Gender Disparities and Excess Risks of place of delivery for neonatal, postnatal and Child Mortality in Ethiopia: A comparative trend analysis

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However, literature reported in fewer countries showed gender disparities in child mortality. On average, boys are expected to have a higher probability of dying before reaching age 5 than girls. On the other hand, it was reported that a gender differential exists in the effect on child mortality with 35% excess girl child mortality. We investigated the trends of gender disparities and its excess risk effect on neonatal, postnatal and child mortality by place of delivery in Ethiopia. This study followed a time-series type of cross-sectional study. The study used the five nationally representative Ethiopian Demographic and Health Surveys data (EDHS 2000, 2005, 2011, 2016 and 2019). The Child and Birth recode data files were accessed as electronic version of STATA file format. The trends and mortality disparities of gender for neonates, post-neonates and children by place of delivery were presented with tables. The sex-age stratified mortality risk difference (RD) of place of delivery with 95% confidence level was calculated using the “ csi” STATA command. In addition, we estimated the Population Attributable Risk (PAR) and the Population Attributable Fraction (PAF) of place of delivery using the regpar and punaf STATA commands, respectively. Finally, multiple variable binomial regression analysis was conducted to identify the independent excess risk effect of child sex adjusted for mothers’ educational status, ANC visit, time of breastfeeding initiation and survey year by place of delivery. Among those who got birth at health facility, male child had 0.626 [Adj.RD = 0.000626, 95%CI:-0.000791, 0.00204], male post-neonate had 2.15 [Adj.RD = 0.00215, 95%CI: 0.0000228, 0.00428] and male neonate had 68.5 [Adj.RD = 0.0685, 95%CI: 0.0576, 0.0795] excess risk of mortality per 1000LB compared to female counterparts. Of those who got birth at home, the excess risk of male child, postnatal and neonatal mortalities were 0.445 [Adj.RD = 0.000445, 95%CI: -0.00179–0.00268], 6.62 [Adj.RD = 0.00662, 95%CI: 0.00398–0.00926] and 21.3 [Adj.RD = 0.0213, 95%CI: 0.0181, 0.0245] per 1000LB compared to females, respectively.The male neonates, post-neonates and children had excess risk of mortality compared to female neonates, post-neonates and children irrespective of place of delivery. The magnitude and significance of the excess risk of mortality of males had a declining trend as the age of new-borns increased from neonate to post-neonate and to child age irrespective of place of birth. A gender based care programmatic approaches ranging from time of foetus sex determination to infancy period and investigations of sex-chromosome linked risk factors with genetics study on new-borns are recommended. Health sciences/Health care/Public health Health sciences/Health care/Public health/Epidemiology Neonatal postnatal and child mortality excess risk trend place of delivery Introduction Males may have a biological disadvantage over females from birth. Both newborn mortality and congenital anomaly deaths are more common in males. Males may be more prone to infections due to a less active immune system and may be more vulnerable to diseases associated with sex chromosomes than females. Women's reproductive ability is linked to biological processes that could offer a defensive edge[ 1 ]. There were fewer countries with gender differences in child mortality, according to published research. Boys are often predicted to have a larger chance than girls of passing away before turning five [ 2 ]. However, a study that looked at the possibility of a gender difference in the impact on child mortality found that India had an excess of 35% of girl child mortality, with a cumulative death rate of 57.5 per 1000 live births [ 3 ]. Additionally, there were 18.5 excess female U5CMR per 1000 live births, translating to an estimated 239,000 additional deaths annually in India [ 4 ]. Gender inequality affects women considerably more severely in low- and middle-income countries (LMICs). The rates of neonatal, infant, and under-five mortality were strongly correlated with the gender inequality[ 5 ]. Improvement in gender equality significantly reduced U5MR[ 6 ]. In the first year of life, boys had a higher chance of dying than girls. In 2006, girls were more likely than boys to die between the ages of one and five, but by 2016, this gender disparity had been eliminated due to improvements in survival[ 7 ]. The sex differential in child mortality is high in India [ 8 ]. The gender differences in the U5MR and the NMR were relatively stable in China[ 9 ]. Another study from Burundi reported a considerable absolute and relative sex-based inequality both in 2010 and 2016 with higher concentration of neonatal mortality rate among male new-borns[ 10 ]. According to a sub-Sahara Africa decomposition analysis, in 16 nations, girls' U5MR was considerably lower than boys'. In Chad (2014–15), Côte d’Ivoire (2011–12), Ethiopia (2016), Gabon (2012), Malawi (2015–16), and Sierra Leone (2013), child sex was the sole primary contributor among the 12 countries where it accounted for more than 25% of the variability in U5MR[ 11 ]. In most LMICs, male children had a greater frequency of U5 mortality, according to a fairlie decomposition analysis[ 12 ]. In sub-Saharan Africa, a cross-national comparative investigation revealed that male child mortality was higher than that of female child mortality. Out of the thirty countries, nineteen have a major link. Males in the region are 17–54% more likely than females to pass away before turning five[ 13 ]. Various countries may have various gender disparities in under-5 mortality. While boys have comparatively lower under-5 death rates than girls in impoverished nations, newborn females naturally have an edge over newborn boys in terms of survival[ 14 ]. Additionally, estimations of child health inequalities revealed a persistent gender difference in IMR and U5MR. With 68 female newborn deaths for every 100 male infants, Jordan has the lowest infant mortality disadvantage for females. Every country under investigation demonstrated a female edge in both metrics, with the exception of Saudi Arabia (IMR 103) and Egypt (IMR 101 and U5MR 107)[ 15 ]. In 1996, girls had a higher U5MR (122.9 vs 113.3) and a higher RD (8.7) than boys had. However, in 2011, girls had a slightly higher U5MR (53.7 versus 53.1) and a lower RD (0.6)[ 16 ]. Boys' disproportionate chance of dying as newborns early was in line with biological assumptions. It was not shown that general gender preference or preferential care-seeking for males explained the excess risk of late neonatal death in girls[ 17 ]. Biological variations between boys and girls affect their chances of dying at different times during infancy and early childhood. Girls may be less likely than males to die in the end, since this is the expected outcome of their biology, but they may still be deprived of their entire biological advantage if they receive treatment that deprives them of part of their advantages[ 18 ]. Global surveillance agencies demand that gender be taken into account when analyzing child mortality data [ 19 ]. Girls have a significantly higher likelihood of dying in the age group 1–5 but a significantly lower probability of dying in the age group 0–1 in the disaggregated data compared to males [ 20 ]. In recent years, infant and under-5 male mortality has been over 50% greater than female mortality [ 21 ]. Males were shown to be at higher risk than females in every variable examined, with the exception of maternal age, according to an Indian study [ 22 ]. Only in the neonatal period does male mortality surpass that of female death. After then, female death rates rise faster than male death rates until the age of three, at which point they are between 46% and 53% higher than the corresponding male rate [ 23 ]. However, mortality seems to be higher for boys overall, with neonates being the most affected (+ 28%), followed by 1–12 months (+ 8%) and 1–4 years old (+ 4%)[ 24 ]. Despite many studies have been done on the gender differences in the excess risk of childhood mortality, most of them have estimated the ratio measures (Odds Ratio, Risk Ratio, and Hazard Ratio) using regression models [ 1 , 8 , 13 , 17 , 18 , 20 , 21 ]. Few used the World Health Organization’s (WHO’s) Health equity Assessment Tool (HEAT) [ 10 ] and the newly developed HDCalc software by the World Health Organization (WHO) [ 16 ] to estimate the absolute difference measures (Risk Difference, Population Attributable Risk, Exposure Attributable Fraction and Population Attributable Fraction and other measures of gender disparities like the between group variance (BGV) and the Theil index (T). Ratio measures are rarely understood by the general public. We therefore assumed that they would more readily comprehend the difference that may arise if all U5 children were born in a health facility for the purposes of this study, increased risk of home delivery for under-five mortality. Furthermore, the impact of preventing the exposure variable (home delivery) in preventing childhood mortality might be clearly presented by the difference measure. Research published two theoretical stances of biological advantage/disadvantage and environmental mechanisms as causes of the differences in male and female childhood mortality [ 13 ]. We attempted to examine the role of place of delivery over time, one of the critical environmental factors for under-five child mortality, in the current five consecutive years of EDHS data analysis. We stratified by age and sex of children, two of the most important epidemiological confounding-biological factors in order to have age-sex specific mortality rates by place of delivery. The main aim of this study was to analyse the disparities and excess risk of gender for neonatal, postnatal and child mortality by place of delivery in Ethiopia using the 2000 to 2019 Ethiopian demographic and health survey data. Methodology Data source and sample The five consecutive nationally representative Ethiopia Demographic and Health Surveys (EDHS) that were conducted between February and May 2000 (EDHS, 2000), from April 27 to August 30, 2005 (EDHS, 2005), from December 27, 2010 to June 3, 2011 (EDHS, 2011), from January 18, 2016 to June 27, 2016 (EDHS, 2016), and from March 21, 2019 to June 28, 2019 (EDHS, 2019) provided the data for this study[ 25 – 29 ]. With 14,642 sample houses covered by the 2000 EDHS, 14,072 were successfully interviewed, resulting in a response rate of 99.3%, and 15,716 eligible women were found, of whom 15,367 were successfully interviewed, with a response rate of 97.8% [ 25 ]. The EDHS conducted in 2005 included 14,645 sample houses; 13,721 of these were successfully interviewed, resulting in a 99% response rate; 14,717 eligible women were found; 14,070 of these were successfully interviewed, yielding a 96% response rate[ 26 ]. Out of the 17,817 sample houses included in the 2011 EDHS, 17,461 were successfully interviewed, resulting in a 98% response rate. Ninety-five percent of the 17,387 eligible women in the 15–49 age group who were interviewed were successful [ 27 ]. Within the 18,008 sample households covered by the 2016 EDHS, 16,650 were successfully interviewed, resulting in a 98% response rate. Additionally, 16,583 eligible women were found; 15,683 women underwent interviews, gave a 95% response rate [ 28 ]. The 2019 EDHS had 9,150 sample households; 8,663 of them were successfully interviewed, resulting in a 99% response rate; 9,012 eligible women were found; 8,885 of these women had interviews completed, producing a 99% response rate[ 29 ]. Five full-scale DHS surveys were conducted in 2000, 2005, 2011, 2016 and 2019. Five questionnaires were used for the surveys one of these was the Woman’s Questionnaire. The Woman’s Questionnaire was used to collect information from all eligible women age 15–49. It used to collect place of deliveries including age at death and date of child birth within five years preceding the date of interview of each survey year [ 25 – 29 ]. Data processing and analysis The current gender disparities and trend analysis of the excess risk of place of delivery for neonatal, postnatal and child mortality used primarily the Birth Recode data files (ETBR41.dta to ETBR81.dta). The Birth recode data files were accessed as electronic version of STATA file format and unwanted variables and observations were dropped. This was done for each of the five surveys in line with the objective of this analysis. In order to make the Birth Recode data appropriate for the objectives of this analysis, the DHS_U5_rates shared code of Chap. 08_CM (STATA do file) was obtained from dhsmeasure of gethub website[ 30 ] Annex1 . Subsequently, the shared code (STATA do file) was modified in order to keep the variables of interest of the analysis including place of delivery, child sex, educational status of mothers, survey year, ANC visits and time of initiation of breast feeding along with strata, cluster and weighting variables for each of the five consecutive surveys separately. In the DHS_U5_rates shared code of the section that can be used to extract the total risk as a denominator for the specified age interval, we made important modification in order to save the number (counts) of under-five child deaths and at risks for the respective eight age intervals along with the variables of interest. Following the modification, execution of the code for the respective surveys generated a new data set which was appropriate to extract the number of deaths and at risks of neonates, post-neonates and children by variables of interest for each surveys. Subsequently, a new STATA do file was created and applied for calculating number of deaths, number of at risks and mortality rates for those home and facility births with a STATA command of “collapse (sum)” by variables of the interest using the newly generated data. The five individual surveys data set then pooled to a single data set using a STATA command, appending data. The data were processed to have a cross-sectional time-series data format through sequential steps for the time series analysis. Three age groups which are mutually exclusive (0–28 days, 1–12 months, 1–4 years) with two sex (male and female) specific six stratum in total were used in calculating the age-sex stratified mortality rates by place of delivery as follows; Neonatal mortality for home and health facility born neonates by sex Post-neonatal mortality for home and health facility born post-neonates by sex Child mortality for home and health facility born child by sex were calculated for each surveys following generating new variables namely died_HOM, died_HF, risk_HOM and risk_HF to have deaths and risks for home and health facility births, respectively Annex2 . Subsequently, the absolute risk difference measures including the risk difference (RD) was calculated. In addition, we estimated the Population Attributable Risk (PAR) and the Population Attributable Fraction (PAF) of place of delivery using the regpar and punaf STATA commands respectively following the svy, subpop(if sex_child = = 0): glm died_child birth_place, family(binomial risk) link(logit) iter(50) estimation of mortality probabilities Annex4 . This was done considering place of delivery as exposure while sex and age as confounding variables. Finally, multiple variable binomial regression analysis was conducted by place of delivery to identify the independent excess risk effects or risk difference of child sex controlling the confounding effect of ANC visit, breastfeeding initiation time and mothers educational status for neonatal, post-neonatal and child mortality. The detailed steps used in the methods were documented in additional files Annex3 and 5 . Results This five consecutive surveys data analysis showed that 51.58% of the under-five children born at home within the 25 years preceding the 2019 survey were male. More than half (51.65%) of the under-five children born at health facility during the 25 years preceding the 2019 survey year were female. The trends in sex distribution of under- five children by place of birth were stable throughout the five consecutive surveys with sex ratio of 1.06 to 1 for children born both at home and at health facility (Table 1 ). Table 1 Trends and child sex disparities of under five-children by place of delivery, Ethiopia, 2024 Survey Year Home Delivery Health facility Delivery Total Male Female Male Female Male Female n % n % n % n % n % n % 2000 5,954 51.22 5,671 48.78 325 52.88 290 47.12 6,288 51.29 5,972 48.71 2005 5,372 51.16 5,130 48.84 309 52.39 281 47.61 5,723 51.27 5,440 48.73 2011 5,530 52.03 5,098 47.97 605 51.22 576 48.78 6,168 51.95 5,704 48.05 2016 4,174 52.19 3,823 47.81 1,462 51.12 1,398 48.88 5,725 51.94 5,298 48.06 2019 1,454 51.16 1,388 48.84 1,338 51.99 1,236 48.01 2,842 51.42 2,685 48.58 Total 22,485 51.58 21,109 48.42 4,039 51.65 3,780 48.35 26,746 51.59 25,099 48.41 Trends and disparities in risk difference of place of delivery for Child (age 1 to 4 year/s old) mortality stratified by sex Among the male children who got birth at home 18.42 per 1000LB died, but among those who got birth at health facility, 4.66 per 1000LB died in the five years preceding the 2016 survey. The difference between these rates, the risk difference (RD) among male children was 13.21[RD = 13.21, 95%CI: 6.31, 20.11] per 1000 live births in the five years preceding the 2016 survey (Table 2 ). The analysis showed that among female children who got birth at home, 20.9, 18.02 and 13.65 per 1000LB died, but among those who got birth at health facility, 3.93, 6.38 and 2.12 per 1000LB died in the five years preceding the 2011, 2016 and 2019 surveys, respectively. The difference between these rates, the risk difference(RD) among female children were 17.91 [RD = 17.91, 95%CI: 10.51, 25.31], 12.31 [RD = 12.31, 95%CI: 4.77, 19.86] and 10.95[RD = 10.95, 95%CI: 2.88, 19.02] per 1000 live births in the five years preceding the 2011, 2016 and 2019 surveys, respectively (Table 2 ). Table 2 Trends and disparities in risk difference (RD) of place of delivery for child (age 1–4 year/s old) mortality by sex, Ethiopia 2024. Male Survey Year Home Delivery facility Delivery Risk diff. (95%CI) chi2 P-value died At risk Rate died At risk Rate RD LB UB 2000 205 3690.32 55.45 5 201.60 23.94 30.8 -8 53.5 3.56 0.06 2005 86 3300.64 26.07 4 161.90 25.73 1.4 -20 25.9 0.01 0.92 2011 75 3555.13 21.17 3 363.95 8.683 12.9 -2 23.3 2.8 0.09 2016 54 2958.22 18.42 4 792.41 4.664 13.2* 6 20.1 7.17 0.01* 2019 10 1017.28 9.986 5 812.62 6.305 3.7 -4.4 11.8 0.75 0.39 Female Survey Year Home Delivery facility Delivery Risk diff. (95%CI) chi2 P-value died At risk Rate died At risk Rate RD LB UB 2000 159 3611.84 43.92 9 178.49 52.03 -7 -40 26.3 0.17 0.68 2005 119 3301.55 36.11 2 167.03 12.09 24.1 -6 41.8 2.74 0.1 2011 69 3323.46 20.9 1 350.72 3.931 17.9 11 25.3 5.43 0.02* 2016 47 2610.72 18.02 4 703.99 6.384 12.3 5 19.9 5.54 0.02* 2019 13 964.97 13.65 2 792.45 2.115 10.9 3 19 6.17 0.01* Notes: RD is risk difference, LB is lower boundary and UB is upper boundary of the risk difference *Significant at P-value < 0.05 (dup: abstract ?) Among the male children (age 1–4 year/s old) who got birth at home and health facility 12.97 per 1000LB died, but among those who got birth at health facility 4.66 per 1000LB died in the five years preceding the 2016 survey year. The difference between these rates, the population attributable risk (PAR) was 8.32 [PAR = 8.32, 95%CI: 1.88, 14.77] per 1000LB. The estimate for possibility of male child mortality prevention, the population attributable fraction (PAF) showed that 64.15% [PAF = 0.6415, 95%CI: 0.0585, 0.8635] of the mortality burden of male children in the five-years preceding the 2016 surveys would have been prevented if none of them were born at home (Table 3 ). Similarly, among female children who got birth at home and health facility 28.36 per 100LB and 15.85 per 1000LB died, but among those who got birth at health facility 5.98 per 1000LB and 3.93 per 1000LB died in the five years preceding the 2005 and 2011 surveys respectively. The difference between these mortality rates, the population attributable risks (PAR) were 22.38 [PAR = 22.38, 95%CI: 9.7635.01] per 1000LB and 11.92 [PAR = 11.92, 95%CI: 5.03, 18.82] per 1000LB in the five years preceding the 2005 and 2011 surveys, respectively. The estimate for possibility of female child mortality prevention, the population attributable fraction (PAF) showed that 75% (PAF = 0.75, 95%CI 0.2823, 0.9144] of the mortality burden of female children in the five-years preceding the 2011 survey would have been prevented if none of them were born at home (Table 3 ). Table 3 Trends and disparities in Population Attributable Risk (PAR) and Population Attributable Fraction (PAF) of place of delivery for child (age 1–4 year/s old) mortality by sex, Ethiopia 2024. Male Survey Year Both births (95%CI) HF births ( 95%CI) PAR (95%CI) / 1000LB PAF (95%CI) per 100LB CMR LB UB CMR LB UB PAR LB UB PAF LB UB 2000 43.47 35.19 53.61 23.72 9.54 57.77 19.75 -3.01 42.49 45.43 -36.14 78.13 2005 21.33 15.84 28.67 12.85 4.17 38.86 8.49 -6.23 23.2 39.78 -82.58 80.14 2011 16.51 11.97 22.72 8.61 1.34 53.28 7.9 -7.88 23.67 47.85 -220.75 91.52 2016 12.97 8.62 19.49 4.66 1.85 11.63 8.32* 1.88 14.77 64.15* 5.85 86.35 2019 6.84 3.74 12.5 6.28 1.97 19.83 0.56 -5.4 6.53 8.25 -137.68 64.58 Female Survey Year Both birth (95%CI) HF birth (95%CI) PAR (95%CI) / 1000LB PAF (95%CI) per 100LB CMR LB UB CMR LB UB PAR LB UB PAF LB UB 2000 35.93 28.69 44.91 50.75 23.1 108 -14.8 -53.1 23.46 -41.3 -200.48 33.6. 2005 28.36 21.52 37.3 5.98 1 34.71 22.38* 9.76 35.01 78.92 -24.36 96.4. 2011 15.85 10.98 22.83 3.93 1.42 10.80 11.92* 5.03 18.82 75.22* 28.23 91.44 2016 12.84 8.52 19.29 6.38 1.76 22.90 6.45 -2.17 15.08 50.28 -74.15 85.80 2019 7.00 3.55 13.76 2.11 0.48 9.22 4.89 -0.26 10.05 69.85 -35.55 93.30 Note: Both births are all children who got birth at home and health facility. *Significant at P-value < 0.05 Trends and disparities in risk difference (RD) of place of delivery for Postnatal (age 28 days to 1 year old) mortality stratified by sex Among the male post-neonates who got birth at home, 25.16 and 21.18 per 1000LB died, but that, among those who got health facility birth, 10.63 and 13.4 per 1000LB died in the five years preceding the 2016 and 2019 surveys, respectively. The differences between these rates, the risk differences (RDs) among male post-neonates were 10.83 [RD = 10.83, 95%CI: 3.59, 18.06] and 13.40 [RD = 13.4, 95%CI: 4.16, 22.64] per 1000LB in the 2016 and 2019 surveys, respectively. In addition, among the female post-neonates who got birth at home, 21.25 and 19.23 per 1000LB died, but that, among those who got birth at health facility 3.06 and 6.97 per 1000LB died in the five years preceding the 2011 and 2016 surveys, respectively. The differences between these rates, the risk differences (RD) were 17.52 [RD = 17.52, 95%CI: 10.83, 24.21] and 12.25 [RD = 12.25, 95%CI: 5.83, 18.67] per 1000LB in the five years preceding the 2011 and 2016 surveys, respectively (Table 4 ). Table 4 Trends and disparities in risk difference (RD) of place of delivery for post-neonatal (age 28 days − 1year old) mortality by sex, Ethiopia 2024. Male Survey Year Home Delivery Health facility Delivery Risk difference (95%CI) chi2 P-value died At risk Rate died At risk Rate RD LB UB 2000 268 5383.25 49.71 9 291.46 32.19 18.85 -1.87 39.57 2.11 0.15 2005 212 4862.38 43.62 10 265.96 38.07 6.01 -17.56 29.58 0.22 0.64 2011 117 5012.89 23.32 9 531.44 17.33 6.39 -5.36 18.14 0.88 0.35 2016 83 3849.44 21.56 14 1304.13 10.63 10.83 3.59 18.06 6.18 0.01* 2019 29 1345.90 21.18 10 1228.75 8.428 13.40 4.16 22.64 7.73 0.01* Female Survey Year Home Delivery Health facility Delivery Risk difference (95%CI) chi2 P-value died At risk Rate died At risk Rate RD LB UB 2000 228 5194.28 43.85 23 261.55 89.76 -44.23 -79.06 -9.39 11.07 0.00* 2005 143 4709.51 30.39 8 259.07 30.95 -0.52 -22.15 21.11 0.00 0.96 2011 100 4690.01 21.25 2 525.92 3.055 17.52 10.83 24.21 7.57 0.01* 2016 70 3623.32 19.23 9 1273.95 6.974 12.25 5.83 18.67 8.92 0.00* 2019 16 1301.66 12.12 12 1142.60 10.34 1.79 -6.62 10.20 0.17 0.68 Notes: RD is risk difference, LB is lower boundary and UB is upper boundary of the risk difference *Significant at P-value < 0.05 Among those male post-neonates who got birth at home and health facility 14.67 per 1000LB died, but among those who got birth at health facility 8.4 per 1000LB died in the five years preceding the 2019 survey year. The difference between these mortality rates, the population attributable risk (PAR) was 6.27 per 1000LB [PAR = 6.27, 95%CI: 0.03, 12.51]. Moreover, the estimate for possibility of male post-neonate mortality prevention, the population attributable fraction (PAF) showed that 42.76% [PAF = 0.4276, 95%CI: -15.23, 71.57] of the mortality burden of male post-neonate in the five-years preceding the 2019 survey would have been prevented if none of them were born at home. Whereas among those female post-neonate who got birth at home and health facility 18.64 and 15.55 per 1000LB died, but among those who got birth at health facility alone 3.05 and 6.94 per 1000LB died in the five years preceding 2011 and 2016 surveys, respectively. The difference in these mortality rates, the population attributable risk (PAR) were 15.59 [PAR = 15.59, 95%CI: 10.19, 20.99] and 8.62 [PAR = 8.62, 95%CI: 1.79, 15.45] per 1000LB in the five years preceding the 2011 and 2016 survey years, respectively. The possibility of female post-neonates mortality prevention, the population attributable fraction (PAF) showed that 83.65% [PAF = 0.8365, 95%CI: 0.56, 0.9392] in the five year preceding the 2011 survey and 55.41% [PAF = 0.5541, 95%CI: 0.0283, 0.7954] in the five year preceding the 2016 survey year of the mortality burden of female post-neonates would have been prevented if all post-neonates were born at health facility (Table 5 ). Table 5 Trends and disparities in Population Attributable Risk (PAR) and Population Attributable Fraction (PAF) of place of delivery for post neonatal (age 28 days-1years old) mortality by sex, Ethiopia 2024. Male Survey Year Both births (95%CI) HF births ( 95%CI) PAR (95%CI) / 1000LB PAF (95%CI) per 100LB PNMR LB UB PNMR LB UB PAR LB UB PAF LB UB 2000 45.97 39.46 53.48 31.66 14.33 68.50 14.31 -10.33 38.93 31.13 -49.29 68.23 2005 40.95 34.35 48.77 36.94 17.44 76.58 4.01 -22.56 30.57 9.79 -85.19 56.06 2011 21.65 16.84 27.80 17.10 7.85 36.86 4.55 -8.42 17.51 21.01 -67.31 62.71 2016 18.00 13.53 23.91 10.59 5.04 22.14 7.41 -0.59 15.41 41.16 -19.77 71.09 2019 14.67 10.04 21.39 8.40 3.73 18.81 6.27* 0.03 12.51 42.76 -15.23 71.57 Female Survey Year Both births (95%CI) HF births (95%CI) PAR (95%CI) / 1000LB PAF (95%CI) per 100LB PNMR LB UB PNMR LB UB PAR LB UB PAF LB UB 2000 43.34 35.94 52.18 83.17 35.58 182.39 -39.8 -106.2 26.92 -91.9 -322.87 12.92 2005 28.82 23.39 35.45 30.21 12.59 70.73 -1.39 -27.15 24.36 -4.84 -146.09 55.34 2011 18.64 14.49 23.95 3.05 1.15 8.04 15.59* 10.19 20.99 83.65* 56.00 93.92 2016 15.55 10.85 22.26 6.94 3.18 15.05 8.62* 1.79 15.45 55.41* 2.83 79.54 2019 10.86 5.72 20.49 10.28 3.76 27.78 0.58 -7.24 8.39 5.32 -102.46 55.72 Note: Both births are all children who got birth at home and health facility. *Significant at P-value < 0.05 Trends and disparities in risk difference (RD) of place of delivery for Neonatal (age < 28 days old) mortality stratified by sex The risk differences (RD) were negative for the five years preceding all except the 2000 survey which indicated that those who got birth at home had reduced risk of mortality compared to those who got birth at health facility for male neonates. The mortality risk differences of place of delivery among male neonates were − 38.51 [RD= -38.51, 95%CI: -69.88, -7.14] and − 31.86 [RD=-31.86, 95%CI: -53.45, -10.27] for the five years preceding the 2011 and 2016 surveys, respectively. Similarly, the risk differences were negative except for the five years preceding the 2000 and 2019 surveys which indicated that home delivery had reduced risk of mortality than health facility delivery for female neonates. Even though the risk differences for most survey years were negative, we found them not statistically significant since the 95% CI of the differences cross zero (Table 6 ). Table 6 Trends and disparities in risk difference of place of delivery for neonatal (age < 28 days old) mortality by sex, Ethiopia 2024. Male Survey Year Home Delivery Health facility Delivery Risk difference (95%CI) Chi2 P-value died At risk Rate died At risk Rate RD LB UB 2000 316 5844.07 53.99 16 315.52 51.49 3.27 -21.66 28.20 0.06 0.80 2005 230 5259.89 43.71 25 304.35 83.11 -38.51 -69.88 -7.14 9.75 0.00* 2011 226 5416.55 41.7 44 597.29 73.14 -31.86 -53.45 -10.27 12.75 0.00* 2016 150 4093.57 36.61 68 1428.90 47.72 -10.95 -23.39 1.50 3.35 0.07 2019 47 1424.48 32.72 55 1315.06 42.03 -8.84 -23.09 5.41 1.49 0.22 Female Survey Year Home Delivery Health facility Delivery Risk difference (95%CI) Chi2 P-value died At risk Rate died At risk Rate RD LB UB 2000 241 5571.71 43.22 7 282.07 25.86 18.43 -0.50 37.36 2.25 0.13 2005 160 5019.91 31.85 9 275.07 32.8 -0.85 -22.44 20.73 0.01 0.94 2011 143 4984.53 28.67 17 567.57 29.29 -1.24 -16.00 13.52 0.03 0.87 2016 60 3769.98 15.97 29 1353.68 21.74 -5.52 -14.21 3.17 1.78 0.18 2019 40 1371.87 29.2 30 1213.91 25.04 4.44 -8.03 16.91 0.48 0.49 Notes: RD is risk difference, LB is lower boundary and UB is upper boundary of the risk difference *Significant at P-value < 0.05 Among those male neonates who got birth at home and health facility 44.34 per 1000LB died, but among those who got birth at health facility 73.14 per 1000LB died in the five years preceding the 2011 survey. The difference between this mortality rate, the male neonate population attributable risk (PAR) was − 28.80 [PAR = -28.80, 95%CI: -55.79, -1.76] per 1000LB. Moreover, the estimate for possibility of male neonates mortality prevention, the male neonate population attributable fraction (PAF) showed that 64.94% [PAF= -0.6494, 95%CI: -1.3588, -0.1533] of the mortality burden of male neonates in the five-years preceding the 2011 survey had been reduced due to home delivery. The neonate population attributable risk (PAR) of place of delivery for both male and female neonatal mortality was negative for most surveys; however, it was found not statistically significant. Therefore, place of delivery has no significant excess risk of neonatal mortality if sex of the neonates is controlled through stratification (Table 7 ). Table 7 Trends and disparities in Population Attributable Risk (PAR) and Population Attributable Fraction (PAF) of place of delivery for neonatal (age less than 28 days) mortality by sex, Ethiopia 2024. Male Survey Year Both births (95%CI) HF birth ( 95%CI) PAR (95%CI) per 1000LB PAF (95%CI) per 100LB NMR LB UB NMR LB UB PAR LB UB PAF LB UB 2000 53.38 45.25 62.86 51.49 25.57 100.94 1.89 -32.42 36.19 3.54 -87.87 50.47 2005 45.36 37.90 54.20 83.11 48.23 139.50 -37.75 -80.53 5.18 -83.22* -206.81 -9.41 2011 44.34 36.83 53.31 73.14 48.12 109.67 -28.80 * -55.79 -1.76 -64.94* -135.88 -15.33 2016 39.13 31.83 48.03 47.72 33.57 67.42 -8.59 -23.08 5.91 -21.96 -65.56 10.16 2019 36.77 26.49 50.85 42.03 27.03 64.81 -5.26 -15.52 5.01 -14.29 -44.22 9.42 Female Survey Year Both births (95%CI) HF birth (95%CI) PAR (95%CI) per 1000LB PAF (95%CI) per 100LB NMR LB UB NMR LB UB PAR LB UB PAF LB UB 2000 41.97 35.01 50.25 25.86 9.23 70.33 16.12 -10.19 42.40 38.39 -68.85 77.52 2005 31.53 25.20 39.39 31.80 12.25 80.00 -0.27 -28.54 28.01 -0.85 -145.39 58.55 2011 28.40 22.75 35.41 29.29 14.78 57.21 -0.89 -19.65 17.87 -3.12 -95.67 45.65 2016 17.30 12.49 23.93 21.74 12.21 38.42 -4.43 -14.18 5.31 -25.63 -94.16 18.72 2019 26.96 19.50 37.16 25.04 14.57 42.71 1.91 -7.07 10.90 7.10 -33.62 35.41 Note: Both births are all children who got birth at home and health facility. *Significant at P-value < 0.05 Multiple variable binomial regression analysis The risk difference or excess risk effects of child-sex, mothers’ educational status, ANC visit, time of breastfeeding initiation and the EDHS year on child mortality by place of delivery was analysed using a binomial regression model. A separate multiple variable binomial regression analysis by place of delivery for neonatal, post-neonatal and child excess risks of mortality were conducted following imputation for missed values of ANC visit and time of breastfeeding initiation (look method description in the additional file). Child (1 year to 4 years old) mortality Male children had a 62.6 [ARD = 0.000626, 95%CI: -0.000791, 0.00204] and 44.5 [ARD = 0.000445, 95%CI: -0.00179–0.00268] per 100,000LB excess risk of mortality compared to female children for those who got birth at health facility and home, respectively, however, it is not statistically significant. Mothers’ educational status of at least read and write had averted 5.49 [ARD = -0.00549, 95%CI: -0.00818, -0.00281] and 6.02 [ARD = -0.00602, 95%CI: -0.00857, -0.00348] per 1000LB excess risks of child mortality compared to illiterate mothers’ educational status for children who got birth at health facility and home, respectively (Table 8 ). In addition, at least one ANC visit/s of mothers had prevented 7.88 [ARD = -0.00788, 95%CI: -0.0119, -0.00384] and 9.98 [ARD = -0.00998, 95%CI: -0.0124, -0.00758] per 1000LB child mortality risk compared to mothers who had no ANC visit for children who got birth at health facility and home, respectively. Moreover, within one hour initiation of breastfeeding following delivery had averted 9.53 [ARD = -0.000953, 95%CI: -0.00242–0.000518] and 18.3 [ARD = -0.00183, 95%CI: -0.00433, 0.000659] per 10,000LB child mortality risk compared to after an hour initiation of breastfeeding for children who got birth at health facility and home, respectively. However, it had no statistically significant mortality reduction (Table 8 ). Among children who got birth at health facility, 36.2 [ARD = -0.0362, 95%CI: -0.0491, -0.0234], 42.4 [ARD = -0.0424, 95%CI: -0.0539, -0.0308], 37 [ARD = -0.037, 95%CI: -0.0490, -0.0250] and 42.6 [ARD = -0.0426, 95%CI: -0.0541, -0.0310] per 1000LB were the averted risk of child mortality during the five years preceding the 2005, 2011, 2016 and 2019 EDHS years compared to the 2000 survey, respectively. Similarly, among children who got home birth, 21.7 [ARD = -0.0217, 95%CI: -0.0257, -0.0176], 31.1 [ARD = -0.0311, 95%CI: -0.035, -0.0273], 37.7 [ARD = -0.0377, 95%CI: -0.0416, -0.0338] and 39.3 [ARD = -0.0393, 95%CI: -0.0436, -0.0351] per 1000LB reduced child mortality risk during the five years preceding the 2005, 2011, 2016 and 2019 surveys were observed compared to the 2000 survey, respectively (Table 8 ). Table 8 multiple variable binomial regression analysis result for the independent effects of child-sex, mothers’ educational status, ANC visit and time of breastfeeding initiation and the EDHS year on mortality risk by place of delivery among children aged 1–4 year/s old, Ethiopia, 2024. VARIABLES category Health facility delivery Home delivery Adj. Risk. Diff 95% CI Adj. Risk. Diff 95%CI Child sex Female 0 - 0 - Male 0.000626 -0.000791–0.00204 0.000445 -0.00179–0.00268 Mother’s educational status illiterate 0 0 Primary & above -0.00549*** -0.00818 - -0.00281 -0.00602*** -0.00857 - -0.00348 At least one Anc visit No 0 0 yes -0.00788*** -0.0119 - -0.00384 -0.00998*** -0.0124 - -0.00758 time of Breastfeeding initiation ≤ 1 hour 0 0 > 1 hour -0.000953 -0.00242–0.000518 -0.00183 -0.00433–0.000659 Survey year 2000 0 0 2005 -0.0362*** -0.0491 - -0.0234 -0.0217*** -0.0257 - -0.0176 2011 -0.0424*** -0.0539 - -0.0308 -0.0311*** -0.0350 - -0.0273 2016 -0.0370*** -0.0490 - -0.0250 -0.0377*** -0.0416 - -0.0338 2019 -0.0426*** -0.0541 - -0.0310 -0.0393*** -0.0436 - -0.0351 Constant 0.0575*** 0.0450–0.0700 0.0632*** 0.0596–0.0668 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Post-neonate (aged 28 days − 1year old) Male post-neonates had 2.15 [ARD = 0.00215, 95%CI: 0.0000228, 0.00428] and 6.62 [ARD = 0.00662, 95%CI: 0.00398–0.00926] per 1000LB excess risk of mortality compared to females for facility and home births, respectively. Primary and above educational status of mothers had prevented 13.5 [ARD = -0.0135, 95%CI: -0.0181, -0.00881] and 2.66 [ARD = -0.00266, 95%CI: -0.00597, 0.000651] per 1000LB excess risk of