Observational Datasets Research Articles

The performance of Climate Information Service in delivering scientific, local, and hybrid weather forecasts: A study case in Bangladesh

Access to reliable and skillful Climate Information Service (CIS) is crucial for smallholder farmers in Bangladesh to mitigate the impacts of rainfall variability and extremes. This study aims to systematically evaluate the performance of CIS in providing Scientific Forecast (SF) and Local Forecast (LF) to smallholders in Bangladesh. The results were then compared with farmers’ perceptions of the forecast accuracy. Additionally, the skill of a simple hybrid forecast (HF), which is an integrated system of SF and LF, was assessed using the ERA5 and ground observation datasets as benchmarks. The SF and LF data were obtained from the meteoblue hindcast and from the interview, respectively. The results indicate that, overall, LF exhibits slightly higher skill compared to SF when evaluated against the ERA5 dataset. The forecast performance, however, declines by almost half when the ground-based observations are used, associated with high false alarms. Farmers, on the other hand, perceived SF to possess superior performance compared to LF. This study demonstrates that combining the SF and LF into a simple HF yields higher forecast skill than either individual forecast, highlighting the importance of HF to deliver a reliable and trustworthy weather forecast.

Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data

Offshore wind energy is a promising resource for renewable energy development. Reanalysed wind data are unmatched by other wind data sources in providing a long-term assessment of wind power potential. In this study, 10 of the selected offshore locations close to the Moroccan coast were used to evaluate the ERA5 wind reanalysis dataset against the IFREMER-blended observational dataset covering the years 1993–2016. The ERA5 wind data’s capacity to represent wind variability in the area was confirmed by the results of the statistical methodologies used. All the reanalysed data scored better at capturing the observed wind variability at the southern sites than at the northern ones, where the wind variability was more complex. In a long-term evaluation from 1981 to 2020, the wind power potential in the Moroccan Atlantic coast was found to be very stable except in the northern sites and between Agadir and Bou Arich. Seven of the 10 sites considered were ranked as promising sites for offshore wind power generation, with wind power densities above 420 W/m2 at 100 m in height. Additionally, the change in signs in the variability toward the middle of the ERA5 record, which was seen at all locations and was also evident in the observations, did not significantly affect the yearly wind power density. However, the seasonal distribution of the latter was modified according to the local features of the seasonal variability.

Emergent Constraint on Projection of the North Pacific Monsoon Trough and Its Implications for Typhoon Activity Using CMIP6 Models

AbstractThe North Pacific monsoon trough (NPMT) is an imperative large‐scale circulation pattern influencing tropical cyclone (TC) activity in the western North Pacific (WNP), thus its future change has great implications for the WNP TC activity. Here future change in the NPMT and its uncertainty are examined by using 35 climate models from Phase six of Coupled Model Intercomparison Project (CMIP6). Due to the El Niño‐like sea surface temperature (SST) pattern, the multi‐model ensemble (MME) mean projects an eastward extension of the NPMT in the latter half of the 21st century. However, considerable inter‐model uncertainty exists in the projection, which is associated with the diversity in the zonal SST gradient across the tropical Pacific. This diversity in the projected SST is largely rooted in the simulated precipitation biases in the tropical Pacific in the historical experiments of CMIP6 models, which can modulate future Pacific SST distribution through the shortwave‐SST feedback. This linkage between biases in historical climate and future climate allows us to constrain the projection by using observational data sets. Emergent constraint using observational precipitation data set reduces the projection uncertainty by 48% and projects an eastward extension of the north Pacific NPMT by 6.2°. This eastward extension of the NPMT indicates an eastward migration of TC genesis location, and elongated TC track and thus strengthened TC intensity, suggesting an increased TC‐related disaster for residents near the WNP.

