Estimating Regression Models Research Articles

Global trends in prevalence of maternal overweight and obesity: A systematic review and meta-analysis of routinely collected data retrospective cohorts

Pregnant women with obesity are at greater risk of complications during pregnancy, peripartum and post-partum, compared to women with healthy BMI. Worldwide data demonstrating the changes in trends of maternal overweight and obesity prevalence informs service development to address maternal obesity, while directing resources to areas of greatest need. This systematic review and meta-analysis of population level data sought to evaluate global temporal changes in prevalence of maternal obesity and overweight/obesity, and compare trends between regions. Pooled prevalence of obesity and overweight/obesity was estimated using random effects meta-analysis. Temporal and geographical trends in prevalence of obesity and overweight/obesity were examined using linear regression. From 11,684 publications, 94 met inclusion criteria representing 121 study cohorts (Europe n = 71; North America n = 23; Australia/Oceania n = 10; Asia n = 5; South America n = 12), totalling 49,009,168 pregnancies. No studies from Africa met the inclusion criteria. Eighty studies (85.1%) were evaluated as having a low risk of bias and 14 studies (14.9%) moderate. In the most recent full decade (2010-2019), global prevalence of maternal obesity was estimated as 16.3% (95% confidence interval (CI): 15.1-17.5%), or approximately one in six pregnancies. Combined overweight/obesity in pregnancy had a pooled prevalence of 43.8% (95%CI: 42.2-45.4%), approaching half of all pregnancies. In each continent, an upward trend similar to the global trend was observed. North America demonstrated the highest prevalence (obesity: 18.7% (95%CI: 15.0-23.2%)); overweight/obesity: 47.0% (95%CI: 45.7-48.3%)) and Asia demonstrated the lowest prevalence (obesity: 10.8% (95%CI: 7.0-16.5%)); overweight/obesity: 28.5% (95%CI: 18.3-41.5%)). Both maternal obesity and combined overweight/obesity prevalence increased annually by 0.34% and 0.64% (p < 0.001), respectively. Our linear regression model estimates current global prevalence of maternal obesity as 20.9% (95%CI 18.6-23.1%) and projects that this will increase to 23.3% (95%CI 20.3-26.2%) by 2030. Globally, maternal obesity and overweight/obesity prevalence is high and increasing, but varies greatly between regions, being highest in North America and lower in Asia. Maternity services across the globe should be adequately resourced to cope with the complexity of needs of pregnant women living with obesity. Future public health interventions should focus on reversing the high prevalence of maternal obesity observed across the globe. The availability of population-level data and research varies between regions, with more data required to understand the needs of maternal populations in the continents of Africa and Asia. Globally, there is a need for improved harmonisation and publication of data for monitoring and improvement of maternal inequalities.

Quantifying weather-induced unreliable public transportation service in cold regions under future climate model scenarios

Climate change, particularly in cold regions, significantly challenges public transportation systems. This study conducts a comprehensive analysis of weather patterns and public transit reliability in the context of climate change impacts. Leveraging advanced modeling techniques, including a ridge regression model for snow water equivalent data estimation and a long short-term memory (LSTM) based on recurrent neural network, the study aims to assess the reliability trends of the rapid transit system under various climate scenarios. The findings reveal that climate change in general increases weather-related delays in the Toronto transit system. The number of short delays decreased accordingly due to changes in winter temperatures but exacerbated long delays as the number of weather extremes increased. The LSTM model performed effectively in predicting delays, especially for the rapid transit system sensitive to weather variations. This study emphasizes the need for robust planning and interventions to increase the resilience of transit systems against climate change and highlights the importance of the integration of climate and extreme weather considerations into transportation management.

Uncertain statistics models for characterizing annual total energy production

In the field of annual total energy production, it is hard to determine whether the experimental data is linear or nonlinear growth characteristics. Therefore, different regression models are used to test the properties of the observed data. This article uses actual data to perform parameter estimation and residual analysis of linear and nonlinear regression models. In addition, uncertain nonlinear time series model is used to fit the experimental data in order to find a more suitable statistics model after obtaining the growth properties of the observed data. A test is introduced to prove that the disturbance terms presented in the regression and time series models are actually uncertain variables, not random variables. Moreover, the uncertain hypothesis tests show that the uncertain time series model is in better agreement with the real data of the annual total energy production.