post-neonatal mortality compared to illiterate mothers’ educational status for facility and home birth, respectively, but it was not statistically significant for those who got birth at home. At least one ANC visit/s of the mothers had averted 8 [ARD = -0.0080, 95%CI: -0.0152, -0.000811] and 13.1 [ARD = -0.0131, 95%CI: -0.0159, -0.0103] per 1000LB post-neonatal mortality risk compared to no ANC visit of the mothers for those post-neonates who got birth at health facility and home, respectively. Moreover, within one hour initiation of breastfeeding following delivery had 2.25 [ARD = 0.00225, 95%CI: -0.000585–0.00509] and 4.71 [ARD = 0.00471, 95%CI: 0.00162, 0.00781] per 1,000LB excess risk of post-neonatal mortality compared to after an hour initiation of breastfeeding for health facility and home births, respectively. However, it had no statistically significant excess risk of mortality for facility births, but initiation of breastfeeding within an hour had statistically significant excess risk compared to initiation after an hour for home births (Table 9 ). Among post-neonates who got birth at health facility, there were 38.8 [ARD = -0.0388, 95%CI: -0.0599, -0.0177], 62.1 [ARD = -0.0641, 95%CI: -0.083, -0.0452], 56.1 [ARD = -0.0561, 95%CI: -0.0753, -0.0368] and 64 per 1000LB [ARD = -0.064, 95%CI: -0.0829, -0.045] reduced risks of post-neonatal mortality during the five years preceding the 2005, 2011, 2016 and 2019 EDHS years compared to the 2000 survey, respectively. Similarly, among those who got birth at home, 21.7 [ARD = -0.0217, 95%CI: -0.0257, -0.0176], 31.1 [ARD = -0.0311, 95%CI: -0.035, -0.0273], 37.7 [ARD = -0.0377, 95%CI: -0.0416, -0.0338] and 39.3 per 1000LB [ARD = -0.0393, 95%CI: -0.0436, -0.0351] post-neonatal mortality risks were reduced during the five years preceding the 2005, 2011, 2016 and 2019 surveys compared to the 2000 survey’s mortality risk, respectively (Table 9 ). Table 9 multiple variable binomial regression analysis result for the independent effects of child-sex, mothers’ educational status, ANC visit ,time of breastfeeding initiation and the EDHS year on PNM risk by place of delivery among post-neonates age 28 days to 1 year old, Ethiopia, 2024. VARIABLES category Health facility delivery Home delivery Adj. Risk. Diff 95% CI Adj. Risk. Diff 95%CI Child sex Female 0 0 Male 0.00215** 2.28e-05–0.00428 0.00662*** 0.00398–0.00926 Mother’s educational status illiterate 0 0 Primary & above -0.0135*** -0.0181 - -0.00881 -0.00266 -0.00597–0.000651 At least one ANC visit No 0 0 yes -0.00800** -0.0152 - -0.000811 -0.0131*** -0.0159 - -0.0103 time of Breastfeeding initiation ≤ 1 hour 0 0 > 1 hour 0.00225 -0.000585–0.00509 0.00471*** 0.00162–0.00781 EDHS year 2000 0 0 2005 -0.0388*** -0.0599 - -0.0177 -0.00780*** -0.0120 - -0.00364 2011 -0.0641*** -0.0830 - -0.0452 -0.0248*** -0.0288 - -0.0208 2016 -0.0561*** -0.0753 - -0.0368 -0.0303*** -0.0346 - -0.0260 2019 -0.0640*** -0.0829 - -0.0450 -0.0340*** -0.0390 - -0.0290 Constant 0.0870*** 0.0665–0.107 0.0608*** 0.0573–0.0643 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Neonates (aged less than 28 days old) Male neonates had 68.5 [ARD = 0.0685, 95%CI: 0.0576, 0.0795] and 21.3 [ARD = 0.0213, 95%CI: 0.0181–0.0245] excess risk of mortality per 1000 LB compared to female neonates who got birth at health facility and home, respectively. Primary and above educational status of mothers had prevented 12 [ARD = -0.012, 95%CI: -0.0231, -0.000963] and 6.83 [ARD = -0.00266, 95%CI: -0.00464, 0.00327] per 1000LB excess risk of neonatal mortality compared to illiterate educational status of mothers for neonates who got birth at health facility and home, respectively. However, at least read and write educational status of the mothers had no a statistically significant risk reduction of neonatal mortality for those neonates who got birth at home. In addition, at least one ANC visit/s of mothers had averted 32.1 [ARD = -0.0321, 95%CI: -0.0472, -0.0171] and 14.6 [ARD = -0.0146, 95%CI: -0.0181, -0.0111] per 1000LB risk of neonatal mortality compared to no at least one ANC visit of mothers for health facility and home birth places, respectively. Moreover, within one hour breastfeeding initiation following delivery had 11.5 [ARD = 0.0115, 95%CI: -0.00157–0.0246] and 5.74 [ARD = 0.00574, 95%CI: 0.00218, 0.0093] per 1000LB excess risk of neonatal mortality compared to after an hour initiation of breastfeeding for health facility and home births, respectively. However, breastfeeding initiation time had no statistically significant association with risk of mortality for facility births, but it had statistically significant excess risk for home births (Table 10 ). Among neonates who got birth at health facility, there were 53.3 [ARD = 0.0533, 95%CI: 0.0311, 0.0756], 60.7 [ARD = 0.0607, 95%CI: 0.0413, 0.0801], 32.6 [ARD = 0.0326, 95%CI: 0.0167, 0.0484] and 13.9 [ARD = 0.0139, 95%CI: -0.0006, 0.0284] per 1000LB excess risks of neonatal mortality during the five years preceding the 2005, 2011, 2016 and 2019 surveys compared to the 2000 survey, respectively. On the contrary, among neonates who got birth at home, 27.8 [ARD = -0.0278, 95%CI: -0.0328, -0.0229], 26.9 [ARD = -0.0269, 95%CI: -0.0319, -0.0219], 45.8 [ARD = -0.0458, 95%CI: -0.0509, -0.0409] and 37 [ARD = -0.037, 95%CI: -0.0437, -0.0304] per 1000LB neonatal mortality risks were averted during the five years preceding the 2005, 2011, 2016 and 2019 surveys compared to the 2000 survey risk of neonatal mortality, respectively (Table 10 ). Table 10 multiple variable binomial regression analysis result for the independent effects of child-sex, mothers’ educational status, ANC visit, time of breastfeeding initiation and the EDHS year on neonatal mortality by place of delivery among neonates age < 28 day VARIABLES category Health facility delivery Home delivery Adj. Risk. Diff 95% CI Adj. Risk. Diff 95%CI Child sex Female 0 0 Male 0.0685*** 0.0576–0.0795 0.0213*** 0.0181–0.0245 Mother’s educational status illiterate 0 0 Primary & above -0.0120** -0.0231 - -0.000963 -0.000683 -0.00464–0.00327 At least one ANC visit No 0 0 yes -0.0321*** -0.0472 - -0.0171 -0.0146*** -0.0181 - -0.0111 Breastfeeding initiation time ≤ 1 hour 0 0 > 1 hour 0.0115* -0.00157–0.0246 0.00574*** 0.00218–0.00930 EDHS year 2000 0 0 2005 0.0533*** 0.0311–0.0756 -0.0278*** -0.0328 - -0.0229 2011 0.0607*** 0.0413–0.0801 -0.0269*** -0.0319 - -0.0219 2016 0.0326*** 0.0167–0.0484 -0.0458*** -0.0509 - -0.0408 2019 0.0139* -0.000600–0.0284 -0.0370*** -0.0437 - -0.0304 Constant 0.0704*** 0.0525–0.0883 0.0886*** 0.0843–0.0929 Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Discussion The analysis on the disparities of risk difference of place of delivery for child, postnatal and neonatal mortality stratified by sex showed that there is no any variation in the frequency distribution of child-sex by place of delivery. However, the male children (age 1–4 year/s old) had higher risk of death than female children for the 2000, 2011 and 2016 consecutive surveys irrespective of place of delivery. The male and female mortality rates were 43.47 versus 35.93, 16.51 versus 15.85, and 12.97 versus 12.84 per 1000 LBs in the five years preceding the 2000, 2011 and 2016 surveys, respectively (Table 3 ). The male post-neonates had higher mortality risk compared to female post-neonates during the five years preceding each consecutive survey irrespective of place of delivery. The male and female mortality rates were 45.97 versus 43.34, 40.95 versus 28.82, 21.65 versus 18.64, 18 versus 15.55 and 14.67 versus 10.86 per 1000LB during the five years preceding the 2000, 2005, 2011, 2016 and 2019 surveys respectively (Table 5 ). Similarly, the male neonates had higher risk of mortality compared to female neonates throughout the five consecutive surveys irrespective of place of delivery. The male and female neonatal mortality rates were 53.38 versus 41.97, 45.36 versus 31.53, 44.34 versus 28.4, 39.13 versus 17.3 and 36.77 versus 26.96 per 1000LB in the five years preceding the 2000, 2005, 2011, 2016 and 2019 surveys respectively (Table 7 ). In line with this finding studies reported that males may be biologically disadvantaged compared to females starting at birth [ 6 ]. The evidence from global report and some studies stated that fewer countries showed gender disparities, some said boys are at high risk of mortality while others reported excess risk of female child mortality [ 2 – 4 ]. A study from Nigeria reported that higher risk among males than females in all the variables under study with the exception of maternal age groups, depicts the risk of infant mortality is higher among female infants than males[ 22 ]. Another study reported that on average, boys are expected to have a higher probability of dying before reaching age 5 than girls [ 2 ]. In Sub-Saharan African countries males have 17–54% higher odds of dying before age five[ 13 ]. Furthermore, a prediction study from Ghana reported that sex-differentiated U5CMR had projected to reach 33.9 per 1000LBs for males, but it was projected to 26.64 per 1000LBs for females[ 19 ] indicating the presence of excess risk of male U5CMR compared to female U5CMR would persist in Ghana. However, it contradicts with studies that reported the female mortality rate was higher than the male mortality rates. A study conducted to test the hypothesis that a gender differential exists in the effect on child mortality reported that 35% excess girl child mortality in India [ 3 ]. In addition, the excess female U5CMR was 18·5 per 1000 live births, which corresponds to an estimated 239, 000 excess deaths per year in India and more than 90% of districts had excess female mortality [ 4 ]. The possible justification for the contradiction might be the biological factors of mortality which has a clearly distinct excess risk for male mortality during infancy, then after the strong effect of the sex linked biological factors alleviated during childhood (1–4 years old) [ 13 ]. In addition, a scoping review reported that “given the biological disadvantage of male children, usually, gender bias is suspected when the mortality of girls is higher than expected, which may be due to the cultural favouritism of male children resulting in neglect of girls”[ 31 ] which in turn elevate mortality of female children. The review also discussed that although such culturally-driven ‘son preference’ is likely the main reason for higher-than-expected mortality of girls, infanticide may also affect sex ratios, although it seems to be restricted to a few societies” [ 31 ]. Moreover, the review added that the frequency of infanticide is extremely hard to measure. Whatever the reason, bias against girls is likely to affect the sex ratios, leading to either similar mortality rates for both sexes or higher mortality for girls than for boys[ 31 ]. A multiple variable binary logistic regression analyses: incorporating child-sex, mothers’ educational status, at least one ANC visit, initiation of breastfeeding within one hour and survey year was conducted in order to estimate the independent excess risk effect of child-sex adjusting for included variables by place of delivery. As a result, among those who got birth at health facility, the male children had 6 per 10,000LB excess deaths than the female children; however, the difference in the risk of mortality was not statistically significant. On the other hand the male post-neonates had 2.15 excess risk of death per 1000LB than the female post-neonates. Similarly, the male neonates had 68.5 excess risk of death per 1000LB compared to female neonates.. Among those who got birth at home, the male children had 4.45 excess risk of death per 10000LB compared to female children, but it was not statistically significant. The male post-neonates however had 6.62 excess risk of mortality per 1000LB compared to female post-neonates. In addition, the male neonates had 21.3 excess mortality risks per1000LB compared to female neonates. The mortality differences between males and females were statistically significant for both post-neonates and neonates irrespective of place of delivery. Controlling the confounding effects of mothers’ educational status, ANC visit, time of breastfeeding initiation and survey year, the findings had implications, primarily; males had excess risk of mortality compared to females irrespective of place of birth. Secondly, the magnitude of the excess risk of mortality of males had a declining trend as the new-borns’ age increased from neonatal to post-neonate and to child age irrespective of place of birth. It was 68.5, 2.15 and 0.6 per 1000LB for those who got facility birth and 21.3, 6.62 and 4.45 per 1000LB for those who got home birth, respectively. The other implication of the finding was the presence of observed great variation in the magnitude of the excess risk of mortality between health facility and home births. The excess risk of male post-neonatal mortality among health facility births was low (2.15 versus 6.62 per 1000LB) compared to the excess risk of male post-neonatal mortality of home births. On the contrary, the excess risk of male neonatal mortality among those who got birth at health facility was higher (68.5 verses 21.3 per 1000LB) than the excess risk of male neonatal mortality of those who got birth at home. The findings might indicate that male under five children are biologically more at risk of mortality at birth than female under five children [ 2 , 6 , 13 , 19 , 22 ]. The higher the magnitude of the male neonatal excess mortality risk among those who got birth at heath facility than at home might indicate the number of complicated births attended at health facility probably higher the number attended at home for male than female new-borns. This in turn might affect the subsequent survival status of live born neonates and the variation in the magnitude of the male excess mortality risk between facility and home births. This finding is in line with a study about neonatal outcomes in relation to sex differences that reported preterm birth, macrosomia, neonatal death, Caesarean Section (SC), and congenital anomaly were more common in males than females[ 32 ]. Conclusion In this comparative trend analysis of the gender disparity in the excess risk of neonatal, post-neonatal and child mortality by place of delivery: the frequency distribution of male and female new-borns was found constant throughout the five consecutive surveys irrespective of place of delivery with sex-ratio of 1.06 to 1. In the sex and age stratified comparative trend analysis: the male had excess risk of mortality compared to female neonates, post-neonates and children irrespective of place of delivery; giving birth at home had excess risk of post-neonatal and child mortality compared to giving birth at health facility: but giving birth at health facility had excess risk of neonatal mortality compared to giving birth at home in the five years preceding the consecutive surveys, except the 2000 survey. In the multiple variable binomial regression analysis: the magnitude of the excess risk of mortality of males had a declining trend as the age of new-borns increased from neonatal to post-neonatal and to child age irrespective of place of birth. The excess risk of male post-neonatal mortality among health facility births was low compared to the excess risk of male post-neonatal mortality of home births. On the contrary, the excess risk of male neonatal mortality among those who got birth at health facility was higher than the excess risk of male neonatal mortality of those who got birth at home. In general adjusting for mothers’ educational status, ANC visit, initiation time of breastfeeding and survey year, the male neonates, post-neonates and children had excess risk of mortality compared to the female neonates, post-neonates and children, respectively. A gender based care programmatic approaches ranging from time of foetal sex determination to infancy period and investigations of sex-chromosome linked risk factors with genetics study on new-borns are needed to be conducted. Declarations Ethics approval and consent to participate The study is based on the data available in the public domain to use; therefore, no ethics statement is required for this work. Consent for publication Consent for publication is not applicable- this study did not take individual person’s detail such as name, images, or videos. Availability of data and material All the data generated or analyzed during this study are included in this published article as additional file and can be accessed through request of the corresponding Author. Competing interests The authors declare that we have no competing interests. Funding There was no external funding for this paper. Authors' contributions G.H, conceptualization, methodology, formal analysis, and writing-original draft. G.A, and M.A edit and write the manuscript. Acknowledgements The authors would like to thank the measure DHS for allowing us to freely access the five consecutive years Ethiopian demographic and health survey data. References Ahmed FA: Gender difference in child mortality . Egypt Popul Fam Plann Rev 1990, 24 (2):60-79. Fund UNCs: Levels & Trends in child Mortality: Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation . In . ; 2019. Krishnan A, Srivastava R, Dwivedi P, Ng N, Byass P, Pandav CS: Non-specific sex-differential effect of DTP vaccination may partially explain the excess girl child mortality in Ballabgarh, India . Trop Med Int Health 2013, 18 (11):1329-1337. Guilmoto CZ, Saikia N, Tamrakar V, Bora JK: Excess under-5 female mortality across India: a spatial analysis using 2011 census data . Lancet Glob Health 2018, 6 (6):e650-e658. Brinda EM, Rajkumar AP, Enemark U: Association between gender inequality index and child mortality rates: a cross-national study of 138 countries . BMC Public Health 2015, 15 :1-6. Bagade T, Chojenta C, Harris M, Oldmeadow C, Loxton D: A women’s rights-based approach to reducing child mortality: data from 193 countries show that gender equality does affect under-five child mortality . Maternal and child health journal 2022, 26 (6):1292-1304 %@ 1092-7875. Alderman H, Nguyen PH, Tran LM, Menon P: Trends and geographic variability in gender inequalities in child mortality and stunting in India, 2006–2016 . Maternal & Child Nutrition 2021, 17 (3):e13179 %@ 11740-18695. Kuntla S, Goli S, Jain K: Explaining sex differentials in child mortality in India: trends and determinants . International Journal of Population Research 2014, 2014 %@ 2090-4029 . Li Y, Zhang Y, Fang S, Liu S, Liu X, Li M, Liang H, Fu H: Analysis of inequality in maternal and child health outcomes and mortality from 2000 to 2013 in China . International journal for equity in health 2017, 16 :1-11. Yaya S, Zegeye B, Ahinkorah BO, Ameyaw EK, Seidu A-A, Shibre G: Time trends, geographical, socio-economic, and gender disparities in neonatal mortality in Burundi: evidence from the demographic and health surveys, 2010–2016 . Archives of Public Health 2020, 78 :1-10. Van Malderen C, Amouzou A, Barros AJD, Masquelier B, Van Oyen H, Speybroeck N: Socioeconomic factors contributing to under-five mortality in sub-Saharan Africa: a decomposition analysis . BMC Public Health 2019, 19 :1-19. Fagbamigbe AF, Morakinyo OM, Balogun FM: Sex inequality in under-five deaths and associated factors in low and middle-income countries: a Fairlie decomposition analysis . BMC Public Health 2022, 22 (1):334 %@ 1471-2458. Boco AG: Assessing sex differentials in under-five mortality in sub-Saharan Africa: A cross-national comparative analysis . Canadian Studies in Population [ARCHIVES] 2014, 41 (3-4):49-87 %@ 1927-1629X. Sohail H: Prevalence and risk factors associated with under-5 mortality: a multi-country comparative study in South Asia . 