level of evaluation of Rwanda hospital's accreditation was associated with improvements in selected outcome indicators, others showing concerning trends and several other indicators showed limited or no response. We recommend Policy makers to continue investment in accreditation but also implementing targeted interventions to address persistent gaps in service quality and health outcomes.\u003c/p\u003e","manuscriptTitle":"The Impact of Hospital Accreditation on Health Utilization and Outcome in Rwanda: An Interrupted Time Series Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:47:19","doi":"10.21203/rs.3.rs-7379706/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"26097510428280076856667666949418145228","date":"2025-09-27T15:38:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T15:09:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10253170885395446555367094279561120923","date":"2025-09-23T13:12:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-20T07:20:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123376826247663268409422261465544297028","date":"2025-09-15T06:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T05:55:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T05:41:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-22T03:31:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-21T22:37:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-08-21T22:34:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4be2fd23-cd8e-49a8-b021-7b2e1979428e","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T07:47:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 07:47:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7379706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7379706","identity":"rs-7379706","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}