Data Analysis Approach Research Articles

Advancing Indigenous primary health care research and education capacity: a qualitative study in Alberta, Canada

Background/Purpose The study explores the perspectives of family physicians in Alberta regarding Indigenous health research education within the framework of primary health care (PHC). Recognizing the key role of PHC in healthcare system, the research identified the preferences and needs of family physicians for relevant and accessible research skills education. To design effective research education, it is critical to understand the skills, content, and delivery methods desired by family physicians engaged in Indigenous health research. The Indigenous Primary Health Care and Policy Research (IPHCPR) Network is examined as a potential transformative platform aligned with Truth and Reconciliation Commission principles and Calls to Action. Methods Through purposive sampling, 12 family physicians practicing in Alberta were recruited to participate in semi-structured qualitative interviews . The study utilized a thematic content approach for data analysis. Results Four key themes emerged from the analysis: 1) Indigenous role modeling; 2) cohort-based learning; 3) relational learning; and 4) the epistemic importance of Indigenous perspectives on learning. Participants emphasized the need for mentorship, collaborative learning settings, and meaningful connections within a research network. Discussion The IPHCPR Network stands out as a pivotal platform for transformative change in healthcare research education, offering opportunities for family physicians, researchers, trainees, and communities to collaboratively improve Indigenous health outcomes. Through its inclusive practices, the network aligns with TRC principles to advance Indigenous health equity and addressing critical gaps in Indigenous primary health research and practice.

EXPRESS: From Stars to Dogs – Identifying “Out-Of-Favor” Products on E-Commerce Platforms? Data Analytic Approach to System Design

Online retail platforms are increasingly challenged by the proliferation of low-quality products, which may damage their reputation and sales. To address this problem, we propose a system architecture to proactively identify products that are likely to go "out of favor". Our approach uses historical data to extract useful information from customer ratings and textual reviews. Available data are fed into a state-of-the-art deep learning sequence model to forecast future ratings. We then analyze rating trends, extracting hyperparameters that a binary classifier uses to label products as "out of favor" or not. We tested this system on an Amazon dataset comprising nearly 800,000 observations across 2,826 electronics products. Our results show that the Long Short-Term Memory (LSTM) model excels in forecasting future product ratings compared to other benchmarks. Ablation analysis shows sentiment-related features significantly improve rating forecasts by up to 40%, with review topics adding 10% and other review characteristics 4%. Counterintuitively, topic extraction from reviews does not provide substantial benefits, despite the heavy computational resources it requires. Finally, the two-stage classification process, which leverages time-series data and rating trends, offers a more stable and robust performance than conventional single-stage methods. We provide considerations for system architecture development through robustness checks ensuring its resilience to stressors. Our experiments indicate that rating trends can change in subtle ways over time, leading a promising “star” product to turn into a liability (“dog”). E-commerce platforms can use the proposed system architecture proactively to identify and remove potentially dubious products instead of waiting to take reactive action.

Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data

BackgroundNewborns are shaped by prenatal maternal experiences. These include a pregnant person’s physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models to predict markers of fetal growth and development—specifically, newborn birthweight and head circumference (HC).MethodsWe used a pre-registered archival data analytic approach. These data consisted of maternal and newborn characteristics of 594 maternal-infant dyads in the western U.S. Participants also completed a measure of emotion dysregulation. In total, there were 22 predictors of newborn HC and birthweight. We used regularized regression for predictor selection and linear prediction, followed by nonlinear models if linear models were overfit.ResultsHC was predicted best with a linear model (ridge regression). Newborn sex (male), number of living children, and maternal BMI predicted a larger HC, whereas maternal preeclampsia, number of prior preterm births, and race/ethnicity (Latina) predicted a smaller HC. Birthweight was predicted best with a nonlinear model (support vector machine). Occupational prestige (a marker similar to socioeconomic status) predicted higher birthweight, maternal race/ethnicity (non-White and non-Latina) predicted lower birthweight, and the number of living children, prior preterm births, and difficulty with emotional clarity had nonlinear effects.ConclusionsHC and birthweight were predicted by a variety of variables associated with prenatal stressful experiences, spanning medical, psychological, and social markers of health and stress. These findings may highlight the importance of viewing prenatal maternal health across multiple dimensions. Findings also suggest that assessing difficulties with emotional clarity during standard obstetric care (in the U.S.) may help identify risk for adverse newborn outcomes.