2017. Khawaja M, Dawns J, Meyerson-Knox S, Yamout R: Disparities in child health in the Arab region during the 1990s . International Journal for Equity in Health 2008, 7 :1-10. Sreeramareddy CT, Harsha Kumar HN, Sathian B: Time trends and inequalities of under-five mortality in Nepal: a secondary data analysis of four demographic and health surveys between 1996 and 2011 . PLoS One 2013, 8 (11):e79818 %@ 71932-76203. Rosenstock S, Katz J, Mullany LC, Khatry SK, LeClerq SC, Darmstadt GL, Tielsch JM: Sex differences in neonatal mortality in Sarlahi, Nepal: the role of biology and environment . J Epidemiol Community Health 2013, 67 (12):986-991 %@ 0143-0005X. Alkema L, Chao F, Sawyer C: Gender differences in infant and child mortality: Estimation and identification of countries with outlying levels or trends . In : 2013 ; 2013. Essel NO, Appiah SK, Mensah IA: Analysis of sex disparities in under-five mortality rates in Ghana: Insights from vector autoregressive modeling . medRxiv 2023:2023.2002. 2017.23286087. Gangadharan L, Maitra P: Does child mortality reflect gender bias? Evidence from Pakistan . Indian Economic Review 2000:113-131 %@ 0019-4670. Alkema L, Chao F, Sawyer CC: Gender Differences in Infant and Child Mortality: Estimation of Sex-Specific Mortality and an Assessment of Excess Female Deaths . 2014. Pal KJHSK, Sirohi A, Rathi V: Gender Discrepancy in Infant Mortality in Nigeria: Evidence from NDHS Data 2018 . Chen SDSaLC: Sex Differentials in Mortality in Rural Bangladesh . Population and Development Review 1980, 6 (2):257-270. Garenne M: Sex differences in health indicators among children in African DHS surveys . (0021-9320 (Print)). Central Statistical AE, Macro ORC: Ethiopia Demographic and Health Survey 2000 . In . Addis Ababa, Ethiopia: Central Statistical Authority/Ethiopia and ORC Macro; 2001. Macro O: Central Statistical Agency Addis Ababa, Ethiopia . Central Statistical Agency Addis Ababa, Ethiopia 2006. Central Statistical Agency [Ethiopia] and ICF International. 2012. Ethiopia Demographic and Health Survey 2011. Addis Ababa, Ethiopia and Calverton, Maryland, USA: Central Statistical Agency and ICF International . In . ; 2011. Central Statistical Agency (CSA) [Ethiopia] and ICF. 2016. Ethiopia Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: CSA and ICF . In . ; 2016. Ethiopian Public Health Institute (EPHI) [Ethiopia] and ICF. 2021. Ethiopia Mini Demographic and Health Survey 2019: Final Report. Rockville, Maryland, USA: EPHI and ICF . In . ; 2019. Code Library, The DHS program [https://github.com/DHSProgram] Costa JC, Victora CG: A scoping review of methods for assessment of sex differentials in early childhood mortality . BMC pediatrics 2021, 21 :1-17. Weng YH, Yang CY, Chiu YW: Neonatal outcomes in relation to sex differences: a national cohort survey in Taiwan . Biol Sex Differ 2015, 6 :30. Additional Declarations No competing interests reported. Supplementary Files Annex1.rar Annex 1: The stata shared code used to extract the numerator (deaths) and denominator (at risks) for eight age segments of three cohorts. Annex2.rar Annex 2: The stata code developed and used to calculate the sum number of deaths, at risks and risk differences by place of delivery and sex of neonate, postnatal and child Annex3.rar Annex 3: The stata code developed and used for multiple imputations of missed data of time of breastfeeding initiation and ANC visit. Annex4.rar Annex 4: The stata code developed and used to calculate the PAR and PAF of place of delivery by sex of neonates, postnatal and child Annex5.rar Annex 5: The stata code developed and used to identify the independent excess risk effect (adjusted risk difference) of sex on neonatal, postnatal and child mortality by place of delivery adjusting for mother’s educational status, ANC visit, breast feeding initiation time and year of survey using multiple variable binomial regression analysis. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6330556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491202945,"identity":"bc29d0f4-9e71-4c37-bcf7-5382a0321862","order_by":0,"name":"Mr Getachew Hailu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYLACxgYQycP8+EcFkGZmbiCgnhmuhc2Y4QxIgJF4LQzSjG0IS3ECeff+gx9+7rBJXDu794Bx4bzaaP52oJYfFdtwajE8c5hZsvdMWuK2O+cSHs/cdjx3xmHGBsaeM7dxa5mRDHLP4dxtN3IMDHi3HcttAGphZmzDo2X+Y+bfjG3/wVokeOccy51PSIu8BDMb0JYDYC3SvA01uRsIaTHgSTaz7G1Lrt9254yZ4YxjB3I3ArUcxOcX+faDj2/8bLMzNrvdY/zgQ01d7rzzhw8++FGBx5YDMJYEmDwMJg9gUwq3pQFVSx0+xaNgFIyCUTBCAQBglWMdFpiywQAAAABJRU5ErkJggg==","orcid":"","institution":"Bahir Dar University","correspondingAuthor":true,"prefix":"Mr","firstName":"Getachew","middleName":"","lastName":"Hailu","suffix":""},{"id":491202946,"identity":"903dbe8f-2ae0-40d0-9b8a-12f5ff75d622","order_by":1,"name":"Gedefaw Abeje","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Gedefaw","middleName":"","lastName":"Abeje","suffix":""},{"id":491202947,"identity":"0716b2c4-ae9c-4cb2-b020-c0d0303fcb70","order_by":2,"name":"Mulusew Andualem Asemahagn","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Mulusew","middleName":"Andualem","lastName":"Asemahagn","suffix":""}],"badges":[],"createdAt":"2025-03-28 19:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6330556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6330556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108484985,"identity":"e631a682-381e-49d7-8d41-3e1f4e01a2ab","added_by":"auto","created_at":"2026-05-05 08:41:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1070356,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6330556/v1/38ba0f6c-ed26-41bc-b94b-70ae9774cd40.pdf"},{"id":87769354,"identity":"e26a75c9-e7b0-4f90-984d-fa8d7cc496ce","added_by":"auto","created_at":"2025-07-28 19:14:27","extension":"rar","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":236529,"visible":true,"origin":"","legend":"\u003cp\u003eAnnex 1: The stata shared code used to extract the numerator (deaths) and denominator (at risks) for eight age segments of three cohorts.\u003c/p\u003e","description":"","filename":"Annex1.rar","url":"https://assets-eu.researchsquare.com/files/rs-6330556/v1/8fc51f9301a4b2a41eb89245.rar"},{"id":87770103,"identity":"2c6820bc-1930-4b13-9cf6-81c76214daed","added_by":"auto","created_at":"2025-07-28 19:30:27","extension":"rar","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":236502,"visible":true,"origin":"","legend":"\u003cp\u003eAnnex 2: The stata code developed and used to calculate the sum number of deaths, at risks and risk differences by place of delivery and sex of neonate, postnatal and child\u003c/p\u003e","description":"","filename":"Annex2.rar","url":"https://assets-eu.researchsquare.com/files/rs-6330556/v1/416872d86f5f842d3fd89849.rar"},{"id":87769622,"identity":"1b149a60-2694-4a79-8fb6-33c44bc91297","added_by":"auto","created_at":"2025-07-28 19:22:26","extension":"rar","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13697,"visible":true,"origin":"","legend":"\u003cp\u003eAnnex 3: The stata code developed and used for multiple imputations of missed data of time of breastfeeding initiation and ANC visit.\u003c/p\u003e","description":"","filename":"Annex3.rar","url":"https://assets-eu.researchsquare.com/files/rs-6330556/v1/802872bcd106f83142a94319.rar"},{"id":87769357,"identity":"24c4bd38-bf1e-4a90-bf17-4b6d6bebc15b","added_by":"auto","created_at":"2025-07-28 19:14:27","extension":"rar","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3269294,"visible":true,"origin":"","legend":"\u003cp\u003eAnnex 4: The stata code developed and used to calculate the PAR and PAF of place of delivery by sex of neonates, postnatal and child\u003c/p\u003e","description":"","filename":"Annex4.rar","url":"https://assets-eu.researchsquare.com/files/rs-6330556/v1/100f7983eb85db4a96ff52de.rar"},{"id":87769364,"identity":"19a26c05-0ea0-4136-9926-2d5c9dd926e2","added_by":"auto","created_at":"2025-07-28 19:14:27","extension":"rar","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3269141,"visible":true,"origin":"","legend":"\u003cp\u003eAnnex 5: The stata code developed and used to identify the independent excess risk effect (adjusted risk difference) of sex on neonatal, postnatal and child mortality by place of delivery adjusting for mother’s educational status, ANC visit, breast feeding initiation time and year of survey using multiple variable binomial regression analysis.\u003c/p\u003e","description":"","filename":"Annex5.rar","url":"https://assets-eu.researchsquare.com/files/rs-6330556/v1/442b2f76b0cfc11091a628b3.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Disparities and Excess Risks of place of delivery for neonatal, postnatal and Child Mortality in Ethiopia: A comparative trend analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMales may have a biological disadvantage over females from birth. Both newborn mortality and congenital anomaly deaths are more common in males. Males may be more prone to infections due to a less active immune system and may be more vulnerable to diseases associated with sex chromosomes than females. Women's reproductive ability is linked to biological processes that could offer a defensive edge[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere were fewer countries with gender differences in child mortality, according to published research. Boys are often predicted to have a larger chance than girls of passing away before turning five [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, a study that looked at the possibility of a gender difference in the impact on child mortality found that India had an excess of 35% of girl child mortality, with a cumulative death rate of 57.5 per 1000 live births [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, there were 18.5 excess female U5CMR per 1000 live births, translating to an estimated 239,000 additional deaths annually in India [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGender inequality affects women considerably more severely in low- and middle-income countries (LMICs). The rates of neonatal, infant, and under-five mortality were strongly correlated with the gender inequality[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Improvement in gender equality significantly reduced U5MR[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the first year of life, boys had a higher chance of dying than girls. In 2006, girls were more likely than boys to die between the ages of one and five, but by 2016, this gender disparity had been eliminated due to improvements in survival[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe sex differential in child mortality is high in India [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The gender differences in the U5MR and the NMR were relatively stable in China[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Another study from Burundi reported a considerable absolute and relative sex-based inequality both in 2010 and 2016 with higher concentration of neonatal mortality rate among male new-borns[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to a sub-Sahara Africa decomposition analysis, in 16 nations, girls' U5MR was considerably lower than boys'. In Chad (2014\u0026ndash;15), C\u0026ocirc;te d\u0026rsquo;Ivoire (2011\u0026ndash;12), Ethiopia (2016), Gabon (2012), Malawi (2015\u0026ndash;16), and Sierra Leone (2013), child sex was the sole primary contributor among the 12 countries where it accounted for more than 25% of the variability in U5MR[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In most LMICs, male children had a greater frequency of U5 mortality, according to a fairlie decomposition analysis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In sub-Saharan Africa, a cross-national comparative investigation revealed that male child mortality was higher than that of female child mortality. Out of the thirty countries, nineteen have a major link. Males in the region are 17\u0026ndash;54% more likely than females to pass away before turning five[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eVarious countries may have various gender disparities in under-5 mortality. While boys have comparatively lower under-5 death rates than girls in impoverished nations, newborn females naturally have an edge over newborn boys in terms of survival[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, estimations of child health inequalities revealed a persistent gender difference in IMR and U5MR. With 68 female newborn deaths for every 100 male infants, Jordan has the lowest infant mortality disadvantage for females. Every country under investigation demonstrated a female edge in both metrics, with the exception of Saudi Arabia (IMR 103) and Egypt (IMR 101 and U5MR 107)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In 1996, girls had a higher U5MR (122.9 vs 113.3) and a higher RD (8.7) than boys had. However, in 2011, girls had a slightly higher U5MR (53.7 versus 53.1) and a lower RD (0.6)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Boys' disproportionate chance of dying as newborns early was in line with biological assumptions. It was not shown that general gender preference or preferential care-seeking for males explained the excess risk of late neonatal death in girls[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBiological variations between boys and girls affect their chances of dying at different times during infancy and early childhood. Girls may be less likely than males to die in the end, since this is the expected outcome of their biology, but they may still be deprived of their entire biological advantage if they receive treatment that deprives them of part of their advantages[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobal surveillance agencies demand that gender be taken into account when analyzing child mortality data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Girls have a significantly higher likelihood of dying in the age group 1\u0026ndash;5 but a significantly lower probability of dying in the age group 0\u0026ndash;1 in the disaggregated data compared to males [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In recent years, infant and under-5 male mortality has been over 50% greater than female mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Males were shown to be at higher risk than females in every variable examined, with the exception of maternal age, according to an Indian study [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOnly in the neonatal period does male mortality surpass that of female death. After then, female death rates rise faster than male death rates until the age of three, at which point they are between 46% and 53% higher than the corresponding male rate [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, mortality seems to be higher for boys overall, with neonates being the most affected (+\u0026thinsp;28%), followed by 1\u0026ndash;12 months (+\u0026thinsp;8%) and 1\u0026ndash;4 years old (+\u0026thinsp;4%)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite many studies have been done on the gender differences in the excess risk of childhood mortality, most of them have estimated the ratio measures (Odds Ratio, Risk Ratio, and Hazard Ratio) using regression models [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Few used the World Health Organization\u0026rsquo;s (WHO\u0026rsquo;s) Health equity Assessment Tool (HEAT) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and the newly developed HDCalc software by the World Health Organization (WHO) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to estimate the absolute difference measures (Risk Difference, Population Attributable Risk, Exposure Attributable Fraction and Population Attributable Fraction and other measures of gender disparities like the between group variance (BGV) and the Theil index (T). Ratio measures are rarely understood by the general public. We therefore assumed that they would more readily comprehend the difference that may arise if all U5 children were born in a health facility for the purposes of this study, increased risk of home delivery for under-five mortality. Furthermore, the impact of preventing the exposure variable (home delivery) in preventing childhood mortality might be clearly presented by the difference measure.\u003c/p\u003e\u003cp\u003eResearch published two theoretical stances of biological advantage/disadvantage and environmental mechanisms as causes of the differences in male and female childhood mortality [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We attempted to examine the role of place of delivery over time, one of the critical environmental factors for under-five child mortality, in the current five consecutive years of EDHS data analysis. We stratified by age and sex of children, two of the most important epidemiological confounding-biological factors in order to have age-sex specific mortality rates by place of delivery. The main aim of this study was to analyse the disparities and excess risk of gender for neonatal, postnatal and child mortality by place of delivery in Ethiopia using the 2000 to 2019 Ethiopian demographic and health survey data.