Cosmological test of dark energy parameterizations in Hořava–Lifshitz gravity

In this work, we assume that the FLRW universe, which is filled with dark matter along with dark energy, is in the framework of Hořava–Lifshitz (HL) gravity. The dark energy is considered as the linear (Model I) and CPL (Model II) parameterizations of the equation of state parameter. For both models, we express the Hubble parameter H(z) in terms of the model parameters and redshift z. To rigorously constrain the model, we have employed a comprehensive set of recent observational datasets, including cosmic chronometers (CC), type Ia supernovae (SNIa), baryon acoustic oscillations (BAO), gamma-ray burst (GRB), quasar (Q) and cosmic microwave background radiation (CMB). Through the joint analysis of this diverse collection of datasets, we have achieved tighter constraints on the model’s parameters. This, in turn, allows us to delve into both the physical and geometric aspects of the model with greater precision. Furthermore, our analysis has enabled us to determine the present values of crucial cosmological parameters, including H0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H_{0}$$\\end{document}, Ωm0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{m0}$$\\end{document}, Ωk0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{k0}$$\\end{document} and ΩΛ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega _{\\Lambda 0}$$\\end{document}. It is noteworthy that our results are consistent with recent findings from Planck 2018, underscoring the reliability and relevance of our models in the current cosmological context. We also conduct analysis of cosmographic parameters and apply statefinder and diagnostic tests to explore the evolution of the Universe. In addition, the statistical analysis suggests that the Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document}CDM model is the preferred model among all our considered models. Our investigation into the models has unveiled intriguing features of the late Universe.

Examining the counterinsurgency-counterterrorism tradeoff: the effects of targeted strikes on militant attacks

ABSTRACT Do targeted strikes against militant groups affect insurgent operations differently from terrorist operations? One of the reasons it is challenging for states to counter militant groups who simultaneously wage an insurgency and a terrorist campaign is that there are offsetting effects between counterinsurgency and counterterrorism. As both an insurgent group and a terrorist group, the Islamic State of Iraq and the Levant (ISIL) poses particular challenges to states. Since 2014, the United States and coalition allies have conducted thousands of targeted strikes against ISIL in Iraq and Syria as part of Operation Inherent Resolve (OIR). In this article, we examine the effects of targeted strikes against ISIL. Using a novel dataset of time-series observations from Iraq and Syria between 2015 and 2018, we examine the relationship between targeted strikes conducted under OIR and the number of insurgent and terrorist attacks conducted by ISIL. Our findings give empirical support to the theoretical argument that the same military strategy can have different effects on insurgency and terrorism.

Impact of Land Surface Snow Processes on the Arctic Stable Boundary Layer

Abstract The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

Estimating Hydrological Regimes from Observational Soil Moisture, Evapotranspiration, and Air Temperature Data

Abstract Evapotranspiration has long been understood to vary with soil moisture in drier regions and to be relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability. Significance Statement When a soil is somewhat dry, an increase in soil moisture can lead to an increase in evapotranspiration E. In contrast, when a soil is wet, E is limited instead by the availability of energy. Determining where E is water limited, energy limited, or some combination of both is important because it tells us where accurate soil moisture initialization in a forecast system might contribute to more accurate forecasts of E and thus air temperature. Here we use a combination of independent datasets (satellite-derived estimates of soil moisture and E as well as air temperature measurements from weather stations) to provide new monthly maps of the water-limited, energy-limited, and combination regimes over the continental United States and across the world.

Dendrochronology: Large datasets help explain when and how trees grow but not why

A meta-analysis of a large dataset of wood cell formation observations from several tree species in the Northern Hemisphere suggests that the 5th of June is favorable for cell division and enlargement, while the summer solstice promotes cell wall thickening.

