Linear Model Approach Research Articles

Rapid Zooplankton Assessment: Evaluating a Tool for Ecosystem‐Based Fisheries Management in the Large Marine Ecosystems of Alaska

ABSTRACTEcosystem‐based fisheries management (EBFM) remains an aspirational goal for management throughout the world. One of the primary limitations of EBFM is the incorporation of basic lower trophic level information, particularly for zooplankton, despite the importance of zooplankton to fish. The generation of zooplankton abundance estimates requires significant time and expertise to generate. The rapid zooplankton assessment (RZA) is introduced as a tool whereby nontaxonomic experts may produce rapid zooplankton counts shipboard that can be applied to management in near real time. Zooplankton are rapidly counted shipboard and placed into three broad groups of zooplankton relevant to higher trophic levels: large copepods (> 2 mm), small copepods (< 2 mm), and euphausiids. A Bayesian, hierarchical linear regression modeling approach was used to validate the relationship between RZA abundances and laboratory‐processed abundances to ensure the rapid method is a reliable indicator. Additional factors likely to impact the accuracy of the RZA abundance predictions were added to the initial regression model: RZA sorter, survey, season, and large marine ecosystem (Bering Sea, Chukchi/Beaufort Sea, and Gulf of Alaska). We tested models that included the random effect of sorter nested within survey, which improved fits for both large copepods (Bayes R2 = 0.80) and euphausiids (Bayes R2 = 0.84). These factors also improved the fit for small copepods when the fixed effect of season was also included (Bayes R2 = 0.65). Additional RZA data were used to predict laboratory‐processed abundances for each zooplankton category and the results were consistent with model training data: large copepods (Bayes R2 = 0.80), small copepods (Bayes R2 = 0.64), and euphausiids (Bayes R2 = 0.88). The Bayesian models were therefore able to predict laboratory‐processed abundances with an associated error when accounting for these fixed and random effects. To demonstrate the utility of zooplankton data in management, zooplankton time series from the Bering Sea shelf were shown to vary in relation to warm and cold conditions. This variability impacted commercially important fish, notably Walleye Pollock (Gadus chalcogrammus), and these time series were used by managers using a risk table approach. The RZA method provides a rapid zooplankton population estimation in near real time that can be applied to the management process quickly, thus helping to fill a gap in EBFM.

An integrated approach for decomposing time series data into trend, cycle and seasonal components

ABSTRACT This study presents a methodology for decomposing time series data into trend, seasonal, and cyclical components using the moving linear model approach by Kyo and Kitagawa (Journal of Business Cycle Research, 19(3): 373-397, 2023). Our approach integrates seasonal adjustment and decomposition into a single framework. We evaluated our approach with two case studies: examining daily COVID-19 case data and the Index of Industrial Production (IIP) in Japan, comparing the results to seasonally adjusted IIP data. Performance metrics included the discrimination power index and the variance of the adjusted cyclical component. Our findings show that our method effectively extracts business cycle information, achieving higher discrimination power and greater adjusted variance compared to seasonally adjusted IIP data, highlighting the superior performance of our integrated seasonal adjustment method. We compared the proposed approach with a state-space modelling method by introducing an overall stability as a new indicator. The results demonstrated the stability of the estimations obtained with our proposed method.

Pathway enrichment in genome-wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes.

The genetic pathways that influence longitudinal heterogeneous changes in Alzheimer's disease (AD) may provide insight into disease mechanisms and potential therapeutic targets. Longitudinal endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) representing amyloid, tau, neurodegeneration (A/T/N), and cognition were selected. Genome-wide association analysis was performed using a linear mixed model (LMM) approach, followed by gene and pathway enrichment with significant and functionally relevant SNPs. A total of 33 and 19 statistically significant pathways were identified associating with the intercept and longitudinal trajectory, respectively. The longitudinal intercept pathways represent eight groups: immune, metabolic, cell growth and survival, DNA maintenance, neuronal signaling, RAS/MAPK/ERKsignalingpathways, vesicle and lysosomal transport, and transcription modification. Longitudinal trajectory pathways represented six groups: Immune, metabolic, cell signaling, cytoskeleton, and glycosylation. Longitudinal enrichment identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition, providing new insight into AD course-related mechanisms and potential new therapeutic targets. A systematic genome-wide analysis with longitudinal AD biomarker endophenotypes was performed. Enriched pathways were identified with functionally derived SNP to gene analysis. Fifty-two pathways were associated with longitudinal trajectory and intercept. Many of the identified pathways are specific steps in larger pathways implicated in AD. The identified pathways may provide therapeutic targets and areas for further study.