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source and sample\u003c/h2\u003e\u003cp\u003eThe five consecutive nationally representative Ethiopia Demographic and Health Surveys (EDHS) that were conducted between February and May 2000 (EDHS, 2000), from April 27 to August 30, 2005 (EDHS, 2005), from December 27, 2010 to June 3, 2011 (EDHS, 2011), from January 18, 2016 to June 27, 2016 (EDHS, 2016), and from March 21, 2019 to June 28, 2019 (EDHS, 2019) provided the data for this study[\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWith 14,642 sample houses covered by the 2000 EDHS, 14,072 were successfully interviewed, resulting in a response rate of 99.3%, and 15,716 eligible women were found, of whom 15,367 were successfully interviewed, with a response rate of 97.8% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The EDHS conducted in 2005 included 14,645 sample houses; 13,721 of these were successfully interviewed, resulting in a 99% response rate; 14,717 eligible women were found; 14,070 of these were successfully interviewed, yielding a 96% response rate[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Out of the 17,817 sample houses included in the 2011 EDHS, 17,461 were successfully interviewed, resulting in a 98% response rate. Ninety-five percent of the 17,387 eligible women in the 15\u0026ndash;49 age group who were interviewed were successful [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Within the 18,008 sample households covered by the 2016 EDHS, 16,650 were successfully interviewed, resulting in a 98% response rate. Additionally, 16,583 eligible women were found; 15,683 women underwent interviews, gave a 95% response rate [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The 2019 EDHS had 9,150 sample households; 8,663 of them were successfully interviewed, resulting in a 99% response rate; 9,012 eligible women were found; 8,885 of these women had interviews completed, producing a 99% response rate[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFive full-scale DHS surveys were conducted in 2000, 2005, 2011, 2016 and 2019. Five questionnaires were used for the surveys one of these was the Woman\u0026rsquo;s Questionnaire. The Woman\u0026rsquo;s Questionnaire was used to collect information from all eligible women age 15\u0026ndash;49. It used to collect place of deliveries including age at death and date of child birth within five years preceding the date of interview of each survey year [\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData processing and analysis\u003c/h3\u003e\n\u003cp\u003eThe current gender disparities and trend analysis of the excess risk of place of delivery for neonatal, postnatal and child mortality used primarily the Birth Recode data files (ETBR41.dta to ETBR81.dta). The Birth recode data files were accessed as electronic version of STATA file format and unwanted variables and observations were dropped. This was done for each of the five surveys in line with the objective of this analysis. In order to make the Birth Recode data appropriate for the objectives of this analysis, the DHS_U5_rates shared code of Chap.\u0026nbsp;08_CM (STATA do file) was obtained from dhsmeasure of gethub website[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAnnex1\u003c/span\u003e. Subsequently, the shared code (STATA do file) was modified in order to keep the variables of interest of the analysis including place of delivery, child sex, educational status of mothers, survey year, ANC visits and time of initiation of breast feeding along with strata, cluster and weighting variables for each of the five consecutive surveys separately.\u003c/p\u003e\u003cp\u003eIn the DHS_U5_rates shared code of the section that can be used to extract the total risk as a denominator for the specified age interval, we made important modification in order to save the number (counts) of under-five child deaths and at risks for the respective eight age intervals along with the variables of interest. Following the modification, execution of the code for the respective surveys generated a new data set which was appropriate to extract the number of deaths and at risks of neonates, post-neonates and children by variables of interest for each surveys. Subsequently, a new STATA do file was created and applied for calculating number of deaths, number of at risks and mortality rates for those home and facility births with a STATA command of \u0026ldquo;collapse (sum)\u0026rdquo; by variables of the interest using the newly generated data. The five individual surveys data set then pooled to a single data set using a STATA command, appending data. The data were processed to have a cross-sectional time-series data format through sequential steps for the time series analysis.\u003c/p\u003e\u003cp\u003eThree age groups which are mutually exclusive (0\u0026ndash;28 days, 1\u0026ndash;12 months, 1\u0026ndash;4 years) with two sex (male and female) specific six stratum in total were used in calculating the age-sex stratified mortality rates by place of delivery as follows;\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eNeonatal mortality for home and health facility born neonates by sex\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePost-neonatal mortality for home and health facility born post-neonates by sex\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eChild mortality for home and health facility born child by sex were calculated for each surveys following generating new variables namely died_HOM, died_HF, risk_HOM and risk_HF to have deaths and risks for home and health facility births, respectively \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eAnnex2\u003c/span\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eSubsequently, the absolute risk difference measures including the risk difference (RD) was calculated. In addition, we estimated the Population Attributable Risk (PAR) and the Population Attributable Fraction (PAF) of place of delivery using the regpar and punaf STATA commands respectively following the \u003cb\u003esvy, subpop(if sex_child\u0026thinsp;=\u0026thinsp;=\u0026thinsp;0): glm died_child birth_place, family(binomial risk) link(logit) iter(50)\u003c/b\u003e estimation of mortality probabilities \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eAnnex4\u003c/span\u003e. This was done considering place of delivery as exposure while sex and age as confounding variables. Finally, multiple variable binomial regression analysis was conducted by place of delivery to identify the independent excess risk effects or risk difference of child sex controlling the confounding effect of ANC visit, breastfeeding initiation time and mothers educational status for neonatal, post-neonatal and child mortality. The detailed steps used in the methods were documented in additional files \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eAnnex3 and 5\u003c/span\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis five consecutive surveys data analysis showed that 51.58% of the under-five children born at home within the 25 years preceding the 2019 survey were male. More than half (51.65%) of the under-five children born at health facility during the 25 years preceding the 2019 survey year were female. The trends in sex distribution of under- five children by place of birth were stable throughout the five consecutive surveys with sex ratio of 1.06 to 1 for children born both at home and at health facility (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends and child sex disparities of under five-children by place of delivery, Ethiopia, 2024\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSurvey Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eHome Delivery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eHealth facility Delivery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e52.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e47.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6,288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e51.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e5,972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e48.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e52.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e47.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5,723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e51.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e5,440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e48.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e51.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e48.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6,168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e51.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e5,704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e48.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e51.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1,398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e48.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5,725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e51.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e5,298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e48.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e51.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1,236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e48.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2,842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e51.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2,685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e48.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22,485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21,109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4,039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e51.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3,780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e48.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e26,746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e51.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e25,099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e48.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends and disparities in risk difference of place of delivery for Child (age 1 to 4 year/s old) mortality stratified by sex\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the male children who got birth at home 18.42 per 1000LB died, but among those who got birth at health facility, 4.66 per 1000LB died in the five years preceding the 2016 survey. The difference between these rates, the risk difference (RD) among male children was 13.21[RD\u0026thinsp;=\u0026thinsp;13.21, 95%CI: 6.31, 20.11] per 1000 live births in the five years preceding the 2016 survey (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analysis showed that among female children who got birth at home, 20.9, 18.02 and 13.65 per 1000LB died, but among those who got birth at health facility, 3.93, 6.38 and 2.12 per 1000LB died in the five years preceding the 2011, 2016 and 2019 surveys, respectively. The difference between these rates, the risk difference(RD) among female children were 17.91 [RD\u0026thinsp;=\u0026thinsp;17.91, 95%CI: 10.51, 25.31], 12.31 [RD\u0026thinsp;=\u0026thinsp;12.31, 95%CI: 4.77, 19.86] and 10.95[RD\u0026thinsp;=\u0026thinsp;10.95, 95%CI: 2.88, 19.02] per 1000 live births in the five years preceding the 2011, 2016 and 2019 surveys, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends and disparities in risk difference (RD) of place of delivery for child (age 1\u0026ndash;4 year/s old) mortality by sex, Ethiopia 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHome Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003efacility Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eRisk diff. (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3690.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e201.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e53.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3300.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e161.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3555.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e363.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2958.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e792.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1017.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e812.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHome Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003efacility Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eRisk diff. (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3611.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e178.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e26.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3301.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e167.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e41.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3323.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e350.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2610.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e703.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e964.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e792.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNotes: RD is risk difference, LB is lower boundary and UB is upper boundary of the risk difference\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e*Significant at P-value \u003c 0.05 (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003eAmong the male children (age 1\u0026ndash;4 year/s old) who got birth at home and health facility 12.97 per 1000LB died, but among those who got birth at health facility 4.66 per 1000LB died in the five years preceding the 2016 survey year. The difference between these rates, the population attributable risk (PAR) was 8.32 [PAR\u0026thinsp;=\u0026thinsp;8.32, 95%CI: 1.88, 14.77] per 1000LB. The estimate for possibility of male child mortality prevention, the population attributable fraction (PAF) showed that 64.15% [PAF\u0026thinsp;=\u0026thinsp;0.6415, 95%CI: 0.0585, 0.8635] of the mortality burden of male children in the five-years preceding the 2016 surveys would have been prevented if none of them were born at home (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ). Similarly, among female children who got birth at home and health facility 28.36 per 100LB and 15.85 per 1000LB died, but among those who got birth at health facility 5.98 per 1000LB and 3.93 per 1000LB died in the five years preceding the 2005 and 2011 surveys respectively. The difference between these mortality rates, the population attributable risks (PAR) were 22.38 [PAR\u0026thinsp;=\u0026thinsp;22.38, 95%CI: 9.7635.01] per 1000LB and 11.92 [PAR\u0026thinsp;=\u0026thinsp;11.92, 95%CI: 5.03, 18.82] per 1000LB in the five years preceding the 2005 and 2011 surveys, respectively. The estimate for possibility of female child mortality prevention, the population attributable fraction (PAF) showed that 75% (PAF\u0026thinsp;=\u0026thinsp;0.75, 95%CI 0.2823, 0.9144] of the mortality burden of female children in the five-years preceding the 2011 survey would have been prevented if none of them were born at home (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends and disparities in Population Attributable Risk (PAR) and Population Attributable Fraction (PAF) of place of delivery for child (age 1\u0026ndash;4 year/s old) mortality by sex, Ethiopia 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBoth births (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHF births ( 95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003ePAR (95%CI) / 1000LB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003ePAF (95%CI) per 100LB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e42.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e45.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-36.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e78.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-6.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e39.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-82.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e80.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-7.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e47.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-220.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e91.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.32*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e64.15*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e86.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-137.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e64.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBoth birth (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHF birth (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003ePAR (95%CI) / 1000LB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003ePAF (95%CI) per 100LB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-53.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-41.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-200.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e33.6.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e34.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.38*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e35.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e78.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-24.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e96.4.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.92*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e75.22*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e28.