Factors associated with satisfaction and perceived helpfulness of mental healthcare: a World Mental Health Surveys report

BackgroundMental health service providers are increasingly interested in patient perspectives. We examined rates and predictors of patient-reported satisfaction and perceived helpfulness in a cross-national general population survey of adults with 12-month DSM-IV disorders who saw a provider for help with their mental health.MethodsData were obtained from epidemiological surveys in the World Mental Health Survey Initiative. Respondents were asked about satisfaction with treatments received from up to 11 different types of providers (very satisfied, satisfied, neither satisfied nor dissatisfied, somewhat dissatisfied, very dissatisfied) and helpfulness of the provider (a lot, some, a little, not at all). We modelled predictors of satisfaction and helpfulness using a dataset of patient-provider observations (n = 5,248).ResultsMost treatment was provided by general medical providers (37.4%), psychiatrists (18.4%) and psychologists (12.7%). Most patients were satisfied or very satisfied (65.9-87.5%, across provider) and helped a lot or some (64.4-90.3%). Spiritual advisors and healers were most often rated satisfactory and helpful. Social workers in human services settings were rated lowest on both dimensions. Patients also reported comparatively low satisfaction with general medical doctors and psychiatrists/psychologists and found general medical doctors less helpful than other providers. Men and students reported lower levels of satisfaction than women and nonstudents. Respondents with high education reported higher satisfaction and helpfulness than those with lower education. Type of mental disorder was unrelated to satisfaction but in some cases (depression, bipolar spectrum disorder, social phobia) was associated with low perceived helpfulness. Insurance was unrelated to either satisfaction or perceived helpfulness but in some cases was associated with elevated perceived helpfulness for a given level of satisfaction.ConclusionsSatisfaction with and perceived helpfulness of treatment varied as a function of type of provider, service setting, mental status, and socio-demographic variables. Invariably, caution is needed in combining data from multiple countries where there are cultural and service delivery variations. Even so, our findings underscore the utility of patient perspectives in treatment evaluation and may also be relevant in efforts to match patients to treatments.

Spatio‐Temporal Dynamics of Aboveground Biomass in China's Oasis Grasslands Between 1989 and 2021

AbstractGrassland provides multiple ecosystem services and plays a key role in preventing desert encroachment and maintaining oasis stability. In China, the area of cropland in oases has expanded significantly in recent decades, which results in a rapid increase in agricultural water demand and encroachment on grassland subsistence space. However, our knowledge about how the expansion of cropland affects oasis grasslands remains limited. We used machine learning, temporal segmentation of spectral trajectories, and maximum covariance analysis to generate an annual oasis grassland aboveground biomass (AGB) data set at 30‐m resolution from 1989 to 2021 based on multiple remotely sensed and ground observation data sets, and investigated the dynamics of grassland AGB under cropland expansion in oases. We found that overall oasis grassland AGB increased significantly (0.3 gm−2 yr−1, P < 0.01) during 1989–2021, but trends in AGB were not consistent across basins. In the Yellow River, Turpan Hami, Qaidam, and southern Altai Mountains River Basins, AGB was dominated by a significant increase. Conversely, in all basins in southern Xinjiang, AGB showed a decreasing trend due to the rapid cropland expansion. Spatially, cropland expansion and AGB dynamics were strongly coupled. In regions characterized by agricultural concentration, grassland AGB benefitted from resource spillover via edge effects. However, in downstream areas or those with a low proportion of cropland, where most grasslands are distributed, the relationship between the two shifted to a trade‐off. Our study provides a scientific basis for identifying priority areas for ecological restoration and for science‐based planning of the scale of cropland in oases.

Research on optimization method of evaporation duct prediction model based on particle swarm algorithm

The sea surface roughness parameterization and universal stability functions, as key components of the evaporation duct prediction models rooted in the Monin-Obukhov similarity theory, dictate the model performance which further impacts the efficiency and accuracy of offshore electromagnetic applications. In this paper, layered meteorological and hydrological observations are collected during two cruises and processed to obtain the reference modified refractivity profiles close to the sea surface, and then particle swarm algorithm is utilized to optimize the parameters of the sea surface roughness parameterization and universal stability functions. The results show that compared with the pre-optimization model, the prediction accuracy of the optimized model is improved by 5.09% and 8.12% under stable conditions, and by 9.97% and 31.51% under unstable conditions for observation dataset from each cruise, which proves the feasibility of the proposed method for evaporation duct prediction model optimization.

