Modern contraceptive use and intention to use: implication for under-five mortality in Ethiopia

Modern contraceptive use and associated factors among reproductive-age women in Ethiopia: multilevel analysis evidence from 2019 Ethiopia mini demographic and health survey

BACKGROUND

Despite the high fertility and population growth rates, the use of modern contraceptives remains low in low- and middle-income countries. Different pocket-sized studies on the use of modern contraceptive methods conducted in various parts of Ethiopia have been extremely varied and ambiguous. Therefore, this study aimed to assess modern contraceptive use and its associated factors in women of reproductive age in Ethiopia.

METHODS

Cross-sectional data from the Ethiopia Interim Demographic Health Survey (EMDHS) 2019 in a stratified, two-stage, and cluster sampling study. Multilevel binary logistic regression analysis was used to fit the associated factors. The interclass correlation (ICC), median odds ratio (MOR), proportional change variance (PVC), and deviance were used for model comparison and fitness. The adjusted odds ratio (AOR) with 95% confidence interval (CI) was used to identify the significant factors of modern contraceptive use.

RESULT

The multilevel analysis demonstrated that Orthodox religion [AOR = 1.7; 95%CI: 1.4-2.10] protestant religion [AOR = 1.2; 95%CI: 0.93-1.62], married [AOR = 4.2; 95%CI: 1.93-9.07], primary education [AOR = 1.5; 95%CI: 1.26-1.76], secondary education [AOR = 1.36; 95%CI: 1.04-1.77 [AOR = 1.89; 95%CI: 1.37-2.61], middle [AOR = 1.4; 95%CI: 1.14-1.73], rich [AOR = 1.3; 95%CI: 1.06-2.68] were positively associated with modern contraceptive utilization, while the age group of 40-49 [AOR = 0.45, 95% CI: 0.34-0.58], and high community poverty [AOR = 0.62; 95%CI: 0.46-0.83] were negatively associated with modern contraceptive utilization.

CONCLUSION

The prevalence of modern contraception in Ethiopia remains low. Maternal age, religion, maternal education, marital status, wealth index, region, and community poverty were significant predictors of modern contraceptive utilization in Ethiopia. Governments and non-governmental organizations should expand their public health programs to poorer communities to increase the use of modern contraception in the country.

Influence of contraceptive use and other socio-demographic factors on under-five child mortality in Bangladesh: semi-parametric and parametric approaches

BACKGROUND

The under-five child mortality rate is a widely accepted indicator of the development of a country as well as reflects the country's health care system and quality of life. Although the child mortality rate is decreasing over time in Bangladesh, the rate is still high among South Asian countries. The target of the Sustainable Development Goal-3.2 is to reduce the under-five mortality rate in all countries of the world to 25 or fewer per 1000 live births by 2030. The purpose of this study is to identify the socio-demographic factors which have an influence on under-five child mortality in Bangladesh as well as to examine whether contraceptive use has any effect on under-five mortality in Bangladesh. Finally, a comparison has been made between the results obtained from the Cox proportional Hazard Model and Weibull model to find out which model is more efficient for the study data.

METHODS

For the study, data was extracted from Bangladesh Demographic Health Surveys 2017-2018 (BDHS 2017-2018). The Kaplan-Meier survival function has been used to demonstrate the survival probabilities of under-five children. While multivariate analyses of the Cox Proportional Hazard model and Weibull model are used to estimate the under-five mortality risks for various predictors.

RESULTS

The study results show consistently higher survival probabilities for children of mothers who used modern contraceptives during survival periods. Other significant predictors for under-five child mortality include mother's education level (higher education), mother's age (> 20), wealth index (rich), source of drinking water (tube well), and division (Chittagong, Khulna, Mymensingh). Weibull model has given more efficient results than the Cox Proportional Hazard model except for one covariate (water source).

CONCLUSION

Contraceptives use significantly improves the survival chances of children under-five age. This underscores the importance of contraceptive use in the pursuit of a sustainable reduction in under-five mortality in Bangladesh. It also intensifies the need to address the present level of contraceptive use in the country. This may not be due to the use of contraceptives in itself but may be due to the substantial biological and socioeconomic benefits that are concomitant with contraceptive use which may promote both maternal and child health. So, Extra effort should be given by the policymakers to ensure the use of modern contraceptive methods to improve the under-five survival in Bangladesh.

The impact of community-based health insurance on health service utilization and financial risk protection in Ethiopia

BACKGROUND

Evidence on the effectiveness of community-based health insurance (CBHI) in low-income countries is inconclusive. This study assessed the impact of CBHI on health service utilization and financial risk protection in Ethiopia.