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e91.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e50.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-74.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e85.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e69.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-35.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e93.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote: Both births are all children who got birth at home and health facility. *Significant at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends and disparities in risk difference (RD) of place of delivery for Postnatal (age 28 days to 1 year old) mortality stratified by sex\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the male post-neonates who got birth at home, 25.16 and 21.18 per 1000LB died, but that, among those who got health facility birth, 10.63 and 13.4 per 1000LB died in the five years preceding the 2016 and 2019 surveys, respectively. The differences between these rates, the risk differences (RDs) among male post-neonates were 10.83 [RD\u0026thinsp;=\u0026thinsp;10.83, 95%CI: 3.59, 18.06] and 13.40 [RD\u0026thinsp;=\u0026thinsp;13.4, 95%CI: 4.16, 22.64] per 1000LB in the 2016 and 2019 surveys, respectively. In addition, among the female post-neonates who got birth at home, 21.25 and 19.23 per 1000LB died, but that, among those who got birth at health facility 3.06 and 6.97 per 1000LB died in the five years preceding the 2011 and 2016 surveys, respectively. The differences between these rates, the risk differences (RD) were 17.52 [RD\u0026thinsp;=\u0026thinsp;17.52, 95%CI: 10.83, 24.21] and 12.25 [RD\u0026thinsp;=\u0026thinsp;12.25, 95%CI: 5.83, 18.67] per 1000LB in the five years preceding the 2011 and 2016 surveys, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends and disparities in risk difference (RD) of place of delivery for post-neonatal (age 28 days \u0026minus;\u0026thinsp;1year old) mortality by sex, Ethiopia 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHome Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHealth facility Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eRisk difference (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5383.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e291.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e39.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4862.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e265.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-17.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e29.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5012.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e531.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-5.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3849.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1304.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1345.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1228.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHome Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHealth facility Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eRisk difference (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003echi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5194.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e261.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e89.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-44.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-79.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-9.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4709.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e259.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-22.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4690.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e525.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e24.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3623.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1273.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1301.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1142.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-6.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNotes: RD is risk difference, LB is lower boundary and UB is upper boundary of the risk difference *Significant at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong those male post-neonates who got birth at home and health facility 14.67 per 1000LB died, but among those who got birth at health facility 8.4 per 1000LB died in the five years preceding the 2019 survey year. The difference between these mortality rates, the population attributable risk (PAR) was 6.27 per 1000LB [PAR\u0026thinsp;=\u0026thinsp;6.27, 95%CI: 0.03, 12.51]. Moreover, the estimate for possibility of male post-neonate mortality prevention, the population attributable fraction (PAF) showed that 42.76% [PAF\u0026thinsp;=\u0026thinsp;0.4276, 95%CI: -15.23, 71.57] of the mortality burden of male post-neonate in the five-years preceding the 2019 survey would have been prevented if none of them were born at home.\u003c/p\u003e\u003cp\u003eWhereas among those female post-neonate who got birth at home and health facility 18.64 and 15.55 per 1000LB died, but among those who got birth at health facility alone 3.05 and 6.94 per 1000LB died in the five years preceding 2011 and 2016 surveys, respectively. The difference in these mortality rates, the population attributable risk (PAR) were 15.59 [PAR\u0026thinsp;=\u0026thinsp;15.59, 95%CI: 10.19, 20.99] and 8.62 [PAR\u0026thinsp;=\u0026thinsp;8.62, 95%CI: 1.79, 15.45] per 1000LB in the five years preceding the 2011 and 2016 survey years, respectively. The possibility of female post-neonates mortality prevention, the population attributable fraction (PAF) showed that 83.65% [PAF\u0026thinsp;=\u0026thinsp;0.8365, 95%CI: 0.56, 0.9392] in the five year preceding the 2011 survey and 55.41% [PAF\u0026thinsp;=\u0026thinsp;0.5541, 95%CI: 0.0283, 0.7954] in the five year preceding the 2016 survey year of the mortality burden of female post-neonates would have been prevented if all post-neonates were born at health facility (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends and disparities in Population Attributable Risk (PAR) and Population Attributable Fraction (PAF) of place of delivery for post neonatal (age 28 days-1years old) mortality by sex, Ethiopia 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBoth births (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHF births ( 95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003ePAR (95%CI) / 1000LB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003ePAF (95%CI) per 100LB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-10.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e38.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e31.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-49.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e68.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-22.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e30.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-85.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e56.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e21.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-67.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e62.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e41.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-19.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e71.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.27*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e42.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-15.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e71.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBoth births (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHF births (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003ePAR (95%CI) / 1000LB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003ePAF (95%CI) per 100LB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eLB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e182.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-39.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-106.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e26.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-91.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-322.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e12.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e70.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-27.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e24.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-4.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-146.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e55.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.59*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e83.65*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e56.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e93.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.62*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e55.41*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e79.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-7.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-102.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e55.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote: Both births are all children who got birth at home and health facility. *Significant at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends and disparities in risk difference (RD) of place of delivery for Neonatal (age\u0026thinsp;\u0026lt;\u0026thinsp;28 days old) mortality stratified by sex\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe risk differences (RD) were negative for the five years preceding all except the 2000 survey which indicated that those who got birth at home had reduced risk of mortality compared to those who got birth at health facility for male neonates. The mortality risk differences of place of delivery among male neonates were \u0026minus;\u0026thinsp;38.51 [RD= -38.51, 95%CI: -69.88, -7.14] and \u0026minus;\u0026thinsp;31.86 [RD=-31.86, 95%CI: -53.45, -10.27] for the five years preceding the 2011 and 2016 surveys, respectively. Similarly, the risk differences were negative except for the five years preceding the 2000 and 2019 surveys which indicated that home delivery had reduced risk of mortality than health facility delivery for female neonates. Even though the risk differences for most survey years were negative, we found them not statistically significant since the 95% CI of the differences cross zero (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends and disparities in risk difference of place of delivery for neonatal (age\u0026thinsp;\u0026lt;\u0026thinsp;28 days old) mortality by sex, Ethiopia 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHome Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHealth facility Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eRisk difference (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5844.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e315.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-21.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5259.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e304.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e83.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-38.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-69.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5416.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e597.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-31.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-53.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-10.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4093.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1428.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-10.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-23.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1424.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1315.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-23.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHome Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHealth facility Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eRisk difference (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5571.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e282.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e37.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5019.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e275.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-22.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4984.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e567.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-16.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3769.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1353.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-14.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1371.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1213.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-8.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNotes: RD is risk difference, LB is lower boundary and UB is upper boundary of the risk difference\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e*Significant at P-value \u003c 0.05\u003c/h3\u003e\n\u003cp\u003eAmong those male neonates who got birth at home and health facility 44.34 per 1000LB died, but among those who got birth at health facility 73.14 per 1000LB died in the five years preceding the 2011 survey. The difference between this mortality rate, the male neonate population attributable risk (PAR) was \u0026minus;\u0026thinsp;28.80 [PAR = -28.80, 95%CI: -55.79, -1.76] per 1000LB. Moreover, the estimate for possibility of male neonates mortality prevention, the male neonate population attributable fraction (PAF) showed that 64.94% [PAF= -0.6494, 95%CI: -1.3588, -0.1533] of the mortality burden of male neonates in the five-years preceding the 2011 survey had been reduced due to home delivery. The neonate population attributable risk (PAR) of place of delivery for both male and female neonatal mortality was negative for most surveys; however, it was found not statistically significant. Therefore, place of delivery has no significant excess risk of neonatal mortality if sex of the neonates is controlled through stratification (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends and disparities in Population Attributable Risk (PAR) and Population Attributable Fraction (PAF) of place of delivery for neonatal (age less than 28 days) mortality by sex, Ethiopia 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBoth births (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHF birth ( 95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003ePAR (95%CI) per 1000LB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003ePAF (95%CI) per 100LB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-32.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e36.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-87.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e50.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e139.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-37.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-80.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-83.22*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-206.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-9.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e109.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-28.80 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-55.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-64.94*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-135.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-15.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-8.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-23.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-21.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-65.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-5.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-15.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-14.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-44.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e9.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSurvey Year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBoth births (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHF birth (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003ePAR (95%CI) per 1000LB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003ePAF (95%CI) per 100LB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eLB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e70.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-10.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e42.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e38.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-68.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e77.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-28.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-145.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e58.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-19.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-95.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e45.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-14.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-25.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-94.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e18.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-7.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-33.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e35.