Sleep Quality and Associated Factors among Pregnant Women Attending Antenatal Care Unit at the Referral Hospitals in Oromia National Regional State, Ethiopia, 2021: A Multicenter Cross-Sectional Study.

INTRODUCTION: Poor sleep quality during pregnancy leads to adverse neonatal outcomes such as low birth weight, intrauterine growth retardation, preterm birth, and operative birth. Though it has many consequences, a limited study was conducted on the prevalence and associated factors of poor sleep quality among pregnant women in Ethiopia. OBJECTIVE: This study is aimed to determine poor sleep quality and associated factors among pregnant women attending antenatal care units at the selected referral hospitals. METHODS: Institution-based cross-sectional study was used from April 20 to June 10, 2021. A The data were collected through systematic random sampling. The Pittsburgh Sleep Quality Index (PSQI) was used to assess the outcome variable based on interview. RESULTS: Out of 423, almost 414 participated in the study with a response rate of 97.9%. The prevalence of poor sleep quality was 54.6% (95% CI: 49.7%, 59%). Based on the trimester; about 44.8%, 36.8%, and 64.2% were observed poor sleep quality in the first, second, and third trimesters, respectively. Third trimester [AOR (Adjusted Odd Ratio) = 4.33; 95% CI (Confidence Interval) (1.43, 13.7)], primigravida [AOR = 4.03; 95% CI (2.59, 7.97)], para ≥2 [AOR = 1.95: 95% CI (1.09, 3.48)], depression [AOR = 4.59: 95% CI (2.31, 9.15)], and perceived stress [AOR = 1.15: 95% CI (1.1, 1.22)] were factors significantly associated. CONCLUSION: One in every two pregnant women has poor sleep quality. Depression, perceived stress, gestational age, gravida, and parity were identified as associated factors with poor sleep quality. Therefore, healthcare providers should work on screening and counseling for sleep problems during prenatal checkups.

Determinants of knowledge of the highest conception probability period among reproductive age women in Low-Income African countries: A multilevel analysis based on the recent Demographic and Health Survey.

BACKGROUND: Adequate knowledge about the highest conception probability period in the reproductive cycle allows individuals and couples to attain or avoid their fertility probabilities. Poor knowledge of conception probability period leads to undesirable outcomes like unwanted pregnancy, miscarriage, and abortion. Determinants of knowledge of highest conception probability period were not well studied on economically disadvantaged countries. Therefore, our study aimed to identify individual and community level factors of knowledge of the highest conception probability period among women of reproductive age in low income African countries. METHODS: The appended and latest Demographic and Health Survey datasets of 15 low-income African countries was used for analysis. Model fitness was done using the Intraclass correlation coefficient, median odds ratio, and deviance. A model with the lowest deviance (model-III) was selected as the best model. Multilevel logistic regression model was used to identify determinant factors of knowledge of the highest conception probability period. In the final model, adjusted odds ratio with 95% confidence interval was reported and variables with p<0.05 were considered as statistically significant with knowledge of the highest conception probability period. RESULTS: Total weighted sample of 235,574 reproductive aged women with a median age of 27 years were included. The correct knowledge of the highest conception probability period among the study participants was 24.04% (95%CI = 23.87-24.22%). Maternal age groups ((20-24 (AOR = 1.49; 95%CI = 1.44-1.55), 25-29 (AOR = 1.62; 1.56-1.68), 35-39 (AOR = 1.76; 1.68-1.84), 40-44 (AOR = 1.75; 1.67-1.83), and 45-49 (AOR = 1.83; 1.74-1.93)), marital status((currently in union (AOR = 1.75; 1.16; 1.13-1.20), formerly in union (AOR = 1.75; 1.11; 1.06-1.16)), better educational status ((secondary (AOR = 2.08; 2.01-2.14) and higher(AOR = 3.36; 3.18-3.55)), higher wealth index ((middle (AOR = 1.08; 1.04-1.12), richer (AOR = 1.24; 1.20-1.28), and richest (AOR = 1.51; 1.45-1.57)), knowledge of contraceptive methods (AOR = 2.63; 2.49-2.77), current contraceptive use (AOR = 1.14; 1.11-1.16), and urban residency (AOR = 1.26; 1.21-1.29) were statistically significant with knowledge of the highest conception probability period. CONCLUSION: In this study, knowledge of the highest conception probability period among women of reproductive age in low-income African countries was low. Therefore, improving the fertility awareness through comprehensive reproductive education or counseling could be one of the operational ways to control unintended pregnancy.

Geographical variation of common childhood illness and its associated factors among under-five children in Ethiopia: spatial and multilevel analysis.

Although substantial progress has been made in reducing child mortality over the last three decades, the magnitude of the problem remains immense. Ethiopia is one of the countries with a high under-five mortality rate due to childhood illnesses including acute respiratory infections, diarrhea, and fever that varies from place to place. It is vital to have evidence of the factors associated with childhood illnesses and the spatial distribution across the country to prioritize and design targeted interventions. Thus, this study aimed to investigate the spatial cluster distribution and associated factors with common childhood illnesses. Secondary data analysis based on the 2016 Ethiopian Demographic and Health Survey data was carried out. A total weighted sample of 10,417 children was included. The study used ArcGIS and SaTScan software to explore spatial distribution. For associated factors, a multilevel binary logistic regression model was fitted using STATA V.14 software. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤ 0.05 in the multivariable model were used to declare significant factors associated with the problem. ICC, MOR, PCV, and deviance (-2LLR) were used to check model fitness and model comparison. In this study, the prevalence of common childhood illnesses among under-five children was 22.5% (95% CI: 21.6-23.3%). The spatial analysis depicted that common childhood illnesses have significant spatial variation across Ethiopia. The SaTScan analysis identified significant primary clusters in Tigray and Northern Amhara regions (log-likelihood ratio (LLR) = 60.19, p < 0.001). In the multilevel analysis, being rural residence [AOR = 1.39, 95% CI (1.01-1.98)], small child size at birth [AOR = 1.36, 95% CI (1.21-1.55)], high community poverty [AOR = 1.26, 95% CI (1.06-1.52)], mothers aged 35-49 [AOR = 0.81, 95% CI (0.69-0.94)], the household had electricity [AOR = 0.77, 95% CI (0.61-0.98)], the household had a refrigerator [AOR = 0.60, 95% CI (0.42-0.87)], improved drinking water [AOR = 0.82, 95% CI (0.70-0.95)], improved toilet [AOR = 0.72, 95% CI (0.54-0.94)], average child size at birth [AOR = 0.83, 95% CI (0.75-0.94)] were significantly associated with common childhood illnesses. Common childhood illnesses had spatial variations across Ethiopia. Hotspot areas of the problem were found in the Tigray, Northern Amhara, and Northeast SNNPR. Both individual and community-level factors affected common childhood illnesses distribution and prevalence in Ethiopia. Therefore, public health intervention should target the hotspot areas of common childhood illnesses to reduce their incidence in the country.