The ecology of opportunity: connecting socioecological health and school achievement in Alabama

ABSTRACT This study investigates the association between school-level achievement and the relative health of the social environment in which children are embedded, to gain a more comprehensive understanding of how behavioural, social, physical, and socio-emotional contexts may holistically impact learning. While research has recognised how specific aspects of a child’s environment can impact learning, what is less understood is how multiple facets of socioecological conditions may holistically contribute to learning advantages or disadvantages. To address this, we combine seven years and over 9,000 observations of school-level achievement scores from the U.S. state of Alabama with a comprehensive scale of “socioecological health” – a measure that includes multiple indicators such as nutrition access, housing quality, neighbourhood violence, social supports, income inequality, and mental health access. We then employ a three-level hierarchical linear modelling approach to assess the association between socioecological health and school achievement. Results suggest that (1) higher levels of socioecological health are associated with higher levels of school achievement; (2) effects are strongest for older students and rural students; and (3) healthy behaviours, social supports, and positive physical environment correspond with higher school achievement. These results highlight the breadth to which social and environmental determinants may influence learning opportunities and underscores the need for a more holistic and inclusive approach to educational leadership and social policy.

A sarcoptic mange outbreak drives long-term patterns of a fitness trait in an exotic ungulate

Species phenotypes are driven by persistent selective pressures that condition their adaptation to the environment. However, stochastic critical events, such as disease outbreaks, may also act as selective pressures because of drastic population decline. External structures like horns of ungulate species may reflect the consequences of these selective pressures on animal fitness. Using a generalized linear model approach, we analysed how a massive mortality event caused by a sarcoptic mange outbreak in 1992 affected the horn size of an Iberian aoudad population (dataset from 1980 to 2014) by comparing horn length before and after the outbreak. We found that this episodic, albeit massive, outbreak changed long-term trends in age-size horn relations. Both females and males presented shorter horns after the sarcoptic mange outbreak, although more marked in males. Even when animals reached mature ages, horn length did not increase with age to the same degree as before the outbreak. We consider that two processes may have acted on the population: a bottleneck favoured “shorter horns” phenotype and a post-mange enzootic period limited horn growth, as infected animals may invest more resources in the immune system. Overall, our results show a top-down regulation of a fitness trait for an ungulate population mediated by a parasite. This disease outbreak showed long-lasting effects on the ungulate population, with ecological and management implications.Graphical

Repeatability of two methods for estimating scapular kinematics during dynamic functional tasks

Best practices for scapular motion tracking are still being determined. The repeatability of different scapular kinematic procedures needs to be evaluated. The purpose of this study was to assess the test-retest reliability of two scapular kinematic procedures: double calibration with AMC (D-AMC) and individualized linear modelling (LM). Ten healthy participants had their upper body movement tracked with optical motion capture in two identical sessions. Five scapular calibration poses were performed, and seven dynamic functional tasks were tested. Scapular angles were calculated from both procedures (D-AMC vs LM). The D-AMC approach uses two poses (neutral and maximum elevation) and tracks the scapula with a rigid cluster, while the LM approach predicts scapular positioning from humeral angles based on equations built from the calibration pose data. Angle waveforms and repeatability outcomes were compared. Internal and upward rotation angle waveforms were significantly different (p < 0.05) between kinematic procedures for some tasks, with maximum mean differences up to 17.3° and 23.2°, respectively. Overall, repeatability outcomes were similar between procedures, but the LM approach was slightly better for tilt and the D-AMC approach was notably improved for upward rotation in certain tasks. For example, minimal detectable changes during the Forward Transfer ranged from 6.9° to 11.9° for the D-AMC and 8.9° to 25.3° for the LM. Discrepancies between procedures may be a function of the calibration poses chosen. Additional calibration poses may improve the comparisons between procedures.

Exploring the spatial interference effects elicited by social and non-social targets: A conditional accuracy function approach.

