Markov Chain Model Research Articles

Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh

This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (χ²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, χ², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns.

Urban Ecological Economic Resilience in China: Spatio-Temporal Evolution, Influencing Factors, and Trend Prediction

This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show that EER has been significantly enhanced, and high-level cities have a “rhombus” spatial distribution pattern. EER has a noticeable spatial agglomeration effect and the range of high–high agglomeration areas has gradually expanded. The LISA time path reflects that the spatial structure of EER is relatively stable, and urban units and neighboring cities show a more apparent synergistic growth trend. Social development, economic support, ecological restoration, and innovation and transformation strongly influence the development of EER, and the interaction between factors is more significant. In the future, EER will still tend to maintain the existing stable and unchanged state, and cross-grade leapfrogging development will not be achieved.

Spatial and Temporal Evolution of the Coupling of Industrial Agglomeration and Carbon Emission Efficiency—Evidence from China’s Animal Husbandry Industry

Drawing upon the data of China’s animal husbandry industry from 2000 to 2020 in 30 provinces, an EBM model incorporating non-desired outputs was employed to gauge the carbon emission efficiency of the animal husbandry industry. Coupling degree models, spatial autocorrelation models, and Markov chain models were utilized to assess the coupling degree between the industrial agglomeration of the animal husbandry sector and its carbon emission efficiency, and to analyze its spatio-temporal distribution and evolution. The outcomes showed that (1) the coupling degree of China’s animal husbandry industry agglomeration and carbon emission efficiency exhibited an overall downward inclination. Notably, the diminishing tendency of the coupling degree was more pronounced in the eastern, central, and western parts of the country; (2) the coupling degree of the 30 provinces showed a spatial distribution of “western > central > northeast > eastern”; (3) the coupling degree showed obvious agglomeration distribution characteristics, wherein a substantial quantity of provinces was located in high–high clustering zones and low–low clustering zones; (4) the coupling degree of various provinces remained fairly stable, but after considering the spatial and geographical correlation, the coupling degree of each province would be influenced by the coupling degree of its adjacent provinces. Evidently, there remained a substantial scope for the enhancement of the coupling coordination degree between the industrial agglomeration of China’s animal husbandry and the carbon emission efficiency. This research is capable of furnishing a theoretical allusion for promoting regional cooperation, leveraging agglomeration advantages, and implementing carbon emission abatement regimes and directives to enhance the low-carbon development level of animal husbandry industry agglomeration in China.

Measurement, dynamic evolution and pollution emission effects of the coupling of green finance and digital technology-evidence from China

This study uses the game theory combination weighting method to measure the level of coordinated development of green finance and digital technology coupling in China. An analysis was conducted using the Kernel density estimation method, traditional Markov chain model, and spatial Markov chain model to study the dynamic evolution characteristics and trends of the coupling and coordination level of green finance and digital technology in Chinese provinces. The results showed an upward trend in the coupling and coordination level of green finance and digital technology in the entire sample of Chinese, eastern, central, western, and northeastern provinces. Additionally, there is an observable club convergence phenomenon in the coordinated development of green finance and digital technology. The phenomenon of high-level convergence and low-level convergence is significant. The areas on the diagonal that are low-level and high-level have a higher probability of remaining stable. The Markov chain model and the spatial Markov chain model both show the “Matthew effect” in the level of coupling and coordinated development of green finance and digital technology. Empirical testing suggests that the coordinated horizontal development of green finance and digital technology can significantly promote pollution emission. Further analysis found that the coupling and coordination of green finance and digital technology achieves pollution emission by driving green innovation.

Assessing and predicting the dynamics of land use/land cover in northern Bangladesh using cellular Automata-Markov chain model

ABSTRACT Rapid urbanization and land use changes significantly impact environmental sustainability and resource management, particularly in developing regions. Therefore, this study examines the spatiotemporal dynamics of land use and land cover (LULC) in Rangpur, Bangladesh, from 1991 to 2021 and projects future trends to 2041. Using supervised and unsupervised classification techniques, along with cellular automata and Markov-chain models, we assessed historical LULC changes and predicted future scenarios. Results show a 38.86% increase in built-up areas (BAs) and a 49.86% decrease in vegetation land (VL) during the study period, with classification accuracy above 87%. Projections indicate a further loss of over 210 km² of VL and an increase of more than 123 km² in urban areas by 2041. Notably, urban expansion is linked to the development of road networks, with significant growth from 115.06 km2 in 2021 to 124.33 km2 by 2041 within a 15-kilometer radius around the city center. These findings offer crucial insights for urban planning, emphasizing the need for sustainable strategies to manage urban expansion and protect environmental and socio-economic resilience in Rangpur.

