Estimated Regression Coefficients Research Articles

Effect of Agricultural Subsidy on Farm Income of Commercial Vegetable Farmers of Makwanpur and Dhading Districts, Nepal

Recent agricultural policies of Nepal prioritize subsidy programs. However, there has always been concern about the realistic assessment of the economic implications of subsidy programs. This study was carried out to assess the effect of agricultural subsidy on farm income in the Makwanpur and Dhading districts of Nepal. Altogether 120 households from Thaha-2 of Makwanpur and Benighat Rorang-7 of Dhading were selected using a multi-stage sampling technique. Descriptive statistical tools, correlation analysis, independent sample t-test, and multiple linear regression were used to analyse the data. Among the total sampled households, 54.17% had access to at least one agricultural subsidy program. The result revealed that farm income was the primary source of household income, contributing 64.17% to total household income. Pearson product correlation shows positive and statistically significant relation between subsidy and technology adoption, technology adoption and annual farm income, subsidy and annual farm income. The estimates of multiple regression coefficients show that subsidy had a significant (p< 0.01) and positive effect on the annual farm income of commercial vegetable farmers. Also, the area under cultivation was a highly significant (p<0.01) factor influencing the farm income. From the independent t-test, the annual farm income of subsidy recipients and non-recipients was found significantly different (p<0.01). So, this study suggests the concerned stakeholders to identify the most needed and best-suited technologies and implement the subsidy program accordingly to boost the annual farm income of commercial vegetable farmers. Keywords: Effect, Farm income, Regression, Subsidy

The Brazilian National Oral Health Policy and oral cancer mortality trends: An autoregressive integrated moving average (ARIMA) model.

This study analyzes the effect of the Brazilian National Oral Health Policy (NOHP) on oral cancer mortality rates (OCMR). This is an ecological study with secondary oral cancer death data, using interrupted time series analysis (ARIMA, Autoregressive Integrated Moving Average). Annual death data were collected from the Mortality Information System (1996-2019). The outcome was the OCMR, standardized by gender and age We considered the NOHP, categorized as "0" (before its implementation), from 1996 to 2004, and "1 to 15", from 2005 to 2019. ARIMA modeling was carried out for temporal analysis, and regression coefficient estimation (RC). The Brazilian NOHP implementation was associated with an increase in OCMR in the North region (CR = 0.16; p = 0.022) and with a decrease in the Southeast region (CR = -0.04; p<0.001), but did not affect the other macro-regions nor Brazil. The forecast models estimated an increase in OCMR for the North, and Northeast, a decrease for the Southeast, and stability for the South and Brazil. The Brazilian NOHP is not being effective in reducing the OCMR. The trends behaved differently in the Brazilian territory, highlighting health inequities. We recommend that the NOHP strengthen the oral health care network, incorporating oral cancer as a notifiable disease, adopting strategies for prevention, screening, and providing opportunities for early treatment of the disease.

Development of Fuel Production Facility Using Waste Plastic as Feedstock

The raising worldwide issue of plastic waste gathering has driven the investigation of imaginative and supportable arrangements. Among these arrangements, the development and evaluation of fuel offices that change squander plastic into significant energy assets have acquired noticeable quality. This far reaching survey dives into the present status of information and progressions in this field, enveloping plausibility studies, arranging and preprocessing strategies, transformation innovation determination, facility plan, and performance assessment. By reusing waste plastic as a feedstock for fuel production, these undertakings present a double an open door to oversee plastic waste while at the same time tending to energy requests. Through a blend of examination discoveries, this research highlights the significance of informed direction and streamlining systems chasing building and really working this production of fuel. Besides, the study highlighted cooperative exploration, strategy support, mechanical development, and worldwide collaboration as crucial drivers for accomplishing fruitful waste plastic-to-fuel change, adding to more reasonable waste administration rehearses and a progress to a roundabout economy. Materials used include; plastic waste, sorting equipment, reactor vessel, gas burner, condenser, analytical equipment, and storage tank. The reactor was placed on the gas burner and connected to the condenser through the holes pipe and the fuel collect was placed under the condenser outlet to receive our end product. Before running the test, the weight of the plastic waste was measured. the estimated regression coefficients alongside their standard errors, t-values, and p-values. The coefficients address the connection between the independent variables and the dependent variable in the regression model. The intercept term in the relapse model shows the anticipated worth of the reliant variable (fuel production) when all autonomous factors are zero. In Table 4.4, the intercept coefficient was assessed as 8.000 with a standard error of 5.873. However, the fact that the coefficient does not have a statistically significant value (t = 1.363, p = 0.264) suggests that the intercept value may not have a significant effect on predicting fuel production.

