scholarly journals Levels, Trends and Determinants of Infant Mortality in Nigeria: An Analysis using the Logistic Regression Model

2021 ◽  
pp. 17-40
Author(s):  
Donalben Onome Eke ◽  
Friday Ewere
Keyword(s):  
Logistic Regression ◽  
Infant Mortality ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mortality Rates ◽  
Mortality Data ◽  
Demographic Health Survey ◽  
P Values ◽  
Educational Levels ◽  
Maternal Characteristics

This paper presents a statistical analysis of the levels, trends and determinants of infant mortality in Nigeria using the logistic regression model. Infant mortality data for each of the five years preceding the 2003, 2008, 2013 and 2018 Nigeria Demographic Health Survey (NDHS) was retrieved and used for the analysis. Findings from the study revealed that infant mortality rates decline have stagnated in the five year period prior to the 2018 survey with an Annual Rate of Reduction (ARR) of 0% relative to an initial ARR of 5.7% between 2003 and 2008. The ARR of 2.039% over the 15 year period spanning 2003 to 2018 suggests that the rate of infant mortality reduction is slow. This study also showed that maternal characteristics such as age and educational levels as well as cultural practises like use of clean water and toilet facilities were statistically significant determinants of infant mortality in Nigeria with P-values < 0.05 across each of the survey years.

scholarly journals Clinical characteristics of patients with bronchiectasis with nontuberculous mycobacterial disease in Mainland China: a single center cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongjun Yin ◽  
Xiaoying Gu ◽  
Yimin Wang ◽  
Guohui Fan ◽  
Binghuai Lu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Pulmonary Disease ◽  
Logistic Regression Model ◽  
Mainland China ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Single Center ◽  
Cross Sectional ◽  
P Values

Abstract Background The diagnosis and treatment of patients with bronchiectasis and nontuberculous mycobacterium (NTM) pulmonary disease are challenging issues and the treatment is also prolonged and depends on the species. There is limited information on patients with bronchiectasis and NTM pulmonary disease in Mainland China. Methods This cross-sectional study was conducted at the China–Japan Friendship Hospital, Beijing, China. Those adult patients who met the diagnostic criteria for bronchiectasis and obtained a culture result of mycobacteria from lower respiratory tract specimens or lung tissue were included in this study. A logistic regression model was used to identify the related factors in patients with NTM pulmonary disease. Results A total of 202 patients with bronchiectasis from 19 cities, 155 without and 47 (23.3%) with NTM pulmonary disease, were included. In all the 47 patients with NTM pulmonary disease, Mycobacterium avium complex was the most common species (66.0%), and 72.3% of them were initiated on standard anti-NTM treatment within 3 months after the diagnosis of NTM pulmonary disease. A larger proportion of patients with NTM pulmonary disease had acute exacerbations of ≥ 3 times within 1 year and were diagnosed bronchiectasis ≥ 50 years among patients with NTM pulmonary disease. The HRCT chest images revealed higher proportions of nodular shadow (100% vs. 35.3%), tree-in-bud sign (97.9% vs. 29.0%), cavities (29.8% vs. 5.8%), and airway dilation of the right middle lobe or the left lingular lobe (63.8% vs. 23.9%) in patients with NTM pulmonary disease than in those without NTM pulmonary disease (all P values = 0.001). The multivariable logistic regression model indicated that three and more abnormal features (OR 33.8; 95% CI 11.1–102.8) and main lesions of bronchial expansion in the middle or lingual lobe (OR 6.4; 95% CI 2.4–16.6) in HRCT chest images were independently associated with NTM pulmonary disease (P values = 0.001). Conclusion In a single center of Mainland China, > 23% of patients with bronchiectasis had NTM pulmonary disease, and most patients were started on standard treatment within 3 months after the diagnosis of NTM pulmonary disease. These findings suggest that patients with bronchiectasis should be thoroughly examined for the presence of NTM pulmonary disease. Trial registration NCT03594032.

