Magnitude of unintended pregnancy among rural reproductive women in Ethiopia: A Multilevel analysis using 2016 EDHS data

Magnitude of unintended pregnancy among rural reproductive women in Ethiopia: A Multilevel analysis using 2016 EDHS data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Magnitude of unintended pregnancy among rural reproductive women in Ethiopia: A Multilevel analysis using 2016 EDHS data Melak Jejaw, Kaleb Assegid Demissie, Misganaw Guadie Tiruneh, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4137645/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Despite growing utilization of family planning in Ethiopia, many pregnancies in rural areas are still unintended and it remains the main global public and reproductive health challenges with devastating impact on women and child health and general public. Hence, this study was sought to determine the prevalence and associated factors of unintended pregnancy in rural women of Ethiopia. This study used the 2016 Ethiopian Demography and Health Survey data. Total weighted samples of 974 reproductive-aged rural women were included in the analysis. Multilevel mixed logistic regression analysis was employed to consider the effect of hierarchal nature of EDHS data by using stata version 14 to determine individual and community level factors. Variables significantly associated with unintended pregnancy were declared with adjusted odds ratio with 95% CI at p-value < 0.05. The prevalence of unintended pregnancy in rural women was 31.66%( 95%CI: 28.8%, 34.66%). Have no media exposure (AOR: 2.67, 95%CI: 1.48, 4.83), not working (AOR: 0.33, 95%CI: 0.21, 0.52), household size of one to three (AOR: 0.44 95%CI: 0.2, 0.96), primiparous (AOR: 0.41, 95%CI: 0.17, 0.99), poor women (AOR: 2.4, 95%CI: 1.24, 4.56), didn’t have intention to use contraceptive (AOR: 0.24, 95%CI: 0.14, 0.44) were individual factors associated to unintended pregnancy. Large central region (AOR: 4.2, 95%CI: 1.19, 14.62) and poor community level (AOR: 4.3, 95%CI: 1.85, 10.22) were community level factors associated to unintended pregnancy. The present study prevalence of unintended pregnancy in rural women was high. Maternal occupation, household size, media exposure, parity, women wealth, intention to use contraceptive, region and community level wealth were factors statistically associated with unintended pregnancy. Hence, demographer and public health practitioners give great emphasis to set strategies to increase accessibility women to media and improve women financial capacity, and strengthen availability of maternal health service to decrease unintended pregnancy adverse outcome in rural areas. Health sciences/Health care Health sciences/Health occupations Health sciences/Medical research Magnitude Unintended Pregnancy Multilevel Analysis Ethiopia Figures Figure 1 Figure 2 Introduction Unintended pregnancy is a pregnancy of woman either not wanted to be pregnant at that time or didn’t have plan to become pregnant in the future[ 1 , 2 ]. Mothers with unintended pregnancy end up with premature delivery, and other pregnancy related adverse outcomes[ 3 , 4 ]. Unintended pregnancy remains a neglected global public health challenge with global burden of 121 million per year with on average of 331,000 per day from 2015 to 2019 and prevalence rate in Sub-Saharan Africa was 29% with lowest 10.8% in Nigeria to highest 54.5% in Namibia. Annually around 14 million unintended pregnancies were occurred in sexually active women[ 5 ]. Unintended pregnancy may occurred due to different reasons such as unable to use contraception, contraception discontinuation, contraceptive defeat, inappropriate and inconsistent use of contraception, lack of knowledge related to family planning[ 6 ]. Unintended pregnancy substantially increase in developing countries that leads to considerable adverse outcome to the mother, child, decrease mother to child relationship quality, induces physical abuse and violence against women, women and girls may suffer from the cultural, religion and marital status stigma in the community[ 7 – 9 ]. In addition, unintended pregnant mothers are high risk to develop serious complication namely; unsafe abortion the main adverse outcome, increase crime rate, maternal depression, induces family and parent stress, decrease workforce productivity and limit job option, and decrease academic performance till derail from educational plan specially rural women. According to WHO report (2020) around 60% of unintended pregnancy end up with abortion and about 45% of them were due to unsafe abortion followed by serious complications of hemorrhage, uterine perforation, cervical injury and infection related procedure that may leads to maternal death. Annually, around 7 million of unsafe abortion of unintended pregnancy leads to hospital admission in developing countries [ 10 – 13 ]. Moreover, since the pregnancy is unintended the pregnant mothers are unhappy and become careless for this pregnancy that leads to late commence of antenatal care visit and disinterested to give birth at the health facility[ 4 ]. Generally, unintended pregnancy has a negative impact for mother’s personal life and their family, financial and social cost as well as greatly contributed to maternal morbidity and mortality. Previous study done in SSA revealed that unintended pregnancy is more common in rural dwellers women. Besides, the 2016 EDHS report showed that the magnitude of unintended pregnancy and teenage pregnancy is high in rural areas[ 5 ]. Moreover, rural women are less educated, lack of access to visit health facility to utilize family planning service, lack of power in decision making about family planning and to get pregnancy, rural women have high parity and most rural women are economically poor[ 14 ]. Furthermore, most rural women have no access social media to get information about maternal health service and to make judgments related to different issue[ 15 ] Even though there is growing utilization of family planning in Ethiopia, unintended pregnancies is still the main public health challenge with prevalence rate range of 13.7–41.5%[ 16 – 18 ]. From global and local reviewed literature socio-demographic factors such as; maternal age[ 6 , 17 , 19 – 21 ], maternal educational level[ 21 – 23 ], religion[ 18 , 24 , 25 ], maternal marital status[ 26 – 29 ], home distance from the nearest health facility[ 27 ] parity[ 27 , 28 ], household size[ 30 , 31 ], income/wealth status[ 5 , 22 , 25 ], knowledge of ovulation cycle[ 32 ], knowledge of family planning[ 6 ], ever had history of terminated pregnancy[ 17 , 20 ], residence[ 6 , 25 ] and region[ 21 , 30 ] are the key factors statistically associated with unintended pregnancy. The Sustainable Development Goals (SDG) of 2030 has target of ensuring the right of all women and couple to choose to have children up to determination of their number and spacing of birth with global ambition of all birth is wanted and all child is valued[ 11 ]. Alleviating unintended pregnancy via vigorous effort has a great importance to prevent negative consequence of abortion, save financial and social costs, and reduce maternal and child mortality[ 33 ]. Even though unintended pregnancy has devastating adverse outcome to the women and child health as well as economic and social impact to women and her parents, as to my our search, there was paucity of evidence of unintended pregnancy among rural women based on nationally representative data in Ethiopia. Hence, this study aimed to determine the prevalence and associated factors of unintended pregnancy among rural women. This study finding will provide knowledge to health workers and policy makers to understand the magnitude of unintended pregnancy and its associated factors to fill gaps by developing attainable intervention. Methods Data source and population This a cross-sectional study used the national representative (nine region and two city administration) data of 2016 Ethiopian Demography and Health Survey (EDHS) that was done from January 18, 2016, to June 27, 2016[ 36 ] The 2016 EDHS used a two stage stratified sampling technique to select a sample. In the first stage, the regions were stratified in to urban and rural areas, and every stratum was clustered into 645 enumeration areas( 202 in urban and 443 in rural) by using the 2007 Ethiopian Population and Housing Census (PHC)[ 34 ] as sampling frame and each clustered were selected by using probability proportional size allocation. In the second stage, 28 household per cluster with total 16,650 households` (15,683 female respondents and 12,688 male respondents) were selected through equal probability systematic sampling technique. The survey data was focus on reproductive health (fertility and fertility preference, marriage, awareness and the use of family planning methods), adult and childhood morbidity and mortality, and reproductive women awareness and attitudes towards HIV/AIDS and other important public health issues. The overall information about the sampling procedure and the questionnaire can be accessed from the EDHS 2016 report[ 35 ]. This study used women’s data (IR data) set of 2016 EDHS that was obtained from the demographic and health survey (DHS) program website ( http://www.measuredhs.com ) For this study analysis, a total weighted sample of 974 pregnant women were selected from the total of 10, 335 rural dwellers reproductive age women(15–49 years) interviewed in the 2016 EDHS (Fig. 1 ). Study variables and definition The dependent variable name for this study is “current pregnancy wanted” in EDHS datasets. In this concern, the reproductive age women who involved in this survey were asked whether they were currently pregnant and those women who answered yes were further asked whether they wanted the current pregnancy. This produced three responses options such as “wanted then”, “wanted later” and “not at all”. To generate the dependent variable for unintended pregnancy that includes pregnancies of either unwanted(the mother didn’t wanted to be pregnant at all) or the pregnancy was mistimed( the mother didn’t have plan to become pregnant at that time but wanted later)[ 13 ]. Then the dependent variable of unintended pregnancy was dichotomous created by re-coding the original variable as “wanted then”=0 as “intended”, “wanted later” and “not at all” = 1 as “unintended”. The independent variables were individual level variables such as maternal age, maternal occupation status, maternal educational status, marital status, religion, parity, household size, wealth status, media exposure status, ever had of termination pregnancy, knowledge of ovulation cycle, sex of household head, unmet need for contraceptive, family size, fertility preference, knowledge of contraception, intention of contraceptive use, and community level variable such as region, community perceived