Multistate Markov chain modeling for child undernutrition transitions in Ethiopia: a longitudinal data analysis, 2002–2016

BackgroundThe use of the multistate Markov chain model is a valuable tool for studying child undernutrition. This allows us to examine the trends of children's transitions from one state to multiple states of undernutrition.ObjectivesIn this study, our objective was to estimate the median duration for a child to first transition from one state of undernutrition to another as well as their first recurrence of undernutrition and also to analyze the typical duration of undernourishment. This involves understanding the central tendency of these transitions and durations in the context of longitudinal data.MethodsWe used a longitudinal dataset from the Young Lives cohort study (YLCS), which included approximately 1997 Ethiopian children aged 1–15 years. These children were selected from five regions and followed through five survey rounds between 2002 and 2016. The surveys provide comprehensive health and nutrition data and are designed to assess childhood poverty. To analyze this dataset, we employed a Markov chain regression model. The dataset constitutes a cohort with repeated measurements, allowing us to track the transitions of individual children across different states of undernutrition over time.ResultsThe findings of our study indicate that 46% of children experienced concurrent underweight, stunting, and wasting (referred to as USW). The prevalence of underweight and stunted concurrent condition (US) was 18.7% at baseline, higher among males. The incidence density of undernutrition was calculated at 22.5% per year. On average, it took 3.02 months for a child in a wasting state to transition back to a normal state for the first time, followed by approximately 3.05 months for stunting and 3.89 months for underweight. It is noteworthy that the median duration of undernourishment among children in the US (underweight and stunted concurrently) state was 48.8 months, whereas those concurrently underweight and wasting experienced a median of 45.4 months in this state. Additionally, rural children (HR = 1.75; 95% CI: 1.53–1.97), those with illiterate fathers (HR = 1.50; 95% CI: 1.38–1.62) and mothers (HR = 1.45; 95% CI: 1.02–3.29), and those in households lacking safe drinking water (HR = 1.70; 95% CI: 1.26–2.14) or access to cooking fuel (HR = 1.95; 95% CI: 1.75–2.17) exhibited a higher risk of undernutrition and a slower recovery rate.ConclusionsThis study revealed that rural children, especially those with illiterate parents and households lacking safe drinking water but cooking fuels, face an increased risk of undernutrition and slower recovery.

Spatiotemporal Changes and Influencing Factors of the Coupled Production–Living–Ecological Functions in the Yellow River Basin, China

The imbalance in the “production–living–ecology” function (PLEF) has become a major issue for global cities due to the rapid advancement of urbanization and industrialization worldwide. The realization of PLEF coupling and coordination is crucial for a region’s sustainable development. Existing research has defined the concept of PLEF from the perspective of land function and measured its coupling coordination level using relevant models. However, there is still room for improvement in the indicator system, research methods, and other aspects. This work builds a PLEF coupling coordination evaluation-index system based on the perspective of human habitat using multi-source data in order to examine the spatial differences in PLEF coupling coordination level and the influencing factors in the Yellow River Basin (YRB). Using the modified coupling coordination model, the Moran index, spatial Markov chain model, and geographically weighted random forest model were introduced to analyze its spatial and temporal differentiation and influencing factors. The results found that (a) the level of PLEF coupling coordination in the YRB from 2010 to 2022 has been improving, and the number of severely imbalanced cities has been reduced from 23 to 15, but the level of downstream cities’ coupling coordination is significantly higher than that of upstream cities. The probability of cities maintaining their own level is greater than 50%, and there is basically no cross-level transfer. (b) The Moran index of the PLEF coupling coordination level has risen from 0.137 to 0.229, which shows a significant positive clustering phenomenon and is continually strengthening. The intercity polarization effect is being continually enhanced as seen in the LISA clustering diagram. (c) There is significant heterogeneity between the influencing factors in time and space. In terms of importance level, the series is per capita disposable income (0.416) > nighttime lighting index (0.370) > local general public budget expenditure (0.332) > number of beds per 1000 people (0.191) > NO2 content in the air (0.110). This study systematically investigates the dynamic evolution of the coupled coordination level of PLEF in the YRB and its influencing mechanism, which is of great practical use.