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote: Both births are all children who got birth at home and health facility. *Significant at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMultiple variable binomial regression analysis\u003c/h2\u003e\u003cp\u003eThe risk difference or excess risk effects of child-sex, mothers\u0026rsquo; educational status, ANC visit, time of breastfeeding initiation and the EDHS year on child mortality by place of delivery was analysed using a binomial regression model. A separate multiple variable binomial regression analysis by place of delivery for neonatal, post-neonatal and child excess risks of mortality were conducted following imputation for missed values of ANC visit and time of breastfeeding initiation (look method description in the additional file).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChild (1 year to 4 years old) mortality\u003c/h3\u003e\n\u003cp\u003eMale children had a 62.6 [ARD\u0026thinsp;=\u0026thinsp;0.000626, 95%CI: -0.000791, 0.00204] and 44.5 [ARD\u0026thinsp;=\u0026thinsp;0.000445, 95%CI: -0.00179\u0026ndash;0.00268] per 100,000LB excess risk of mortality compared to female children for those who got birth at health facility and home, respectively, however, it is not statistically significant. Mothers\u0026rsquo; educational status of at least read and write had averted 5.49 [ARD = -0.00549, 95%CI: -0.00818, -0.00281] and 6.02 [ARD = -0.00602, 95%CI: -0.00857, -0.00348] per 1000LB excess risks of child mortality compared to illiterate mothers\u0026rsquo; educational status for children who got birth at health facility and home, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, at least one ANC visit/s of mothers had prevented 7.88 [ARD = -0.00788, 95%CI: -0.0119, -0.00384] and 9.98 [ARD = -0.00998, 95%CI: -0.0124, -0.00758] per 1000LB child mortality risk compared to mothers who had no ANC visit for children who got birth at health facility and home, respectively. Moreover, within one hour initiation of breastfeeding following delivery had averted 9.53 [ARD = -0.000953, 95%CI: -0.00242\u0026ndash;0.000518] and 18.3 [ARD = -0.00183, 95%CI: -0.00433, 0.000659] per 10,000LB child mortality risk compared to after an hour initiation of breastfeeding for children who got birth at health facility and home, respectively. However, it had no statistically significant mortality reduction (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong children who got birth at health facility, 36.2 [ARD = -0.0362, 95%CI: -0.0491, -0.0234], 42.4 [ARD = -0.0424, 95%CI: -0.0539, -0.0308], 37 [ARD = -0.037, 95%CI: -0.0490, -0.0250] and 42.6 [ARD = -0.0426, 95%CI: -0.0541, -0.0310] per 1000LB were the averted risk of child mortality during the five years preceding the 2005, 2011, 2016 and 2019 EDHS years compared to the 2000 survey, respectively. Similarly, among children who got home birth, 21.7 [ARD = -0.0217, 95%CI: -0.0257, -0.0176], 31.1 [ARD = -0.0311, 95%CI: -0.035, -0.0273], 37.7 [ARD = -0.0377, 95%CI: -0.0416, -0.0338] and 39.3 [ARD = -0.0393, 95%CI: -0.0436, -0.0351] per 1000LB reduced child mortality risk during the five years preceding the 2005, 2011, 2016 and 2019 surveys were observed compared to the 2000 survey, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003emultiple variable binomial regression analysis result for the independent effects of child-sex, mothers\u0026rsquo; educational status, ANC visit and time of breastfeeding initiation and the EDHS year on mortality risk by place of delivery among children aged 1\u0026ndash;4 year/s old, Ethiopia, 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ecategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHealth facility delivery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eHome delivery\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdj. Risk. Diff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdj. Risk. Diff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChild sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000791\u0026ndash;0.00204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00179\u0026ndash;0.00268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMother\u0026rsquo;s\u003c/p\u003e\u003cp\u003eeducational\u003c/p\u003e\u003cp\u003estatus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eilliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary \u0026amp; above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00549***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00818 - -0.00281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00602***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00857 - -0.00348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAt least\u003c/p\u003e\u003cp\u003eone Anc visit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00788***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0119 - -0.00384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00998***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0124 - -0.00758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003etime of\u003c/p\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003cp\u003einitiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1 hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1 hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.000953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00242\u0026ndash;0.000518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00433\u0026ndash;0.000659\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSurvey year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0362***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0491 - -0.0234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0217***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0257 - -0.0176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0424***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0539 - -0.0308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0311***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0350 - -0.0273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0370***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0490 - -0.0250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0377***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0416 - -0.0338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0426***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0541 - -0.0310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0393***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0436 - -0.0351\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0575***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0450\u0026ndash;0.0700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0632***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0596\u0026ndash;0.0668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003ePost-neonate (aged 28 days − 1year old)\u003c/h3\u003e\n\u003cp\u003eMale post-neonates had 2.15 [ARD\u0026thinsp;=\u0026thinsp;0.00215, 95%CI: 0.0000228, 0.00428] and 6.62 [ARD\u0026thinsp;=\u0026thinsp;0.00662, 95%CI: 0.00398\u0026ndash;0.00926] per 1000LB excess risk of mortality compared to females for facility and home births, respectively. Primary and above educational status of mothers had prevented 13.5 [ARD = -0.0135, 95%CI: -0.0181, -0.00881] and 2.66 [ARD = -0.00266, 95%CI: -0.00597, 0.000651] per 1000LB excess risk of post-neonatal mortality compared to illiterate mothers\u0026rsquo; educational status for facility and home birth, respectively, but it was not statistically significant for those who got birth at home. At least one ANC visit/s of the mothers had averted 8 [ARD = -0.0080, 95%CI: -0.0152, -0.000811] and 13.1 [ARD = -0.0131, 95%CI: -0.0159, -0.0103] per 1000LB post-neonatal mortality risk compared to no ANC visit of the mothers for those post-neonates who got birth at health facility and home, respectively. Moreover, within one hour initiation of breastfeeding following delivery had 2.25 [ARD\u0026thinsp;=\u0026thinsp;0.00225, 95%CI: -0.000585\u0026ndash;0.00509] and 4.71 [ARD\u0026thinsp;=\u0026thinsp;0.00471, 95%CI: 0.00162, 0.00781] per 1,000LB excess risk of post-neonatal mortality compared to after an hour initiation of breastfeeding for health facility and home births, respectively. However, it had no statistically significant excess risk of mortality for facility births, but initiation of breastfeeding within an hour had statistically significant excess risk compared to initiation after an hour for home births (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong post-neonates who got birth at health facility, there were 38.8 [ARD = -0.0388, 95%CI: -0.0599, -0.0177], 62.1 [ARD = -0.0641, 95%CI: -0.083, -0.0452], 56.1 [ARD = -0.0561, 95%CI: -0.0753, -0.0368] and 64 per 1000LB [ARD = -0.064, 95%CI: -0.0829, -0.045] reduced risks of post-neonatal mortality during the five years preceding the 2005, 2011, 2016 and 2019 EDHS years compared to the 2000 survey, respectively. Similarly, among those who got birth at home, 21.7 [ARD = -0.0217, 95%CI: -0.0257, -0.0176], 31.1 [ARD = -0.0311, 95%CI: -0.035, -0.0273], 37.7 [ARD = -0.0377, 95%CI: -0.0416, -0.0338] and 39.3 per 1000LB [ARD = -0.0393, 95%CI: -0.0436, -0.0351] post-neonatal mortality risks were reduced during the five years preceding the 2005, 2011, 2016 and 2019 surveys compared to the 2000 survey\u0026rsquo;s mortality risk, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003emultiple variable binomial regression analysis result for the independent effects of child-sex, mothers\u0026rsquo; educational status, ANC visit ,time of breastfeeding initiation and the EDHS year on PNM risk by place of delivery among post-neonates age 28 days to 1 year old, Ethiopia, 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ecategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHealth facility delivery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eHome delivery\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdj. Risk. Diff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdj. Risk. Diff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eChild sex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00215**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.28e-05\u0026ndash;0.00428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00662***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00398\u0026ndash;0.00926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eMother\u0026rsquo;s\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eeducational\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003estatus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eilliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary \u0026amp; above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0135***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0181 - -0.00881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.00597\u0026ndash;0.000651\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eAt least one\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eANC visit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00800**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0152 - -0.000811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0131***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0159 - -0.0103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003etime of\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBreastfeeding\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003einitiation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1 hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1 hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000585\u0026ndash;0.00509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00471***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00162\u0026ndash;0.00781\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eEDHS year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0388***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0599 - -0.0177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00780***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0120 - -0.00364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0641***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0830 - -0.0452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0248***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0288 - -0.0208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0561***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0753 - -0.0368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0303***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0346 - -0.0260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0640***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0829 - -0.0450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0340***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0390 - -0.0290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0870***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0665\u0026ndash;0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0608***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0573\u0026ndash;0.0643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eNeonates (aged less than 28 days old)\u003c/h2\u003e\u003cp\u003eMale neonates had 68.5 [ARD\u0026thinsp;=\u0026thinsp;0.0685, 95%CI: 0.0576, 0.0795] and 21.3 [ARD\u0026thinsp;=\u0026thinsp;0.0213, 95%CI: 0.0181\u0026ndash;0.0245] excess risk of mortality per 1000 LB compared to female neonates who got birth at health facility and home, respectively. Primary and above educational status of mothers had prevented 12 [ARD = -0.012, 95%CI: -0.0231, -0.000963] and 6.83 [ARD = -0.00266, 95%CI: -0.00464, 0.00327] per 1000LB excess risk of neonatal mortality compared to illiterate educational status of mothers for neonates who got birth at health facility and home, respectively. However, at least read and write educational status of the mothers had no a statistically significant risk reduction of neonatal mortality for those neonates who got birth at home. In addition, at least one ANC visit/s of mothers had averted 32.1 [ARD = -0.0321, 95%CI: -0.0472, -0.0171] and 14.6 [ARD = -0.0146, 95%CI: -0.0181, -0.0111] per 1000LB risk of neonatal mortality compared to no at least one ANC visit of mothers for health facility and home birth places, respectively. Moreover, within one hour breastfeeding initiation following delivery had 11.5 [ARD\u0026thinsp;=\u0026thinsp;0.0115, 95%CI: -0.00157\u0026ndash;0.0246] and 5.74 [ARD\u0026thinsp;=\u0026thinsp;0.00574, 95%CI: 0.00218, 0.0093] per 1000LB excess risk of neonatal mortality compared to after an hour initiation of breastfeeding for health facility and home births, respectively. However, breastfeeding initiation time had no statistically significant association with risk of mortality for facility births, but it had statistically significant excess risk for home births (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong neonates who got birth at health facility, there were 53.3 [ARD\u0026thinsp;=\u0026thinsp;0.0533, 95%CI: 0.0311, 0.0756], 60.7 [ARD\u0026thinsp;=\u0026thinsp;0.0607, 95%CI: 0.0413, 0.0801], 32.6 [ARD\u0026thinsp;=\u0026thinsp;0.0326, 95%CI: 0.0167, 0.0484] and 13.9 [ARD\u0026thinsp;=\u0026thinsp;0.0139, 95%CI: -0.0006, 0.0284] per 1000LB excess risks of neonatal mortality during the five years preceding the 2005, 2011, 2016 and 2019 surveys compared to the 2000 survey, respectively. On the contrary, among neonates who got birth at home, 27.8 [ARD = -0.0278, 95%CI: -0.0328, -0.0229], 26.9 [ARD = -0.0269, 95%CI: -0.0319, -0.0219], 45.8 [ARD = -0.0458, 95%CI: -0.0509, -0.0409] and 37 [ARD = -0.037, 95%CI: -0.0437, -0.0304] per 1000LB neonatal mortality risks were averted during the five years preceding the 2005, 2011, 2016 and 2019 surveys compared to the 2000 survey risk of neonatal mortality, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003emultiple variable binomial regression analysis result for the independent effects of child-sex, mothers\u0026rsquo; educational status, ANC visit, time of breastfeeding initiation and the EDHS year on neonatal mortality by place of delivery among neonates age\u0026thinsp;\u0026lt;\u0026thinsp;28 day\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ecategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHealth facility delivery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eHome delivery\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdj. Risk. Diff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdj. Risk. Diff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChild sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0685***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0576\u0026ndash;0.0795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0213***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0181\u0026ndash;0.0245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMother\u0026rsquo;s\u003c/p\u003e\u003cp\u003eeducational\u003c/p\u003e\u003cp\u003estatus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eilliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary \u0026amp; above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0120**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0231 - -0.000963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.000683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.00464\u0026ndash;0.00327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAt least one\u003c/p\u003e\u003cp\u003eANC visit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0321***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0472 - -0.0171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0146***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0181 - -0.0111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003cp\u003einitiation time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1 hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1 hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0115*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.00157\u0026ndash;0.0246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00574***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00218\u0026ndash;0.00930\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eEDHS