Prediction of carcass weight using the morphometry of ankle bones in hair goats.

Morphologic measurements such as body lenght, wither height, heart girth, chest width, body leght, cannon-bone circumference is used to predict carcass weight. For this purpose, estimating carcass weight with measurements of key bones such as ankle bones, which play a significant role in the balance distribution of body weight, seems possible. The aim of this study is to create new regression models for effective carcass weight estimation by using the morphometric data of the talus and calcaneus bones of hair goats. Study materials consisted of talus and calcaneus bones obtained from abattoir products of hair goat kids (12-18 months old, 20 female and 20 male) and adult hair goats (36-48 months old, 20 female and 20 male). Morphometric measurements of the talus and calcaneus of each animal were taken by a digital caliper. Using the morphometric measurements, an index and a factor were calculated for each bone. Regression analysis and correlations were examined in IBM SPSS 21 programme. As a result, statistical analysis of GLc, GLt, Bd, Calfactor and Talfactor were statistically significant on predicting carcass weight. Specific anatomical structures, such as certain bone measurements, such as talus and calnaneus could serve as indicators of growth performance and also carcass weight performance. In addition new anatomical factors and indices may be produced and new regression methods may be applied with these new parameters to predict carcass weight.

Association of pre-pregnancy anthropometric factors and mediterranean diet score with hyperemesis gravidarum: Results from a hospital-based case-control study

BackgroundHyperemesis gravidarum (HG) is the most widespread reason for admission to the hospital during the first half of pregnancy. HG etiology is not clear in many cases. However, maternal body composition and pre-pregnancy diet are suggested as possible factors. This hospital-based case-control study aims to determine the link between the Mediterranean diet score (MDS) and pre-pregnancy anthropometric indicators with Hyperemesis gravidarum. MethodsA total of 45 women with incident HG and 126 controls were entered into this case-control study between April 2020 and June 2021. A repeatable and accurate 168-item semi-quantitative food frequency questionnaire was used to define the MDS. The association between MDS and HG odds was examined through adjustment by possible confounders by the estimation of multivariate regression models. ResultsThe median (interquartile range) pre-pregnancy body mass index of participants was 22.7 (21.7-25.9) and 22.9 (21.4-25) Kg/m2 in cases and controls, respectively (P=0.284). The adjusted analyses found that the MDS was associated with a 25% reduced odds of HG (odds ratio (OR): 0.75, 95% confidence interval (95%CI): 0.24-0.91). Body mass index and waist circumference before pregnancy were not significantly associated with HG odds in both regression models. ConclusionsThe odds of HG during pregnancy are lower in women with a higher MDS before pregnancy. Also, a high intake of fruits and nuts, vegetables, legumes, and fish before pregnancy is liked with a lower HG odds.

Model of performance indicators for the evaluation of state public health expenditures in Brazil

Objective: To present a proposal on a set of performance indicators that can be used in audits of state audit courts to assess the effectiveness of budget spending on public health. Methodology: This is quantitative research, with the application of statistical techniques related to Data Envelopment Analysis (DEA) in two stages and estimation of linear longitudinal regression models for strongly balanced short panel data, on the budget expenditures made in the period from 2015 to 2019 by the States and the Federal District on public health. Results: It was found that there is no use of performance indicators in the elaboration and execution of Multi-Annual Plans (MAPs) by governments to evaluate the effectiveness of spending on public actions; that the mention of "performance indicators" refers exclusively to percentages to compare what was accomplished in relation to what was predicted; that records of duplicate public actions were found in the MAPs by some entities, and that overvaluation was identified in the records of the resources provided for in most of the MAPs. The degree of statistical explanation of the proposed set of indicators reached an average of 87% of statistical confidence. None of the public entities was 100% effective in the expenditures made. 69% of the auditors of the Courts of Auditors legitimized the set of indicators proposed to evaluate the effectiveness of state public health spending. Finally, it is concluded that the proposed performance evaluation indicators can be used to verify the effectiveness of state public health spending. Contributions of the study: It presents performance indicators, created by means of a statistical method, to evaluate the effectiveness of spending on state public health, since the specialized literature indicates little or no discussion about it. It indicates that the actions included in the MAPs are not audited by the courts of auditors and that the legislative branch is silent in the supervision between what is foreseen and what is carried out, considering that there are repeated and overvalued actions.