Network Analysis of Active and Passive Radar Based on the Extended Kalman Filter

In this current work, a sequential extended Kalman filter (EKF) algorithm is proposed to solve the quandary of regional accuracy disparity for time difference of arrival (TDOA) positioning in passive radar. The proposed approach entails merging the complete set of active radar observation data with the TDOA data of passive radar in a serial fashion, followed by the deployment of the EKF algorithm for the purpose of filtering. The simulation results show that the proposed data fusion method effectively solves the regional accuracy imbalance problem caused by the fuzzy area and no solution area of the TDOA positioning solution through the conflation of the precision merits of active and passive radar, and improves the overall target detection accuracy.

Observation constraints on scalar field cosmological model in anisotropic universe

In this study, we have explored a scalar field cosmological model in the axially symmetric Bianchi type-I universe. In this study, our aim is to constrain the scalar field dark energy model in an anisotropic background. For this purpose, the explicit solution of the developed field equations for the model is determined and analyzed. Constraints on the cosmological model parameters are established utilizing Markov Chain Monte Carlo (MCMC) analysis and using the latest observational datasets of OHD, BAO, and Pantheon. For the combined dataset (OHD, BAO, and Pantheon), the best-fit values of Hubble and density parameters are estimated as [Formula: see text], [Formula: see text] [Formula: see text] and [Formula: see text]. The model shows a flipping nature and redshift transition occurs at [Formula: see text], and the present value of decelerated parameter is computed to be [Formula: see text] for the combined dataset. We have explored characteristics like the universe’s age, particle horizon, deceleration parameter, and jerk parameter. The dynamical properties, such as energy density [Formula: see text], scalar field pressure [Formula: see text], and equation of state parameter [Formula: see text], are analyzed and presented. We have also described the behavior of the scalar potential [Formula: see text] and scalar field. Furthermore, the authors also described the behavior of energy conditions in scalar-tensor cosmology. The scenario of the present accelerated expansion of the universe is described by the contribution of the scalar field.

Effects of land surface temperatures on vegetation phenology along urban–rural local climate zone gradients

ContextUrbanization and local urban climate have multiple impacts on vegetation phenology in urban and suburban areas. Understanding these effects and their interactions with the surface urban heating effect remains limited.ObjectiveWe employed a time series of Earth observation data to analyze land surface phenology (LSP) dynamics and related environmental drivers in the highly urbanized Pearl River Delta (PRD) region.MethodsFirst, local climate zone (LCZ) maps were generated from Earth observation datasets of 2000 and 2019. Second, LSP (i.e., start, end, and length of season) were extracted from vegetation indices for 2000–2019. Thirdly, land surface temperature (LST) was used as an explanatory variable based on the LCZ of cities. Finally, interannual trends of LSP and their association with LST were analyzed, depending on the distance gradient of vegetation to compact high-rise buildings.ResultsUrban surface characteristics showed that LSP in regions dominated by compact and high-rise urban areas presented significant spatiotemporal variation at the start and end of season than those dominated by open, mid-rise, and low-rise areas. The impacts of spring and autumn LST in the daytime on LSP were slightly more substantial than those in the nighttime. The association of decreasing spring LST in the daytime with a delayed start of season is especially pronounced in urban domains.ConclusionsThe results indicate that vegetated areas adjacent to urban domains presented greater spatiotemporal dynamics than suburban and rural regions. Our study emphasizes the dependence of spatiotemporal changes in vegetation phenology on the effects of urban surface warming.

