Factors Affecting Postnatal Care in Bangladesh: Clustered Data Analysis

The study aims to determine the factors affecting postnatal care in Bangladesh using the data extracted from Bangladesh Demographic and Health Survey (BDHS), 2014. For the purpose of regression analysis, mixed logistic regression model has been utilized to take into account the possible correlation among subjects within clusters. It is found that region, place of residence, mother’s education, wealth index, access to media, birth order and antenatal care visits have significant association with postnatal care. Dhaka Univ. J. Sci. 66(1): 59-65, 2018 (January)

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Assessment of the Quality of Antenatal Care Services and the Associated Risk Factors in Tanzania

10.21203/rs.3.rs-30567/v1 ◽

2020 ◽

Author(s):

Abdalla Hussein ◽

Jackline Mbishi

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Antenatal Care ◽

Regression Model ◽

Service Provision ◽

Logistic Regression Model ◽

Wealth Index ◽

Factors Associated ◽

Associated Risk Factors

Abstract Background This study gives the finding of the assessment made on the quality of antenatal care (ANC) services received by women in Tanzania during pregnancy and the associated risk factors. Methods We used the data from the 2015-16Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). The sample included 6,924 women who gave birth in the five years before the survey and having at least one ANC visit in the most current pregnancy. The quality of ANC services provided was assessed using six questions on receipt of recommended components of ANC services. The responses of the individual components were summed up to generate the ANC service provision score. In so doing, the women who received all six components were considered to receive good quality ANC services. A weighted logistic regression model was employed in the examination of the factors associated with the quality of ANC services. Results The average service provision score was 4.4 out of a total of 6. Approximately, 31% of the women received good quality ANC services. The most provided ANC components were blood sample measurement (87%) and iron tablets/syrup (82.1%). The urine sample (60%) was the least offered ANC service. The results of the adjusted logistic regression model revealed that age, place of residence, education level, the time of the first ANC visit, number of ANC visits, and family wealth index were significant factors associated with the quality of ANC services. Conclusions In terms of service provision, the quality of ANC service in Tanzania is suboptimal. With differences by observed risk factors, there must be more efforts to strengthen the quality of ANC services and to remove the differences.

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Prevalence and factors affecting the utilization of antenatal care in rural areas of Southwestern Ethiopia

BMC Pregnancy and Childbirth ◽

10.1186/s12884-021-04362-8 ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Assaye Belay ◽

Tessema Astatkie ◽

Solomon Abebaw ◽

Bekele Gebreamanule ◽

Wegayehu Enbeyle

Keyword(s):

Logistic Regression ◽

Antenatal Care ◽

Regression Model ◽

Rural Area ◽

Rural Areas ◽

Logistic Regression Model ◽

Binary Logistic Regression ◽

Binary Logistic Regression Model ◽

Factors Affecting ◽

Southwestern Ethiopia

Abstract Background Antenatal care (ANC) is a health care intervention intended to ensure the safety of pregnancy. According to the World Health Organization, at least four ANC visits are recommended for a healthy pregnancy. However, whether this recommended number of visits was followed or not in the rural areas of Southwestern Ethiopia is not known. Therefore, the study aimed to investigate the prevalence of, and the associated factors of ANC utilization by pregnant women in the rural areas of Southwestern Ethiopia. Methods A community-based cross-sectional study design was used in three rural zones. The data were collected from n = 978 women through a structured questionnaire with face-to-face interview. The collected data were analyzed using descriptive statistics and a multiple binary logistic regression model. Results The results showed that 56% of women made the recommended minimum number of ANC visits and the remaining 44% of them underutilized the ANC service. The multiple binary logistic regression model identified zone, marital status of the woman, educational level of the husband, occupation of the husband, knowledge of danger signs of pregnancy, birth interval, source of information, timely visits, and transportation problem to be statistically significant factors affecting the prevalence of ANC visit utilization of women. Bench Maji zone had smaller odds ratio of ANC visit prevalence as compared to Kaffa zone. Women who lived in the rural area of Sheko zone are 2.67 times less likely to utilize ANC visit than those who lived in the rural area of Kaffa zone keeping other variables constant. Conclusion The study results highlight the need to increase the number of ANC visits, and the importance of using an appropriate model to determine the important socio-demographic factors that ANC service providers shall focus on to improve the health of the unborn baby and the mother during pregnancy.