distance from the health facility, community women educational status, community women wealth status, community women media exposure, community women awareness of modern contraceptives. Some variables were recorded for simplicity of this study analysis such as wealth status of poorest and poorer category were re-coded as poor = 1, those above the poorest wealth category and below the high category were leveled as middle = 2, whereas richest and richer wealth category were re-coded as rich = 3. On the other hand, region was recorded similarly with the previous study in Ethiopia as “small peripheral” that includes Afar, Somali, Benishangul, and Gambela, and the “large central” which includes Tigray, Amhara, Oromi, Southern Nations Nationalities and Peoples Region (NNPR), Harari, Dire Dawa, and Addis Ababa based on their geopolitical characteristic[ 36 ]. For the analysis purpose other independent variables were recorded. Other community level variables such as community women educational status, community wealth status, community media exposure, community modern contraceptive awareness were generated from the individual level variables by aggregating the data and using median because of asymmetric distribution of the data. Before aggregating the community level variables, each individual level variable were di-chotomized into yes or no by referring to previous studies and considering those clusters as having high proportion whose value above the median and clusters as having low proportion whose value below the median. Data processing and analysis Data extraction and cleaning, coding, labeling and analysis were performed by using stata version 14 software. The effect of sampling bias; due to the unequal probability selection of strata and non-response, was reduced through data weighting by using sample weight factor (v005) of EDHS data set. Multi-level analysis was done after ascertain the eligibility of data by using Inter-cluster Correlation Coefficient (ICC) above 10% (ICC = 57.52% for this study) to consider the hierarchical nature of the DHS data. Bi-variable multi-level logistic regression analysis was done to compute the crude odd ratio at 95% confidence level and variables with p-value < 0.20 were candidate for multi-variable multi-level logistic regression analysis. After selecting the eligible variable for multi variable multi-level logistic regression analysis with serious of four model fitness was done; model 0 (empty or null model without independent variable) only the unintended pregnancy was analyzed through cluster variable to examined the random effect. Model 1 (examined the effect of only individual-level factors), mode 2 (examined the effect of only community-level factors), and model 3 (examined the mixed effect of individual and community factors at the same time). Statistical association for fixed effect of variables to unintended pregnancy was declared at p-value < 0.05 with Adjusted Odd Ratio (AOR) at 95% confidence interval. The variations between clusters (random effect result) were estimated through three different methods such as Intra-Class Correlation (ICC), Median Odd Ratio (MOR) and Proportional change Variance (PCV). ICC was done for each model I, I, II, III and it shows the variation of community characteristics effect on unintended pregnancy of reproductive women in rural areas. The higher ICC value helps to identify the most relevant community characteristics to realize the individual variation in unintended pregnancy. MOR refers the odd ratio between the two randomly selected different areas with highest risk and the lowest risk and it was computed by using the formula of MOR = . The MOR value indicates the level of risk of an individual being resident to that area to have unintended pregnancy of reproductive women[ 37 ]. PCV was done for model I, II, III with respect the variance the model zero to portray the power of factors in the model to examine unintended pregnancy and PCV was computed by subtracting the variance of each model from the empty model by using the formula PCV= (Ve-Vmi)/Ve where Ve; variance of the empty model and Vmi; variance of consecutive model[ 38 ]. Model fitness for the three models was examined by using nested deviance. The missing values were handling based on EDHS guideline. Missing value of current pregnancy intention status was excluded from the numerators and it was included in the denominator. If the outcome variable; current pregnancy intention status, contained the missing or “no response” or “don’t know” considered as undesired pregnancy. Explanatory variables that include missing value more than 5% because the EDHS survey is cross-sectional study were excluded from further analysis Patient and public involvement statement Currently pregnant women were included in this study by furnishing valuable information. However, the study participant have never been participated in the study design, protocol, data collection tools, and disseminating this finding. Result Individual level characteristics of the rural pregnant women A total of 974 reproductive women were included in the analysis. The overall mean age of the women was 27.9(SD ± 6.4) years and about three-fourth (75.2%) of them were between 20–34 years of age. Most (59.6%) of the women didn’t attend formal education. Majorities (96.15%) of the women were married and about 45% women were Muslim followers. Besides, around 55.8% of women had work but; about 53.2% of women had poor wealth status. Regarding to household head, majority (87.87%) of the household head were male. Moreover, nearly half(48.3%) of women had 4 to 6 family size and neatly, two-third (65.9%) the women had desire to have more children (Table 1 ). Table 1 Individual level characteristics of pregnant women in EDHS 2016 Variable Frequency(weighted) Percentage (weighted) Age 15–19 78 8.0 20–34 732 75.2 35–49 164 16.8 Maternal education No formal education 580 59.6 Primary 347 35.6 Secondary and above 47 4.8 Religion Orthodox 292 30.0 Muslim 442 45.3 Protestant 208 21.4 Catholic and Others 32 3.3 Marital status Married 937 96.15 Not married 37 3.85 Maternal occupation Working 544 55.84 Not working 430 44.16 House wealth index Poor 518 53.15 Middle 209 21.50 Rich 247 25.35 Media exposure Yes 277 71.58 No 697 28.42 Household size 1 to 3 259 26.59 4 to 6 471 48.34 7 and above 244 25.07 Sex of household head Male 856 87.87 Female 118 12.13 Parity Primiparous 299 30.72 Multiparous 418 42.88 Grand multiparous 257 26.40 Ever terminated pregnancy Yes 878 90.16 No 96 9.84 Intention to contraceptives Intend to use 689 70.75 Does not intend to use 285 29.25 unmet need for contraception Not unmet need 666 68.33 Unmet need 308 31.67 Awareness on modern contraceptive method Knows no method 28 2.87 Knows modern method 946 97.13 Desire for another child Yes 642 65.94 No 332 34.06 Knowledge of ovulation cycle Know ovulation period 787 80.81 Don’t know ovulation period 187 19.19 * Yes; if the women want to have another child within 2 years or after 2 years *No ; if the women do not want any more children Community level characteristics Majority (92.67%) of women were from larger central region and most (62.37%) of the women perceived that they had big problem to get medical care at the health facility. About 61% of the community women educational status was low and more than half (53%) of community women had media exposure. Moreover, about two-third (66.17%) of the community women wealth status were poor (Table 2 ). Table 2 Community level characteristics of pregnant women in Ethiopia, EDHS 2016 Variable Frequency(weighted) Percentage(weighted) Distance to the health facility Big problem 608 62.37 Not big problem 366 37.63 Region Small peripheral 71 7.33 Larger central 903 92.67 Community media exposure Yes 516 52.98 No 458 47.02 Community women educational level Higher women education 380 39.01 Low women education 594 60.99 Community awareness on modern contraceptive method High 70 7.18 Low 904 92.82 Community wealth status Rich 329 33.83 Poor 645 66.17 The Prevalence of unintended pregnancy The overall prevalence of unintended pregnancy in this study was 31.66%( 308), 95% CI; 28.81%, 34.66%) comprises 21.59% mistimed and 10.07% of unwanted (Fig. 2 .) Random effect and model fitness According to the empty (null) model Intra-Cluster Correlation Coefficient (ICC) result, about 57.52% of the overall variability of unintended pregnancy can be attributed to cluster variability. The median odd ratio (MOR) of unintended pregnancy in the empty model was 6.11. This portrayed that if an individual randomly selected from two different clusters and then those women from clusters having high unintended pregnancy had 6.11 times higher odds to have unintended pregnancy as compared to women from the lower unintended pregnancy clusters. Besides, the proportional change variance (PCV) increase from 26.99% in model I to 38.29% in model III, this shows the final model (model III) best explained the unintended pregnancy variability. The model fitness was checked by using deviance (-2LLR). Since model III had the lowest deviance value that indicates the best fitting model (Table 3 ). Table 3 Model comparison and random effect analysis result of unintended pregnancy among rural women in Ethiopia, EDHS 2016 Random effect Null model Model I Model II Model III Variance 3.63 2.65 2.99 2.24 ICC (%) 57.52% 44.61% 47.62% 40.54% MOR 6.11 4.69 5.17 4.14 Explained variance (PCV) Ref 26.99% 17.63% 38.29% Model fitness Deviance (-2logliklihood) 995.88 1100.98 1067.26 964.38 Individual and community-level factors associated with unintended pregnancy (fixed-effects) After adjusted for the individual level and community level factors in the final model (model III) maternal occupational status, media exposure status of the women, maternal wealth status, household size, parity and contraception intention of the women were individual level factors statistically associated with unintended pregnancy of rural women. Among community level factors region and community level wealth statuses were factors statistically associated with unintended pregnancy. The fixed effect model, in bi-variable multilevel modeling all individual and community level factors had p < 0.2 and exported to multi-variable multilevel logistic regression analysis. The odd of unintended pregnancy was 67% lower among women whose occupational status were not worker as compared to worker (AOR: 0.33, 95%CI: 0.21, 0.52). The odd of unintended pregnancy was 2.4 times higher among women whose wealth status was poor as compared to rich (AOR: 2.4, 95%CI: 1.24, 4.56). Those women who had no media exposure were 2.67 times high risk to have unintended pregnancy as compared to counterparts (AOR: 2.67, 95% CI: 1.48, 4.83). The odd of unintended pregnancy was 56% lower among women who had household size of one to three as compared to household