ADAPTATION OF MSME BUSINESS ACTORS TO SHARIA DIGITAL FINANCE

Purpose: The purpose of this study is to identify how micro, small and medium enterprises "Cuan Store" adapt to using Sharia Digital Finance in their business operations. Design/Methodology/Approach: This research used a descriptive approach along with a qualitative data analysis approach in this study, data collection using interview techniques, observation and literature study. Findings: The results of this study that micro, small and medium enterprises "Cuan Store" have successfully adapted in applying sharia digital finance as a medium in running their business, then the factor of using sharia digital finance in micro, small and medium enterprises is very significant because many "Cuan Store" customers have used sharia digital finance as a payment transaction, as for the impact on the use of sharia digital finance has a positive influence on micro, small and medium enterprises "Cuan Store". Research Implications: The existence of sharia digital services encourages micro, small and medium enterprises to continue learning about sharia principles. Due to the fact that many customers of "Cuan Store" have used Islamic digital finance as a way of payment, the factor of using Islamic digital finance on micro, small and medium enterprises is very significant. In addition, the effect of using Islamic digital finance has a positive impact on the micro, small and medium enterprises of "Cuan Store", because the existence of Islamic digital services encourages micro, small and medium enterprises to continue learning about sharia principles.

FINANCIAL INCLUSION AND THE INFORMAL SECTOR DURING AND POST-COVID 19 ERAS: STATUS, CHALLENGES AND WAY FORWARD

COVID 19 is one of the major disasters that significantly disrupted the socio-economic statuses of various businesses and nations at large. The purpose of the study was to explore the extent to which COVID 19 affected financial inclusion in Zimbabwe. The quantitative research methodology and the purposive sampling technique were employed. Data was collected using in-depth and focus discussion group interviews from 35 participants. Data was analysed using thematic data analysis approach. Barred people in rural areas from accessing financial services which directly and indirectly increased the magnitude of poverty among people running informal businesses. Financial inclusion, is a key driver to poverty reduction; hence the negative impact of on financial inclusion significantly propels poverty in societies. Digitalizing financial services is one of the strategies which can be utilized to promote financial inclusion in the post- era. Financial institutions should use technology in financial services, so that those in rural areas do not incur transport and logistical costs for accessing financial services. Digital finance and other various financial innovations should be used to achieve financial inclusion in rural areas. Policies that encourage the delivery of formal financial services in rural areas should be developed. Governments should consider granting subsidy to providers of financial services so that they can offer basic financial services at low cost to the excluded population. Thus, governments ought to commit themselves to the delivery of accessible, affordable, appropriate and cost-effective financial services to unserved or underserved households. The study provides managers of financial institutions with various techniques of providing financial services to the informal sector in the post- eras in rural areas. It also provides managers with information pertaining to the risks in providing financial services in post-pandemic era in rural areas. Managers of internet service providers has a role in making sure that the rural folk have access to the facility so that digital banking systems become realizable.

Using Vaccine Safety Data to Demonstrate the Potential of Pooled Data Analysis.

In Canada, vaccine safety studies are often conducted at the provincial/territorial level where the primary data on vaccination reside. Combining health services data from multiple jurisdictions using a pooled data analytic approach would reduce the amount of time needed to detect vaccine safety signals. To determine the difference in the time it would take to identify safety signals using different proportions of the Canadian population, we conducted power and sample size calculations for a hypothetical self-controlled case series-based surveillance analysis. We used scenarios modeled after the real-world examples of myocarditis and vaccine-induced immune thrombotic thrombocytopenia (VITT) following COVID-19 vaccination as our base cases. Our calculations demonstrated that in the case of a myocarditis-type event, a pooled analysis would reduce the time needed to detect a safety signal by over 60% compared to using Ontario data alone. In the case of a VITT-type event, a pooled analysis could detect a safety signal 49 days sooner than using Ontario data alone, potentially averting as many as 30 events. Our analysis demonstrates that there is substantial value in using pan-Canadian health services data to evaluate the safety of vaccines. Efforts should be made to develop a pan-Canadian vaccine data source to allow for an earlier evaluation of suspected adverse events following immunization.

