Exploring the association between non-regular employment and adverse birth outcomes: an analysis of national data in Japan.

Background: As few studies have explored the association between non-regular or precarious employment in parents and adverse birth outcomes, this study aimed to investigate this association using national data in Japan. Methods: This study utilized the census data from 2020 and birth data from the vital statistics in 2021 and 2022 in the analysis. Adverse birth outcomes, including preterm birth, term low birth weight (TLBW), and small-for-gestational-age, were examined. Data linkage was conducted between birth data and census data to link parental employment statuses and educational attainments with birth data. Rates of adverse birth outcomes were calculated for each parental employment status. Additionally, regression analysis was used to determine adjusted risk ratios (RRs) of parental employment statuses for each birth outcome. Results: After data linkage, 334,110 birth records were included in the statistical analysis. Rates for non-regular workers were consistently higher than those for regular workers across all adverse birth outcomes for maternal employment status. Results of regression analyses indicated that the risks of preterm birth for non-regular workers were statistically significantly higher than those for regular workers, both in mothers and fathers with a RR (95% confidence intervals [CIs]) of 1.053 (1.004-1.104) and 1.142 (1.032-1.264), respectively. Furthermore, the risk of TLBW birth for non-regular workers was statistically significantly higher than that for regular workers in fathers (RR [95% CI]: 1.092 [1.043-1.143]). Conclusions: Our findings demonstrate that non-regular workers have a higher risk of some adverse birth outcomes compared to regular workers.

Time to initiation of antenatal care and its predictors among pregnant women in Ethiopia: a multilevel mixed-effects acceleration failure time model.

OBJECTIVE: To assess the time to initiation of antenatal care (ANC) and its predictors among pregnant women in Ethiopia. DESIGN: Retrospective follow-up study using secondary data from the 2019 Ethiopian Mini-Demographic and Health Survey. SETTING AND PARTICIPANTS: 2933 women aged 15-49 years who had ANC visits during their current or most recent pregnancy within the 5 years prior to the survey were included in this study. Women who attended prenatal appointments but whose gestational age was unknown at the first prenatal visit were excluded from the study. OUTCOME MEASURES: Participants were interviewed about the gestational age in months at which they made the first ANC visit. Multivariable mixed-effects survival regression was fitted to identify factors associated with the time to initiation of ANC. RESULTS: In this study, the estimated mean survival time of pregnant women to initiate the first ANC visit in Ethiopia was found to be 6.8 months (95% CI: 6.68, 6.95). Women whose last birth was a caesarean section (adjusted acceleration factor (AAF)=0.75; 95% CI: 0.61, 0.93) and women with higher education (AAF)=0.69; 95% CI: 0.50, 0.95) had a shorter time to initiate ANC early in the first trimester of pregnancy. However, being grand multiparous (AAF=1.31; 95% CI: 1.05, 1.63), being previously in a union (AAF=1.47; 95% CI: 1.07, 2.00), having a home birth (AAF=1.35; 95% CI: 1.13, 1.61) and living in a rural area (AAF=1.25; 95% CI: 1.03, 1.52) were the impediments to early ANC initiation. CONCLUSION: Women in this study area sought their initial ANC far later than what the WHO recommended. Therefore, healthcare providers should collaborate with community health workers to provide home-based care in order to encourage prompt ANC among hard-to-reach populations, such as rural residents and those giving birth at home.

Estimated Number of Injection-Involved Overdose Deaths in US States From 2000 to 2020: Secondary Analysis of Surveillance Data.