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Understanding Primary Ciliary Dyskinesia: Experience From a Mediterranean Diagnostic Reference Centre

Journal of Clinical Medicine ◽

10.3390/jcm9030810 ◽

2020 ◽

Vol 9 (3) ◽

pp. 810

Author(s):

Miguel Armengot-Carceller ◽

Ana Reula ◽

Manuel Mata-Roig ◽

Jordi Pérez-Panadés ◽

Lara Milian-Medina ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Regression Model ◽

Logistic Regression Analysis ◽

Situs Inversus ◽

Primary Ciliary Dyskinesia ◽

Logistic Regression Model ◽

Productive Cough ◽

Simple Logistic ◽

Ciliary Dyskinesia

Background: Due to the lack of a gold standard diagnostic test, reference centres with experienced personnel and costly procedures are needed for primary ciliary dyskinesia (PCD) diagnostics. Diagnostic flowcharts always start with clinical symptoms. Therefore, the aim of this work is to define differential clinical criteria so that only patients clinically compatible with PCD are referred to reference centres. Materials and methods: 18 variables from 476 Mediterranean patients with clinically suspicious PCD were collected. After analysing cilia function and ultrastructure, 89 individuals were diagnosed with PCD and 387 had a negative diagnosis. Simple logistic regression analysis, considering PCD as a dependent variable and the others as independent variables, was done. In order to define the variables that best explain PCD, a step-wise logistic regression model was defined. Aiming to classify individuals as PCD or PCD-like patients, based on variables included in the study, a classification and regression tree (CART) was designed. Results and conclusions: Simple logistic regression analysis shows statistically significant association between age at the beginning of their symptomatology, periodicity, fertility, situs inversus, recurrent otitis, atelectasis, bronchiectasis, chronic productive cough, rhinorrea, rhinusinusitis and recurrent pneumonias, and PCD. The step-wise logistic regression model selected situs inversus, atelectasis, rhinorrea, chronic productive cough, bronchiectasis, recurrent pneumonias, and otitis as PCD predictive variables (82% sensitivity, 88% specificity, and 0.92 Area Under the Curve (AUC)). A decision tree was designed in order to classify new individuals based on pansinusitis, situs inversus, periodicity, rhinorrea, bronchiectasis, and chronic wet cough.

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Factors determining child mortality in Uganda

International Journal of Social Economics ◽

10.1108/ijse-08-2015-0201 ◽

2017 ◽

Vol 44 (5) ◽

pp. 633-642 ◽

Cited By ~ 3

Author(s):

Will Kaberuka ◽

Alex Mugarura ◽

Javan Tindyebwa ◽

Debra S. Bishop

Keyword(s):

Health Care ◽

Logistic Regression ◽

Birth Weight ◽

Regression Model ◽

Marital Status ◽

Birth Order ◽

Child Mortality ◽

Logistic Regression Model ◽

Household Wealth ◽

Content Type

Purpose The purpose of this paper is to establish socio-economic factors and maternal practices that determine child mortality in Uganda. Design/methodology/approach The paper examines the role of sex, birth weight, birth order and duration of breastfeeding of a child; age, marital status and education of the mother; and household wealth in determining child mortality. The study employs a logistic regression model to establish which of the factors significantly impacts child mortality in Uganda. Findings The study established that education level, age and marital status of the mother as well as household wealth significantly impact child mortality. Also important are the sex, birth weight, birth order and breastfeeding duration. Research limitations/implications Policies aimed at promoting breastfeeding and education of female children can make a significant contribution to the reduction of child mortality in Uganda. Practical implications Health care intervention programs should focus on single, poor and uneducated mothers as their children are at great risk due to poor and inadequate health care utilization. Originality/value This paper could be the first effort in examining child mortality status in Uganda using a logistic regression model.

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Determinants of acute respiratory infection among under-five children in rural Ethiopia

BMC Infectious Diseases ◽

10.1186/s12879-021-06864-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Amanuel Mengistu Merera