A black box approach to fitting smooth models of mortality

Actuaries have long been interested in the forecasting of mortality for the purpose of the pricing and reserving of pensions and annuities. Most models of mortality in age and year of death, and often year of birth, are not identifiable so actuaries worried about what constraints should be used to give sensible estimates of the age and year of death parameters, and, if required, the year of birth parameters. These parameters were then forecast with an ARIMA model to give the required forecasts of mortality. A recent article showed that, while the fitted parameters were not identifiable, both the fitted and forecast mortalities were. This result holds if the age term is smoothed with P-splines. The present article deals with generalized linear models with a rank deficient regression matrix. We have two aims. First, we investigate the effect that different constraints have on the estimated regression coefficients. We show that it is possible to fit the model under different constraints in R without imposing any explicit constraints. R does all the necessary booking-keeping ‘under the bonnet’. The estimated regression coefficients under a particular set of constraints can then be recovered from the invariant fitted values. We have a black box approach to fitting the model subject to any set of constraints.

Bias reduction and robustness for Gaussian longitudinal data analysis

In the analysis of clustered or longitudinal data structures, such as those coming from surveys or trials, it is desirable to accurately estimate the dependence parameters within repeated measurements. Marginal models together with the generalized estimating equation allow to consistently estimate regression coefficients. Inference may be problematic for small and moderate sample sizes, or with models involving many covariates. Moreover, the correlation structure may be misspecified leading to a substantial bias in the sandwich covariance matrix estimator. The paper focuses on Gaussian with exchangeable correlation matrix model and proposes to use adjustments of the score function aiming at mean and median bias reduction of maximum likelihood estimates. Extensive simulation studies show a remarkable performance of the proposed methods. In addition, we show that the bias reduction methods maintain an appreciable robustness with respect to the maximum likelihood under misspecification of the correlation matrix.

UNCONSUMED FOOD CONCERNS: THE CASE OF THE UNITED ARAB EMIRATES

The article identifies factors influencing consumer attitudes with regard to unconsumed food in the wealthy economy of the United Arab Emirates (UAE), where the vast majority of the population are transient residents. The estimated regression coefficients were used to calculate the marginal effects, which measure change in probability of attitude towards the unconsumed food. The study applied the survey data collected from 1,542 UAE residents. The respondents included individuals representing 70 nationalities – transient residents of the UAE, mostly Filipinos (26%) and citizens of India (20%), and the UAE nationals (11%). Results show that persons showing most frequently concern about unconsumed food are women, older, college educated, and high income. Additionally, those concerned were also the long-term residents of the UAE as well as the supporters of investment in renewable energy. The sole factor that was associated with lower probability of being concerned about unconsumed food was the number of owned cars (on average 2.7 cars per household).

Issues in Implementing Regression Calibration Analyses.

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

Consistent ridge estimation for replicated ultrastructural measurement error models

The presence of measurement errors in data and multicollinearity among the explanatory variables have negative effects on the estimation of regression coefficients. Within this respect, the motivation of this article is to examine measurement errors and multicollinearity problems simultaneously. In this paper, by utilizing three different forms of corrected score functions three consistent ridge regression estimators are proposed. Theoretical comparisons of these new estimators are examined by implementing the mean squared error criterion. Large sample properties of these estimators are investigated without assuming any distributional assumption. Two numerical examples are presented using real data sets and also a simulation study is performed. The findings indicate that the newly proposed three estimators outperform the existing estimators by the criterion of mean squared error.

Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia

BackgroundIn Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak.MethodsThe spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression.ResultsIn the first 8 months of 2021, COVID-19 severely affected Sarawak’s central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts’ rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates.ConclusionThis research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.

Nordic region suicide trends 2000–2018; sex and age groups

Background and aim Overall, suicide rates in the Nordic region, Denmark, Finland, Iceland, Norway and Sweden, have declined in the past 40 years. The aim of this study was to determine trends in suicide mortality from 2000 to 2018. Methods Data were obtained from official suicide statistics for men and women, 15 years and older. Gender and age groups in four calendar periods were analyzed using Joinpoint Estimated Regression Coefficient. Results The crude regional suicide rate was 17.1, 2000–2004, decreased to 14.1 per 100,000 inhabitants in 2015–2018. Age-standardized rates are 13.6–11.3. The crude rate decreased by 19.5% (16.3% age-standardized), 19.3% for males and 20.5% for females. The largest decrease was found in Finland (34.9%), the smallest in Norway (1.4%). In males, the exception was an increased suicide rate among all Icelandic except 15–24-year olds, and in 45–64 year-old Norwegians. Among females, an increase was seen among 15–24-year olds in all countries except Iceland, in all age groups in Norway, and in 25–44-year olds in Sweden. In males, a decline of the suicide rated lower than 10% was noted in 25–44 olds in Norway and in 15–64 year-olds in Sweden. Discussion A robust decrease was observed in the overall regional suicide rate in recent years. Exceptions are rising rates in Icelandic males, in Norwegian females, and the youngest female groups in all except Iceland. The small decline among middle-aged males in Norway and Sweden is of concern.