Pengaruh Financial Distress, Kompleksitas Operasi dan Auditor Switching Terhadap Audit Report Lag Dengan Ukuran Perusahaan Sebagai Pemoderasi

The aim of this research is to determine the influence of Financial Distress, Company Operational Complexity and Auditor Swithing on Audit Report Lag with Company Size as a moderating variable. The method used in this research is associative quantitative, using secondary data. The population of this research is food and beverage companies listed on the Indonesia Stock Exchange for the 2018-2022 period, namely 72 companies. With the research sample method using purposive sampling with six sample criteria, the total sample used was 19 companies so that the observations amounted to 95 data. Data analysis in this research uses descriptive statistical analysis, panel data regression model estimation, classical assumption testing, hypothesis testing and Moderated Regression Analysis (MRA) using eviews 10. The results of this research are that Financial Distress has an effect on Audit Report Lag, the complexity of company operations has no effect. on the Audit Report Lag, Auditor Swithing cannot influence the Audit Report Lag. And for the moderating variable, company size can only strengthen the relationship between company operational complexity and Audit Report Lag, while company size weakens the relationship between Financial Distress and Audit Report Lag, and company size weakens the relationship between Auditor Swithing and Audit Report Lag

The Determinants of Profitability of Western Balkan Countries Commercial Banks: A panel Data Approach

Commercial banks are considered to be important contributors to the country’s financial system and economic development. The aim of this study was to examine the impact of various factors on commercial banks’ profitability. Moreover, the study compares and analyzes the main determinants of the banks’ profitability in Western Balkan (WB) countries. The sample size includes all commercial banks in WB countries, covering the period 2010–2020. The collected data provides information about the two main measurements used to determine a bank’s performance: 1) return on assets (ROA) and 2) return on equity (ROE), which in our study were used as indicators varying from non-performing loans, the number of banks, real gross domestic product growth, the unemployment rate, and consumer price inflation (percent, period average). The estimated regression models with panel data revealed that real GDP growth, the unemployment rate, and inflation are positively correlated and significantly determine the bank’s performance. While two other factors (non-performing loans and the number of banks) were negatively associated with the performance of banks. Since the determining factors of external and internal profitability turn out to be almost the same for all the countries of the Western Balkans, during the observed period of 2010– 2020, the number of cases of non-performing loans was much higher in other countries of the region than in Kosovo. Once again, the banking system in Kosovo, unlike other countries, has scored ROE performance indicators that are almost three times higher than the average of the countries in the region. On the other hand, Kosovo has the highest unemployment rate in the region. While the weakest ROE performance was in Montenegro, which was improving year after year. Our findings have several important implications for policymakers and regulators, especially in Kosovo.

Copula-based regression estimation in the presence of outliers

Ordinary least square estimators for linear regression models are highly influenced by outliers. Copula is a powerful tool for studying the dependence between response and predictor variables and may overcome several limitations associated with the classical linear regression models. In this study, we investigate the efficacy of the copula-based regression models over classical linear regression in the presence of outliers in the x, y, and x-y directions. We also examine the performance of several robust regression estimation techniques when variables are related via elliptical and Archimedean family of copulas and outliers are present in the data. Finally, real and artificial data sets are analyzed to compare the performance of various robust regression techniques.

