Exploratory Analysis Research Articles

Abstract C009: An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities

Abstract C009: An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities

The Impact of Organisational Culture on Transportation Behaviours: An Exploratory Analysis

The Impact of Organisational Culture on Transportation Behaviours: An Exploratory Analysis

The In Vivo Effect of Water-Based Lubricants on the Vaginal Microbiome of Women from Varying Age Groups: Exploratory Analysis of a Randomized Controlled Trial

Vaginal mucosa undergoes physiological changes across the lifespan, such as increased pH and reduced natural lubrication which are known to impact vaginal commensal microorganisms, hence increasing the chances of vaginal infections. An improved understanding of vaginal microbiome composition in different age groups and the effect of social behaviors, such as the use of personal lubricants, could facilitate the development of new strategies to maintain good vaginal health. The objective of this study was to assess the effect of water-based lubricants on the human vaginal microbiome. Fifty females from three age groups (18–29, 30–44, and 45–65 years) with mild-to-moderate vaginal dryness were randomized to one of five lubricants (four of which were formulated to meet expert guidance on osmolality and pH). Subjects entered the study at tolerance or treatment phase (vaginal intercourse minimum once a week using assigned lubricant). Four vaginal swabs per participant were sampled during pre-(“baseline”), post-first (“2 h”, “24 h”) and post-last (“4 weeks”) lubricant application to assess bacterial and fungal diversity via amplicon sequencing. Vaginal pH and relative humidity were measured at baseline, 2 h, and 24 h post-lubricant application. Relative bacteriome abundance was statistically compared between timepoints for each lubricant group. Vaginal moisture, age, BMI, and pH were correlated with bacteriome relative abundance. Lactobacilli and Gardnerella sp. Were predominant across participants. Repeated lubricant application did not significantly alter the vaginal bacteriome during 4 weeks of product use (p > 0.05) when measured by relative abundance and alpha-diversity index. Bacteriome diversity and abundance differed significantly between age groups at baseline whereas lactobacilli relative abundance was negatively associated with age and vaginal pH.

Functional Time Series Analysis and Visualization Based on Records

In many phenomena, data are collected on a large scale and at different frequencies. In this context, functional data analysis (FDA) has become an important statistical methodology for analyzing and modeling such data. The approach of FDA is to assume that data are continuous functions and that each continuous function is considered as a single observation. Thus, FDA deals with large-scale and complex data. However, visualization and exploratory data analysis, which are very important in practice, can be challenging due to the complexity of the continuous functions. Here we introduce a type of record concept for functional data, and we propose some nonparametric tools based on the record concept for functional data observed over time (functional time series). We study the properties of the trajectory of the number of record curves under different scenarios. Also, we propose a unit root test based on the number of records. The trajectory of the number of records over time and the unit root test can be used for visualization and exploratory data analysis. We illustrate the advantages of our proposal through a Monte Carlo simulation study. We also illustrate our method on two different datasets: Daily wind speed curves at Yanbu, Saudi Arabia and annual mortality rates in France. Overall, we can identify the type of functional time series being studied based on the number of record curves observed. Supplementary materials for this article are available online.

Echocardiographic screening for heart failure and optimization of the care pathway for individuals with pacemakers: a randomized controlled trial

AbstractIndividuals with pacemakers are at increased risk of left ventricular systolic dysfunction (LVSD). Whether screening for and optimizing the medical management of LVSD in these individuals can improve clinical outcomes is unknown. In the present study, in a multicenter controlled trial (OPT-PACE), we randomized 1,201 patients (717 men) with a pacemaker to echocardiography screening or usual care. In the screening arm, LVSD was detected in 201 of 600 (34%) patients, who then received management in either primary care or a specialist heart failure (HF) and devices clinic. The primary outcome of the trial was the difference in a composite of time to first HF hospitalization or death. Over 31 months (interquartile range = 30–40 months), the primary outcome occurred in 106 of 600 (18%) patients receiving echocardiography screening, which was not significantly different compared with the occurrence of the primary outcome in 115 of 601 (19%) patients receiving the usual care (hazard ratio = 0.89; 95% confidence interval = 0.69, 1.17). In a prespecified, nonrandomized, exploratory analysis, patients with LVSD managed by the specialist clinic experienced the primary outcome event less frequently than those managed in primary care. The results of this trial indicate that echocardiography screening commonly identifies LVSD in individuals with pacemakers but alone does not alter outcomes. ClinicalTrials.gov registration: NCT01819662.

School Closures and the Spatial Ecology of Education Access in 10 U.S. Cities

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.