size of seven and above (AOR: 0.44, 95% CI: 0.2, 0.96). Besides, the odd of unintended pregnancy was 59% lower among primi-parous women as compared to grand multiparous (AOR: 0.41, 95% CI: 0.17, 0.99) Similarly, the odd of unintended pregnancy was 76% lower among those women who didn’t have intention to use contraceptive (AOR: 0.24, 95%CI: 0.14, 0.41). Moreover, those women who lived in larger central region were 4.2 times higher as compared to small periphery region (AOR: 4.2, 95% CI: 1.19, 14.62). Furthermore, the odd of unintended pregnancy was 4.3 times higher among women from community of poor wealth status as compared their counterparts (AOR: 4.3, 95% CI: 1.85, 10.22) (Table 4 ) Table 4 Multi-variable analyses for factors affecting unintended pregnancy among rural reproductive age women in Ethiopia (n=974) Variables Pregnancy intention Model I AOR (95% CI) Model II AOR(95% CI) Model III AOR(95% CI) Unintended n (%) Wanted n (%) Age 15-19 15 62 1 1 20-34 228 505 0.8(0.32, 2.01) 0.9(0.36, 2.26) 35-49 65 99 1.53(0.49, 4.82) 1.44(0.45, 4.61) Maternal educational status No formal education 188 392 1.05(0.36,3.08) 1.1(0.35, 3.17) Primary 107 240 1.19(0.41, 3.41) 1.09(0.38, 3.13) Secondary and above 13 34 1 1 Occupational status Not working 139 405 0.34(0.22 0 .54) 0.33(0.21, 0 .52)** Working 169 261 1 1 Wealth status Poor 163 355 1.55(0.84, 2.88) 2.4(1.24, 4.56)** Middle 62 147 0.81(0.42,1.56) 0.83(0.44, 1.59) Rich 83 164 1 1 Media exposure Yes 228 469 1 1 No 80 197 2.45(1.40, 4.26) 2.67(1.48, 4.83)** House hold size 1 to 3 53 206 0.55(0.26,1.19) 0.44(0.20, 0.96)** 4 to 6 163 308 0.83(0.45,1.56) 0.68(0.37, 1.28) 7 and above 92 152 1 Parity Primiparous 64 235 0.4(0.17 , 0.97) 0.41(0.17, 0.99)** Multiparous 138 280 0.8(0.40 1.58) 0.82(0.41, 1.61) Grand multiparous 106 152 1 1 Intention to contraceptives Intend to use 256 434 1 1 Does not intend to use 52 232 0.18(0.11, 0 .31) 0.24(0.14, 0 .41)** Region Small peripheral 5 71 1 1 Large central 303 595 6.5(2.09, 20.29) 4.2(1.19, 14.62)** Distance to the health facility Big problem 216 392 1.8(1.13, 2.75) 1.64(0.98, 2.74) Not big problem 92 274 1 1 Community level educational status Higher 100 280 1 Low 208 386 0.9(0.47, 1.63) 1.19(0.56, 2.56) Community level wealth status Poor 230 415 2.6(1.29, 5.11) 4.3(1.85, 10.22)** Rich 78 251 1 1 Community level media exposure Ever had media exposure 166 350 1 1 Never had media exposure 142 316 0.9(0.49 1.64) 0.57(0.27 ,1.22) ** Statistically significant, AOR: Adjusted Odd Ratio, 1; Reference Discussion The current study aimed to assess the prevalence and associated factors of unintended pregnancy among rural women in Ethiopia by using EDHS 2016 data through multilevel analysis. The overall prevalence of unintended pregnancy in this study was 31.66% which is comparable with study conducted in national report of EDHS 2011[ 32 ], Shashmanie, Ethiopia[ 39 ], Egypt[ 40 ] and Sab Saharan Africa[ 41 ]. This study finding is less than the study conducted in Gellan[ 42 ], South Africa[ 43 ], Botswana[ 44 ], Arsi, Ethiopia[ 28 ], Ghana[ 21 ], Addis Ababa[ 45 ] and Debere Markos[ 46 ]. The discrepancy might be due to study population and study period variation, study area difference, methodological variation, difference of maternal health quality, coverage and accessibility including family planning methods, and socio-cultural. This study includes merely the rural women whereas study done in Gellan, Arsi, Debere Markos and Ghana were community based that include both rural and urban that may increase magnitude of unintended pregnancy on those study areas. On the other hand, study report in Addis Ababa revealed that most study participant that had unintended pregnancy were women in schools whereas the current study majority of the participants were not attend formal education. Thus women in schools are the most fire age groups, influenced by peers’ pressure and have access to watch pornography that induces sexual desire for those adolescent girls to have sex and may not have awareness about family planning that leads to unintended pregnancy and abortion. Besides, study conducted in Debere Markos, Ethiopia showed that the study participants had risky behavior such as substance abuser, alcohol drunker and khat user’s practices risky sexual behavior that attribute for unintended pregnancy whereas this study participants are rural women who had less to zero risky behavior for addiction. Moreover, by fearing social discrimination, the cultural taboo and intention of husband to have more children, rural women have a tendency of saying the pregnancy was wanted after the pregnancy already occurred even if they didn’t want really. However; this finding is higher than study done in low and middle income countries[ 47 ], Gambia[ 48 ], Ethiopia national survey 2013[ 32 ], Gondar town[ 26 ], Bangladesh[ 14 ], Europe[ 49 ], and Cambodia[ 20 ]. The possible reason for the difference between in this study and Europe might be European women has better income, the women has autonomy have a great chance to be aware about contraceptive methods that makes the women high likely to use family planning that decrease the risk of unintended pregnancy. Likewise, women in Europe has chance of open communication with her husband about family planning in contrast, rural women in Ethiopia may not have chance of open communication with her husband about the family planning, may not have chance of autonomy to decide about her family planning and their fertility that may contribute for unintended pregnancy. Study done in Bangladesh revealed that all study participate women were married whereas the current study were both married and unmarried. Indeed, married women are less likely to have unintended pregnancy than unmarried women. In addition, study conducted in Gondar was in urban areas whereas this study was done in rural areas. Rural women may have limited maternal health information and health institution due to lack of awareness and knowledge towards pregnancy status that may increase unintended pregnancy in rural areas than urban. Traditionally, majority of the rural women wants to bear many children thus women may not use contraceptive that in turn end up with unintended pregnancy. Again, life style, cultural and socio-demographic difference may play role for the variation. Moreover, Southwest Ethiopia study was institutional based that may underestimates of the unintended pregnancy in contrast this study was community based that may show real figures of unintended pregnancy. In multivariable multilevel logistic regression analysis household size, media exposure, maternal occupational status, wealth status, parity, contraception intention, region and community level wealth. Household size of one to three has lower odds of unintended pregnancy compared to household size of seven and above. This study finding is similar with study done in Ethiopia[ 26 , 30 ], Sudan[ 50 ] [ 55 ] and Niger[ 31 ]. The reason may be due women with larger household size spent hard time in caring of their family that greatly affecting women to accesses information and utilizations of maternal health services such as the family planning services that in turn end up with unintended pregnancy. In addition, women with larger household size may perceive that they reached their ideal number of children since any more pregnancy may consider as unintended pregnancy. Women who didn’t have media exposure were more likely to have unintended pregnancy. This finding is contradictory with study done in low and middle income countries which revealed that women who had media exposure had higher odd of unintended pregnancies. However; the present study result is in line with study done in Ethiopia[ 51 , 52 ], Nepal[ 53 ] and Pakistan[ 54 ]. The potential reason may be women who don’t have media exposure may not got information regarding to contraceptive methods and importance of maternal health service utilization. Women who are not currently working have lower odds of unintended pregnancy compared to women who are currently working. This result is congruent with a study done in Ethiopia[ 55 , 56 ]. However this finding is contradictory to study done in Ethiopia[ 57 ] and Cambodia[ 20 ]. The potential explanation for the variation might be women who don’t have work could have low level of social contact and self-confidence to have relationship, and may be free from the influence of work that induces to practice unplanned sex that in turn leads to unintended pregnancy[ 55 ]. Poor women have higher odds of unintended pregnancy compared to rich women and this is consistent with other study done in United States America[ 58 ], Venator[ 59 ], India[ 60 ] and South Asia[ 61 ]. The possible reason may be poor women may not have opportunity to attained formal education, less likely to use contraceptive and high risk for unprotected sex that leads to unintended pregnancy. Besides, primiparous women were less likely to have unintended pregnancy. The potential explanation may be primiparous women may have opportunity to be aware and to utilized family planning service whereas grand multiparous women may believe that they are infertile and reached menopause, thus women are restricted to use family planning service that increases the likelihood of unintended pregnancy. Women who don’t have intention to use contraceptive were less likely to have unintended pregnancy than women who have intention to use contraceptive. This result is inconsistent with study South Asian countries[ 61 ]. The plausible justification may be women who don’t have intention to use contraceptive would perceive themselves as low risk to have pregnancy and they may gave socially desirable response for already occurred pregnancy by responding didn’t have intention to use contraception. This finding is contradictory to study done in Ethiopia[ 62 ]. Moreover, women reside in large central region were more likely to have unintended pregnancy as compared to women reside in small periphery region. This finding is similar with other studies done in Ethiopia[ 30 ], Kenya[ 19 ] and Ghana[ 21 ]. The variation might be due to population size in small peripheral region is lower as compared to larger central region, thus women’s pregnancy may be recommended and acceptable in small peripheral region whereas women in the large central region women may be busy due to their intention to improve their financial status and majority of the women’s pregnancies tends to be unintended[ 36 ]. Furthermore, community wealth status was another community level factor associated to unintended pregnancy. Poor community women have higher odds of unintended pregnancy compared to counterparts and it is consistent with other similar study[ 61 ]. The discrepancy may be poor community women may not have access to education and media, women may not have chance to obtained information about family