Detection and assessment of heavy metals concentration in wheat leaves collected from the vicinity of Sahiwal coal power plant, Pakistan, employing a modern data analysis approach

ABSTRACT Heavy metal pollution in food crops has become a global issue, especially in staple crops like wheat and rice. Electricity production utilising coal, ignites this issue in crops grown in the surrounding areas of coal power plants. Keeping this view in mind, the concentration of six heavy metals (HMs) Calcium (Ca), Cadmium (Cd), Chromium (Cr), Cobalt (Co), Lead (Pb) and Magnesium (Mg) in wheat leaves have been assessed in this study. Fifty samples of wheat leaves have been collected from the surrounding areas of the coal power plant in Sahiwal. The samples were prepared using the wet digestion method (WDM). Finally, these samples have been sent for analysis via atomic absorption spectroscopy (AAS). The limit of detection (LOD) and limit of Quantification (LOQ) of AAS were calculated with the help of blank samples. The concentration of Ca, Cd, Cr, Co, Pb and Mg in wheat leaves were 1.0187 ± 0.549 mg/kg, 0.00484 ± 0.00403 mg/kg 00,416 ± 0.00334 mg/kg, 0.0534 ± 0.0351 mg/kg, 0.035 ± 0.018 mg/kg, 0.464 ± 0.200 mg/kg respectively. The concentration of all understudy metals was below the Food and Agriculture Organization/World Health Organization (WHO/FAO) standards except for Co, in one sample. However, the concentration of heavy metals may increase, which may have profound repercussions. Therefore, preventive measures should be taken in this regard. The neutrosophic approach has been utilised to examine the variation of the heavy metals content in wheat leaves. This study provides a solid scientific foundation for future appropriate actions and management of heavy metals contamination in food crops near coal-fired power stations.

Assessment of household preferences for net textile type (polyester versus polyethylene) for decision-making of the National Malaria Control Programme in Burkina Faso: methods for a quasi-experimental study

BackgroundA quasi-experimental comparative trial will be designed in Burkina Faso. The study will compare the use and preferences for two groups types of insecticide-treated nets textile: polyester-based and polyethylene-based, according to their use and preferences in selected health districts. These health districts will be selected in three eco-climate zones (Sahelian, dry savannah and wet savannah) in the country. These findings will inform decisions on future net procurements for national malaria control programme in 2025.MethodsQuantitative surveys and qualitative data collection will be carried out to gather information on the type of net textile most commonly used and preferred by the community. They will be performed between the end of the dry season and the early rainy season. The quantitative surveys involved household interviews with households and individuals’ questionnaires, while the qualitative data collection involved in-depth individual interviews and focus group discussions to explore and clarify some key evaluation criteria. A total of 9450 insecticide-treated nets were surveyed for quantitative survey purposes. For the qualitative study, 48 in-depth individual interviews and 12 focus group discussions were carried out. A mixed model approach combining the results from quantitative surveys and qualitative studies will be used for decision-making on the type of insecticide-treated net preference.ConclusionThis methodological approach will be used by the National Malaria Control Programme to conduct this study on determinants of net use in Burkina Faso in order to provide robust evidence across diverse settings. This mixed-methods approach for data collection and analysis could be used in other countries to provide evidence that would help to increase the uptake of insecticide-treated nets, the main vector control tool in Africa.

Real-time solar PV generation in a building using LSTM-based time series forecasting

The increasing demand for renewable energy has forced the researchers to lay importance on studying the behavior of these sources by monitoring the operational data and performing data analysis. The application of data analytics to renewable energy is one of the most powerful methods to maintain the system's reliability and stability. As the power system becomes more complex the necessity to perform data analytics also increases. When data analytic techniques are applied to solar energy generations through Photovoltaic (PV) dataset, the possible behavior of PV generation performance which is affected by changes in environmental conditions can be predicted and further analytical approaches allow us to detect possible PV panel and inverter failures. This paper is an attempt towards applying the intelligent data analytics approaches to solar PV generation of a real-time photovoltaic plant. The main purpose of the data analytics platform is to analyze the energy yield, assess the PV system performance, and forecast solar PV generation. In this article, Long Short-Term Memory (LSTM) machine learning model is developed to assess and interpret the available information from the gathered data of the PV plant. The proposed model is a sequence-to-sequence regression-based forecasting model which performs time series forecasting in a real-time 100 kW PV plant in the University building of SRM Institute of Science and Technology, Kattankulathur, Tamilnadu.