Suitability of ERA5-Land reanalysis dataset for hydrological modelling in the Alpine region

Study region: The region of interest is the South-Tyrol in the southeastern Alps, Italy. A comparison of meteorological forcing is performed with reference to this region while hydrological simulations are conducted in the Passirio river basin.Study focus: The objective of this work is to evaluate the suitability of ERA5-Land reanalysis product as a reference dataset for hydrological modelling in topographically complex Alpine regions. ERA5-Land is compared with observational gridded dataset, obtained by means of the Kriging interpolation, available at two contrasting spatial resolutions a coarse grid size of 11 × 8 kilometres and a high-resolution grid size 1 × 1 kilometres. The hydrological coherence of the three datasets (ERA5-Land and the two observational datasets) with observed streamflow time series is then assessed with refer the Passirio/Passer river basin and to its nested and snow-dominated sub-catchment Plan/Pfelders.New hydrological insights for the region: The study assesses ERA5-Land’s suitability for hydrological simulations in the South-Tyrol region by comparing streamflow outcomes. At the annual scale, an overestimation of precipitation of about 40mm/month and a temperature underestimation of about 2.1 °C is reported. These discrepancies influence hydrological simulations, resulting in an overestimation of both streamflow and snow water equivalent. It is thus crucial to consider the limitations of ERA5-Land when using it as forcing for hydrological application in watersheds characterized by complex terrains, such as Alpine region.

Earth as an Exoplanet. III. Using Empirical Thermal Emission Spectra as an Input for Atmospheric Retrieval of an Earth-twin Exoplanet

In this study, we treat Earth as an exoplanet and investigate our home planet by means of a potential future mid-infrared space mission called the Large Interferometer For Exoplanets (LIFE). We combine thermal spectra from an empirical data set of disk-integrated Earth observations with a noise model for LIFE to create mock observations. We apply a state-of-the-art atmospheric retrieval framework to characterize the planet, assess the potential for detecting the known bioindicators, and investigate the impact of viewing geometry and seasonality on the characterization. Our key findings reveal that we are observing a temperate habitable planet with significant abundances of CO2, H2O, O3, and CH4. Seasonal variations in the surface and equilibrium temperature, as well as in the Bond albedo, are detectable. Furthermore, the viewing geometry and the spatially and temporally unresolved nature of our observations only have a minor impact on the characterization. Additionally, Earth’s variable abundance profiles and patchy cloud coverage can bias retrieval results for the atmospheric structure and trace-gas abundances. Lastly, the limited extent of Earth’s seasonal variations in biosignature abundances makes the direct detection of its biosphere through atmospheric seasonality unlikely. Our results suggest that LIFE could correctly identify Earth as a planet where life could thrive, with detectable levels of bioindicators, a temperate climate, and surface conditions allowing liquid surface water. Even if atmospheric seasonality is not easily observed, our study demonstrates that next generation space missions can assess whether nearby temperate terrestrial exoplanets are habitable or even inhabited.

Mixed-Effects Height Prediction Model for Juniperus procera Trees from a Dry Afromontane Forest in Ethiopia

Tree height is a crucial variable in forestry science. In the current study, an accurate height prediction model for Juniperus procera Hochst. ex Endl. trees were developed, using a nonlinear mixed-effects modeling approach on 1215 observations from 101 randomly established plots in the Chilimo Dry Afromontane Forest in Ethiopia. After comparing 14 nonlinear models, the most appropriate base model was selected and expanded as a mixed-effects model, using the sample plot as a grouping factor, and adding stand-level variables to increase the model’s prediction ability. Using a completely independent dataset of observations, the best sampling alternative for calibration was determined using goodness-of-fit criteria. Our findings revealed that the Michaelis–Menten model outperformed the other models, while the expansion to the mixed-effects model significantly improved the height prediction. On the other hand, incorporating the quadratic mean diameter and the stem density slightly improved the model’s prediction ability. The fixed-effects of the selected model can also be used to predict the mean height of Juniperus procera trees as a marginal solution. The calibration response revealed that a systematic selection of the three largest-diameter trees at the plot level is the most effective for random effect estimation across new plots or stands.