Predicting land cover changes in Xuan Lien Nature Reserve, Thanh Hoa province, Vietnam

The research was carried out in Xuan Lien Nature Reserve, which is a specially protected natural area and one of the five main centers of biodiversity in Vietnam. The work is aimed at analyzing changes and predicting the state of land cover in Xuan Lien Reserve. For these purposes, Landsat-7, Landsat-5 and Landsat-8 images obtained in 2000, 2010 and 2020 were studied. Normalized Difference Vegetation Index (NDVI) method was used to isolate the vegetation cover on the land areas, and Normalized Difference Water Index (NDWI) method was used. The results of the study indicate that the vegetation on the land is transforming in Xuan Lien Reserve. The area of broadleaf forests decreased by 6.2% from 2000 to 2020, and the area of wetlands increased by 4.9% due to the construction of Cua Dat hydroelectric complex in Reserve. The model of Markov chains and cellular automata was used to predict the state of the allocated lands in the study area. The predicted results of the model have been successfully confirmed using the agreement indices. According to the results of the predicted, by 2035 the area of broadleaf forests and wetlands will increase to 86.3% and 7.4%. The change in the structure of the vegetation cover of Reserve's land is explained by the measures taken by the Vietnamese government to regulate the water level in Cua Dat reservoir and expand the forest area.

Assessment of the interaction between digital infrastructure and ecological resilience: Insights from Yangtze River Delta Urban Agglomeration in China

Exploring the interaction between digital infrastructure (DI) and ecological resilience (ER) is essential to understand whether urban development and the ecosystem have formed an integration trend. First, clarifying the interactive and driving mechanism between DI and ER, the evaluation indicator systems for them are constructed based on the core connotation of DI and the theory of evolutionary resilience. Then, using multiple methods such as the interactive coercion and the Markov chain model, the study reveals the spatiotemporal evolution of DI, ER, and their interactive levels in the Yangtze River Delta Urban Agglomeration (YRDUA)from 2005 to 2021. Furthermore, we employ the optimal parameter-based geographical detector (OPGD) to identify the core driving factors. The results indicate that (1) The comprehensive levels of DI and ER show an upward trend, and both systems have significant spatial heterogeneity. (2) The interactive coercion model verifies a coercive effect and a constraining relationship between DI and ER; notably, the interaction between the two systems is still in its moderate stage. (3) The analysis of driving factors reveals that socioeconomic conditions are the key influencing factors. Additionally, the proportion of cases showing a bi-enhancement type account for 92%, suggesting that the interactive role of the two factors mainly exhibits a multiplier effect. The study provides empirical evidence for solidifying the foundation of digital development, establishing robust ecological safety barriers, and promoting the deep integration of DI and ER, thereby facilitating high-quality regional development.

Beyond bigrams: call sequencing in the common marmoset (Callithrix jacchus) vocal system.

Over the last two decades, an emerging body of research has demonstrated that non-human animals exhibit the ability to combine context-specific calls into larger sequences. These structures have frequently been compared with language's syntax, whereby linguistic units are combined to form larger structures, and leveraged to argue that syntax might not be unique to language. Currently, however, the overwhelming majority of examples of call combinations are limited to simple sequences comprising just two calls which differ dramatically from the open-ended hierarchical structuring of the syntax found in language. We revisit this issue by taking a whole-repertoire approach to investigate combinatoriality in common marmosets (Callithrix jacchus). We use Markov chain models to quantify the vocal sequences produced by marmosets providing evidence for structures beyond the bigram, including three-call and even combinations of up to eight or nine calls. Our analyses of these longer vocal sequences are suggestive of potential further internal organization, including some amount of recombination, nestedness and non-adjacent dependencies. We argue that data-driven, whole-repertoire analyses are fundamental to uncovering the combinatorial complexity of non-human animals and will further facilitate meaningful comparisons with language's combinatoriality.