Educator perceptions of the complex needs of young people in Pupil Referral Units: An exploratory qualitative analysis

Background Alternative education provision such as Pupil Referral Units support young people who have been excluded from mainstream school settings and often from disadvantaged backgrounds. However, there is limited research to date exploring educators’ perceptions of the complex needs of young people in PRUs, and the extent to which PRUs as currently configured can meet such needs. Methods Between March 2019 and October 2020 twenty-two participants holding various educational roles from five different Pupil Referral Units across London and Southeast England were interviewed. The interviews aimed to explore the participants’ experiences of working with students in PRU’s and examine some of the challenges that they might encounter. Semi-structured interviews were analysed using Reflexive thematic analysis. Results The three identified themes and their sub-themes highlighted the complex needs of these young people and identified significant barriers to effective service provision. The first theme ‘Complexities of PRU population’ highlighted the challenges that young people in PRUs face and perceived systemic short falls in addressing such complexity. The second theme ‘Challenges of the PRU environment’ highlights the frustrations that educators experience when it comes to providing adequate support to young people in PRU’s, the absence of agency support, and the uncertainty that these educational settings can bring. The third theme ‘Peer Group Influences’ highlights the impact of peer groups from beyond the classroom on engagement within the classroom. Conclusions Despite the clear complex needs of young people in PRUs, staff reported feeling ill-equipped to support these individuals and lacked access to effective inter-agency support. Participants reported that pupils’ mental health difficulties were exacerbated by exclusion and reintegration practices, an over-zealous focus on educational outcomes and the impact of gang influences on their school lives. Implications include more specific mental health training for staff working in PRU’s, improved inter-agency working and the incorporation of trauma-informed approaches in educational practice.

Combating Repeated Lies: The Impact of Fact-Checking on Persistent Falsehoods by Politicians

<span id="docs-internal-guid-523bc895-7fff-c004-d998-e65fcb75fa8f"><span>The rise of repeated false claims within political discourse is undermining fact-checking efforts. By reiterating similar statements that perpetuate previous falsehoods, political actors shift from misinformation to deliberate disinformation and even propagandistic tactics. Through an analysis of 1,204 political fact-checks conducted by the Spanish fact-checking organization Newtral, this study quantifies and characterizes the prevalence of repeated false claims in political discourse, revealing that a substantial 24.8% of false statements are repeated, with each being repeated an average of four times. By delving into the nature and types of claims most susceptible to recurrence, the study identifies five primary patterns employed by political actors: nuanced variations, data manipulation, multilateral attacks, discourse qualification, and cumulative repetition. These tactics blur the lines between deception and self-correction. The annotated database of these repeated false statements can serve as a valuable resource for exploratory qualitative analysis as well as claim-matching research in automated fact-checking.</span></span>

Exploiting Speech Tremors: Machine Learning for Early Diagnosis of Amyotrophic Lateral Sclerosis

Abstract Neurodegenerative diseases pose significant challenges in healthcare, with Amyotrophic Lateral Sclerosis (ALS) being one such rare yet debilitating condition affecting motor neurons. Machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in healthcare, offering insights and solutions for various medical conditions. This study focuses on utilizing ML and AI to establish an early diagnosis of ALS through tremor detection in sustained speech. The study aims to establish an early diagnosis of ALS by analyzing tremors detected in sustained speech. The dataset comprises 54 patients from the Republican Research and Clinical Centre of Neurology and Neurosurgery in Belarus, Minsk. The study adopts a two-faceted approach: (1) Exploratory voice analysis to identify tremors associated with ALS in speech samples. (2) Development of ML algorithms to construct predictive models for early ALS diagnosis based on the identified tremors. The ML models exhibit promising results in distinguishing ALS patients from healthy controls based on speech analysis. Tremor detection in sustained speech proves to be an effective marker for early ALS diagnosis. While initial findings are encouraging, larger-scale studies are required to validate the clinical applicability of this approach. The successful application of ML and AI in early ALS diagnosis by leveraging innovative approaches, such as tremor detection in sustained speech, we can enhance early diagnosis and improve patient outcomes in neurodegenerative diseases like ALS on a broader scale.