planning, and lack of transportation cost to utilize maternal health service may contribute unintended pregnancy. Strength and limitation of this study The present study had strength because it used large nationally representative data. This study used the appropriate multilevel statistical approach to account the hierarchical nature of the data. Since this study used nationally representative data, it has high chance of providing insights for policy-makers and program designers to set the most potential intervention strategies at all levels(nationally and regional level). This study might have the possible limitation of recall bias since this study used the EDH survey data that was heavily relay on the respondent's self-report. Moreover, this study was unable to ascertain the temporal relationship between unintended pregnancy rather it shows the association between unintended pregnancies and factors. Conclusion and recommendations The present study prevalence of unintended pregnancy in rural women was high. From multivariable multilevel logistic regression analysis, individual level variable such as maternal occupation status, household size, media exposure, parity, wealth status and contraception, and community level variables such as region and community level wealth were statistically associated with unintended pregnancy. Hence, priority attention has to be given to increase accessibility of media and set strategies to improve women financial capacity to those high vulnerable groups (unintended pregnancy) and enhancing availability maternal health service to decrease adverse outcome of unintended pregnancy in rural areas. Abbreviations AOR Adjusted Odds Ratio CI Confidence Interval DHS Demographic and Health Survey EAs Enumeration Areas EDHS Ethiopian Demographic and Health Survey ICC Intra Class Correlation MOR Median Odd Ratio PCV Proportional Change in Variance PHC Population and Housing census, SNNPR Southern Nations Nationalities and Peoples Region Declarations The ethical approval and permission to obtain the data were freely access from the DHS website www.measuredhs.com . During the original DHS data collection process, international and national ethical guidelines were considered. Ethical clearance for the original DHS data was approved by the ICF Macro Institutional Review Board, the Center for Disease Control and Prevention (CDC) and Institutional Review Board (IRB) in each country, in accordance with United States Department of Health and Human Services requirement for human subject protection. According to DHS, all respondents and /or their legal guardians(s) of minors’ (age below 16) provided written consent to participant. All the methods were done based on Helsinki declarations. No information obtained from the data set was disclosed to any third person. The study is not an experimental study. Further explanation of how DHS uses data and its ethical standards can be found at: http://goo.gl/ny8T6X. Data availability The data used for the present study will be accessible with rational request from responsible corresponding author and every one can access the data set online from www.measuredhs.com. Authors’ contributions MJ, initiated the research concept, wrote up the research proposal, extract and analyze the data, presented the result and wrote up the manuscript. AH participated in data analysis, interpretation of the result and wrote up of the manuscript. KD, MT, KA and YT participated in data extraction and data analysis. AE, WN, AW, LY, MG and NW involved in the interpretation of the result and discussion. LA, HA, DG and AY involved in the review and finalized the manuscript document. All authors read and approved the final manuscript. Consent for publication It is not applicable for the present study because this study used a secondary data analysis done by Ethiopian central statistical agency. Competing interests All authors declare that they have no competing interests. Funding source No funding was secured for this study. Acknowledgments We would like to forward our special acknowledgement to measure DHS program for given us to access and use the 2016 EDHS data set. 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Teller, Prevalence and determinants of unintended childbirth in Ethiopia. BMC Pregnancy and Childbirth, 2014. 14: p. 1–9. Izugbara, C., Household characteristics and unintended pregnancy among ever-married women in Nigeria. Social medicine, 2014. 8(1): p. 4–10. Habte, D., et al., Correlates of unintended pregnancy in Ethiopia: results from a national survey. PLOS ONE, 2013. 8(12): p. e82987. Le, H.H., et al., The burden of unintended pregnancies in Brazil: a social and public health system cost analysis. International Journal of Women's Health, 2014: p. 663–670. Larsen, L., et al., The impact of rapid urbanization and public housing development on urban form and density in Addis Ababa, Ethiopia. Land, 2019. 8(4): p. 66. Tusa, B.S., A.B. Weldesenbet, and S.A. Kebede, Spatial distribution and associated factors of underweight in Ethiopia: an analysis of Ethiopian demographic and health survey, 2016. PLOS ONE, 2020. 15(12): p. e0242744. Teshale, A.B. and G.A. Tesema, Magnitude and associated factors of unintended pregnancy in Ethiopia: a multilevel analysis using 2016 EDHS data. BMC Pregnancy and Childbirth, 2020. 20: p. 1–8. Yalew, M., et al., Individual and community-level factors associated with unmet need for contraception among reproductive-age women in Ethiopia; a multi-level analysis of 2016 Ethiopia Demographic and Health Survey. BMC public Health, 2020. 20: p. 1–9. Ononokpono, D.N. and C.O. Odimegwu, Determinants of maternal health care utilization in Nigeria: a multilevel approach. The Pan African medical journal, 2014. 17(Suppl 1). Alemu, B.W., Third stage of Labor Practice and Associated Factors among Skilled Birth Attendants Working in Gamo and Gofa Zone Public Health Facility, Southern, Ethiopia. Ethiopian Journal of Reproductive Health, 2021. 13(2): p. 10–10. Mohamed, E.A.-E.B., et al., Prevalence, determinants, and outcomes of unintended pregnancy in Sohag district, Egypt. Journal of the Egyptian Public Health Association, 2019. 94: p. 1–9. Ayalew, H.G., et al., Prevalence and factors associated with unintended pregnancy among adolescent girls and young women in sub-Saharan Africa, a multilevel analysis. BMC Women's Health, 2022. 22(1): p. 464. Yohannes, E. and B. Balis, Unintended pregnancy and associated factors among women who live in Ilu Gelan District, Western Ethiopia, 2021. International Journal of Reproductive Medicine, 2022. 2022. Woldesenbet, S., et al., The prevalence of unintended pregnancy and its association with HIV status among pregnant women in South Africa, a national antenatal survey, 2019. Scientific Reports, 2021. 11(1): p. 23740. Doherty, K., et al., Unintended pregnancy in Gaborone, Botswana: A cross sectional study. African Journal of Reproductive Health, 2018. 22(2): p. 76–82. Mulatu, T., A. Cherie, and L. Negesa, Prevalence of unwanted pregnancy and associated factors among women in reproductive age groups at selected health facilities in Addis Ababa, Ethiopia. J Women’s Health Care, 2017. 6(392): p. 2167–420. Nigussie, K., et al., Magnitude of unintended pregnancy and associated factors among pregnant women in Debre Markos Town, East Gojjam Zone, Northwest Ethiopia: a cross-sectional study. International journal of women's health, 2021: p. 129–139. Aragaw, F.M., et al., Magnitude of unintended pregnancy and its determinants among childbearing age women in low and middle-income countries: evidence from 61 low and middle income countries. Frontiers in Reproductive Health, 2023. 5. Barrow, A., et al., Prevalence and factors associated with unplanned pregnancy in The Gambia: findings from 2018 population-based survey. BMC Pregnancy and Childbirth, 2022. 22(1): p. 17. Goossens, J., et al., The prevalence of unplanned pregnancy ending in birth, associated factors, and health outcomes. Human Reproduction, 2016: p. 1–13. Sabahelzain, M.M., et al., Prevalence and factors associated with unintended pregnancy among married women in an urban and rural community, Khartoum state, Sudan. Global J Med Public Health, 2014. 3(4): p. 1–9. Admasu, E., et al., Level of unintended pregnancy among reproductive age women in Bahir Dar city administration, Northwest Ethiopia. BMC Research Notes, 2018. 11: p. 1–5. Abita, Z. and D. Girma, Exposure to mass media family planning messages and associated factors among youth men in Ethiopia. Heliyon, 2022. 8(9). Acharya, P., R. Gautam, and A.R. Aro, Factors influencing mistimed and unwanted pregnancies among Nepali women. Journal of biosocial science, 2016. 48(2): p. 249–266. Razzaq, S., et al., Unintended pregnancy and the associated factors among pregnant females: Sukh Survey-Karachi, Pakistan. JPMA. The Journal of the Pakistan Medical Association, 2021. 71(11 (Suppl 7)): p. S50. Kassie, T., et al., Magnitude and factors associated with unintended pregnancy among pregnant women in Addis Ababa, Ethiopia. Global journal of medicine and public health, 2017. 6(4): p. 15. Mulat, S., et al., Prevalence of unplanned pregnancy and associated factors among mothers attending antenatal care at Hawassa City Public Hospitals, Hawassa, SNNPR, Ethiopia. Ethiopia J Women's Health Care, 2017. 6(387): p. 2167–0420. Moges, Y., et al., Factors associated with the unplanned pregnancy at Suhul General Hospital, Northern Ethiopia, 2018. Journal of Pregnancy, 2020. 2020. Iseyemi, A., et al., Socioeconomic status as a risk factor for unintended pregnancy in the contraceptive CHOICE project. Obstetrics & Gynecology, 2017. 130(3): p. 609–615. Magnus, A., Poverty in the United States: An analysis of Its Measurement and the Long-term Social and Economic Costs, 2020, University of Denver. Anand, A., S. Mondal, and B. Singh, Changes in Socioeconomic Inequalities in Unintended Pregnancies Among Currently Married Women in India. Global Social Welfare, 2023: p. 1–12. Sarder, A., et al., Prevalence of unintended pregnancy and its associated factors: evidence from six south Asian countries. PLOS ONE, 2021. 16(2): p. e0245923. Wobse, B.A. and T.A. Gashaw, Multilevel modeling of unintended current pregnancy: In the case of Ethiopian Demographic and Health Survey, 2016. Digital Health, 2023. 9: p. 20552076231173306. Additional Declarations No competing interests reported. 