On Remedying the Presence of Heteroscedasticity in a Multiple Linear Regression Modelling

This study demonstrated the very essence of remedying the presence of heteroscedasticity, where it existed, in regression modelling. Two different hypothetical data, Data A (the Original) and Data B (the Original), were used in this study for the purpose of illustration. The normality, multicollinearity and autocorrelation assumptions were satisfied, but the Breusch-Pagan test and the White test established the existence of heteroscedasticity in the two datasets. The estimated multiple linear regression model for Data A (the Original) was statistically significant with an R-square value of 0.976, an AIC value of 332.5929, and an SBC value of 347.2533; and the one for Data B (the Original) was also statistically significant with an R-square value of 0.553, an AIC value of 69.89669, and an SBC value of 82.15499. The Log-transformation was applied on the variables in Data A (the Original) and Data B (the Original) to give rise to new sets of data, Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied); which equally satisfied the normality, multicollinearity and autocorrelation assumptions, and also satisfied that there were no existences of heteroscedasticity in the two datasets. Now, the estimated multiple linear regression model for Data A (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.986, an AIC value of -135.021, and an SBC value of -120.361; and the estimated model for Data B (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.624, an AIC value of -32.0801, and an SBC value of -19.8218. From the points of view of the values of the R-square (0.986>0.976 and 0.624>0.553), AIC (-135.021<332.5929 and -32.0801<69.89669) and SBC (-120.361<347.2533 and -19.8218<82.15499), it was evident that the estimated regression models for Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied) were, respectively, better models when compared to the regression models for Data A (the Original) and Data B (the Original).

On some two parameter estimators for the linear regression models with correlated predictors: simulation and application

Regression analysis is widely used to predict the response variable utilizing one or more predictor variables. In many fields of study, the predictors are highly correlated causing multicollinearity problem that severely affects the efficiency of ordinary least square (OLS) estimators by significantly inflating their variances. To solve the multicollinearity problem, various one and two parameter ridge estimators are available in literature. In this article, a class of modified two parameter Lipovetsky–Conklin ridge estimators is proposed based on eigen values of X ′ X matrix that provide an automatic dealing option for treating different levels of multicollinearity. An extensive simulations study followed by real life example is used to evaluate the performance of proposed estimators based on MSE criterion. In most of the simulation conditions, our proposed estimators outperformed the existing estimators.

The role of firm complexity in the relationship between integrated reporting and earnings management

PurposeThis study aims to evaluate the relationship between integrated reporting and management’s opportunistic behavior (i.e., accrual and real earnings management) and the moderating role of firm complexity.Design/methodology/approachData of firms at the Johannesburg Stock Exchange were collected and analyzed. The Johannesburg Stock Exchange is currently the primary exchange that mandates the practice of integrated reporting. Regression estimation models and robustness tests were applied to the analysis.FindingsThis study concludes that integrated reporting quality reduces firms’ accrual and real earnings management practices. It further concludes that the significant negative effect of integrated reporting quality on firms’ earnings management practices is impeded by higher firm complexity.Originality/valueThis study enhances the literature on the behavioral effect of a combined financial and sustainability disclosure practice on both accrual and real earnings management, specifically targeting South Africa’s listed companies – the primary market currently mandates integrated reporting practice.

Age affects drug survival rates of interleukin (IL)-17 and IL-23 inhibitors in patients with plaque psoriasis: Results from a retrospective, multicentric, multi-country, cohort study.

Scarce data related to the drug survival of biologic agents in psoriasis patients aged ≥65 years is available. To evaluate the drug survival of interleukin (IL)-23 or the IL-17 inhibitors approved for the treatment of moderate-to-severe psoriasis in elderly patients (aged ≥65 years), compared with younger adult patients (aged <65 years), and to identify clinical predictors that can influence the drug survival. This retrospective multicentric cohort study included adult patients with moderate-to-severe psoriasis, dissecting two-patient subcohorts based on age: elderly versus younger adults. Kaplan-Meier estimator and proportional hazard Cox regression models were used for drug survival analysis. We included 4178 patients and 4866 treatment courses; 934 were elderly (1072 treatment courses), and 3244 were younger patients (3794 treatment courses). Drug survival, considering all causes of interruption, was higher in patients aged <65 years than in elderly patients overall (log-rank p < 0.006). This difference was significant for treatment courses involving IL-23 inhibitors (p < 0.001) but not for those with IL-17 inhibitors (p = 0.2). According to both uni- and multi-variable models, elder age was associated with an increased risk of treatment discontinuation (univariable analysis: HR: 1.229, 95% CI 1.062-1.422; p < 0.006; multivariable analysis: HR: 1.199, 95% CI 1.010-1.422; p = 0.0377). Anti-IL-23 agents were associated with a reduced likelihood of treatment discontinuation after adjusting for other variables (HR: 0.520, 95% CI 0.368-0.735; p < 0.001). Being previously treated with IL-17 inhibitors increased the probability of discontinuation. Elderly patients with psoriasis have an increased risk of biologic treatment discontinuation compared with younger adult patients, particularly, if being treated with IL-23 inhibitors. However, in stratified analyses conducted in elderly patients, IL-23 inhibitors showed higher drug survival rates than IL-17 inhibitors.