Screening of Silver-Based Single-Atom Alloy Catalysts for NO Electroreduction to NH3 by DFT Calculations and Machine Learning.

Exploring NO reduction reaction (NORR) electrocatalysts with high activity and selectivity toward NH3 is essential for both NO removal and NH3 synthesis. Due to their superior electrocatalytic activities, single-atom alloy (SAA) catalysts have attracted considerable attention. However, the exploration of SAAs is hindered by a lack of fast yet reliable prediction of catalytic performance. To address this problem, we comprehensively screened a series of transition-metal atom doped Ag-based SAAs. This screening process involves regression machine learning (ML) algorithms and a compressed-sensing data-analytics approach parameterized with density-functional inputs. The results demonstrate that Cu/Ag and Zn/Ag can efficiently activate and hydrogenate NO with small Φmax(η), a grand-canonical adaptation of the Gmax(η) descriptor, and exhibit higher affinity to NO over H adatoms to suppress the competing hydrogen evolution reaction. The NH3 selectivity is mainly determined by the s orbitals of the doped single-atom near the Fermi level. The catalytic activity of SAAs is highly correlated with the local environment of the active site. We further quantified the relationship between the intrinsic features of these active sites and Φmax(η). Our work clarifies the mechanism of NORR to NH3 and offers a design principle to guide the screen of highly active SAA catalysts.

Sleep Quality and Urinary Incontinence in Prostate Cancer Patients: A Data Analytics Approach with the ASCAPE Dataset.

The ASCAPE project aims to improve the health-related quality of life of cancer patients using artificial intelligence (AI)-driven solutions. The current study employs a comprehensive dataset to evaluate sleep and urinary incontinence, thus enabling the development of personalized interventions. This study focuses on prostate cancer patients eligible for curative treatment with surgery. Forty-two participants were enrolled following their diagnosis and were followed up at baseline and 3, 6, 9, and 12 months after surgical treatment. The data collection process involved a combination of standardized questionnaires and wearable devices, providing a holistic view of patients' QoL and health outcomes. The dataset is systematically organized and stored in a centralized database, with advanced statistical and AI techniques being employed to reveal correlations, patterns, and predictive markers that can ultimately lead to implementing personalized intervention strategies, ultimately enhancing patient QoL outcomes. The correlation analysis between sleep quality and urinary symptoms post-surgery revealed a moderate positive correlation between baseline insomnia and baseline urinary symptoms (r = 0.407, p = 0.011), a positive correlation between baseline insomnia and urinary symptoms at 3 months (r = 0.321, p = 0.049), and significant correlations between insomnia at 12 months and urinary symptoms at 3 months (r = 0.396, p = 0.014) and at 6 months (r = 0.384, p = 0.017). Furthermore, modeling the relationship between baseline insomnia and baseline urinary symptoms showed that baseline insomnia is significantly associated with baseline urinary symptoms (coef = 0.222, p = 0.036). The investigation of sleep quality and urinary incontinence via data analysis through the ASCAPE project suggests that better sleep quality could improve urinary disorders.

Improving Student Learning Outcomes in Science and Science Subjects Through a Problem-Based Learning Model for Class V.A SD Negeri 27 Palembang

This study aims to improve the learning outcomes of students in the subject of Natural and Social Sciences (IPAS) by applying the Problem-Based Learning (PBL) learning model in class V.A SD Negeri 27 Palembang. This research used the Classroom Action Research (CAR) method which was carried out in three cycles. Each cycle involved planning, implementation, observation, and reflection. For data analysis, both quantitative and qualitative approaches were employed. The results showed a significant increase in students' learning outcomes in Chapter 2 “Harmony in Ecosystems”. The average score of students increased from 62.6 in cycle I with 62% learning completeness, to 70.1 in cycle II with 70% learning completeness, and reached 83.3 in cycle III with 83% learning completeness. Thus, the PBL learning model proved effective in improving student learning outcomes in IPAS subjects in class V.A SD Negeri 27 Palembang.