BACKGROUND: In the United States, both drug overdose mortality and injection-involved drug overdose mortality have increased nationally over the past 25 years. Despite documented geographic differences in overdose mortality and substances implicated in overdose mortality trends, injection-involved overdose mortality has not been summarized at a subnational level. OBJECTIVE: We aimed to estimate the annual number of injection-involved overdose deaths in each US state from 2000 to 2020. METHODS: We conducted a stratified analysis that used data from drug treatment admissions (Treatment Episodes Data Set-Admissions; TEDS-A) and the National Vital Statistics System (NVSS) to estimate state-specific percentages of reported drug overdose deaths that were injection-involved from 2000 to 2020. TEDS-A collects data on the route of administration and the type of substance used upon treatment admission. We used these data to calculate the percentage of reported injections for each drug type by demographic group (race or ethnicity, sex, and age group), year, and state. Additionally, using NVSS mortality data, the annual number of overdose deaths involving selected drug types was identified by the following specific multiple-cause-of-death codes: heroin or synthetic opioids other than methadone (T40.1, T40.4), natural or semisynthetic opioids and methadone (T40.2, T40.3), cocaine (T40.5), psychostimulants with abuse potential (T43.6), sedatives (T42.3, T42.4), and others (T36-T59.0). We used the probabilities of injection with the annual number of overdose deaths, by year, primary substance, and demographic groups to estimate the number of overdose deaths that were injection-involved. RESULTS: In 2020, there were 91,071 overdose deaths among adults recorded in the United States, and 93.1% (84,753/91,071) occurred in the 46 jurisdictions that reported data to TEDS-A. Slightly less than half (38,253/84,753, 45.1%; 95% CI 41.1%-49.8%) of those overdose deaths were estimated to be injection-involved, translating to 38,253 (95% CI 34,839-42,181) injection-involved overdose deaths in 2020. There was large variation among states in the estimated injection-involved overdose death rate (median 14.72, range 5.45-31.77 per 100,000 people). The national injection-involved overdose death rate increased by 323% (95% CI 255%-391%) from 2010 (3.78, 95% CI 3.33-4.31) to 2020 (15.97, 95% CI 14.55-17.61). States in which the estimated injection-involved overdose death rate increased faster than the national average were disproportionately concentrated in the Northeast region. CONCLUSIONS: Although overdose mortality and injection-involved overdose mortality have increased dramatically across the country, these trends have been more pronounced in some regions. A better understanding of state-level trends in injection-involved mortality can inform the prioritization of public health strategies that aim to reduce overdose mortality and prevent downstream consequences of injection drug use.

Uncovering the dual role of dimensionless radius in buckling of spherical shells with random geometric imperfections.

Localized deformation and randomly shaped imperfections are salient features of buckling-type instabilities in thin-walled load-bearing structures. However, it is generally agreed that their complex interactions in response to mechanical loading are not yet sufficiently understood, as evidenced by buckling-induced catastrophic failures which continue to today. This study investigates how the intimate coupling between localization mechanisms and geometric imperfections combine to determine the statistics of the pressure required to buckle (the illustrative example of) a hemispherical shell. The geometric imperfections, in the form of a surface, are defined by a random field generated over the nominally hemispherical shell geometry, and the probability distribution of the buckling pressure is computed via stochastic finite element analysis. Monte-Carlo simulations are performed for a wide range of the shell's radius to thickness ratio, as well as the correlation length of the spatial distribution of the imperfection. The results show that over this range, the buckling pressure is captured by the Weibull distribution. In addition, the analyses of the deformation patterns observed during the simulations provide insights into the effects of certain characteristic lengths on the local buckling that triggers global instability. In light of the simulation results, a probabilistic model is developed for the statistics of the buckling load that reveals how the dimensionless radius plays a dual role which remained hidden in previous deterministic analyses. The implications of the present model for reliability-based design of shell structures are discussed.

Development of an enhanced scoring system to predict ICU readmission or in-hospital death within 24 hours using routine patient data from two NHS Foundation Trusts.

RATIONALE: Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration. OBJECTIVES: We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU. DESIGN: A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a 'static' score of the patient's condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient's risk of deterioration throughout their hospital stay. SETTING: Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model. PARTICIPANTS: A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort. RESULTS: Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653). CONCLUSIONS: We showed that a scoring system incorporating data from a patient's stay in the ICU has better performance than commonly used EWS systems based on vital signs alone. TRIAL REGISTRATION NUMBER: ISRCTN32008295.

Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling.

BACKGROUND: Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power. METHODS: Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks. RESULTS: The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies. CONCLUSIONS: SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.