Comparing pedigree and genomic inbreeding coefficients, and inbreeding depression of reproductive traits in Japanese Black cattle

BackgroundPedigree-based inbreeding coefficients have been generally included in statistical models for genetic evaluation of Japanese Black cattle. The use of genomic data is expected to provide precise assessment of inbreeding level and depression. Recently, many measures have been used for genome-based inbreeding coefficients; however, with no consensus on which is the most appropriate. Therefore, we compared the pedigree- ({F}_{PED}) and multiple genome-based inbreeding coefficients, which were calculated from the genomic relationship matrix with observed allele frequencies ({F}_{GRM}), correlation between uniting gametes ({F}_{UNI}), the observed vs expected number of homozygous genotypes ({F}_{HOM}), runs of homozygosity (ROH) segments ({F}_{ROH}) and heterozygosity by descent segments ({F}_{HBD}). We quantified inbreeding depression from estimating regression coefficients of inbreeding coefficients on three reproductive traits: age at first calving (AFC), calving difficulty (CD) and gestation length (GL) in Japanese Black cattle.ResultsThe highest correlations with {F}_{PED} were for {F}_{ROH} (0.86) and {F}_{HBD} (0.85) whereas {F}_{GRM} and {F}_{UNI} provided weak correlations with {F}_{PED}, with range 0.33–0.55. Except for {F}_{GRM} and {F}_{UNI}, there were strong correlations among genome-based inbreeding coefficients (ge 0.94). The estimates of regression coefficients of inbreeding depression for {F}_{PED} was 2.1 for AFC, 0.63 for CD and -1.21 for GL, respectively, but {F}_{PED} had no significant effects on all traits. Genome-based inbreeding coefficients provided larger effects on all reproductive traits than {F}_{PED}. In particular, for CD, all estimated regression coefficients for genome-based inbreeding coefficients were significant, and for GL, that for {F}_{UNI} had a significant.. Although there were no significant effects when using overall genome-level inbreeding coefficients for AFC and GL, {F}_{ROH} provided significant effects at chromosomal level in four chromosomes for AFC, three chromosomes for CD, and two chromosomes for GL. In addition, similar results were obtained for {F}_{HBD}.ConclusionsGenome-based inbreeding coefficients can capture more phenotypic variation than {F}_{PED}. In particular, {F}_{ROH} and {F}_{HBD} can be considered good estimators for quantifying inbreeding level and identifying inbreeding depression at the chromosome level. These findings might improve the quantification of inbreeding and breeding programs using genome-based inbreeding coefficients.

Regression of binary network data with exchangeable latent errors

AbstractUndirected, binary network data consist of indicators of symmetric relations between pairs of actors. Regression models of such data allow for the estimation of effects of exogenous covariates on the network and for prediction of unobserved data. Ideally, estimators of the regression parameters should account for the inherent dependencies among relations in the network that involve the same actor. To account for such dependencies, researchers have developed a host of latent variable network models; however, estimation of many latent variable network models is computationally onerous and which model is best to base inference upon may not be clear. We propose the probit exchangeable (PX) model for undirected binary network data that is based on an assumption of exchangeability, which is common to many of the latent variable network models in the literature. The PX model can represent the first two moments of any exchangeable network model. We leverage the EM algorithm to obtain an approximate maximum likelihood estimator of the PX model that is extremely computationally efficient. Using simulation studies, we demonstrate the improvement in estimation of regression coefficients of the proposed model over existing latent variable network models. In an analysis of purchases of politically aligned books, we demonstrate political polarization in purchase behavior and show that the proposed estimator significantly reduces runtime relative to estimators of latent variable network models, while maintaining predictive performance.

Modeling Factors Associated with Dialysis Adequacy Using Longitudinal Data Analysis: Generalized Estimating Equation Versus Quadratic Inference Function.

