multilevel logistic regression
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2022 ◽  
Vol 14 (2) ◽  
pp. 809
Author(s):  
Xiaowen Wang ◽  
Meiyue Li

The severity of the 2007–2008 economic crisis and the spatial heterogeneity of its impact have accelerated the study of regional economic resilience. The economic crisis has affected most parts of the world, and its impact is highly heterogeneous within China. The aim of this study was to explore the determinants of regional economic resilience across 284 Chinese cities from 2003–2018. Both nation-based and province-based regional economic resilience were examined. A multilevel logistic regression model was established, finding a disparity of provincial effects on regional performance during the economic crisis. Regional economic resilience is significantly affected by provincial trajectories, economy size, and resources. There are five significant determinants of economic resilience: income inequality, innovation, government intervention, human capital, and financial development. The results provide evidence for the government to design region-based policies, taking into consideration the size and the resources of the region’s economy to build a resilient wall to defend against external shocks and to form a basis for sustainable development.


Author(s):  
Berhanu Bekele Debelu

The purpose of this study was to identify the factors that affect the mortality among adult HIV/TB co-infected patients and to see the nutritional difference among mortality in residence level. Retrospective cohort studies of 417 patients which fulfill our criteria were included. Multilevel logistic regression models were used. MLwiN and SPSS software are used to estimate the parameter. The variance of the random factor in the empty model was significant which indicates that there were residential differences in TB-HIV co-infected mortality and it shows multilevel analysis was an appropriate approach for further analysis. The prevalence of HIV/TB co-infected patients' death was 12.9% in study time. Functional status, age of patients, WHO clinical stages, nutritional status, CD4 counts, regimen, and BMI were found to be significant determinants of HIV/TB co-infected mortality. In our study, patients with the bedridden category of functional status, the fourth stages of WHO clinical stages (stage IV), patients with higher age, patients whose treatments were second-line regimen and low CD4 cell counts were more at risk of death. The study also revealed that; poor nutritional status increased the risk of mortality among HIV/TB co-infected patients and it varies among the residence of the patients (rural area were more at risk).


2021 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Lea Fobel ◽  
Nina Kolleck

(1) Background: The equality of life chances in Germany is often assessed along the lines of a west/east and urban/rural differentiation in which the latter usually perform worse. One currently popular proposal for addressing these inequalities is to strengthen cultural and arts education. The question arises to what extent regional characteristics genuinely influence participation opportunities and to what extent individual resources still play a decisive role. (2) Methods: Using descriptive analyses and multilevel logistic regression modelling, we investigate the distribution of and participation in non-formal cultural education amongst German youth. (3) Results: We find that differences are more complex than a simple west/east or urban/rural divides. Rather, cultural activities must be considered in terms of their character in order to assess the mechanisms at play. There seem to be differences in the dependency on district funding between very peripheral and very central districts that frame the cultural infrastructure. (4) Conclusions: Regional discrepancies are not uniformly distributed across different fields of education or infrastructure. Simplifying statements that classify peripheral regions the general losers can be refuted here. Simultaneously, more comprehensive data could yield significantly more results than we are currently able to produce.


2021 ◽  
Vol 8 ◽  
Author(s):  
Mengsha Sun ◽  
Qiyu Bo ◽  
Bing Lu ◽  
Xiaodong Sun ◽  
Minwen Zhou

Objective: This study aims to investigate the association of sleep duration with vision impairment (VI) in middle-aged and elderly adults.Methods: This cross-sectional study used the data from the baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) 2011–2012, a national survey of adults aged 45 years or older. Weighted multilevel logistic regression models were used to evaluate the association between self-reported sleep duration and VI.Results: Of the 13,959 survey respondents, a total of 4,776 (34.2%) reported VI. The prevalence of short (≤6 h/night) and long (>8 h/night) sleep durations was higher among respondents with VI than those without VI (P < 0.001). Multilevel logistic regression models showed that compared with a sleep duration of 6–8 h/night, a sleep duration of ≤6 h/night was associated with a 1.45-fold [95% confidence interval (CI) = 1.34–1.56] higher VI risk, and a sleep duration of >8 h/night was associated with a 1.18-fold (95% CI = 1.03–1.34) higher VI risk, after adjusting for sociodemographic data, lifestyle factors, and health conditions. Vision impairment was associated with short sleep duration in respondents from all age or gender categories. However, VI was associated with long sleep duration in respondents from the elderly or female categories. The association between VI and long sleep duration disappeared in respondents of middle-aged or male categories.Conclusions: The potential impact of sleep on the risk of visual functions requires further attention. A more comprehensive and integrated health care and rehabilitation system covering vision and sleep is also needed.