Online eurythmy therapy for cancer-related fatigue: a prospective repeated-measures observational study exploring fatigue, stress, and mindfulness

IntroductionCancer is a debilitating disease with an often chronic course. One of the most taxing and prevalent sequelae in this context is cancer-related fatigue (CRF) resulting from the disease and/or associated treatments. Over the last years mindfulness-based interventions such as eurythmy therapy (ERYT), a mindful-movement therapy from anthroposophic medicine, have emerged as promising adjunct therapies in oncology. This prospective study investigated an online implementation of ERYT for CRF using a single arm repeated-measures design based on two consecutive studies.MethodStudy 1 consisted of an initial assessment before, during, after, and at follow up of a 6-week online ERYT-based program in a mixed sample of N = 165 adults with or without cancer diagnosis. Study 2 involved a similar design with an adapted 8-week online ERYT-based program in a sample of N = 125 adults who had been diagnosed with cancer. Outcomes were assessed using the Functional Assessment of Chronic Illness Therapy–Fatigue, Perceived Stress Scale, Mindful Attention Awareness Scale, and Insomnia Severity Index (for Study 1 all, for Study 2 only the former three). We additionally performed an exploratory analysis regarding practice frequency and duration. Data were analyzed using Linear Mixed-Effect Models per outcome; ANOVA was used for practice times.ResultsFor Study 1, mixed-effects model estimates showed no significant effect on fatigue, but pointed to significantly improved emotional and physical well-being, reduced stress, as well as increased mindfulness (mixed subjects). Functional and social well-being or sleep quality did not change significantly. Study 2 model estimates on the other hand showed significantly improved CRF in conjunction with the ERYT-based online intervention, as well as improved stress and mindfulness scores (cancer-diagnosed subjects).ConclusionTaken together, while our results should be interpreted with caution given the single-arm design and relatively high dropout, they suggest online ERYT may be associated with a reduction in fatigue for individuals diagnosed with cancer, an increase in mindfulness, and benefits for stress and certain well-being indicators. The online group format is advantageous in view of affordability and accessibility, the latter being particularly relevant for individuals who due to high symptom severity cannot leave their homes. Randomized-controlled studies will be needed to confirm these findings.

Role of dorsal striatum circuits in relapse to opioid seeking after voluntary abstinence

AbstractHigh relapse rate during abstinence is a defining characteristic of drug addiction. We previously found that opioid seeking progressively increases after voluntary abstinence induced by adverse consequences of oxycodone seeking (crossing an electric barrier). Functional MRI revealed that this effect is associated with changes in functional connectivity within medial orbitofrontal cortex (mOFC)- and dorsomedial striatum (DMS)-related circuits. Here, we used a pharmacological manipulation and fMRI to determine the causal role of mOFC and DMS in oxycodone seeking after electric barrier-induced abstinence. We trained rats to self-administer oxycodone (6 h/day, 14 days). Next, we induced voluntary abstinence by exposing them to an electric barrier for 2 weeks. We inactivated the mOFC and DMS with muscimol+baclofen (GABAa and GABAb receptor agonists) and then tested them for relapse to oxycodone seeking on abstinence days 1 or 15 without the electric barrier or oxycodone. Inactivation of DMS (p < 0.001) but not mOFC decreased oxycodone seeking before or after electric barrier-induced abstinence. Functional MRI data revealed that DMS inactivation decreased cerebral blood volume levels in DMS and several distant cortical and subcortical regions (corrected p < 0.05). Furthermore, functional connectivity of DMS with several frontal, sensorimotor, and auditory regions significantly increased after DMS inactivation (corrected p < 0.05). Finally, an exploratory analysis of an existing functional MRI dataset showed that DMS inactivation restored voluntary abstinence-induced longitudinal changes in DMS functional connectivity with these brain regions (p < 0.05). Results indicate a role of DMS and related brain circuits in oxycodone seeking after voluntary abstinence, suggesting potential targets for intervention.

Native and non-native vowel discrimination in 6-month-old Norwegian infants

In the current preregistered study, we tested n = 67 6-month-old Norwegian infants’ discrimination of a native vowel contrast /y-i/ and a non-native (British) vowel contrast /ʌ-æ/ in an eye-tracking habituation paradigm. Our results showed that, on a group level, infants did not discriminate either contrast. Yet, exploratory analyses revealed a negative association between infants’ performance in each experiment, that is, better discrimination of the native contrast was associated with worse discrimination of the non-native contrast. Potentially, infants in this study might have been on the cusp of perceptual reorganisation towards their native language.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

People judge others more harshly after talking to bots

Abstract People now commonly interact with Artificial Intelligence (AI) agents. How do these interactions shape how humans perceive each other? In two pre-registered studies (total N = 1,261), we show that people evaluate other humans more harshly after interacting with an AI (compared with an unrelated purported human). In Study 1, participants who worked on a creative task with AIs (versus purported humans) subsequently rated another purported human’s work more negatively. Study 2 replicated this effect and demonstrated that the results hold even when participants believed their evaluation would not be shared with the purported human. Exploratory analyses of participants’ conversations show that prior to their human evaluations they were more demanding, more instrumental and displayed less positive affect towards AIs (versus purported humans). These findings point to a potentially worrisome side effect of the exponential rise in human-AI interactions.