year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0533***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0311\u0026ndash;0.0756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0278***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0328 - -0.0229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0607***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0413\u0026ndash;0.0801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0269***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0319 - -0.0219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0326***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0167\u0026ndash;0.0484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0458***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0509 - -0.0408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0139*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.000600\u0026ndash;0.0284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0370***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0437 - -0.0304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0704***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0525\u0026ndash;0.0883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0886***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0843\u0026ndash;0.0929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis on the disparities of risk difference of place of delivery for child, postnatal and neonatal mortality stratified by sex showed that there is no any variation in the frequency distribution of child-sex by place of delivery. However, the male children (age 1\u0026ndash;4 year/s old) had higher risk of death than female children for the 2000, 2011 and 2016 consecutive surveys irrespective of place of delivery. The male and female mortality rates were 43.47 versus 35.93, 16.51 versus 15.85, and 12.97 versus 12.84 per 1000 LBs in the five years preceding the 2000, 2011 and 2016 surveys, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe male post-neonates had higher mortality risk compared to female post-neonates during the five years preceding each consecutive survey irrespective of place of delivery. The male and female mortality rates were 45.97 versus 43.34, 40.95 versus 28.82, 21.65 versus 18.64, 18 versus 15.55 and 14.67 versus 10.86 per 1000LB during the five years preceding the 2000, 2005, 2011, 2016 and 2019 surveys respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, the male neonates had higher risk of mortality compared to female neonates throughout the five consecutive surveys irrespective of place of delivery. The male and female neonatal mortality rates were 53.38 versus 41.97, 45.36 versus 31.53, 44.34 versus 28.4, 39.13 versus 17.3 and 36.77 versus 26.96 per 1000LB in the five years preceding the 2000, 2005, 2011, 2016 and 2019 surveys respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn line with this finding studies reported that males may be biologically disadvantaged compared to females starting at birth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The evidence from global report and some studies stated that fewer countries showed gender disparities, some said boys are at high risk of mortality while others reported excess risk of female child mortality [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A study from Nigeria reported that higher risk among males than females in all the variables under study with the exception of maternal age groups, depicts the risk of infant mortality is higher among female infants than males[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Another study reported that on average, boys are expected to have a higher probability of dying before reaching age 5 than girls [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Sub-Saharan African countries males have 17\u0026ndash;54% higher odds of dying before age five[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, a prediction study from Ghana reported that sex-differentiated U5CMR had projected to reach 33.9 per 1000LBs for males, but it was projected to 26.64 per 1000LBs for females[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] indicating the presence of excess risk of male U5CMR compared to female U5CMR would persist in Ghana.\u003c/p\u003e\u003cp\u003eHowever, it contradicts with studies that reported the female mortality rate was higher than the male mortality rates. A study conducted to test the hypothesis that a gender differential exists in the effect on child mortality reported that 35% excess girl child mortality in India [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, the excess female U5CMR was 18\u0026middot;5 per 1000 live births, which corresponds to an estimated 239, 000 excess deaths per year in India and more than 90% of districts had excess female mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The possible justification for the contradiction might be the biological factors of mortality which has a clearly distinct excess risk for male mortality during infancy, then after the strong effect of the sex linked biological factors alleviated during childhood (1\u0026ndash;4 years old) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, a scoping review reported that \u0026ldquo;given the biological disadvantage of male children, usually, gender bias is suspected when the mortality of girls is higher than expected, which may be due to the cultural favouritism of male children resulting in neglect of girls\u0026rdquo;[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] which in turn elevate mortality of female children. The review also discussed that although such culturally-driven \u0026lsquo;son preference\u0026rsquo; is likely the main reason for higher-than-expected mortality of girls, infanticide may also affect sex ratios, although it seems to be restricted to a few societies\u0026rdquo; [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, the review added that the frequency of infanticide is extremely hard to measure. Whatever the reason, bias against girls is likely to affect the sex ratios, leading to either similar mortality rates for both sexes or higher mortality for girls than for boys[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA multiple variable binary logistic regression analyses: incorporating child-sex, mothers\u0026rsquo; educational status, at least one ANC visit, initiation of breastfeeding within one hour and survey year was conducted in order to estimate the independent excess risk effect of child-sex adjusting for included variables by place of delivery. As a result, among those who got birth at health facility, the male children had 6 per 10,000LB excess deaths than the female children; however, the difference in the risk of mortality was not statistically significant. On the other hand the male post-neonates had 2.15 excess risk of death per 1000LB than the female post-neonates. Similarly, the male neonates had 68.5 excess risk of death per 1000LB compared to female neonates.. Among those who got birth at home, the male children had 4.45 excess risk of death per 10000LB compared to female children, but it was not statistically significant. The male post-neonates however had 6.62 excess risk of mortality per 1000LB compared to female post-neonates. In addition, the male neonates had 21.3 excess mortality risks per1000LB compared to female neonates. The mortality differences between males and females were statistically significant for both post-neonates and neonates irrespective of place of delivery.\u003c/p\u003e\u003cp\u003eControlling the confounding effects of mothers\u0026rsquo; educational status, ANC visit, time of breastfeeding initiation and survey year, the findings had implications, primarily; males had excess risk of mortality compared to females irrespective of place of birth. Secondly, the magnitude of the excess risk of mortality of males had a declining trend as the new-borns\u0026rsquo; age increased from neonatal to post-neonate and to child age irrespective of place of birth. It was 68.5, 2.15 and 0.6 per 1000LB for those who got facility birth and 21.3, 6.62 and 4.45 per 1000LB for those who got home birth, respectively. The other implication of the finding was the presence of observed great variation in the magnitude of the excess risk of mortality between health facility and home births. The excess risk of male post-neonatal mortality among health facility births was low (2.15 versus 6.62 per 1000LB) compared to the excess risk of male post-neonatal mortality of home births. On the contrary, the excess risk of male neonatal mortality among those who got birth at health facility was higher (68.5 verses 21.3 per 1000LB) than the excess risk of male neonatal mortality of those who got birth at home. The findings might indicate that male under five children are biologically more at risk of mortality at birth than female under five children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The higher the magnitude of the male neonatal excess mortality risk among those who got birth at heath facility than at home might indicate the number of complicated births attended at health facility probably higher the number attended at home for male than female new-borns. This in turn might affect the subsequent survival status of live born neonates and the variation in the magnitude of the male excess mortality risk between facility and home births. This finding is in line with a study about neonatal outcomes in relation to sex differences that reported preterm birth, macrosomia, neonatal death, Caesarean Section (SC), and congenital anomaly were more common in males than females[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this comparative trend analysis of the gender disparity in the excess risk of neonatal, post-neonatal and child mortality by place of delivery: the frequency distribution of male and female new-borns was found constant throughout the five consecutive surveys irrespective of place of delivery with sex-ratio of 1.06 to 1. In the sex and age stratified comparative trend analysis: the male had excess risk of mortality compared to female neonates, post-neonates and children irrespective of place of delivery; giving birth at home had excess risk of post-neonatal and child mortality compared to giving birth at health facility: but giving birth at health facility had excess risk of neonatal mortality compared to giving birth at home in the five years preceding the consecutive surveys, except the 2000 survey. In the multiple variable binomial regression analysis: the magnitude of the excess risk of mortality of males had a declining trend as the age of new-borns increased from neonatal to post-neonatal and to child age irrespective of place of birth. The excess risk of male post-neonatal mortality among health facility births was low compared to the excess risk of male post-neonatal mortality of home births. On the contrary, the excess risk of male neonatal mortality among those who got birth at health facility was higher than the excess risk of male neonatal mortality of those who got birth at home. In general adjusting for mothers\u0026rsquo; educational status, ANC visit, initiation time of breastfeeding and survey year, the male neonates, post-neonates and children had excess risk of mortality compared to the female neonates, post-neonates and children, respectively. A gender based care programmatic approaches ranging from time of foetal sex determination to infancy period and investigations of sex-chromosome linked risk factors with genetics study on new-borns are needed to be conducted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study is based on the data available in the public domain to use; therefore, no ethics statement is required for this work.\u003c/p\u003e\n\u003cp id=\"_Toc520119691\"\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eConsent for publication is not applicable- this study did not take individual person’s detail such as name, images, or videos.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc520119692\"\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eAll the data generated or analyzed during this study are included in this published article as additional file and can be accessed through request of the corresponding Author.\u003c/p\u003e\n\u003cp id=\"_Toc520119693\"\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that we have no competing interests.\u003c/p\u003e\n\u003cp id=\"_Toc520119694\"\u003eFunding\u003c/p\u003e\n\u003cp\u003eThere was no external funding for this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eG.H, conceptualization, methodology, formal analysis, and writing-original draft. G.A, and M.A edit and write the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the measure DHS for allowing us to freely access the five consecutive years Ethiopian demographic and health survey data. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed FA: \u003cstrong\u003eGender difference in child mortality\u003c/strong\u003e. \u003cem\u003eEgypt Popul Fam Plann Rev \u003c/em\u003e1990, \u003cstrong\u003e24\u003c/strong\u003e(2):60-79.\u003c/li\u003e\n\u003cli\u003eFund UNCs:\u003cstrong\u003e Levels \u0026amp; Trends in child Mortality: Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation\u003c/strong\u003e. 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In\u003cem\u003e.\u003c/em\u003e; 2019.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCode Library, The DHS program \u003c/strong\u003e[https://github.com/DHSProgram]\u003c/li\u003e\n\u003cli\u003eCosta JC, Victora CG: \u003cstrong\u003eA scoping review of methods for assessment of sex differentials in early childhood mortality\u003c/strong\u003e. \u003cem\u003eBMC pediatrics \u003c/em\u003e2021, \u003cstrong\u003e21\u003c/strong\u003e:1-17.\u003c/li\u003e\n\u003cli\u003eWeng YH, Yang CY, Chiu YW: \u003cstrong\u003eNeonatal outcomes in relation to sex differences: a national cohort survey in Taiwan\u003c/strong\u003e. \u003cem\u003eBiol Sex Differ \u003c/em\u003e2015, \u003cstrong\u003e6\u003c/strong\u003e:30.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonatal, postnatal and child mortality, excess risk, trend, place of delivery","lastPublishedDoi":"10.21203/rs.3.rs-6330556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6330556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMales may be biologically disadvantaged compared with females starting at birth. However, literature reported in fewer countries showed gender disparities in child mortality. On average, boys are expected to have a higher probability of dying before reaching age 5 than girls. On the other hand, it was reported that a gender differential exists in the effect on child mortality with 35% excess girl child mortality. We investigated the trends of gender disparities and its excess risk effect on neonatal, postnatal and child mortality by place of delivery in Ethiopia. This study followed a time-series type of cross-sectional study. The study used the five nationally representative Ethiopian Demographic and Health Surveys data (EDHS 2000, 2005, 2011, 2016 and 2019). The Child and Birth recode data files were accessed as electronic version of STATA file format. The trends and mortality disparities of gender for neonates, post-neonates and children by place of delivery were presented with tables. The sex-age stratified mortality risk difference (RD) of place of delivery with 95% confidence level was calculated using the \u0026ldquo;\u003cem\u003ecsi\u0026rdquo;\u003c/em\u003e STATA command. In addition, we estimated the Population Attributable Risk (PAR) and the Population Attributable Fraction (PAF) of place of delivery using the regpar and punaf STATA commands, respectively. Finally, multiple variable binomial regression analysis was conducted to identify the independent excess risk effect of child sex adjusted for mothers\u0026rsquo; educational status, ANC visit, time of breastfeeding initiation and survey year by place of delivery. Among those who got birth at health facility, male child had 0.626 [Adj.RD\u0026thinsp;=\u0026thinsp;0.000626, 95%CI:-0.000791, 0.00204], male post-neonate had 2.15 [Adj.RD\u0026thinsp;=\u0026thinsp;0.00215, 95%CI: 0.0000228, 0.00428] and male neonate had 68.5 [Adj.RD\u0026thinsp;=\u0026thinsp;0.0685, 95%CI: 0.0576, 0.0795] excess risk of mortality per 1000LB compared to female counterparts. Of those who got birth at home, the excess risk of male child, postnatal and neonatal mortalities were 0.445 [Adj.RD\u0026thinsp;=\u0026thinsp;0.000445, 95%CI: -0.00179\u0026ndash;0.00268], 6.62 [Adj.RD\u0026thinsp;=\u0026thinsp;0.00662, 95%CI: 0.00398\u0026ndash;0.00926] and 21.3 [Adj.RD\u0026thinsp;=\u0026thinsp;0.0213, 95%CI: 0.0181, 0.0245] per 1000LB compared to females, respectively.The male neonates, post-neonates and children had excess risk of mortality compared to female neonates, post-neonates and children irrespective of place of delivery. The magnitude and significance of the excess risk of mortality of males had a declining trend as the age of new-borns increased from neonate to post-neonate and to child age irrespective of place of birth. A gender based care programmatic approaches ranging from time of foetus sex determination to infancy period and investigations of sex-chromosome linked risk factors with genetics study on new-borns are recommended.\u003c/p\u003e","manuscriptTitle":"Gender Disparities and Excess Risks of place of delivery for neonatal, postnatal and Child Mortality in Ethiopia: A comparative trend analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 19:14:22","doi":"10.21203/rs.3.rs-6330556/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebd47082-3548-4b60-8e3a-69b68858f6b0","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-05T08:34:29+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52159574,"name":"Health sciences/Health care/Public health"},{"id":52159575,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2026-05-05T08:41:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 19:14:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6330556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6330556","identity":"rs-6330556","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}