Multi-task Learning with High-Dimensional Noisy Images.

Recent medical imaging studies have given rise to distinct but inter-related datasets corresponding to multiple experimental tasks or longitudinal visits. Standard scalar-on-image regression models that fit each dataset separately are not equipped to leverage information across inter-related images, and existing multi-task learning approaches are compromised by the inability to account for the noise that is often observed in images. We propose a novel joint scalar-on-image regression framework involving wavelet-based image representations with grouped penalties that are designed to pool information across inter-related images for joint learning, and which explicitly accounts for noise in high-dimensional images via a projection-based approach. In the presence of non-convexity arising due to noisy images, we derive non-asymptotic error bounds under non-convex as well as convex grouped penalties, even when the number of voxels increases exponentially with sample size. A projected gradient descent algorithm is used for computation, which is shown to approximate the optimal solution via well-defined non-asymptotic optimization error bounds under noisy images. Extensive simulations and application to a motivating longitudinal Alzheimer's disease study illustrate significantly improved predictive ability and greater power to detect true signals, that are simply missed by existing methods without noise correction due to the attenuation to null phenomenon.

Integrating Electronic Health Records and Polygenic Risk to Identify Genetically Unrelated Comorbidities of Schizophrenia That May Be Modifiable.

Background: Patients with schizophrenia have substantial comorbidity that contributes to reduced life expectancy of 10 to 20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore are enriched for potentially modifiable associations. Methods: Phenome-wide comorbidity was calculated from electronic health records of 250,000 patients across 2 independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham); associations with schizophrenia polygenic risk scores were calculated across the same phenotypes in linked biobanks. Results: Schizophrenia comorbidity was significantly correlated across institutions (r = 0.85), and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and polygenic risk score associations were significantly correlated (r = 0.55, p = 1.29 × 10-118). However, directly testing for the absence of genetic effects identified 36 comorbidities that had significantly equivalent schizophrenia polygenic risk score distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications (e.g., movement disorders) or of the disease such as reduced hygiene (e.g., diseases of the nail), thereby validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effects such as tobacco use disorder and diabetes. Conclusions: This work demonstrates the consistency and robustness of electronic health record-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk, indicating other causes that may be modifiable and where further study of causal pathways could improve outcomes for patients.

Patients with schizophrenia have many co-occurring diseases that contribute substantially to premature mortality of 10 to 20 years. Conditions that are comorbid but lack shared genetic risk with schizophrenia are likely to have causes that are more modifiable. Here, we calculated comorbidity from electronic health records from 2 independent health care institutions and associations with schizophrenia polygenic risk scores across the same phenotypes in linked biobanks. We identified known and novel diseases comorbid with schizophrenia, thereby validating our approach.

When Nature Requires a Resource to Be Used-The Case of Callinectes sapidus: Distribution, Aggregation Patterns, and Spatial Structure in Northwest Europe, the Mediterranean Sea, and Adjacent Waters.

The Atlantic blue crab Callinectes sapidus, which is native to the western Atlantic coast and listed among the 100 most invasive alien species in the Mediterranean Sea, is attracting a great deal of interest because of its rapid colonisation of new areas, the significant increase in its population, and the impacts it may have on ecosystems and ecosystem services. Outside its natural distribution range, the species was first found on European Atlantic coasts in the early 1900s and was introduced into the Mediterranean Sea a few decades later, probably through ballast water. Currently, it is found in almost the entire Mediterranean Basin and is also expanding into the Black Sea and along the north African and Iberian Atlantic coasts. Based on a systematic review of C. sapidus occurrences, this study describes its distribution, aggregation patterns, and spatial structure in Northwest Europe, the Mediterranean Sea, and adjacent waters through a series of ecological indicators elaborated using GIS spatial-temporal statistics. The main results highlight that the species is expanding in the Mediterranean and adjacent waters, while in northern Europe, the population remains confined in some areas. Furthermore, the main species detection methods are analysed, finding that traps and nets are the most frequently used methods, and management suggestions are provided.