Livelihood diversification and household welfare among farm households in the Philippines

AbstractDiversification of income sources is one of the most common strategies households employ to minimize household income variability and to ensure a minimum level of income that guarantee their food security. This study examines the impact of livelihood diversification on farm profits among smallholder rice farmers in the Central Luzon Region (CLR) of the Philippines using long‐term farm‐level panel data spanning from 1966 to 2016. We employed a random‐effects ordered probit model to investigate the drivers of livelihood diversification and then used the mixed Markov chain model to analyze the transition of households from less to more diversified livelihoods and its impact on farm profits over time. Our findings reveal substantial diversification among households over time. In particular, the elevation of the farm location is a key driver of rice farm households’ probability to diversify. The estimates show that an increase in the latitude of the farm location increases the probability of rice farmers in the CLR to diversify. We find that 64% of the rice farm households constitute farmers for whom diversification can be a strategy for survival. For medium‐ to high‐profit farm categories, diversification tends to protect farmers against farm profit losses arising from adverse climatic and weather variability.

Modeling Emergency Traffic Using a Continuous-Time Markov Chain

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated.

A Conceptual Model for Health Impact Assessment of Personalized Prevention Policies

Abstract Background Personalized prevention and pharmacogenomics are emerging fields in global health, promoting tailored approaches to disease prevention and individualized drug therapy, based on the individuals’ genetic profile. However, adoption of these technologies in public health is challenged by the possibility of health disparities. Health Impact Assessment (HIA) is a useful tool to address this issue, by examining how policies differentially impact population groups, guiding stakeholders to more equitable and inclusive outcomes. Solid frameworks for assessing health impacts are needed to avoid heterogeneity when accounting for equity outcomes in HIA. This study tested a conceptual model for HIA in personalized medicine, using a case study in pharmacogenetics to map potential impacts and outcomes, with a focus on health equity. Methods A conceptual model for a HIA was developed to map expected impacts and outcomes of a health policy, aimed at enforcing DPYD genotype-guided dosing to prevent fluoropyrimidine toxicity in colorectal cancer patients. The conceptual model was developed based on literature reviews and consultation with stakeholders. Identification of relevant impacts guided selection of indicators and quantitative impact assessment using a Markov chain model. Results Impact pathways were identified using a three-tiered reference framework encompassing individual and population perspectives, organizational factors, and overarching health system considerations. The final diagram outlined key outcomes in four categories: health, organizational, economic and equity. HIA projections using this model were obtained for the DPYD testing policy. Conclusions In the rise of personalized medicine and pharmacogenomics, health inequalities challenges need to be addressed before implementation in healthcare. This study provided a novel HIA conceptual model for personalized prevention policies based on genomic information, with an emphasis on equity. Key messages • HIA is an adequate tool to evaluate potential health inequalities resulting from personalized medicine policies in healthcare. • This study provides a validated framework for HIA of personalized prevention policies grounded in genomic data.

Real‐time reliability evaluation method for IGBT module in MMC based on junction temperature and bond wire failure online monitoring

AbstractThe lack of an MMC reliability evaluation approach that incorporates data on essential components' health monitoring in the already related research prevents reliability from being further improved. Therefore, this article proposes a real‐time reliability evaluation method of the multi‐state IGBT module in MMC through iterative calculation of the Markov chain model. The degradation process of the IGBT module is divided into multiple states based on the monitored bond wire failure, and the failure rate of the IGBT module is updated in real time using the accumulated monitoring data of junction temperature. The proposed method can more accurately reflect the real state of MMC compared with the binary state evaluation method. Besides, a multi‐channel high‐speed data acquisition board is designed to measuring the gate voltage and collector voltage of the IGBT for the health evaluation of IGBT modules in MMC; the typically measured results validate the effectiveness of the data acquisition board.

Predicting the Impact of Distributed Denial of Service (DDoS) Attacks in Long-Term Evolution for Machine (LTE-M) Networks Using a Continuous-Time Markov Chain (CTMC) Model

The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gather data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such attacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we developed a predictive model called Markov Detection and Prediction (MDP) using a Continuous-Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing, and predicting DDoS attacks in Long-Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious, and Malicious traffic. Additionally, we are able to predict the system behavior when facing different DDoS attacks.