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13:18:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9182,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of pregnancy intention among pregnant rural women in Ethiopia, EDHS 2016\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4137645/v1/fc72b547aaf32f8af286c285.png"},{"id":73094766,"identity":"3e965dad-c3d0-41aa-b323-64d6c97421c3","added_by":"auto","created_at":"2025-01-06 16:24:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1169228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4137645/v1/4bbbd7fe-6409-4343-9362-de99a6f7627b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Magnitude of unintended pregnancy among rural reproductive women in Ethiopia: A Multilevel analysis using 2016 EDHS data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnintended pregnancy is a pregnancy of woman either not wanted to be pregnant at that time or didn\u0026rsquo;t have plan to become pregnant in the future[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Mothers with unintended pregnancy end up with premature delivery, and other pregnancy related adverse outcomes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnintended pregnancy remains a neglected global public health challenge with global burden of 121\u0026nbsp;million per year with on average of 331,000 per day from 2015 to 2019 and prevalence rate in Sub-Saharan Africa was 29% with lowest 10.8% in Nigeria to highest 54.5% in Namibia. Annually around 14\u0026nbsp;million unintended pregnancies were occurred in sexually active women[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnintended pregnancy may occurred due to different reasons such as unable to use contraception, contraception discontinuation, contraceptive defeat, inappropriate and inconsistent use of contraception, lack of knowledge related to family planning[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnintended pregnancy substantially increase in developing countries that leads to considerable adverse outcome to the mother, child, decrease mother to child relationship quality, induces physical abuse and violence against women, women and girls may suffer from the cultural, religion and marital status stigma in the community[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, unintended pregnant mothers are high risk to develop serious complication namely; unsafe abortion the main adverse outcome, increase crime rate, maternal depression, induces family and parent stress, decrease workforce productivity and limit job option, and decrease academic performance till derail from educational plan specially rural women. According to WHO report (2020) around 60% of unintended pregnancy end up with abortion and about 45% of them were due to unsafe abortion followed by serious complications of hemorrhage, uterine perforation, cervical injury and infection related procedure that may leads to maternal death. Annually, around 7\u0026nbsp;million of unsafe abortion of unintended pregnancy leads to hospital admission in developing countries [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, since the pregnancy is unintended the pregnant mothers are unhappy and become careless for this pregnancy that leads to late commence of antenatal care visit and disinterested to give birth at the health facility[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Generally, unintended pregnancy has a negative impact for mother\u0026rsquo;s personal life and their family, financial and social cost as well as greatly contributed to maternal morbidity and mortality.\u003c/p\u003e \u003cp\u003ePrevious study done in SSA revealed that unintended pregnancy is more common in rural dwellers women. Besides, the 2016 EDHS report showed that the magnitude of unintended pregnancy and teenage pregnancy is high in rural areas[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, rural women are less educated, lack of access to visit health facility to utilize family planning service, lack of power in decision making about family planning and to get pregnancy, rural women have high parity and most rural women are economically poor[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, most rural women have no access social media to get information about maternal health service and to make judgments related to different issue[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eEven though there is growing utilization of family planning in Ethiopia, unintended pregnancies is still the main public health challenge with prevalence rate range of 13.7\u0026ndash;41.5%[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom global and local reviewed literature socio-demographic factors such as; maternal age[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], maternal educational level[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], religion[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], maternal marital status[\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], home distance from the nearest health facility[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] parity[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], household size[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], income/wealth status[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], knowledge of ovulation cycle[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], knowledge of family planning[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], ever had history of terminated pregnancy[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], residence[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and region[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] are the key factors statistically associated with unintended pregnancy.\u003c/p\u003e \u003cp\u003eThe Sustainable Development Goals (SDG) of 2030 has target of ensuring the right of all women and couple to choose to have children up to determination of their number and spacing of birth with global ambition of all birth is wanted and all child is valued[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Alleviating unintended pregnancy via vigorous effort has a great importance to prevent negative consequence of abortion, save financial and social costs, and reduce maternal and child mortality[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEven though unintended pregnancy has devastating adverse outcome to the women and child health as well as economic and social impact to women and her parents, as to my our search, there was paucity of evidence of unintended pregnancy among rural women based on nationally representative data in Ethiopia. Hence, this study aimed to determine the prevalence and associated factors of unintended pregnancy among rural women. This study finding will provide knowledge to health workers and policy makers to understand the magnitude of unintended pregnancy and its associated factors to fill gaps by developing attainable intervention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and population\u003c/h2\u003e \u003cp\u003eThis a cross-sectional study used the national representative (nine region and two city administration) data of 2016 Ethiopian Demography and Health Survey (EDHS) that was done from January 18, 2016, to June 27, 2016[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe 2016 EDHS used a two stage stratified sampling technique to select a sample. In the first stage, the regions were stratified in to urban and rural areas, and every stratum was clustered into 645 enumeration areas( 202 in urban and 443 in rural) by using the 2007 Ethiopian Population and Housing Census (PHC)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] as sampling frame and each clustered were selected by using probability proportional size allocation. In the second stage, 28 household per cluster with total 16,650 households` (15,683 female respondents and 12,688 male respondents) were selected through equal probability systematic sampling technique.\u003c/p\u003e \u003cp\u003eThe survey data was focus on reproductive health (fertility and fertility preference, marriage, awareness and the use of family planning methods), adult and childhood morbidity and mortality, and reproductive women awareness and attitudes towards HIV/AIDS and other important public health issues. The overall information about the sampling procedure and the questionnaire can be accessed from the EDHS 2016 report[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This study used women\u0026rsquo;s data (IR data) set of 2016 EDHS that was obtained from the demographic and health survey (DHS) program website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.measuredhs.com\u003c/span\u003e\u003cspan address=\"http://www.measuredhs.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor this study analysis, a total weighted sample of 974 pregnant women were selected from the total of 10, 335 rural dwellers reproductive age women(15\u0026ndash;49 years) interviewed in the 2016 EDHS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables and definition\u003c/h2\u003e \u003cp\u003eThe dependent variable name for this study is \u0026ldquo;current pregnancy wanted\u0026rdquo; in EDHS datasets. In this concern, the reproductive age women who involved in this survey were asked whether they were currently pregnant and those women who answered yes were further asked whether they wanted the current pregnancy. This produced three responses options such as \u0026ldquo;wanted then\u0026rdquo;, \u0026ldquo;wanted later\u0026rdquo; and \u0026ldquo;not at all\u0026rdquo;. To generate the dependent variable for unintended pregnancy that includes pregnancies of either unwanted(the mother didn\u0026rsquo;t wanted to be pregnant at all) or the pregnancy was mistimed( the mother didn\u0026rsquo;t have plan to become pregnant at that time but wanted later)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Then the dependent variable of unintended pregnancy was dichotomous created by re-coding the original variable as \u0026ldquo;wanted then\u0026rdquo;=0 as \u0026ldquo;intended\u0026rdquo;, \u0026ldquo;wanted later\u0026rdquo; and \u0026ldquo;not at all\u0026rdquo; = 1 as \u0026ldquo;unintended\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe independent variables were individual level variables such as maternal age, maternal occupation status, maternal educational status, marital status, religion, parity, household size, wealth status, media exposure status, ever had of termination pregnancy, knowledge of ovulation cycle, sex of household head, unmet need for contraceptive, family size, fertility preference, knowledge of contraception, intention of contraceptive use, and community level variable such as region, community perceived distance from the health facility, community women educational status, community women wealth status, community women media exposure, community women awareness of modern contraceptives. Some variables were recorded for simplicity of this study analysis such as wealth status of poorest and poorer category were re-coded as poor\u0026thinsp;=\u0026thinsp;1, those above the poorest wealth category and below the high category were leveled as middle\u0026thinsp;=\u0026thinsp;2, whereas richest and richer wealth category were re-coded as rich\u0026thinsp;=\u0026thinsp;3. On the other hand, region was recorded similarly with the previous study in Ethiopia as \u0026ldquo;small peripheral\u0026rdquo; that includes Afar, Somali, Benishangul, and Gambela, and the \u0026ldquo;large central\u0026rdquo; which includes Tigray, Amhara, Oromi, Southern Nations Nationalities and Peoples Region (NNPR), Harari, Dire Dawa, and Addis Ababa based on their geopolitical characteristic[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For the analysis purpose other independent variables were recorded.