2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Masayoshi Oka

Abstract Background Standardization and normalization of continuous covariates are used to ease the interpretation of regression coefficients. Although these scaling techniques serve different purposes, they are sometimes used interchangeably or confused for one another. Therefore, the objective of this study is to demonstrate how these scaling techniques lead to different interpretations of the regression coefficient in multilevel logistic regression analyses. Methods Area-based socioeconomic data at the census tract level were obtained from the 2015–2019 American Community Survey for creating two measures of neighborhood socioeconomic status (SES), and a hypothetical data on health condition (favorable versus unfavorable) was constructed to represent 3000 individuals living across 300 census tracts (i.e., neighborhoods). Two measures of neighborhood SES were standardized by subtracting its mean and dividing by its standard deviation (SD) or by dividing by its interquartile range (IQR), and were normalized into a range between 0 and 1. Then, four separate multilevel logistic regression analyses were conducted to assess the association between neighborhood SES and health condition. Results Based on standardized measures, the odds of having unfavorable health condition was roughly 1.34 times higher for a one-SD change or a one-IQR change in neighborhood SES; these reflect a health difference of individuals living in relatively high SES (relatively affluent) neighborhoods and those living in relatively low SES (relatively deprived) neighborhoods. On the other hand, when these standardized measures were replaced by its respective normalized measures, the odds of having unfavorable health condition was roughly 3.48 times higher for a full unit change in neighborhood SES; these reflect a health difference of individuals living in highest SES (most affluent) neighborhoods and those living in lowest SES (most deprived) neighborhoods. Conclusion Multilevel logistic regression analyses using standardized and normalized measures of neighborhood SES lead to different interpretations of the effect of neighborhood SES on health. Since both measures are valuable in their own right, interpreting a standardized and normalized measure of neighborhood SES will allow us to gain a more rounded view of the health differences of individuals along the gradient of neighborhood SES in a certain geographic location as well as across different geographic locations.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicholas Siame Adam ◽  
Halima S. Twabi ◽  
Samuel O.M. Manda

Abstract Background Multilevel logistic regression models are widely used in health sciences research to account for clustering in multilevel data when estimating effects on subject binary outcomes of individual-level and cluster-level covariates. Several measures for quantifying between-cluster heterogeneity have been proposed. This study compared the performance of between-cluster variance based heterogeneity measures (the Intra-class Correlation Coefficient (ICC) and the Median Odds Ratio (MOR)), and cluster-level covariate based heterogeneity measures (the 80% Interval Odds Ratio (IOR-80) and the Sorting Out Index (SOI)). Methods We used several simulation datasets of a two-level logistic regression model to assess the performance of the four clustering measures for a multilevel logistic regression model. We also empirically compared the four measures of cluster variation with an analysis of childhood anemia to investigate the importance of unexplained heterogeneity between communities and community geographic type (rural vs urban) effect in Malawi. Results Our findings showed that the estimates of SOI and ICC were generally unbiased with at least 10 clusters and a cluster size of at least 20. On the other hand, estimates of MOR and IOR-80 were less accurate with 50 or fewer clusters regardless of the cluster size. The performance of the four clustering measures improved with increased clusters and cluster size at all cluster variances. In the analysis of childhood anemia, the estimate of the between-community variance was 0.455, and the effect of community geographic type (rural vs urban) had an odds ratio (OR)=1.21 (95% CI: 0.97, 1.52). The resulting estimates of ICC, MOR, IOR-80 and SOI were 0.122 (indicative of low homogeneity of childhood anemia in the same community); 1.898 (indicative of large unexplained heterogeneity); 0.345-3.978 and 56.7% (implying that the between community heterogeneity was more significant in explaining the variations in childhood anemia than the estimated effect of community geographic type (rural vs urban)), respectively. Conclusion At least 300 clusters with sizes of at least 50 would be adequate to estimate the strength of clustering in multilevel logistic regression with negligible bias. We recommend using the SOI to assess unexplained heterogeneity between clusters when the interest also involves the effect of cluster-level covariates, otherwise, the usual intra-cluster correlation coefficient would suffice in multilevel logistic regression analyses.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Betregiorgis Zegeye ◽  
Bight Opoku Ahinkorah ◽  
Edward Kwabena Ameyaw ◽  
Abdul-Aziz Seidu ◽  
Mpho Keetile ◽  
...  