The Relationship Career Adaptability on Employee Turnover and Mediation Role of Perceived Overqualification at PT. Kimia Farma Diagnostika

The goal of this study was to identify the effect of career adaptability on turnover intention and to analyze the mediation role of perceived overqualification on employees of PT. Kimia Farma Diagnostika. This study involved 80 respondents with a quantitative approach and SEM-PLS multivariate analysis method for data analysis. This research found that career adaptability showed a positive and significant effect on turnover intention. In addition, it was also found that perceived overqualification partially mediated the relationship between career adaptability and turnover intention. The practical implications of this study are to provide insight to the management of PT. Kimia Farma Diagnostika to design more effective employee retention strategies. Further research is recommended to expand the scope of analysis at the organizational level, consider additional moderating variables, and address existing limitations. Thus, this study makes an important contribution to understanding and addressing employee retention issues in these companies.

From ensemble learning to deep ensemble learning: A case study on multi-indicator prediction of pavement performance

Recently, Big data analytics approaches were combined with emerging machine learning techniques, which can provide more sophisticated insights into information-intensive activity. Compared to traditional shallow-architecture machine learning algorithms, Deep learning could excavate more potential information from the raw features. However, its powerful representational capacity relies on the support of enormous samples. The ensemble trees system performs superior on small sample problems due to better generalization capacity. To merge both benefits of deep learning and ensemble tree system, this paper developed a deep ensemble algorithm applied to multiple indicators prediction of pavement performance, including International Roughness Index and pavement 3-layer modulus. The deep ensemble algorithm is developed by merging a deep neural network (DNN) with the decision manifold property of the decision trees (TabNet) into a cascade ensemble system, combined with a sliding window algorithm to extract dependency information from raw data. During the training stage, the Bayesian Optimization Algorithm (BOA) is used to search for the optimal combination of sub-decision makers in the cascade ensemble. And equipped with GPU, it can speed up by 2.6–4.0 times. In the case study of pavement engineering, with sufficient training samples, it can achieve an average accuracy of 98.74 %, higher than DNN (97.49 %) and XGBoost (96.12 %) in predicting pavement indicators. With insufficient training samples, it can achieve an accuracy improvement of 12 % than XGBoost (75 %) and 24.5 % than DNN (62.5 %).

#Dolphins: communication and engagement in marine mammal tourism attractions

ABSTRACT This paper delves into the ethical dimensions and public perceptions surrounding dolphin tourism, both seeing them in the wild and in the marine park. Using a comprehensive analysis of social media data over seven years, we focus on dolphins as a central element of marine mammal tourism, investigating how digital narratives shape tourist attitudes and behaviours towards these intelligent creatures. Informed by a multi-method data analysis approach, this study reveals an “echo chamber” effect in social media, perpetuating selective exposure to information that aligns with visitor motivations: hedonism, relevance and reverence. The theoretical contributions of this research extend the existing literature on wildlife tourism by offering a deeper understanding of how public sentiments towards animals in tourism settings evolve and influence tourism practices. The finding underscores the need for informed, ethical communication strategies that consider the complex interplay of entertainment, education, and conservation in wildlife tourism.

Breaking the taboo of using the nursing process: lived experiences of nursing students and faculty members

BackgroundDespite the numerous advantages of the nursing process, nursing students often struggle with utilizing this model. Therefore, studies suggest innovative teaching methods to address this issue. Teaching based on real clinical cases is considered a collaborative learning method that enhances students’ active learning for the development of critical thinking and problem-solving skills. In this method, students can acquire sufficient knowledge about patient care by accessing authentic information.ObjectiveThe aim of the present study was to investigate the experiences of nursing students and faculty members regarding the implementation of nursing process educational workshops, based on real case studies.DesignA qualitative descriptive study.Participants9 Nursing students and 7 faculty members from the Isfahan School of Nursing and Midwifery who attended the workshops.MethodsThis qualitative descriptive study was conducted from 2021 to 2023. Data was collected through semi-structured individual and focus group interviews using a qualitative content analysis approach for data analysis.ResultsAfter analyzing the data, a theme titled “Breaking Taboos in the Nursing Process” was identified. This theme consists of four categories: “Strengthening the Cognitive Infrastructure for Accepting the Nursing Process,” “Enhancing the Applicability of the Nursing Process,” “Assisting in Positive Professional Identity,” and “Facilitating a Self-Directed Learning Platform.” Additionally, thirteen subcategories were obtained.ConclusionThe data obtained from the present study showed that conducting nursing process educational workshops, where real clinical cases are discussed, analyzed, and criticized, increases critical thinking, learning motivation, and understanding of the necessity and importance of implementing the nursing process. Therefore, it is recommended that instructors utilize this innovative and effective teaching method for instructing the nursing process.