\u003c/p\u003e \u003cp\u003eOther community level variables such as community women educational status, community wealth status, community media exposure, community modern contraceptive awareness were generated from the individual level variables by aggregating the data and using median because of asymmetric distribution of the data. Before aggregating the community level variables, each individual level variable were di-chotomized into yes or no by referring to previous studies and considering those clusters as having high proportion whose value above the median and clusters as having low proportion whose value below the median.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData processing and analysis\u003c/h2\u003e \u003cp\u003eData extraction and cleaning, coding, labeling and analysis were performed by using stata version 14 software. The effect of sampling bias; due to the unequal probability selection of strata and non-response, was reduced through data weighting by using sample weight factor (v005) of EDHS data set.\u003c/p\u003e \u003cp\u003eMulti-level analysis was done after ascertain the eligibility of data by using Inter-cluster Correlation Coefficient (ICC) above 10% (ICC\u0026thinsp;=\u0026thinsp;57.52% for this study) to consider the hierarchical nature of the DHS data. Bi-variable multi-level logistic regression analysis was done to compute the crude odd ratio at 95% confidence level and variables with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.20 were candidate for multi-variable multi-level logistic regression analysis. After selecting the eligible variable for multi variable multi-level logistic regression analysis with serious of four model fitness was done; model 0 (empty or null model without independent variable) only the unintended pregnancy was analyzed through cluster variable to examined the random effect. Model 1 (examined the effect of only individual-level factors), mode 2 (examined the effect of only community-level factors), and model 3 (examined the mixed effect of individual and community factors at the same time). Statistical association for fixed effect of variables to unintended pregnancy was declared at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with Adjusted Odd Ratio (AOR) at 95% confidence interval.\u003c/p\u003e \u003cp\u003eThe variations between clusters (random effect result) were estimated through three different methods such as Intra-Class Correlation (ICC), Median Odd Ratio (MOR) and Proportional change Variance (PCV). ICC was done for each model I, I, II, III and it shows the variation of community characteristics effect on unintended pregnancy of reproductive women in rural areas. The higher ICC value helps to identify the most relevant community characteristics to realize the individual variation in unintended pregnancy.\u003c/p\u003e \u003cp\u003eMOR refers the odd ratio between the two randomly selected different areas with highest risk and the lowest risk and it was computed by using the formula of MOR = \u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1712650215.png\" style=\"width: 97px;\"\u003e. The MOR value indicates the level of risk of an individual being resident to that area to have unintended pregnancy of reproductive women[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. PCV was done for model I, II, III with respect the variance the model zero to portray the power of factors in the model to examine unintended pregnancy and PCV was computed by subtracting the variance of each model from the empty model by using the formula PCV= (Ve-Vmi)/Ve where Ve; variance of the empty model and Vmi; variance of consecutive model[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Model fitness for the three models was examined by using nested deviance.\u003c/p\u003e \u003cp\u003eThe missing values were handling based on EDHS guideline. Missing value of current pregnancy intention status was excluded from the numerators and it was included in the denominator. If the outcome variable; current pregnancy intention status, contained the missing or \u0026ldquo;no response\u0026rdquo; or \u0026ldquo;don\u0026rsquo;t know\u0026rdquo; considered as undesired pregnancy. Explanatory variables that include missing value more than 5% because the EDHS survey is cross-sectional study were excluded from further analysis\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePatient and public involvement statement\u003c/h2\u003e \u003cp\u003eCurrently pregnant women were included in this study by furnishing valuable information. However, the study participant have never been participated in the study design, protocol, data collection tools, and disseminating this finding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndividual level characteristics of the rural pregnant women\u003c/h2\u003e \u003cp\u003eA total of 974 reproductive women were included in the analysis. The overall mean age of the women was 27.9(SD\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4) years and about three-fourth (75.2%) of them were between 20\u0026ndash;34 years of age. Most (59.6%) of the women didn\u0026rsquo;t attend formal education. Majorities (96.15%) of the women were married and about 45% women were Muslim followers. Besides, around 55.8% of women had work but; about 53.2% of women had poor wealth status. Regarding to household head, majority (87.87%) of the household head were male. Moreover, nearly half(48.3%) of women had 4 to 6 family size and neatly, two-third (65.9%) the women had desire to have more children (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\u003eIndividual level characteristics of pregnant women in EDHS 2016\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency(weighted)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (weighted)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal education\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrthodox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtestant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatholic and Others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal occupation\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHouse wealth index\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia exposure\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 to 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of household head\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrand multiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver terminated pregnancy\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntention to contraceptives\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntend to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoes not intend to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eunmet need for contraception\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot unmet need\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmet need\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAwareness on modern contraceptive method\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnows no method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnows modern method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDesire for another child\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnowledge of ovulation cycle\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnow ovulation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDon\u0026rsquo;t know ovulation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003e* Yes;\u003c/b\u003e if the women want to have another child within 2 years or after 2 years\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003e*No\u003c/b\u003e; if the women do not want any more children\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCommunity level characteristics\u003c/h2\u003e \u003cp\u003eMajority (92.67%) of women were from larger central region and most (62.37%) of the women perceived that they had big problem to get medical care at the health facility. About 61% of the community women educational status was low and more than half (53%) of community women had media exposure. Moreover, about two-third (66.17%) of the community women wealth status were poor (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\u003eCommunity level characteristics of pregnant women in Ethiopia, EDHS 2016\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency(weighted)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage(weighted)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the health facility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot big problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall peripheral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarger central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity media exposure\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity women educational level\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher women education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow women education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity awareness on modern contraceptive method\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity wealth status\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe Prevalence of unintended pregnancy\u003c/h2\u003e \u003cp\u003eThe overall prevalence of unintended pregnancy in this study was 31.66%( 308), 95% CI; 28.81%, 34.66%) comprises 21.59% mistimed and 10.07% of unwanted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRandom effect and model fitness\u003c/h2\u003e \u003cp\u003eAccording to the empty (null) model Intra-Cluster Correlation Coefficient (ICC) result, about 57.52% of the overall variability of unintended pregnancy can be attributed to cluster variability. The median odd ratio (MOR) of unintended pregnancy in the empty model was 6.11. This portrayed that if an individual randomly selected from two different clusters and then those women from clusters having high unintended pregnancy had 6.11 times higher odds to have unintended pregnancy as compared to women from the lower unintended pregnancy clusters. Besides, the proportional change variance (PCV) increase from 26.99% in model I to 38.29% in model III, this shows the final model (model III) best explained the unintended pregnancy variability. The model fitness was checked by using deviance (-2LLR). Since model III had the lowest deviance value that indicates the best fitting model (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\u003eModel comparison and random effect analysis result of unintended pregnancy among rural women in Ethiopia, EDHS 2016\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplained variance (PCV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel fitness\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeviance (-2logliklihood)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e995.