Background. Anemia constitutes a major public health concern, which is associated with maternal and perinatal mortality. In low- and middle-income countries, the burden of anemia is profoundly high. Cameroon, as one of the low- and middle-income countries, has a disproportionate anemia burden. Factors associated with anemia prevalence are largely unknown in Cameroon. Hence, we determined the prevalence of anemia and its individual/household and community-level factors among adult women in Cameroon. Methods. We derived data from the 2018 Cameroon Demographic and Health Survey for analysis in this study. Using the Stata version 14 software, univariate multilevel logistic regression analysis was used to select variables that had significant association with anemia at p < 0.05 . Statistically significant variables were included in a multivariable multilevel logistic regression modelling to examine their associations with anemia. Results were reported using adjusted odds ratios (AOR) with their respective 95% confidence interval (CI). Results. A total of 6,809 women aged 15-49 years were involved in this study with a mean age 30 ± 11.87 years. Approximately two-fifths of women were anemic. Of them, 0.8% were severely anemic, while 17.4% and 21.5% were moderately and mildly anemic, respectively. Current employment status (yes AOR = 0.77 , 95% CI; 0.61-0.96) and parity (1-2 children AOR = 0.61 , 95% CI; 0.44-0.86) were the main individual level factors associated with anemia, whereas region (Douala region AOR = 2.65 , 95% CI; 1.61-4.36, North-West region AOR = 0.53 , 95% CI; 0.28-0.99) was the community-level factor associated with anaemia. Conclusion. Empowerment of women through employment opportunities as well as focusing special attention in region where high prevalence of anemia could be crucial to decrease the burden of anemia and related maternal and perinatal mortality in the country.


2021 ◽  
Author(s):  
Igarashi Yu ◽  
Seiichiro Tateishi ◽  
Arisa Harada ◽  
Ayako Hno ◽  
Mayumi Tsuji ◽  
...  

Objective: This study examined the relationship between support for workers with illness and work functioning impairment during the COVID-19 pandemic. Methods: An internet survey was conducted on December, 2020. We included 22,388 subjects for analysis. A question was used to determine whether subjects need support from their company to continue working in their current health condition. The odds ratios (ORs) of relation between work functioning impairment and necessary of support for sick workers were estimated using multilevel logistic regression analysis. Results: The OR of work functioning impairment among sick workers not receiving support was 5.61 (95% confidence interval (CI) 5.19-6.06, p<0.001) and those receiving support was 1.82 (95% CI 1.64-2.03, p<0.001) compared to healthy workers. Conclusions: This study suggests that providing support to workers with illness may improve their work functioning impairment.


2021 ◽  
Author(s):  
Gurmessa Nugussu ◽  
Akalu Banbeta ◽  
Jaleta Abdisa

Abstract Background: Globally, there is an increase in the prevalence and incidence of fetal macrosomia. In Sub-Sahara African countries including Ethiopia, all infants were not weighed at birth, and there is a limit to knowledge regarding fetal macrosomia in Ethiopia. The main objective of this study is to assess the regional variation and determinants of fetal macrosomia using the multilevel logistic regression model.Methods: The study was based on the recent Ethiopian Demographic and Health Survey of 2016. A total of 2110 weighted infants at birth were extracted. Multilevel logistic regression analysis is performed to identify the factors associated with fetal macrosomia after various candidate models for their efficiency have been compared based on Akaike’s Information Criteria. Chi-square test of association and the inter-class correlation (ICC) are used to test and compute the variation of fetal macrosomia among the regions, respectively.Results: The overall prevalence of fetal macrosomia among the weighted infants at birth is 219 (10.4%). Based on the estimated chi-square test, there is a significant difference in fetal macrosomia across the regions of Ethiopia. The ICC reveals that 14% of the variation in fetal macrosomia can be explained by grouping the infants into the regions. Random intercept with fixed slope model fits the study data well as compared to the other competitors. Based on this model, the age of the mother, residence, educational level of mother, body mass index of mother, gestational age, wealth index, multiple pregnancies, and the infant sex are the significant factors associated with fetal macrosomia in all regions of Ethiopia.Conclusion: Concerned bodies, including the ministry of health and its hierarchical body, need to give special support and attention to women aged between 35 and 49, post-term pregnant women, and overweight or obese women to minimize the prevalence of fetal macrosomia.


Author(s):  
Jeremiah W. Jaggers ◽  
David C. Kondrat ◽  
Kelli E. Candida ◽  
Keith Miller

People with serious mental illness are disproportionately represented among prison/jail populations. Mental health courts (MHC) serve as an alternative to incarceration. In this study, we explore the extent to which MHC participants have members of their social network who were reported as having a history of arrests. Multilevel logistic regression demonstrated friends who used drugs, race, and network density were all predictive of MHC participants’ friends who have a history of arrest. Results demonstrate an association between MHC participation and arrest among individuals in their social network. Given the importance of social support in recovery from mental illness and in desisting from crime, such limitations can be problematic. MHC participants may be disinclined to engage with the very individuals who are able to provide social and emotional support.


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