Recent studies employing the spatial interference paradigm reveal qualitative differences in congruency effects between gaze and arrow targets. Typically, arrows produce a standard congruency effect (SCE), with faster responses when target direction aligns with its location. Conversely, gaze targets often lead to a reversed congruency effect (RCE), where responses are slower in similar conditions. We explored this dissociation using the Conditional Accuracy Function (CAF) to assess accuracy across reaction time bins. Using a hierarchical linear mixed modelling approach to compare cropped eyes, and full faces as social stimuli, and arrows as non-social stimuli, we synthesized findings from 11 studies, which led to three distinct models. The results showed that with non-social targets, incongruent trials exhibited lower accuracy rates in the first bin than in subsequent bins, while congruent trials maintained stable accuracy throughout the distribution. Conversely, social targets revealed a dissociation within the fastest responses; alongside a general reduction in accuracy for both congruency conditions, congruent trials resulted in even lower accuracy rates than incongruent ones. These results suggest with gaze targets that additional information, perhaps social, in addition to the automatic capture by the irrelevant target location, is being processed during the earlier stages of processing.

Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan.

Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.

Impact of asymmetry on exchange rate determination: The role of fundamentals

This study tries to take a new look at the exchange rate determination model by employing recent developments in time series estimation methodologies. For this purpose, the validity of the sticky price monetary exchange rate model has been searched for the Turkish lira / US dollar case. Estimation results considering both linear and non–linear modeling approaches highly support the theoretical foundations and reveal explicitly the asymmetric nature of the model. The paper infers that since the nominal exchange rate seems to be determined through the economic fundamentals, it should not be used as a policy tool with a long–term perspective.

Uncovering Fusarium Species Associated with Fusarium Wilt in Chickpeas (Cicer arietinum L.) and the Identification of Significant Marker–Trait Associations for Resistance in the International Center for Agricultural Research in the Dry Areas’ Chickpea Collection Using SSR Markers

Enhancing plants’ resistance against FW is crucial for ensuring a sustainable global chickpea production. The present study focuses on the identification of fungal pathogens and the assessment of ninety-six chickpea samples for Fusarium wilt from the International Center for Agricultural Research in the Dry Areas (ICARDA)’s collection. Eight fungal isolates were recovered from the symptomatic chickpeas. Polyphasic identification was conducted by comparing the internal transcribed spacer region (ITS), the elongation factor 1-α (tef1-α), and beta-tubulin (tub2). Among them, Neocosmospora solani, N. nelsonii, N. falciformis, N. brevis, Fusarium brachygibbosum, and F. gossypinum were identified. An analysis of the genetic diversity of chickpeas, using 69 polymorphic simple sequence repeat (SSR) markers, revealed a total of 191 alleles across all markers, with, on average, each SSR marker detecting approximately 2.8 alleles. A STRUCTURE analysis delineated lines into two distinct sub-groups (K = 2). Association mapping, using the generalized linear model (GLM) and mixed linear model (MLM) approaches, identified six and five marker–trait associations (MTAs) for FW resistance, respectively. Notably, these TA42, TA125 (A) and TA125 (B), TA37, and TAASH MTAs, commonly found in both models, emerge as potential candidates for the targeted enhancement of FW resistance in chickpeas. To our knowledge, this study represents an inaugural report on the association mapping of genomic loci governing FW resistance in chickpeas from the ICARDA’s accessions.

Relevance of physical activity with decrease in minimal joint space of the nonoperative hip in female patients after unilateral THA.

To clarify the relevance of physical activity (PA) with respect to a decrease in minimal joint space (MJS) of the nonoperative hip in female patients at 24 months post-contralateral total hip arthroplasty (THA). This prospective cohort study was conducted from six to 24 months post-THA. The subjects were 85 female patients with hip osteoarthritis (OA) who had undergone unilateral THA. The primary outcome was a change in MJS (ΔMJS). Daily step counts and moderate to vigorous physical activity were measured. To identify factors related to ΔMJS, a generalized linear model approach was used, with adjustment factors, Kellgren-Lawrence (KL) grade, PA, and interaction terms between PA and KL grade as explanatory variables. The interaction term between daily step count and KL grade was significant. The regression coefficient of the daily step count for ΔMJS was significant in the KL ≥ 1 group, but not in the KL = 0 group. A model with adjustment factors, an interaction term, and PA showed that the daily step count increased ΔMJS. PA was related to ΔMJS of the nonoperative hip in female patients with KL grade ≥1, but was not related to ΔMJS in patients without OA post-THA. Excessive daily step count may be a risk factor for a decrease in MJS of the nonoperative hip.