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Regression Model ◽

Respiratory Infection ◽

Acute Respiratory Infection ◽

Logistic Regression Model ◽

P Value ◽

Under Five ◽

Under Five Children ◽

Rural Ethiopia

Abstract Introduction In low- and middle-income nations, acute respiratory infection (ARI) is the primary cause of morbidity and mortality. According to some studies, Ethiopia has a higher prevalence of childhood acute respiratory infection, ranging from 16 to 33.5%. The goal of this study was to determine the risk factors for acute respiratory infection in children under the age of five in rural Ethiopia. Methods A cross-sectional study involving 7911 children under the age of five from rural Ethiopia was carried out from January 18 to June 27, 2016. A two stage cluster sampling technique was used recruit study subjects and SPSS version 20 was used to extract and analyze data. A binary logistic regression model was used to identify factors associated with a childhood acute respiratory infection. The multivariable logistic regression analysis includes variables with a p-value less than 0.2 during the bivariate logistic regression analysis. Adjusted odds ratios were used as measures of effect with a 95% confidence interval (CI) and variables with a p-value less than 0.05 were considered as significantly associated with an acute respiratory infection. Results The total ARI prevalence rate among 7911 under-five children from rural Ethiopia was 7.8%, according to the findings of the study. The highest prevalence of ARI was found in Oromia (12.8%), followed by Tigray (12.7%), with the lowest frequency found in Benishangul Gumuz (2.4%). A multivariable logistic regression model revealed that child from Poor household (AOR = 2.170, 95% CI: 1.631–2.887), mother’s no education (AOR = 2.050,95% CI: 1.017–4.133), mother’s Primary education (AOR = 2.387, 95% CI:1.176–4.845), child had not received vitamin A (AOR = 1.926, 95% CI:1.578–2.351), child had no diarrhea (AOR = 0.257, 95% CI: 0.210–0.314), mothers not working (AOR = 0.773, 95% CI:0.630–0.948), not stunted (AOR = 0.663, 95% CI: 0.552–0.796), and not improved water source (AOR = 1.715, 95% CI: 1.395–2.109). Similarly, among under-five children, the age of the child, the month of data collection, anemia status, and the province were all substantially linked to ARI. Conclusions Childhood ARI morbidity is a serious health challenge in rural Ethiopia, according to this study, with demographic, socioeconomic, nutritional, health, and environmental factors all having a role. As a result, regional governments, healthcare staff, and concerned groups should place a priority on reducing ARI, and attempts to solve the issue should take these variables into account.

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Modeling and Analyzing the Factors Affecting the Mortality of Pedestrians in Urban Accidents

Mathematical Modelling in Civil Engineering ◽

10.2478/mmce-2018-0010 ◽

2018 ◽

Vol 14 (4) ◽

pp. 10-21

Author(s):

Ahmet Tortum ◽

Alireza Motamadnia

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Motor Vehicle ◽

Binary Logistic Regression ◽

Education Level ◽

Road Surface ◽

Binary Logistic Regression Model ◽

Factors Affecting ◽

Surface Conditions

Abstract The nature of urban and rural accidents has been different from each other in some of the factors and even the severity of damage rate, mayhem, and death. In this research, using statistical methods and binary logistic regression model, we have addressed to analyze important parameters such as age, gender, education level, the color of the pedestrian dress, season of accident, time of accident, the speed of the vehicle colliding with pedestrians and road surface conditions at the time of accident on the way of death (at the scene of the incident or in the hospital) pedestrians who have been traumatized. After the creation of the binary logistic regression model, it was determined that only the parameters of speed and the accident time have been significant in the level less than 5%. And other parameters such as age, gender, the season of accident occurrence, the color of the pedestrian dress, road surface conditions and education level had no significant effect in terms of statistical on the incidence of mortality arising from a pedestrian accident with the motor vehicle. The results revealed that by adopting decisions related to the traffic calming, attention to passages lighting and brightness the mortality rate of a pedestrian due to the urban accidents can be reduced.

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Effects of the Agri-environmental Program Implemented in Sultan Reeds Area of Kayseri, Turkey

International Journal of Scientific Research and Management ◽

10.18535/ijsrm/v7i12.ah01 ◽

2019 ◽

Vol 7 (12) ◽

Author(s):

Ismet Boz

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Agricultural Practices ◽

Farm Size ◽

Chi Square ◽

Factors Affecting ◽

Environmental Program ◽

Chi Square Test ◽

Sustainable Agricultural Practices

This study was initiated to evaluate the effects of agri-environment program implemented in the Sultan reeds area of Kayseri province, Turkey. The specific objectives of the study were to compare the farmers who enrolled in the program with those who didn’t enroll regarding their application of different sustainable agricultural practices, and to determine factors affecting their enrolment in the program. The main comparative indicators were selected from different sustainable agricultural practices either promoted by the agri-environmental program or not promoted but considered very useful for the locality. Two stratified samples of farmers (enrolled and not enrolled) were selected based on their farm size. Chi-square tests of independence were used to compare farmers on the selected sustainable agricultural practices. Logistic regression model was used to determine factors affecting the enrolment of the agri-environment program. The findings of the chi-square test showed that enrolled farmers use grow more forage legumes, are more conscious about pesticides use and chemical applications, and they use more pressurized irrigation systems. Findings of the logistic regression model sowed that using rental land negatively, but contacts with extension personnel, and using long term loans for farming investments positively influenced the enrolment of the agri-environment program. Governmental effort must concentrate on these issues when promoting agri-environmental programs in the region.