In hemodialysis patients, changes in dialysis adequacy (DA) are examined longitudinally. The aim of this study was to determine factors affecting DA using the generalized estimating equation (GEE) and to compare them with the quadratic inference function (QIF). A longitudinal study. This longitudinal study examined the records of 153 end-stage renal disease (ESRD) patients. The longitudinal data on the DA and baseline demographic and clinical characteristics were obtained from patients' files. The GEE1, GEE2, and QIF models were fitted with different correlation structures, and then the best correlation structure was selected using the quasi-likelihood information criterion (QIC), Akaike information criterion (AIC), and Bayes information criterion (BIC) fitting criteria. The majority of patients (59.5%) had unfavorable DA (KT/V<1.2). Women and patients<60 years had more favorable DA. In the GEE model, the coefficients of female gender (β=0.079, 95% confidence interval [CI]: 0.032, 0.062), age at starting dialysis (β=-0.002, 95% CI: -0.004, -0.0001), hypertension (HTN, β=-0.055, 95% CI: -0.007, -0.103), diabetes (β=-0.088,95% CI: -0.021, -0.155), dialysis duration (β=0.132, 95% CI: 0.085, 0.178), and weight (β=-0.004, 95% CI: -0.006, -0.003) demonstrated a significant relationship with DA. The three models resulted in a similar estimate of regression coefficients. The relative efficiencies of QIF versus GEE1, QIF versus GEE2, and GEE2 versus GEE1 were 1.175, 1.056, and 1.113, respectively. DA is not optimal in most hemodialysis patients, and gender, age at the start of dialysis, HTN, diabetes, dialysis duration, and weight had a significant association with DA. The three different models yielded quite similar coefficient estimates, but the QIF model resulted more efficient than GEE1 and GEE2.

Asymptotic properties of M estimators in classical linear models with φ-mixing random errors

In this paper, the asymptotic normality and strong consistency for M estimators of the unknown regression coefficients in classical linear models are established under the assumptions that the errors are identically distributed φ-mixing random variables. The results obtained in this paper generalize the corresponding ones of Chen and Zhao [M-methods in linear model. Shanghai: Scientific and Technical Publishers; 1996] and Zhao [Strong consistency of M-estimates in linear model. Sci China Ser A. 2002;45:1420–1427] for independent errors. Finally, the simulation study is provided to verify the finite sample performance of the theoretical results and a real example is analysed for illustration.

Prenatal exposure to perceived stress, maternal asthma, and placental size

Prenatal exposure to perceived stress, maternal asthma, and placental size

On the Use of Minimum Penalties in Statistical Learning

Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not generalize to other types of models. In this article we proposed the MinPen framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using common assumptions. The MinPen framework uses a novel penalty based on the minimum function to simultaneously detect and exploit relationships between responses. An iterative algorithm is proposed as a solution to the nonconvex optimization. Theoretical results such as high-dimensional convergence rates, model selection consistency, and a framework for post selection inference are provided. We extend the proposed MinPen framework to other exponential family loss functions, with a specific focus on multiple binomial responses. Tuning parameter selection is also addressed. Finally, simulations and two data examples are presented to show the finite sample properties of this framework. Supplemental material providing proofs, additional simulations, code, and datasets are available online.

Preliminary Evidence for Genetic Nurture in Depression and Neuroticism Through Polygenic Scores

Modeling genetic nurture (ie, the effects of parental genotypes through influences on the environment experienced by their children) is essential to accurately disentangle genetic and environmental influences on phenotypic variance. However, these influences are often ignored in both epidemiologic and genetic studies of depression. To estimate the association of genetic nurture with depression and neuroticism. This cross-sectional study jointly modeled parental and offspring polygenic scores (PGSs) across 9 traits to test for the association of genetic nurture with lifetime broad depression and neuroticism using data from nuclear families in the UK Biobank, with data collected between 2006 and 2019. A broad depression phenotype was measured in 38 702 offspring from 20 905 independent nuclear families, with most of these participants also reporting neuroticism scores. Parental genotypes were imputed from sibships or parent-offspring duos and used to calculate parental PGSs. Data were analyzed between March 2021 and January 2023. Estimates of genetic nurture and direct genetic regression coefficients on broad depression and neuroticism. This study of 38 702 offspring with data on broad depression (mean [SD] age, 55.5 [8.2] years at study entry; 58% female) found limited preliminary evidence for a statistically significant association of genetic nurture with lifetime depression and neuroticism in adults. The estimated regression coefficient of the parental depression PGS on offspring neuroticism (β = 0.04, SE = 0.02, P = 6.63 × 10-3) was estimated to be approximately two-thirds (66%) that of the offspring's depression PGS (β = 0.06, SE = 0.01, P = 6.13 × 10-11). Evidence for an association between parental cannabis use disorder PGS and offspring depression was also found (β = 0.08, SE = 0.03, P = .02), which was estimated to be 2 times greater than the association between the offspring's cannabis use disorder PGS and their own depression status (β = 0.04, SE = 0.02, P = .07). The results of this cross-sectional study highlight the potential for genetic nurture to bias results from epidemiologic and genetic studies on depression or neuroticism and, with further replication and larger samples, identify potential avenues for future prevention and intervention efforts.