Migrant Women in the UK’s Digital Economy: The Elimination of Labour Market Barriers in the Digital Labour Market

This research paper delves into the complexities migrant women face within the UK labour market, with a specific focus on the digital economy’s role as both a barrier and a conduit for employment opportunities. Migrant women in the UK encounter dual barriers to labour market entry: systemic challenges rooted in migration and gender biases, and the digital divide that exacerbates access and inclusion issues within the burgeoning digital economy. Through an exploratory descriptive analysis, this study explores how digitalism—defined as the integration of digital technologies into economic and societal practices—circumvents traditional labour market entry barriers such as languages barriers. By setting out the grounds for a potential hypothesis and further research in the era of AI, this paper underlines how through the implementation of AI tools, traditional barriers such as language barriers are eliminated in the digital labour market.

Altered learning from positive feedback in adolescents with anorexia nervosa.

Anorexia nervosa (AN) is characterized by severe restriction of calorie intake, which persists despite serious medical and psychological sequelae of starvation. Several prior studies have identified impaired feedback learning among individuals with AN, but whether it reflects a disturbance in learning from positive feedback (i.e., reward), negative feedback (i.e., punishment), or both, and the extent to which this impairment is related to severity and duration of illness, has not been clarified. Participants were female adolescents with AN (n = 76) and healthy teen volunteers (HC; n = 38) between the ages of 12-18 years who completed a probabilistic reinforcement learning task. A Bayesian reinforcement learning model was used to calculate separate learning rates for positive and negative feedback. Exploratory analyses examined associations between feedback learning and duration of illness, eating disorder severity, and self/parent reports of reward and punishment sensitivity. Adolescents with AN had a significantly lower rate of learning from positive feedback relative to HC. Patients and HC did not differ in learning from negative feedback or on overall task performance measures. Feedback learning parameters were not significantly associated with duration of illness, eating disorder severity, or questionnaire-based reports of reward and punishment sensitivity. Adolescents with AN showed a circumscribed deficit in learning from reward that was not associated with duration of illness or reported sensitivity to reward or punishment. Subsequent longitudinal research should explore whether differences in learning from positive feedback relate to course of illness in youth with AN.

The Relative Reinforcing Value of Menthol Among Young Adult Cigarette Smokers: Results From a Behavioral Choice Task.

Menthol cigarettes are associated with experimentation and progression to regular use. Although reinforcement processes likely underlie menthol's appeal, the reinforcing value of menthol cigarettes remains unknown. This study examined the relative reinforcing value (RRV) of menthol versus nonmenthol cigarettes in young adult menthol (n = 54) and nonmenthol (n = 53) smokers, and differences in menthol's RRV by race, ethnicity, and sexual orientation. Overnight abstinent participants completed a choice task assessing willingness to "work" to click targets on a computer screen to earn menthol or nonmenthol cigarette puffs. A progressive ratio schedule was used where the menthol target had to be clicked progressively more times, over 10 trials, to earn a menthol cigarette puff, while clicks for the nonmenthol target were fixed across trials. RRV for menthol was defined by the breakpoint, or the highest trial (out of to 10) completed for a menthol cigarette puff. Number of clicks for menthol and nonmenthol puffs were also examined. Menthol smokers worked harder for menthol versus nonmenthol cigarette puffs (breakpoint = 9.17; ~1236 clicks vs. 24 clicks). Breakpoint was higher among Hispanic (6.49) versus NH White (4.83) and NH non-White smokers (4.43). In exploratory analyses of interactions of menthol preference with race and ethnicity, nonmenthol Hispanic smokers worked harder for menthol cigarette puffs versus NH non-White and NH White nonmenthol smokers. Menthol cigarettes are highly reinforcing for young adult menthol and Hispanic smokers. A menthol ban may reduce addiction risk among younger individuals and some minoritized groups of smokers. This study provides evidence of the greater relative reinforcing value of menthol compared to nonmenthol cigarettes among young adults who had a cigarette flavor preference, suggesting increased addiction risk of menthol cigarettes. Young adult menthol smokers and Hispanic (vs. non-Hispanic) smokers worked harder to earn menthol (vs. nonmenthol) cigarette puffs. Findings add to the evidence base supporting the U.S. Food and Drug Administration's (FDA) intent to ban menthol in cigarettes. Further, prevention messaging campaigns and cessation programs should take into account the reinforcing value of menthol in cigarettes, especially in vulnerable and at-risk populations.