Relationship between hukou conversion and psychological integration of rural older migrants: the mediating effect of accessibility to health resources

ObjectiveThis study investigates the relationship between hukou conversion and the psychological integration of rural older migrants, exploring the mediating role of accessibility to health resources.MethodsThe 3,963 valid samples of rural older migrants included in the study were sourced from the 2017 China Migrants Dynamic Survey (CMDS). The study established a multiple linear regression model for estimation and utilized inverse probability-weighted regression adjustment (IPWRA) method to correct for the selection bias of hukou conversion.ResultsCompared to older migrants with rural hukou, merit-based (β = 0.384, 95% CI: 0.265 to 0.504), family-based (β = 0.371, 95% CI: 0.178 to 0.565) and policy-based (β = 0.306, 95% CI: 0.124 to 0.487) converters have significantly higher psychological integration. These findings remain robust even after addressing the potential issue of endogenous selection bias using the IPWRA method. Bootstrap mediating effect tests indicate that hukou conversion can indirectly affect psychological integration through the mediator role of health resources accessibility.ConclusionAccessibility of health resources mediates the association between hukou conversion and psychological integration. Policymakers should enhance the implementation of hukou conversion, strengthen the health resource guarantee system, and achieve a deeper psychological integration among rural older migrants.

Estimation of patient-reported outcome measures based on features of knee joint muscle co-activation in advanced knee osteoarthritis

Electromyography (EMG) is considered a potential predictive tool for the severity of knee osteoarthritis (OA) symptoms and functional outcomes. Patient-reported outcome measures (PROMs), such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and visual analog scale (VAS), are used to determine the severity of knee OA. We aim to investigate muscle activation and co-contraction patterns through EMG from the lower extremity muscles of patients with advanced knee OA patients and evaluate the effectiveness of an interpretable machine-learning model to estimate the severity of knee OA according to the WOMAC (pain, stiffness, and physical function) and VAS using EMG gait features. To explore neuromuscular gait patterns with knee OA severity, EMG from rectus femoris, medial hamstring, tibialis anterior, and gastrocnemius muscles were recorded from 84 patients diagnosed with advanced knee OA during ground walking. Muscle activation patterns and co-activation indices were calculated over the gait cycle for pairs of medial and lateral muscles. We utilized machine-learning regression models to estimate the severity of knee OA symptoms according to the PROMs using muscle activity and co-contraction features. Additionally, we utilized the Shapley Additive Explanations (SHAP) to interpret the contribution of the EMG features to the regression model for estimation of knee OA severity according to WOMAC and VAS. Muscle activity and co-contraction patterns varied according to the functional limitations associated with knee OA severity according to VAS and WOMAC. The coefficient of determination of the cross-validated regression model is 0.85 for estimating WOMAC, 0.82 for pain, 0.85 for stiffness, and 0.85 for physical function, as well as VAS scores, utilizing the gait features. SHAP explanation revealed that greater co-contraction of lower extremity muscles during the weight acceptance and swing phases indicated more severe knee OA. The identified muscle co-activation patterns may be utilized as objective candidate outcomes to better understand the severity of knee OA.

Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8/9 Imagery

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

Intermunicipal cooperation in public procurement

ABSTRACT This study evaluates the impact of intermunicipal cooperation on public procurement (PP) performance, based on the Italian experience. We use both a fixed-effects regression model and alternative matching estimators to analyse a sample of 50,905 Italian public works contracts awarded by municipalities and municipal unions (MUs) between 2012 and 2020. Our results indicate that while local centralisation does not necessarily lead to significant cost savings in the procurement phase, MUs outperform individual municipalities in the execution phase, especially in terms of reducing delivery delays. We conclude that while MUs do not necessarily lead to strong economies of scale, they do improve efficiency during contract execution. This highlights the alternative benefits of PP centralisation beyond cost savings.