Markov Chain Analysis of Land-use Changes in India and Maharashtra

Dynamics of Agriculture resembles the history of agriculture in India which examines the emphasizing significance to the nation's legacy, culture and civilization. India's agriculture changed after independence from a food-scarce nation to one that exports food this comes true primarily due to the result of scientific advancements. The present study is based on secondary data with the use of Markov Chain analysis to ascertain land-use changes in India and Maharashtra state. Changes in land use brought on by agriculture, urbanization and environmental factors significantly impact resource management and sustainability. We build a Markov Chain model with secondary land utilization data from government reports to measure the transition probabilities between various land use categories over time. The Markov chain model is easy to understand and communicate. It effectively captures transitions between land-use changes over time. The model works with limited data as compared to other complex models, making it accessible for studies. The results of the study emphasize dynamic interactions between various land-use patterns in India and Maharashtra. There we have concluded a stable land-use changing pattern. A trend towards intensive cropping has been observed during this study. This study reveals important trends in land conversion, most notably the growth of urban areas at the expense of forested and agricultural areas. The predictive abilities of model highlight the significance of well-informed policy-making for sustainable land management by offering insights into potential future land use scenarios.

Enhancing Reliability in Multilevel Inverter: A Fault‐Tolerant Approach With Reduced Switch Count

ABSTRACTReliability is a major concern in multilevel inverters (MLI) due to the increased number of power switches and diodes, which increases the chance of switch failures and complicates the inverter design. The primary challenges faced by the current MLI design include higher control complexity, increased parasitic elements, more extensive protection, and lack of multiple fault tolerance. Therefore, developing reduced switch MLI with fault‐tolerant ability is crucial to ensure system availability and prevent adverse outcomes. To address these challenges, this article introduces a highly resilient fault‐tolerant reduced switch count (FTRSC) MLI topology offering fault tolerance with minimal switch count. The proposed topology provides fault tolerance in case of single‐ and multiple‐switch open‐circuit (OC) failures. The inherent fault‐tolerant operation is analyzed for the proposed topology's symmetric and asymmetric configurations. The simulation and experimental results validate the effectiveness of the proposed topology considering normal, faulty, and postfault conditions. Furthermore, the reliability evaluation of the proposed work is carried out using the Markov chain model, resulting in a high total mean time to failure of approximately 117,550 h/failures, along with an efficiency of 96.3%. These findings demonstrate that the proposed circuit has enhanced reliability and extended mean time to failure, affirming its suitability for sensitive industrial applications. Finally, a thorough comparison with recent studies highlights the superiority of the proposed system.

Bayesian Ensemble Kalman Filter for Gaussian Mixture Models

AbstractInverse theory and data assimilation methods are commonly used in earth and environmental science studies to predict unknown variables, such as the physical properties of underground rocks, from a set of measured geophysical data, like geophysical seismic or electromagnetic data. A new Bayesian approach based on the ensemble Kalman filter using Gaussian mixture models is presented to overcome the assumption of Gaussian distribution of the unknown variables commonly used in the data assimilation literature and to generalize the algorithm to inverse problems with multimodal probability distributions. In applications of subsurface characterization, the multimodality of the unknown variables is generally due to the presence of different rock types, also known as geological facies. In the proposed method, the weights of the Gaussian mixture model represent the facies proportions, and they follow a Markov chain model. The proposed Bayesian model generates the unknown model parameters conditioned on measured data using a Markov chain Monte Carlo sampler. The validity of the method is demonstrated on a data assimilation problem where the goal is to estimate the posterior distribution of the unknown rock density from a set of repeated measurements of acoustic wave velocity measured at different times. The proposed method provides accurate estimates with efficient computational times.

Cost-Effectiveness of an Ultrasound-First Strategy in the Diagnostic Evaluation of Noncalcified Lesions Recalled From Screening Digital Breast Tomosynthesis.