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1067.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e964.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndividual and community-level factors associated with unintended pregnancy (fixed-effects)\u003c/h2\u003e \u003cp\u003eAfter adjusted for the individual level and community level factors in the final model (model III) maternal occupational status, media exposure status of the women, maternal wealth status, household size, parity and contraception intention of the women were individual level factors statistically associated with unintended pregnancy of rural women. Among community level factors region and community level wealth statuses were factors statistically associated with unintended pregnancy.\u003c/p\u003e \u003cp\u003eThe fixed effect model, in bi-variable multilevel modeling all individual and community level factors had p\u0026thinsp;\u0026lt;\u0026thinsp;0.2 and exported to multi-variable multilevel logistic regression analysis. The odd of unintended pregnancy was 67% lower among women whose occupational status were not worker as compared to worker (AOR: 0.33, 95%CI: 0.21, 0.52). The odd of unintended pregnancy was 2.4 times higher among women whose wealth status was poor as compared to rich (AOR: 2.4, 95%CI: 1.24, 4.56). Those women who had no media exposure were 2.67 times high risk to have unintended pregnancy as compared to counterparts (AOR: 2.67, 95% CI: 1.48, 4.83). The odd of unintended pregnancy was 56% lower among women who had household size of one to three as compared to household size of seven and above (AOR: 0.44, 95% CI: 0.2, 0.96). Besides, the odd of unintended pregnancy was 59% lower among primi-parous women as compared to grand multiparous (AOR: 0.41, 95% CI: 0.17, 0.99)\u003c/p\u003e \u003cp\u003eSimilarly, the odd of unintended pregnancy was 76% lower among those women who didn\u0026rsquo;t have intention to use contraceptive (AOR: 0.24, 95%CI: 0.14, 0.41). Moreover, those women who lived in larger central region were 4.2 times higher as compared to small periphery region (AOR: 4.2, 95% CI: 1.19, 14.62). Furthermore, the odd of unintended pregnancy was 4.3 times higher among women from community of poor wealth status as compared their counterparts (AOR: 4.3, 95% CI: 1.85, 10.22) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 4 Multi-variable\u0026nbsp;\u003c/strong\u003eanalyses for factors affecting unintended pregnancy among rural reproductive age women in Ethiopia (n=974)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.093645484949832%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Pregnancy intention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003eModel I AOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel II AOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003eModel III AOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eUnintended n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003eWanted n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e20-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.8(0.32, \u0026nbsp;2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e0.9(0.36, \u0026nbsp;2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1.53(0.49, \u0026nbsp;4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1.44(0.45, 4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal educational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1.05(0.36,3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1.1(0.35, 3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1.19(0.41, \u0026nbsp;3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1.09(0.38, 3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eNot working\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.34(0.22 \u0026nbsp;0 .54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e0.33(0.21, 0 .52)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eWorking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1.55(0.84, \u0026nbsp; 2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e2.4(1.24, \u0026nbsp;4.56)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.81(0.42,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e0.83(0.44, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eRich\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e2.45(1.40, \u0026nbsp; 4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e2.67(1.48, 4.83)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHouse hold size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e1 to 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.55(0.26,1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e0.44(0.20, 0.96)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e4 to 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.83(0.45,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e0.68(0.37, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e7 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.434782608695652%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003ePrimiparous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.4(0.17 , 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.41(0.17, 0.99)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eMultiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.8(0.40 \u0026nbsp; \u0026nbsp; 1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.82(0.41, 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eGrand multiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntention to contraceptives\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eIntend to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eDoes not intend to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e0.18(0.11, 0 .31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.24(0.14, 0 .41)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eSmall peripheral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eLarge central\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e6.5(2.09, \u0026nbsp;20.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.2(1.19, 14.62)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to the health facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eBig problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e1.8(1.13, 2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.64(0.98, 2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eNot big problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity level educational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eHigher\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eLow\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e0.9(0.47, \u0026nbsp;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.19(0.56, 2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity level wealth status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e2.6(1.29, \u0026nbsp;5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.3(1.85, 10.22)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eRich\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity level media exposure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eEver had media exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.762541806020067%\" valign=\"top\"\u003e\n \u003cp\u003eNever had media exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.715719063545151%\" valign=\"top\"\u003e\n \u003cp\u003e0.9(0.49 \u0026nbsp; \u0026nbsp;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.57(0.27 ,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e** Statistically significant, AOR: Adjusted Odd Ratio, 1; Reference\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study aimed to assess the prevalence and associated factors of unintended pregnancy among rural women in Ethiopia by using EDHS 2016 data through multilevel analysis. The overall prevalence of unintended pregnancy in this study was 31.66% which is comparable with study conducted in national report of EDHS 2011[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Shashmanie, Ethiopia[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], Egypt[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and Sab Saharan Africa[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This study finding is less than the study conducted in Gellan[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], South Africa[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], Botswana[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Arsi, Ethiopia[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Ghana[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], Addis Ababa[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and Debere Markos[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The discrepancy might be due to study population and study period variation, study area difference, methodological variation, difference of maternal health quality, coverage and accessibility including family planning methods, and socio-cultural. This study includes merely the rural women whereas study done in Gellan, Arsi, Debere Markos and Ghana were community based that include both rural and urban that may increase magnitude of unintended pregnancy on those study areas. On the other hand, study report in Addis Ababa revealed that most study participant that had unintended pregnancy were women in schools whereas the current study majority of the participants were not attend formal education. Thus women in schools are the most fire age groups, influenced by peers’ pressure and have access to watch pornography that induces sexual desire for those adolescent girls to have sex and may not have awareness about family planning that leads to unintended pregnancy and abortion. Besides, study conducted in Debere Markos, Ethiopia showed that the study participants had risky behavior such as substance abuser, alcohol drunker and khat user’s practices risky sexual behavior that attribute for unintended pregnancy whereas this study participants are rural women who had less to zero risky behavior for addiction. Moreover, by fearing social discrimination, the cultural taboo and intention of husband to have more children, rural women have a tendency of saying the pregnancy was wanted after the pregnancy already occurred even if they didn’t want really.