Automating Government Report Generation: A Generative AI Approach for Efficient Data Extraction, Analysis, and Visualization

This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching & report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 has been used to read different data visualisation graphs and fetching the information from internal knowledge base, respectively, to update the graphs in automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by Department of Economic & Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefit of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.

Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The ‘cleaning’ factor has the highest weight, and ‘updating’ is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

The Effect of Health Worker Performance and Loyalty On Patient Satisfaction Through Service Quality as An Intervening Variable on BPJS Patients At Tamalate Health Center Makassar City

Puskesmas and BPJS are two health service support facilities that are closely related and cannot be separated in health services in Indonesia. Patient satisfaction is one of the important indicators that must be considered in providing health services. Service quality is an indicator of achieving patient satisfaction, including the role of health workers while planning health services, with the efficiency and effectiveness of health care providers being a key factor in the overall success of these services. Patients receiving BPJS at Tamalate Health Center in Makassar City were the subjects of this investigation, which aimed to identify the relationship between health worker performance and loyalty and patient satisfaction via service quality as a mediating variable. The Tamalate Health Center in Makassar City is the site of this quantitative study that employs a causal survey technique. A questionnaire was used as the tool. Path analysis is then used as the data analysis approach. Service quality for BPJS patients at Makassar City's Tamalate Health Center was shown to be directly impacted by health staff performance and loyalty. At Makassar City's Tamalate Health Center, BPJS patient satisfaction is directly impacted by the loyalty and performance of health workers. At the Tamalate Health Center in Makassar City, BPJS patients' satisfaction is indirectly affected by the performance of health workers via the level of service they get. For BPJS patients at the Tamalate Health Center in Makassar City, customer loyalty indirectly affects happiness via service quality.

Two assumptions of the prior event rate ratio approach for controlling confounding can be evaluated by self-controlled case series and dynamic random intercept modeling

ObjectivesThe prior event rate ratio (PERR) is a recently developed approach for controlling confounding by measured and unmeasured covariates in real-world evidence research and observational studies. Despite its rising popularity in studies of safety and effectiveness of biopharmaceutical products, there is no guidance on how to empirically evaluate its model assumptions. We propose two methods to evaluate two of the assumptions required by the PERR, specifically, the assumptions that occurrence of outcome events does not alter the likelihood of receiving treatment, and that earlier event rate does not affect later event rate. Study Design and SettingWe propose using self-controlled case series (SCCS) and dynamic random intercept modeling (DRIM), respectively, to evaluate the two aforementioned assumptions. A nonmathematical introduction of the methods and their application to evaluate the assumptions are provided. We illustrate the evaluation with secondary analysis of deidentified data on pneumococcal vaccination and clinical pneumonia in The Gambia, West Africa. ResultsSCCS analysis of data on 12,901 vaccinated Gambian infants did not reject the assumption of clinical pneumonia episodes had no influence on the likelihood of pneumococcal vaccination. DRIM analysis of 14,325 infants with a total of 1719 episodes of clinical pneumonia did not reject the assumption of earlier episodes of clinical pneumonia had no influence on later incidence of the disease. ConclusionThe SCCS and DRIM methods can facilitate appropriate use of the PERR approach to control confounding. Plain Language SummaryThe prior event rate ratio is a promising approach for analysis of real-world data and observational studies. We propose two statistical methods to evaluate the validity of two assumptions it is based on. They can facilitate appropriate use of the prior even rate ratio.