A variance-upscaling quantile mapping method for gridded precipitation bias correction

Accurate estimation of precipitation in mountainous regions remains a challenge. Quantile Mapping (QM) is a method used extensively to correct gridded precipitation from numerical models or satellite remote sensing. The method can match the intensity histogram of a given gridded precipitation to that of the observation. However, the widely existing oversmoothed effect, a precipitation time series variability representativeness error, in observational gridded datasets may be inherited in the corrected results, impacting the assessment of extreme precipitation events. To address this issue, this study introduces a variance-upscaling QM method (VUQM). Unlike previous methods that obtain frequency parameters by interpolating rain gauge data, the new method creates a model to transform the precipitation time series variance at the rain gauge point level to the model's grid level. When compared to correct ERA5-Land reanalysis precipitation, the results show that the variance using the VUQM is 1.6 times larger than that obtained using QM alone, and it is 2 times larger in the 95th percentile. This indicates that the new method significantly reduces the oversmoothed effect and preserves precipitation variation. Random simulations show that low station density only introduces uncertainty, not theoretical bias in VUQM. In contrast, the variance biases in gridded datasets constructed from areal average or interpolation cannot be avoided, particularly in regions with significant spatial heterogeneity. This suggests that VUQM is well-suited for use in the Qinghai-Tibetan Plateau (QTP) compared with the old methods. Overall, this method sheds some light on mountainous precipitation bias correction.

Supplemental Nutrition Assistance Program and Adherence to Antihypertensive Medications

Nonadherence to antihypertensive medications is associated with uncontrolled blood pressure, higher mortality rates, and increased health care costs, and food insecurity is one of the modifiable medication nonadherence risk factors. The Supplemental Nutrition Assistance Program (SNAP), a social intervention program for addressing food insecurity, may help improve adherence to antihypertensive medications. To evaluate whether receipt of SNAP benefits can modify the consequences of food insecurity on nonadherence to antihypertensive medications. A retrospective cohort study design was used to assemble a cohort of antihypertensive medication users from the linked Medical Expenditure Panel Survey (MEPS)-National Health Interview Survey (NHIS) dataset for 2016 to 2017. The MEPS is a national longitudinal survey on verified self-reported prescribed medication use and health care access measures, and the NHIS is an annual cross-sectional survey of US households that collects comprehensive health information, health behavior, and sociodemographic data, including receipt of SNAP benefits. Receipt of SNAP benefits in the past 12 months and food insecurity status in the past 30 days were assessed through standard questionnaires during the study period. Data analysis was performed from March to December 2021. Status of SNAP benefit receipt. The main outcome, nonadherence to antihypertensive medication refill adherence (MRA), was defined using the MEPS data as the total days' supply divided by 365 days for each antihypertensive medication class. Patients were considered nonadherent if their overall MRA was less than 80%. Food insecurity status in the 30 days prior to the survey was modeled as the effect modifier. Inverse probability of treatment (IPT) weighting was used to control for measured confounding effects of baseline covariates. A probit model was used, weighted by the product of the computed IPT weights and MEPS weights, to estimate the population average treatment effects (PATEs) of SNAP benefit receipt on nonadherence. A stratified analysis approach was used to assess for potential effect modification by food insecurity status. This analysis involved 6692 antihypertensive medication users, of whom 1203 (12.8%) reported receiving SNAP benefits and 1338 (14.8%) were considered as food insecure. The mean (SD) age was 63.0 (13.3) years; 3632 (51.3%) of the participants were women and 3060 (45.7%) were men. Although SNAP was not associated with nonadherence to antihypertensive medications in the overall population, it was associated with a 13.6-percentage point reduction in nonadherence (PATE, -13.6 [95% CI, -25.0 to -2.3]) among the food-insecure subgroup but not among their food-secure counterparts. This analysis of a national observational dataset suggests that patients with hypertension who receive SNAP benefits may be less likely to become nonadherent to antihypertensive medication, especially if they are experiencing food insecurity. Further examination of the role of SNAP as a potential intervention for preventing nonadherence to antihypertensive medications through prospectively designed interventional studies or natural experiment study designs is needed.

Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity

Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond “White”, “Black”, “Asian”, “Mixed” and “Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.