Dataset Information

Generalized anxiety disorder among mothers attending perinatal services during COVID-19 pandemic: using ordinal logistic regression model.

ABSTRACT:

Background

Generalized anxiety disorder is characterized by excessive and uncontrollable worry about a variety of events. It is critical to ensure a pregnant mother's mental health in order to reduce pregnancy and birth-related problems. The major goal of current study was to identify the factors associated with generalized anxiety disorder among mothers attending perinatal services in the study area during COVID-19 using ordinal logistic regression.

Methods

The institution-based cross-sectional study was conducted from July 10^th, 2020 to August 10^th, 2020 at Kembata Tembaro zone, Southern Ethiopia. The current study included 423 mothers. The GAD-7 scale was used to assess the anxiety level among mothers. An Ordered logit model was used to identify the determinants of GAD. Brant test of the parallel line was utilized to check proportionality assumption. The statistical significance was determined using an adjusted proportional odd ratio with a 95%CI, and a p-value <5%. STATA software version 14 was used to analyze statistical data.

Results

Of all 423 mothers attending perinatal service during COVID-19; 134(31.7%), 171(40.4%), 85(20.1%), and 33 (7.8%) had non/minimal to severe generalized anxiety disorder respectively. The results of multivariable proportional odds model (POM) showed that the variables town residents [aPOR = 1.827; 95% CI:1.233-2.708], having alcohol habit [aPOR = 3.437, 95% CI = 1.397-8.454], having occupation [aPOR = 0.509, 95% CI: 0.303-0.857], being health care worker [aPOR = 0.117, 95% CI = 0.044-0.311], having chronic illness [aPOR = 7.685, 95% CI = 3.045-19.39], having family history of anxiety/mood disorder [aPOR = 7.839, 95% CI = 2.656-23.12], fear of contracting COVID-19 [aPOR = 1.704, 95% CI = 1.152-2.521], having moderate social support [aPOR = 0.648, 95% CI = 0.425-0.989], having strong social support [aPOR = 0.495, 95% CI = 0.272-0.901] were significantly associated with generalized anxiety disorder at 5% level of significance.

Conclusion

Current findings concluded that the prevalence of GAD among mothers attending perinatal service during COVID-19 was high. The covariates like being town resident, lower-income status, occupation status, having a chronic illness, having a positive family history of anxiety or mood disorder, perceived social support, and fear of the COVID-19 were significantly associated with generalized anxiety disorder among mothers. Mothers who visit perinatal services should be given special consideration to improve health care services and ensure their mental health.

SUBMITTER: Lelisho ME

PROVIDER: S-EPMC9220756 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPoor maternal nutrition during pregnancy creates a stressful environment that can lead to long-term effects on tissue development. Understanding the food consumption score can be used to prevent problems associated with poor dietary intake of pregnant mothers. In Ethiopia, the food consumption score ranges from 54% to 81.5%, which is far below the World Food Program (WFP) recommendation. Thus, this study aimed to assess food consumption score and associated factors among pregnant women attending antenatal care services in health centers of Addis Ababa, Ethiopia.MethodsThis study has used institution based cross sectional study. Overall, 999 pregnant women were selected for this study. A multistage sampling technique followed by systematic random sampling was used to include pregnant women coming for antenatal care services in the selected health centers of Addis Ababa from June 07 to July 08, 2022. We used interviewer administered questionnaire using the Kobo toolbox. Food consumption score (FCS) was assessed after collecting data on frequency of eight food groups consumed over the previous seven days, which were weighted according to their relative nutritional value. STATA 14 was used to analyse the data. Ordinal logistic regression was used to identify independent predictors of food consumption score. Those variables having p value < 0.25 in the bivariable ordinal logistic regression were considered for the final model. Crude and Adjusted Odds Ratio were used to assess the strength of the association. In the final model, p value < 0.05 at 95% confidence interval was used to declare statistical significance.ResultFrom the total of 949 pregnant women a little over half (51.20% (95%CI: 48.00%-54.40%) had acceptable food consumption score, while just over two fifth (42.60% (95% CI: 39.40%-45.70%)) and a small proportion (6.2% (95%CI: 4.84%-7.94%)) of the study participants had borderline and poor food consumption score, respectively. No meal skip (AOR = 1.37, 95% CI:1.03-1.81), able to read and write (AOR = 3.99, 95% CI: 1.33-11.96), poorest wealth status (AOR = 0.52, 95% CI: 0.34-0.78), positive attitude towards consumption of a diversified diet (AOR = 1.52,95% CI: 1.17-1.98) were independent predictors of acceptable food consumption score.ConclusionIn this study, considerably low level of acceptable food consumption score among the study participants was observed. Besides, not skipping meal, having better educational status, wealth status and attitude towards consumption of a diversified diet were associated with acceptable food consumption score. Therefore, nutritional education considering important dietary modifications should be intensified targeting vulnerable groups.

Project description:BackgroundLogistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models.MethodsWe used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC.Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted.ResultsThe packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient.ConclusionsOn relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen "non-informative" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain.

Project description:PurposeResearch on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks.MethodsAnalyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups-High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk-and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors.ResultsThe key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana.ConclusionsThe COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.

Project description:BackgroundOne of the most frequent reasons for children's poor physical and mental development is malnutrition, becoming a more significant issue in most developing nations, including Ethiopia. Prior research used multiple anthropometric measurements separately to pinpoint undernutrition concerns in children. However, the impact of each explanatory variable on a single response category was not considered in these investigations. This study used a single composite index of anthropometric parameters to identify the factors affecting elementary school students' nutritional condition.MethodsIn Dilla, Ethiopia, 494 primary school students took part in a cross-sectional institutional survey during the 2021 academic year. Principal component analysis was used to create a single composite measure of nutritional status using z-scores for the anthropometric indices of height-for-age and body mass index-for-age. The relative effectiveness of a partial proportional odds model was compared with several other ordinal regression models to identify the important variables for children's nutritional status.Results27.94% of primary school students were undernourished (7.29% severely and 20.65% moderately). According to the fitted partial proportional odds model, the mother's education level (secondary or higher) was positively correlated with the nutritional status of primary school students, given that in this case the students ate three or more times per day and had a high dietary diversity score (OR = 5.94; CI: 2.2-16.0). Nevertheless, there was a negative correlation between larger family size (OR = 0.56; CI: 0.32-0.97), unprotected groundwater (OR = 0.76; CI: 0.6-0.96), and severely food insecure households (OR = 0.3; CI: 0.14-0.68).ConclusionIn Dilla, Ethiopia, undernutrition among primary school students is a serious issue. It is essential to implement nutrition education and school feeding programs, improve drinking water sources, and boost the community's economy to alleviate the problems.