BACKGROUND. Ultrasound may be sufficient in the diagnostic evaluation of many noncalcified lesions recalled from screening digital breast tomosynthesis (DBT). In some scenarios, omission of diagnostic mammography can save health care costs. OBJECTIVE. The purpose of this study was to evaluate the cost-effectiveness of a strategy of performing ultrasound first versus diagnostic mammography first in the diagnostic evaluation of noncalcified lesions recalled from screening DBT. METHODS. Decision tree analysis was performed to compare ultrasound first versus diagnostic mammography first in the diagnostic evaluation of DBT-recalled noncalcified lesions from a U.S. health care system perspective with a 40-year horizon. The analysis used probabilities and prevalence information from published single-institution prospective data, additional literature-derived estimates of diagnostic test performance, and Medicare-allowable reimbursement rates. Health states were represented in a Markov chain model. For each strategy, the total cost and effectiveness (expressed in quality-adjusted life-years [QALYs]) were estimated. Cost-effectiveness was assessed through incremental cost-effectiveness ratios (ICERs) and incremental net monetary benefit, with use of a willingness-to-pay (WTP) threshold of US$100,000 per QALY gained. Deterministic sensitivity analyses were performed to estimate the impact of different input parameters, and probabilistic sensitivity analysis with Monte Carlo simulations was conducted to estimate the impact of combined uncertainty across parameters. RESULTS. In the base-case scenario, for diagnostic evaluation of DBT-recalled noncalcified lesions, a strategy of performing ultrasound first versus diagnostic mammography first resulted in more cost savings (total cost, US$17,672 vs US$18,323) and greater cost-effectiveness (QALYs, 23.1309 vs 23.1306) over the 40-year horizon. The ultrasound-first strategy resulted in an ICER of -2,170,000 (expressed as U.S. dollars per QALY) and an incremental net monetary benefit of US$681 versus the diagnostic mammography-first strategy. Therefore, performing ultrasound first was deemed the more cost-effective strategy at the WTP threshold. In deterministic sensitivity analyses, the most important driver of cost-effectiveness was lost utility from delayed diagnosis, followed by the relative sensitivities of ultrasound and diagnostic mammography. In probabilistic sensitivity analysis, ultrasound first was the better strategy in 93.0% of iterations. CONCLUSION. A strategy of performing ultrasound first, with or without diagnostic mammography, is more cost-effective than a traditional strategy of conducting diagnostic mammography first. CLINICAL IMPACT. This cost-effectiveness analysis supports the growing prioritization of ultrasound as the primary method for evaluating DBT-recalled noncalcified lesions.

Longitudinal transitions of the double burden of overweight and stunting from childhood to early adulthood in India, Peru, and Vietnam.

Examining trajectories of undernutrition and overnutrition separately limits understanding of the double burden of malnutrition. We investigated transitions between normal, stunting, overweight and concurrent stunting and overweight (CSO) and associations with sociodemographic factors in children and adolescents. We used data from the Young Lives cohort in India, Peru and Vietnam, which follow children 1-15 (N = 5413) and 8-22 years (N = 2225) over five rounds between 2002 and 2016. We estimated transitions between nutritional states using a Markov chain model and estimated sociodemographic associations employing a logit parametrization. Transitions into stunting peaked in ages 1-5 years (India: 22.9%, Peru: 17.6%, Vietnam: 14.8%), while stunting reversal was highest during adolescence across all countries. Transitions into overweight peaked in ages 19-22, while overweight reversal increased in ages 1-5 and 12-15 years. Transitions away from stunting to overweight were rare; more commonly, stunted individuals developed overweight while remaining stunted, leading to a CSO state. In Peru, 20.2% of 19-year-olds who were stunted reached CSO by age 22, with 4% shifting from stunted to overweight. Reversion to a normal state is least likely for those in a CSO state. Household wealth gradually reduced the likelihood of transitioning into stunting [odds ratios (ORs) for wealthiest quartile in Peru: 0.29, 95% confidence interval (CI) 0.20-0.41; India: 0.43, 95% CI 0.32-0.57; Vietnam: 0.36, 95% CI 0.26-0.50), with stunting reversal only being more likely in the two wealthiest quartiles across all countries (ORs for wealthiest quartile in Peru: 2.39, 95% CI 1.57-3.65; India: 1.28, 95% CI 1.05-1.54; Vietnam: 1.89, 95% CI 1.23-2.91). In Vietnam, only the richest quartile was at higher risk of transitioning into overweight (OR 1.87, 95% CI 1.28-2.72), while in Peru and India, the risk gradually rose across all wealth quartiles (ORs for wealthiest quartile in Peru: 2.84, 95% CI 2.14-3.77; India: 2.99, 95% CI 1.61-5.54). Childhood and adolescence represent critical periods for prevention and reversal of stunting and overweight, thereby averting the development of CSO later in life. Context-specific interventions are crucial for preventing disparate transitions towards the double burden of malnutrition across socioeconomic groups.