METHODS

We conducted a comparative cross-sectional study nested within a larger national household survey in 2020. Data was collected from three groups of households-CBHI member households (n = 1586), non-member households from CBHI implementing woredas (n = 1863), and non-member households from non-CBHI implementing woredas (n = 789). Indicators of health service utilization, out-of-pocket health spending, catastrophic health expenditure, and impoverishment due to health spending among CBHI members were compared with non-members from CBHI implementing woredas and households from non-CBHI implementing woredas. Propensity score matching (PSM) was used to account for possible selection bias.

RESULTS

The annual number of OPD visits per capita among CBHI member households was 2.09, compared to 1.53 among non-member households from CBHI woredas and 1.75 among households from non-CBHI woredas. PSM estimates indicated that CBHI members had 0.36 (95% CI: 0.25, 0.44) and 0.17 (95% CI: -0.04, 0.19) more outpatient department (OPD) visits per capita per year than their matched non-member households from CBHI-implementing and non-CBHI implementing woredas, respectively. CBHI membership resulted in a 28-43% reduction in annual OOP payments as compared to non-member households. CBHI member households were significantly less likely to incur catastrophic health expenditures (measured as annual OOP payments of more than 10% of the household's total expenditure) compared to non-members (p < 0.01).

CONCLUSION

CBHI membership increases health service utilization and financial protection. CBHI proves to be an important strategy for promoting universal health coverage. Implementing CBHI in all woredas and increasing membership among households in woredas that are already implementing CBHI will further expand its benefits.

Assessment of intention to use contraceptive methods with spatial distributions and associated factors among women in Ethiopia: evidence from EDHS 2016

Background

Modern contraceptive methods have immense influences on the health of mothers and their children. Using contraceptive methods is seen to control family size and unnecessary pregnancies. Considering different factors like resources and various cultural aspects, assessing the intention to use contraception might bring areas with these problems into the light for intervention.

Methods

We analyzed the cross-sectional survey data from EDHS 2016, which comprised 5651 reproductive-age women. Spatial autocorrelation was checked with global Moran’s statistics, at ±1 for dispersion and clustering. Aselin Local moran’s statistics also indicated types of clusters. Hot spot(Getis-Ord Gi) statistics further used to measure autocorrelation over different spatial locations. The significance level was checked by calculating Z-score and hot and cold spots indicated the variation in intention to use contraceptives per catchments. Interpolation was also applied to see the number of intents to use contraceptive areas other than the sampled using ordinary Kriging spatial interpolation. We used Kulldorff’s SatScan for specific local clustering and the Bernoulli model test was applied to check significance. Individual and community-level factors were examined using multilevel logistic regression. Due to the clustering nature of data where p-value< 0.05 signaled associations. The disproportional nature of data was adjusted using sampling weights.

Result

From the total sample of women, the intention to use contraceptive methods was 2366.08(44.11%) and was highly clustered in North and Western Ethiopia. The mean number of children was (4.5 ± 2.90); age at first cohabitation was (16.9 ± 3.99); the ideal number of children was (4.77 ± 2.00). Age and the ideal number of children were negatively associated with the use of contraception. Primary education, number of children, counseling at health facilities, and age at first cohabitation were negatively associated.

Conclusion

We observed various distributions among regions. Educational status and various socio-cultural including working with the religious organization might need serious considerations to increase the intention to use contraceptive methods. Besides the efforts done, policy decisions might need to consider this finding and uphill the intervention against the negatively associated socio-cultural and demographic variables in outplayed areas.

Machine learning approach for predicting under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

There is a dearth of literature on the use of machine learning models to predict important under-five mortality risks in Ethiopia. In this study, we showed spatial variations of under-five mortality and used machine learning models to predict its important sociodemographic determinants in Ethiopia. The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used three machine learning models such as random forests, logistic regression, and K-nearest neighbors as well as one traditional logistic regression model to predict under-five mortality determinants. For each machine learning model, measures of model accuracy and receiver operating characteristic curves were used to evaluate the predictive power of each model. The descriptive results show that there are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be between 46.3 and 67.2% for the models considered, with the random forest model (67.2%) showing the best performance. The best predictive model shows that household size, time to the source of water, breastfeeding status, number of births in the preceding 5 years, sex of a child, birth intervals, antenatal care, birth order, type of water source, and mother’s body mass index play an important role in under-five mortality levels in Ethiopia. The random forest machine learning model produces a better predictive power for estimating under-five mortality risk factors and may help to improve policy decision-making in this regard. Childhood survival chances can be improved considerably by using these important factors to inform relevant policies.