Effect of Physical Resource Allocation Strategies on Public Service Delivery: A Case of Vihiga County Government, Kenya

Resource allocation strategies are important components of the strategic management process. They are applied by organizations during the implementation of a formulated strategy so as to achieve competitive advantage in markets. The purpose of this study was to determine how strategies for allocating physical resources affect the delivery of public services in Vihiga County. Asset-based views and systems theories were embraced to help the review. During the course of the investigation, a descriptive survey research design was used. A target population of 593 individuals was used, and a sample size of 234 respondents was picked in view of Krecjie and Morgan's recipe. The respondents were distributed among the participating institutions, and each participant was selected at random. Respondents were selected from health and Early Childhood Development (ECD) facilities, primary and secondary schools, trade, infrastructure, and public service departments to fill out questionnaires. The researcher conducted in-person interviews with selected administrators as well as document analysis within the finance department. Descriptive statistics like frequencies, percentages, and means were used to collect and analyze the data using both qualitative and quantitative methods. Using SPSS, quantitative data were analyzed to determine how strong the relationship was between the variables and the results shown in tables, graphs, and charts. The regression model's estimates of the regression show that strategies for allocating physical resources have a significant impact on the delivery of public services. The estimates of the significant regression coefficient are r = 0.649, t = 7.819, and the p-value is 0.01. The coefficient estimate's P-value is less than 0.05, indicating significance at the 95% confidence level. According to this significant estimate, the levels of the public service delivery index would rise by 0.649 units if physical resource allocation strategies were increased by one unit in county government. The findings made it possible to draw the conclusion that the Vihiga County government's resource allocation strategies and public service delivery have a positive and significant relationship and that government regulation has a positive and significant effect on moderating the relationship. The study recommends that adequate financing of technological resources should be done at all times to enable efficient monitoring of service provision. The study becomes reference material for all county government staff, policymakers, and other institutions in Kenya.

Multiple imputation of more than one environmental exposure with nondifferential measurement error.

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

Associations between physical and mental health and the utilization of ambulatory and emergency healthcare among asylum-seekers: results from a cross-sectional survey in Berlin, Germany

BackgroundDespite a high burden of chronic and mental illness, asylum-seekers show low utilization of ambulatory specialist healthcare. Forgoing timely healthcare when facing access barriers may direct them toward emergency care. This paper examines interrelations of physical and mental health and utilization of ambulatory and emergency care, and explicitly addresses associations between the different types of care.MethodsA structural equation model was applied to a sample of n = 136 asylum-seekers living in accommodation centers in Berlin, Germany. Utilization patterns of emergency care (outcome) and physical and mental ambulatory care (endogenous predictors) were estimated, while controlling for age, gender, chronic conditions, bodily pain, depression, anxiety, length of stay in Germany (exogenous predictors) and self-rated health (endogenous predictor).ResultsAssociations were observed between ambulatory care utilization and poor self-rated health (0.207, CI: 0.05; 0.364), chronic illness (0.096, CI: 0.017; 0.175) and bodily pain (0.019, CI: 0.002; 0.036); between mental healthcare utilization and anxiety (0.202, CI: 0.051; 0.352); and between emergency care utilization and poor self-rated health (0.621, CI: 0.059; 1.183), chronic illness (0.287, CI: 0.012; 0.563), mental healthcare utilization (0.842, CI: 0.148; 1.535) and anxiety (0.790, CI: 0.141; 1.438) (values in parentheses show estimated regression coefficients and 95% confidence intervals). We found no associations between the utilization of ambulatory and emergency care.ConclusionsOur study generates mixed results concerning associations between healthcare needs and ambulatory and emergency care utilization among asylum-seekers. We found no evidence that low utilization of ambulatory care contributes to emergency care utilization; neither did we find evidence that ambulatory treatment obviates the need to seek emergency care. Our results indicate that higher physical healthcare needs and anxiety are associated with more utilization of both ambulatory and emergency care; whereas healthcare needs related to depression tend to remain unmet. Both the undirected and under-utilization of health services may reflect navigation and accessibility issues. To facilitate more needs-based and effective healthcare utilization and thus contribute to health equity, support services such as interpretation and care navigation as well as outreach are warranted.