scholarly journals Modeling Infant Mortality Risk Factors using Logistic Regression Model and Spatial Analysis in Kenya

2021 ◽  
pp. 21-33
Author(s):  
Erick Cheruiyot Kirui ◽  
Elphas Luchemo ◽  
Ayubu Anapapa
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Infant Mortality ◽  
Regression Model ◽  
Spatial Variation ◽  
Mortality Risk ◽  
Logistic Regression Model ◽  
Important Indicator ◽  
Weather Patterns ◽  
High Level

Globally, infant mortality is used as an important indicator for healthcare status hence an important tool for evaluation and planning of public health strategies. Despite of numerous interventions by governments aimed at reducing infant mortality, high rates are still reported in Kenya. A lot of resources are channeled towards its control leading to low productivity hence impacting the household economic welfare and national GD. The specific objective was to establish risk factors and the spatial variation of infant mortality in Kenya by analyzing the 2014 Kenya Demographic Health Survey data. A fully Bayesian paradigm and logistic regression model were used to determine infant mortality risk factors and spatial variation in Kenya. Demographic, socioeconomic and environmental factors were found to have significant effect on infant mortality. Counties from the northern parts of Kenya, Rift Valley, Central, Eastern, Nyanza, Coastal and Western parts of Kenya showed a high level of infant deaths. Infant mortality is high in arid and semi-arid areas and coastal areas due to high prevalence of infectious diseases and inadequate water supply, health facilities and low education levels. Infant mortality varies significantly across regions in Kenya due to cultural activities, and weather patterns hence exists spatial autocorrelation among neighboring regions.

scholarly journals Exploring the Influencing Factors for Infant Mortality: A Mixed-Method Study of 24 Developing Countries Based on Demographic and Health Survey data

2021 ◽  
Author(s):  
Md. Akhtarul Islam ◽  
Tarana Tabassum ◽  
Mohammad Ali Moni
Keyword(s):  
Developing Countries ◽  
Logistic Regression ◽  
Infant Mortality ◽  
Regression Model ◽  
Mixed Method ◽  
Logistic Regression Model ◽  
Random Effect ◽  
Binary Logistic Regression ◽  
Forest Plot ◽  
Binary Logistic Regression Model

Objective: This study aimed to discover the prevalence of infant mortality and to assess how different factors influence infant mortality in 24 developing countries by utilizing the latest DHS data. Methods: This study used a mixed-method design to assemble cross-sectional studies to integrate data from 24 other countries due to a widening perspective of infant mortality. Most recent available DHS data of 24 different developing countries from the year 2013 to 2019 was used to conduct the study. Descriptive analysis, binary logistic regression model, random-effect meta-analysis, and forest plot have been used for the final analyses. Results: Binary logistic regression model revealed for Bangladesh that, higher education level of fathers (OR: 0.344, 95% CI: 0.147; 0.807), being 2nd born or above order infant (OR: 0.362, 95% CI: 0.248, 0.527), taking ANC (OR: 0.271, 95% CI: 0.192; 0.382 for 1-4 visits), taking PNC (OR: 0.303, 95% CI: 0.216; 0.425) were statistically significant determinants of lowering infant death. While carrying multiple fetus (OR: 6.634, 95% CI: 3.247; 13.555) was exposed as a risk factor of infant mortality. Most significant factors influencing infant mortality for all 24 developing countries were number of fetus (OR: 0.193, 95% CI: 0.176; 0.213), taking ANC (OR: 0.356, 95% CI: 0.311; 0.407) and taking PNC (OR: 0.302, 95% CI: 0.243; 0.375). Conclusion In this study, the number of the fetus, taking ANC and PNC, was the most significant factor affecting the risk of infant mortality in developing countries. So, anticipation and control projects ought to be taken in the field in regard to these hazard factors.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p &lt; 0,0001), education (p &lt; 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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