Patient learning choices in an online health infomediary

ABSTRACT Health infomediaries are online interactive platforms where patients learn about diseases, procedures, and treatments, but how the learning dynamics evolve to help patients make decisions is unknown. Investigating patient learning in health information is essential to enabling patient empowerment using these platforms. In this study, we theoretically anchor patients’ learning processes in a health infomediary to Nonaka-Takeuchi’s Socialization, Externalization, Combination, and Internalization (SECI) learning dynamics model, with the infomediary as the ba or collaborative space for learning. We posit that questions reflect externalization for a focal learning patient, while reviews and comments reflect socialization, driving patients’ knowledge internalization. Learning with more information aids patients in health-relevant choices and actions such as a doctor consultation in a health infomediary. Three hypotheses about continued learning choices are presented and tested using data from 8,900 reconstructive surgery health infomediary users, using a multichoice mixed logit model with panel estimations. Findings show patients continue to review and comment more than questions to learn about trendy surgeries, which is counterintuitive to existing insights, whereas questions lead to learning sentiment-laden information. Exploratory analyses suggest that learning through reviews regarding post-procedural services may deter, but other learning modes motivate patients to consult a doctor. We discuss the theoretical contributions and implications for infomediary design and research.

A hybrid‐ensemble model for software defect prediction for balanced and imbalanced datasets using AI‐based techniques with feature preservation: SMERKP‐XGB

AbstractMaintaining software quality is a significant challenge as the complexity of software is increasing with the rise of the software industry. Software defects are a primary concern in complex modules, and predicting them in the early stages of the software development life cycle (SDLC) is difficult. Previous techniques to address this issue have not been very promising. We have proposed “A hybrid ensemble model for software defect prediction using AI‐based techniques with feature preservation” to overcome this problem. We have used the National Aeronautics and Space Administration (NASA) dataset from the PROMISE repository for testing and validation. By applying exploratory data analysis (EDA), feature engineering, scaling, and standardization, we found that the dataset is imbalanced, which can negatively affect the model's performance. To address this, we have used the Synthetic Minority Oversampling (SMOTE) technique and the edited nearest neighbor (ENN) (SMOTE‐ENN). We have also used recursive feature elimination cross‐validation (RFE‐CV) with a pipeline to prevent data leaking in CV and kernel‐based principal component analysis (K‐PCA) to minimize dimensionality and selectively relevant features. The reduced dimensional data is then given to the eXtreme Gradient Boosting (XGBoost) for classification, resulting in the hybrid‐ensemble (SMERKP‐XGB) model. The proposed SMERKP‐XGB model is better than previously developed models in terms of accuracy (CM1: 97.53%, PC1: 92.05%, and PC2: 97.45%, KC1:95.65%), and area under the receiver operating characteristic curve values (CM1:96.30%, PC1:98.30%, and PC2:99.30%: KC1: 93.54) and other evaluation criteria mentioned in the literature.

Possibility of the optimum monitoring and evaluation (M&E) production frontier for risk-informed health governance in disaster-prone districts of West Bengal, India

An efficient M&E system in public healthcare is crucial for achieving universal health coverage in low- and middle-income countries, especially when the need for service remains unmet due to the exposure of the population to disaster risks and uncertainties. Current research has conducted exploratory and predictive analyses to estimate the determinants of sustainable M&E solutions for ensuring uninterrupted access during and after disasters. The aim was to estimate the efficiency of reaching a higher M&E production frontier via the Cobb‒Douglas model and stochastic frontier model as the basic theoretical and empirical frameworks. The research followed a deductive approach and used a stratified purposive sampling method to collect data from different layers of health and disaster governance in a flood-prone rural setting in the Malda, South 24 Parganas and Purulia districts in West Bengal, India. The present mixed-method study revealed multiple challenges in healthcare seeking during disasters and how a well-structured M&E system can increase system readiness to combat these challenges. The stochastic frontier model estimated the highest M&E frontier producing the most attainable M&E effectiveness through horizontal convergence between departments, enhanced coordination, the availability of frontline health workers at health centers, the adoption of learned innovation and the outsourcing of the evaluation component to external evaluators to improve M&E process quality. Although the study has several limitations, it shows the potential to increase technical and allocative efficiency through building skills in innovative techniques and applying them in process implementation. In the future, research on strategy improvement followed by real-world evidence-based policy advocacy is needed to increase the impact of M&E on access to healthcare services.