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Determinants of Acute Respiratory Infection among Under-Five Children in Rural Ethiopia

10.21203/rs.3.rs-811643/v1 ◽

2021 ◽

Author(s):

Amanuel Mengistu Merera

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Regression Model ◽

Respiratory Infection ◽

Acute Respiratory Infection ◽

Logistic Regression Model ◽

P Value ◽

Under Five ◽

Under Five Children ◽

Rural Ethiopia

Abstract Introduction: In low- and middle-income nations, acute respiratory infection (ARI) is the primary cause of morbidity and mortality. According to some studies, Ethiopia has a higher prevalence of childhood acute respiratory infection, ranging from 16 % to 33.5 %. The goal of this study was to determine the risk factors for acute respiratory infection in children under the age of five in rural Ethiopia. Methods: A cross-sectional study involving 7,911 children under the age of five from rural Ethiopia was carried out from January 18 to June 27, 2016. A two stage cluster sampling technique was used recruit study subjects and SPSS version 20 was used to extract and analyze data. A binary logistic regression model was used to identify factors associated with a childhood acute respiratory infection. The multivariable logistic regression analysis includes variables with a p-value less than 0.2 during the bivariate logistic regression analysis. Adjusted odds ratios were used as measures of effect with a 95% confidence interval (CI) and variables with a p-value less than 0.05 were considered as significantly associated with an acute respiratory infection. Results: The total ARI prevalence rate among 7,911 under-five children from rural Ethiopia was 7.8%, according to the findings of the study. The highest prevalence of ARI was found in Oromia (12.8 %), followed by Tigray (12.7 %), with the lowest frequency found in Benishangul Gumuz (2.4 %). A multivariable logistic regression model revealed that child from Poor household (AOR=2.170, 95% CI: 1.631-2.887), mother’s no education (AOR=2.050,95% CI: 1.017-4.133), mother’s Primary education (AOR=2.387, 95% CI:1.176-4.845), child had not received vitamin A (AOR=1.926, 95% CI:1.578-2.351), child had no diarrhea (AOR=0.257, 95% CI: 0.210-0.314), mothers not working (AOR=0.773, 95% CI:0.630-0.948), not stunted (AOR=0.663, 95% CI: 0.552-0.796), and not improved water source (AOR=1.715, 95% CI: 1.395-2.109). Similarly, among under-five children, the age of the child, the month of data collection, anemia status, and the province were all substantially linked to ARI. Conclusions: Childhood ARI morbidity is a serious health challenge in rural Ethiopia, according to this study, with demographic, socioeconomic, nutritional, health, and environmental factors all having a role. As a result, regional governments, healthcare staff, and concerned groups should place a priority on reducing ARI, and attempts to solve the issue should take these variables into account.

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The Factors Affecting Eye Patients (Cataract) In Jordan by Using the Logistic Regression Model

Modern Applied Science ◽

10.5539/mas.v11n8p38 ◽

2017 ◽

Vol 11 (8) ◽

pp. 38

Author(s):

Adeeb Ahmed Ali AL Rahamneh ◽

Omar M. Hawamdeh

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Likelihood Ratio Test ◽

Logistic Regression Model ◽

Ratio Test ◽

Response Variable ◽

Factors Affecting ◽

Independent Variables ◽

Lemeshow Test ◽

The Right

This study aims to use the logistic regression model to classify patients as infected and without cataracts. The independent variables were used to represent the gender, the age, the pressure in the right eye, the pressure in the left eye, HbA1C, and the anemia, representative variables for the study of Cataract disease affects the eyes, based on a random sample of (116) patients. The results proved that the used logistic regression model is an efficient and representative for data that shows through (Likelihood Ratio Test) and (Hosmer and Lemeshow test), and the study proved that the value of (R Square Nagelkerke=1) this means that 100% of the change in the occurred changes in the response variable explained through the Logistic regression model.

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Factors affecting neonatal mortality in the general population: evidence from the 2016 Ethiopian Demographic and Health Survey (EDHS)—multilevel analysis

BMC Research Notes ◽

10.1186/s13104-019-4668-3 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Haileab Fekadu Wolde ◽

Kedir Abdela Gonete ◽

Temesgen Yihunie Akalu ◽

Adhanom Gebreegziabher Baraki ◽

Ayenew Molla Lakew

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

General Population ◽

Antenatal Care ◽

Neonatal Mortality ◽

Multivariable Logistic Regression Analysis ◽

Factors Affecting ◽

Term Birth ◽

Female Birth ◽

Twin Birth

Abstract Objective This study was aimed to identify factors affecting neonatal mortality in Ethiopia. Results According to the multilevel multivariable logistic regression analysis, the odds of neonatal mortality was significantly associated with husbands with no education (AOR = 2.30, 95% CI 1.10, 4.83), female birth (AOR = 0.57, 95% CI 0.39, 0.83), twin birth (AOR = 13.62, 95% CI 7.14, 25.99), pre-term birth (AOR = 15.07, 95% CI 7.80, 29.12) and mothers with no antenatal care (ANC) visit during pregnancy (AOR = 1.90 95% CI 1.11, 3.25).

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