Proximity to humans is associated with antimicrobial-resistant enteric pathogens in wild bird microbiomes

Humans are radically altering global ecology, and one of the most apparent human-induced effects is urbanization, where high-density human habitats disrupt long-established ecotones. Changes to these transitional areas between organisms, especially enhanced contact among humans and wild animals, provide new opportunities for the spread of zoonotic pathogens. This poses a serious threat to global public health, but little is known about how habitat disruption impacts cross-species pathogen spread. Here, we investigated variation in the zoonotic enteric pathogen Campylobacter jejuni. The ubiquity of C.jejuni in wild bird gut microbiomes makes it an ideal organism for understanding how host behavior and ecology influence pathogen transition and spread. We analyzed 700 C.jejuni isolate genomes from 30 bird species in eight countries using a scalable generalized linear model approach. Comparing multiple behavioral and ecological traits showed that proximity to human habitation promotes lineage diversity and is associated with antimicrobial-resistant (AMR) strains in natural populations. Specifically, wild birds from urban areas harbored up to three times more C.jejuni genotypes and AMR genes. This study provides novel methodology and much-needed quantitative evidence linking urbanization to gene pool spread and zoonoses.

Childhood trauma and differential response to long-term psychoanalytic versus cognitive-behavioural therapy for chronic depression in adults.

Childhood trauma is a major risk factor for chronic depression. It has been suggested that adults with chronic depression who have experienced childhood trauma may require long-term treatment owing to a breakdown of basic trust and related difficulties in developing a productive therapeutic relationship. As empirical studies have been preliminary and scarce, we studied the effects of psychoanalytic therapy (PAT) versus cognitive-behavioural therapy (CBT) for chronic depression in adults with a history of childhood trauma. In this subgroup, we expected a greater symptom reduction in PAT compared with CBT. In a large trial of long-term psychotherapies for chronic depression (LAC-Study; Clinical Trial Register ISRCTN91956346), 210 adults received open-ended CBT or PAT in an out-patient setting and were examined yearly over 5 years on the Beck Depression Inventory - II (BDI-II). Based on a linear mixed model approach, we tested participant-reported childhood trauma based on the Childhood Trauma Questionnaire (CTQ) as a predictor and moderator of treatment outcome. CTQ subscales were examined exploratively. Depressive symptoms decreased over time (b = -4.55, s.e. = 0.90, 95% CI -6.32 to -2.81, T = -5.08; P < 0.001). A significant three-way interaction between childhood trauma, time and therapy group (b = -0.05, s.e. = 0.02, 95% CI -0.09 to -0.01, T = -2.42; P = 0.016) indicated that participants with childhood trauma profited especially well from PATs. Our results indicate differential benefits from PAT compared with CBT among adults with chronic depression and a history of childhood trauma. The results have important implications for differential indication and policy.

Enhancing students’ reasoning skills by asking students to provide evidence-based explanations in science classrooms: findings from TIMSS 2019

ABSTRACT This study explores teachers’ instructional activities to ask students provide evidence-based explanations in science lessons. The sample of this study is 8739 eighth-grade students, 456 science teachers, and 273 schools in the US that participated in TIMSS 2019. Utilising a hierarchical linear modelling approach, the study found a significant correlation between teachers asking students to provide evidence-based explanations and student performance in answering reasoning questions on TIMSS 2019. The study suggests that promoting career growth that emphasises science content, science pedagogy, and critical thinking is closely associated with teachers’ encouragement of students using evidence-based explanations. Furthermore, the study indicates that teachers who undertook professional development (PD) in the last two years are more likely asking students to provide evidence-based explanations. However, the lack of science resources significantly influences teachers’ decisions to asking students to provide evidence-based explanations in science lessons.

Leveraging ecological indicators to improve short term forecasts of fish recruitment

AbstractForecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large‐scale meta‐analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well‐studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long‐term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non‐parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species‐specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single‐species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.

Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data

Telematics boxes integrated into vehicles are instrumental in capturing driving data encompassing behavioral and contextual information, including speed, distance travelled by road type, and time of day. These data can be amalgamated with drivers' individual attributes and reported accident occurrences to their respective insurance providers. Our study analyzes a substantial sample size of 19,214 individual drivers over a span of 55 weeks, covering a cumulative distance of 181.4 million kilometers driven. Utilizing this dataset, we develop predictive models for weekly accident frequency. As anticipated based on prior research with yearly data, our findings affirm that behavioral traits, such as instances of excessive speed, and contextual data pertaining to road type and time of day significantly aid in ratemaking design. The predictive models enable the creation of driving scores and personalized warnings, presenting a potential to enhance traffic safety by alerting drivers to perilous conditions. Our discussion delves into the construction of multiplicative scores derived from Poisson regression, contrasting them with additive scores resulting from a linear probability model approach, which offer greater communicability. Furthermore, we demonstrate that the inclusion of lagged behavioral and contextual factors not only enhances prediction accuracy but also lays the foundation for a diverse range of usage-based insurance schemes for weekly payments.

The Importance of Frequent Engagement With Self-Assessment Resources in Online Learning: A Hierarchical Linear Modeling Approach

This study explored online assessment and highlighted significant assessment strategies, such as capstone projects, quizzes, and self-assessments. Inquiry-based assessment, transformative assessment, and scaffolding with self-assessment resources formed the framework. Our goal was to determine the effects of certain self-assessment parameters on student achievement in online learning, including the frequency of answering online self-assessment questions, the points earned for correct answers, and the time spent answering questions. Data from undergraduate geoscience courses were analyzed using a hierarchical linear model, and the results showed that frequently answering online self-assessment questions is a strong predictor of higher final grades, capstone project success, and quiz results. The frequency of answering online self-assessment questions was a more significant factor than the amount of time spent on these questions and the total points earned. Educators are encouraged to redesign their courses to include self-assessment resources that promote frequent student engagement instead of just one-time interaction.

Impact of El Niño, Indian Ocean dipole, and Madden–Julian oscillation on land surface temperature in Kuching City Sarawak, during the periods of 1997/1998 and 2015/2016: a pilot study

ABSTRACT The severe El Niño events of 1997/1998 and 2015/2016 caused significant disruptions in Southeast Asia, particularly in Borneo, resulting in hazardous haze and acute water shortages. This study examines the influence of El Niño, the Indian Ocean Dipole (IOD), and the Madden–Julian oscillation (MJO) on regional climate, using time-series data from February 1993 to December 2020. Data from El Niño, IOD, and MJO indices were integrated with Landsat 5 and 8 land surface temperature records, allowing for a detailed analysis of their combined effects on regional temperature and precipitation patterns. Time-series trend decomposition and the generalized linear mixed model approach identified the Oceanic Niño Index (ONI) as a significant driver of temperature increases and dry spell occurrences during the peak El Niño years. On the other hand, ONI correlated strongly with mean monthly temperatures, underscoring its dominant influence. In addition, the IOD was found to significantly affect regional temperatures with a regression coefficient of 0.38867 (p = 0.0455), indicating its significant but less pronounced impact compared with ONI. These findings clarify the dynamics between key climate indices and their impact on regional climate extremes, offering critical insights for improving climate resilience and adaptation in tropical regions.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Effects of COVID-19 on Living Standards of Households in the North East Region of Ghana: A Linear Mixed Effects Model Approach

Abstract The world was severely hit by the novel coronavirus (COVID-19) in the latter part of 2019. The coronavirus is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and was first identified in Wuhan, China. The pandemic has devastated every sector of Ghana's economy and has significantly affected the living standards of people in the country. This study seeks to investigate the effects of COVID-19 on the living standards of households in Ghana using the North East Region as a case study. A sample of 399 respondents was drawn from the six (6) districts/municipalities using a single-stage cluster sampling technique. A linear mixed-effects model was used to investigate the fixed effects and the random effects in the study. Results from the study showed that variables such as the marital status of respondents, highest educational level of respondents, dependents, and employment status of respondents had significant fixed effects on the living standard of households in the Region. Overall, the study results showed that COVID-19 had significantly affected most households which has resulted in deteriorating living standards making the overall living standards worse in the Region. It was recommended in the study that a similar investigation be carried out in other parts of the country to get a more holistic view of the effects of the pandemic.