Open-Endedness in Genelife.

We explore the open-ended nature of evolution in Genelife, an evolutionary extension of Conway's Game of Life cellular automaton in which "live" cell states are endowed at birth with a genome that affects their local dynamics and can be inherited. Both genetic sequences and locally connected spatial patterns are analyzed for novelty, keeping track of all new structures, and innovation is quantified using activity statistics. The impacts of both spatial symmetry breaking with nontotalistic rules and superimposed density regulation of the live state proliferation on the open-ended nature of the evolution are explored. Conditions are found where both genetic and spatial patterns exhibit open-ended innovation. This innovation appears to fall short of functional biological innovation, however, and potential reasons for this are discussed.

[Quality control of Tianwang Buxin Pills based on UPLC fingerprint and multi-component quantification].

Tianwang Buxin Pills have demonstrated therapeutic effects in clinical practice, whereas there is a serious lack of comprehensive quality control to ensure the safety and effectiveness of clinical medication. In this study, ultra-performance liquid chromatography(UPLC) was employed to establish the fingerprint and the method for simultaneously determining the content of seven components of Tianwang Buxin Pills. Furthermore, chemometrics was employed to identify the key factors for the stable quality, which provided a reference for the comprehensive quality control and evaluation of this preparation. There were 25 common peaks in the UPLC fingerprints of 15 batches of Tianwang Buxin Pills, from which thirteen compounds were identified. A quantitation method was established for seven pharmacological components(α-linolenic acid, salvianolic acid B, glycyrrhetinic acid, schisandrin A, ß-asarone, 3,6'-disinapoylsucrose, and ligustilide). The principal component analysis(PCA) and partial least square discriminate analysis(PLS-DA) were performed to determine the key pharmacological components for controlling the quality stability of Tianwang Buxin Pills, which included 3,6'-disinapoylsucrose, α-linolenic acid, and ß-asarone. The established fingerprint and multi-component content determination method have strong specificity, stability, and reliability. In addition, 3,6'-disinapoylsucrose, α-linolenic acid, and ß-asarone are the key pharmacological components that ensure the quality stability between batches and can be used to comprehensively control the quality of Tianwang Buxin Pills. The findings provide a scientific basis for the quality evaluation and standard establishment of Tianwang Buxin Pills.

The "Why" behind including "Y" in your imputation model.

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e. single imputation with fixed values) and stochastic imputation (i.e. single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This article aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model.

Reporting and representation of underserved groups in intervention studies for patients with multiple long-term conditions: a systematic review.

OBJECTIVES: Globally, there is a growing number of people who are living with multiple long-term conditions (MLTCs). Due to complex management needs, it is imperative that research consists of participants who may benefit most from interventions. It is well documented that ethnic minority groups and lower socioeconomic status (SES) groups are at an increased risk of developing MLTCs. Therefore, the aim of this systematic review was to determine the level of reporting and representation of underserved groups (ethnic minority and low SES) in intervention studies addressing MLTCs. DESIGN: Systematic review. Four databases including Cochrane Library, MEDLINE, CINAHL and Scopus were searched for intervention studies from North America or Europe published between January 1990 and July 2023. SETTING: Hospital and community-based interventions. We included interventional studies focusing on improving MLTC-related outcomes. PARTICIPANTS: Patients with MLTCs. MAIN OUTCOME MEASURES: Total number of studies reporting on ethnicity and SES. Number and proportion of studies reporting by ethnic/SES group. RESULTS: Thirteen studies met the inclusion criteria. Only 4 of 13 studies (31%) recorded and reported ethnicity information. Of these four studies that reported on ethnicity, three studies consisted of primarily White participants. Ethnic minority groups were underrepresented, but one study included a majority of African American participants. Moreover, 12 of 13 studies (92%) reported on SES with income and educational level being the primary measures used. SES representation of higher deprivation groups was varied due to limited data. CONCLUSIONS: For ethnicity, there was a lack of reporting, and ethnic minority groups were underrepresented in intervention studies. For SES, there was a high level of reporting but the proportion of study samples from across the spectrum of SES varied due to the variety of SES measures used. Findings highlight a need to improve the reporting and representation of ethnic minority groups and provide more detailed information for SES through using consistent measures (e.g. education, income and employment) to accurately determine the distribution of SES groups in intervention studies of people with MLTCs.