Epidemiological survey of accidents with venomous animals treated at the Regional Hospital of Bom Jesus, Piauí, Northeast Brazil

Aim: Venomous animals are responsible for 26.8% of all poisonings occurring in people registered in Brazil, reflecting serious economic, medical, and social public health issues in tropical countries. The objective of this work was to characterize the cases notified of accidents involving venomous animals in humans in a Public Regional Hospital located in Piauí state, Northeast Brazil. Methods: We analyzed the notification forms registered at the National Notifiable Diseases Information System (SINAN), provided by the Hospital Regional Manoel de Souza from July 2009 to December 2017. The data were organized in spreadsheets and were exported to SPSS v. 24 for statistical analysis. A linear regression analysis was performed, and variables were considered significant when the estimated regression model presented P ≤ 0.05. Results: From 2009 to 2017, 833 cases of accidents caused by venomous animals treated were reported on SINAN. Accidents caused by scorpions were the most frequent, accounting for 69.7% (n = 269) of cases, followed by snakes at 17.6% (n = 68) and insects at 12.7% (n = 49). The people most affected by venomous animals were men (53.9%; n = 208) between the ages of 20 and 39 (42.5%; n = 164). The most frequent bite site was on the extremities, with the feet being the most affected region at 39.3% (n = 235), followed by the hands at 25.8% (n = 154) and the legs at 8% (n = 48). The study showed that despite the Regional Hospital covering 18 cities, the municipality of Bom Jesus had the highest incidence of accidents caused by venomous animals, mainly scorpions. Therefore, this investigation is a starting point for understanding the spatial distribution of these accidents. The data show that there are still flaws in the SINAN information flow. In the serotherapy variable, for example, 51.1% of cases were not completed. Conclusion: It is necessary to provide guidance and carry out educational work in order to improve official data records, so that they can be incorporated into the care routine.

Some new asymptotic results for series estimation under clustered dependence

This paper presents some asymptotic results for series estimation of a nonparametric regression model under clustered dependence. A mean square rate of convergence for the series regression estimator is established. Moreover, asymptotic pointwise normality is shown for the series estimator.

Cost Effectiveness of Fremanezumab in Episodic and Chronic Migraine Patients from a Japanese Healthcare Perspective.

Fremanezumab is an effective treatment for episodic (EM) and chronic migraine (CM) patients in Japan, but its cost effectiveness remains unknown. The objective of this study was to determine the cost effectiveness of fremanezumab compared with standard of care (SOC) in previously treated EM and CM patients from a Japanese healthcare perspective. Estimated regression models were implemented in a probabilistic Markov model to inform effectiveness and health-related quality-of-life data for fremanezumab and SOC. The model was further populated with data from the literature. The adjusted Japanese healthcare perspective included productivity losses. The main model outcomes were quality-adjusted life-years (QALYs), costs (2022 Japanese Yen [¥]), and incremental outcomes including the incremental cost-effectiveness ratio (ICER). Analyses were performed separately for the EM and CM patients and combined. Costs and effects were discounted at an annual rate of 2.0%. The mean QALYs over a 25-year time horizon for the EM and CM populations combined were 13.03 for SOC and 13.15 for fremanezumab. The associated costs were ¥27,550,292 for SOC and ¥28,371,048 for fremanezumab. QALYs were higher and costs lower for EM patients compared with CM patients for both fremanezumab and SOC. The deterministic ICERs of fremanezumab versus SOC were ¥6,334,861 for EM, ¥7,393,824 for CM, and ¥6,530,398 for EM and CM combined. Indirect costs and choice of mean migraine days model distribution had a substantial impact on the ICER. Using fremanezumab in a heterogeneous mixture of Japanese EM and CM patients resulted in a reduction of monthly migraine days and thus more QALYs compared with SOC. The cost effectiveness of fremanezumab versus SOC in EM and CM patients resulted in an ICER of ¥6,530,398, from an adjusted Japanese public healthcare perspective.