Modelling motion energy in psychotherapy: A dynamical systems approach.

In this study we introduce an innovative mathematical and statistical framework for the analysis of motion energy dynamics in psychotherapy sessions. Our method combines motion energy dynamics with coupled linear ordinary differential equations and a measurement error model, contributing new clinical parameters to enhance psychotherapy research. Our approach transforms raw motion energy data into an interpretable account of therapist-patient interactions, providing novel insights into the dynamics of these interactions. A key aspect of our framework is the development of a new measure of synchrony between the motion energies of therapists and patients, which holds significant clinical and theoretical value in psychotherapy. The practical applicability and effectiveness of our modelling and estimation framework are demonstrated through the analysis of real session data. This work advances the quantitative analysis of motion dynamics in psychotherapy, offering important implications for future research and therapeutic practice.

Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers.

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

Linking fast and slow: The case for generative models.

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Prognostic factors for disease activity in newly diagnosed teriflunomide-treated patients with multiple sclerosis: a nationwide Danish study.

BACKGROUND: Clinicians frequently rely on relapse counts, T2 MRI lesion load (T2L) and Expanded Disability Status Scale (EDSS) scores to guide treatment decisions for individuals diagnosed with multiple sclerosis (MS). This study evaluates how these factors, along with age and sex, influence prognosis during treatment with teriflunomide (TFL). METHODS: We conducted a nationwide cohort study using data from the Danish Multiple Sclerosis Registry.Eligible participants had relapsing-remitting MS or clinically isolated syndrome and initiated TFL as their first treatment between 2013 and 2019. The effect of age, pretreatment relapses, T2L and EDSS scores on the risk of disease activity on TFL were stratified by sex. RESULTS: In total, 784 individuals were included (57.4% females). A high number of pretreatment relapses (≥2) was associated with an increased risk of disease activity in females only (OR and (95% CI): 1.76 (1.11 to 2.81)). Age group 50+ was associated with a lower risk of disease activity in both sexes (OR females=0.28 (0.14 to 0.56); OR males=0.22 (0.09 to 0.55)), while age 35-49 showed a different impact in males and females (OR females=0.79 (0.50 to 1.23); OR males=0.42 (0.24 to 0.72)). EDSS scores and T2L did not show any consistent associations. CONCLUSION: A high number of pretreatment relapses was only associated with an increased risk of disease activity in females, while age had a differential impact on the risk of disease activity according to sex. Clinicians may consider age, sex and relapses when deciding on TFL treatment.

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming.

Survey sampling has wide range of applications in social and scientific investigation to draw inference about the unknown parameter of interest. In complex surveys, the sample information about the study variable cannot be expressed by a precise number under uncertain environment due fuzziness and indeterminacy. Therefore, this information is expressed by neutrosophic numbers rather than the classical numbers. The neutrosophic statistics, which is generalization of classical statistics, deals with the neutrosophic data that has some degree of indeterminacy and fuzziness. In this study, we investigate the compromise optimum allocation problem for estimating the population means of the neutrosophic study variables in a multi-character stratified random sampling under uncertain per unit measurement cost. We proposed the intuitionistic fuzzy cost function, modeling the fuzzy uncertainty in stratum per unit measurement cost. The compromise optimum allocation problem is formulated as a multi-objective intuitionistic fuzzy optimization problem. The solution methodology is suggested using neutrosophic fuzzy programming and intuitionistic fuzzy programming approaches. A numerical study includes the means estimation of atmospheric variables is presented to explore the real-life application, explain the mathematical formulation, and efficiency comparison with some existing methods. The results show that the suggested methods produce more precise estimates with less utilization of survey resources as compared to some existing methods. The Python is used for statistical analysis, graphical designing and numerical optimization problems are solved using GAMS.