\u003c/p\u003e \u003cp\u003eHowever; this finding is higher than study done in low and middle income countries[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], Gambia[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], Ethiopia national survey 2013[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Gondar town[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Bangladesh[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Europe[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and Cambodia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The possible reason for the difference between in this study and Europe might be European women has better income, the women has autonomy have a great chance to be aware about contraceptive methods that makes the women high likely to use family planning that decrease the risk of unintended pregnancy. Likewise, women in Europe has chance of open communication with her husband about family planning in contrast, rural women in Ethiopia may not have chance of open communication with her husband about the family planning, may not have chance of autonomy to decide about her family planning and their fertility that may contribute for unintended pregnancy. Study done in Bangladesh revealed that all study participate women were married whereas the current study were both married and unmarried. Indeed, married women are less likely to have unintended pregnancy than unmarried women. In addition, study conducted in Gondar was in urban areas whereas this study was done in rural areas. Rural women may have limited maternal health information and health institution due to lack of awareness and knowledge towards pregnancy status that may increase unintended pregnancy in rural areas than urban. Traditionally, majority of the rural women wants to bear many children thus women may not use contraceptive that in turn end up with unintended pregnancy. Again, life style, cultural and socio-demographic difference may play role for the variation.\u003c/p\u003e \u003cp\u003eMoreover, Southwest Ethiopia study was institutional based that may underestimates of the unintended pregnancy in contrast this study was community based that may show real figures of unintended pregnancy.\u003c/p\u003e \u003cp\u003eIn multivariable multilevel logistic regression analysis household size, media exposure, maternal occupational status, wealth status, parity, contraception intention, region and community level wealth.\u003c/p\u003e \u003cp\u003eHousehold size of one to three has lower odds of unintended pregnancy compared to household size of seven and above. This study finding is similar with study done in Ethiopia[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Sudan[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and Niger[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The reason may be due women with larger household size spent hard time in caring of their family that greatly affecting women to accesses information and utilizations of maternal health services such as the family planning services that in turn end up with unintended pregnancy. In addition, women with larger household size may perceive that they reached their ideal number of children since any more pregnancy may consider as unintended pregnancy.\u003c/p\u003e \u003cp\u003eWomen who didn’t have media exposure were more likely to have unintended pregnancy. This finding is contradictory with study done in low and middle income countries which revealed that women who had media exposure had higher odd of unintended pregnancies. However; the present study result is in line with study done in Ethiopia[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], Nepal[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and Pakistan[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The potential reason may be women who don’t have media exposure may not got information regarding to contraceptive methods and importance of maternal health service utilization.\u003c/p\u003e \u003cp\u003eWomen who are not currently working have lower odds of unintended pregnancy compared to women who are currently working. This result is congruent with a study done in Ethiopia[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However this finding is contradictory to study done in Ethiopia[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and Cambodia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The potential explanation for the variation might be women who don’t have work could have low level of social contact and self-confidence to have relationship, and may be free from the influence of work that induces to practice unplanned sex that in turn leads to unintended pregnancy[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePoor women have higher odds of unintended pregnancy compared to rich women and this is consistent with other study done in United States America[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], Venator[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], India[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and South Asia[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The possible reason may be poor women may not have opportunity to attained formal education, less likely to use contraceptive and high risk for unprotected sex that leads to unintended pregnancy. Besides, primiparous women were less likely to have unintended pregnancy. The potential explanation may be primiparous women may have opportunity to be aware and to utilized family planning service whereas grand multiparous women may believe that they are infertile and reached menopause, thus women are restricted to use family planning service that increases the likelihood of unintended pregnancy.\u003c/p\u003e \u003cp\u003eWomen who don’t have intention to use contraceptive were less likely to have unintended pregnancy than women who have intention to use contraceptive. This result is inconsistent with study South Asian countries[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The plausible justification may be women who don’t have intention to use contraceptive would perceive themselves as low risk to have pregnancy and they may gave socially desirable response for already occurred pregnancy by responding didn’t have intention to use contraception. This finding is contradictory to study done in Ethiopia[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Moreover, women reside in large central region were more likely to have unintended pregnancy as compared to women reside in small periphery region. This finding is similar with other studies done in Ethiopia[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Kenya[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and Ghana[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The variation might be due to population size in small peripheral region is lower as compared to larger central region, thus women’s pregnancy may be recommended and acceptable in small peripheral region whereas women in the large central region women may be busy due to their intention to improve their financial status and majority of the women’s pregnancies tends to be unintended[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, community wealth status was another community level factor associated to unintended pregnancy. Poor community women have higher odds of unintended pregnancy compared to counterparts and it is consistent with other similar study[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The discrepancy may be poor community women may not have access to education and media, women may not have chance to obtained information about family planning, and lack of transportation cost to utilize maternal health service may contribute unintended pregnancy.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrength and limitation of this study\u003c/h2\u003e \u003cp\u003eThe present study had strength because it used large nationally representative data. This study used the appropriate multilevel statistical approach to account the hierarchical nature of the data. Since this study used nationally representative data, it has high chance of providing insights for policy-makers and program designers to set the most potential intervention strategies at all levels(nationally and regional level). This study might have the possible limitation of recall bias since this study used the EDH survey data that was heavily relay on the respondent's self-report. Moreover, this study was unable to ascertain the temporal relationship between unintended pregnancy rather it shows the association between unintended pregnancies and factors.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and recommendations","content":"\u003cp\u003eThe present study prevalence of unintended pregnancy in rural women was high. From multivariable multilevel logistic regression analysis, individual level variable such as maternal occupation status, household size, media exposure, parity, wealth status and contraception, and community level variables such as region and community level wealth were statistically associated with unintended pregnancy. Hence, priority attention has to be given to increase accessibility of media and set strategies to improve women financial capacity to those high vulnerable groups (unintended pregnancy) and enhancing availability maternal health service to decrease adverse outcome of unintended pregnancy in rural areas.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnumeration Areas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthiopian Demographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntra Class Correlation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedian Odd Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProportional Change in Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePopulation and Housing census, \u003cdiv class=\"Term\"\u003eSNNPR\u003c/div\u003eSouthern Nations Nationalities and Peoples Region\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe ethical approval and permission to obtain the data were freely access from the DHS website\u0026nbsp;\u003cstrong\u003ewww.measuredhs.com\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eDuring the original\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDHS data collection process, international and national ethical guidelines were considered. Ethical clearance for the original DHS data was approved by the ICF Macro Institutional Review Board, the Center for Disease Control and Prevention (CDC) and Institutional Review Board (IRB) in each country, in accordance with United States Department of Health and Human Services requirement for human subject protection. According to DHS, all respondents and /or their legal guardians(s) of minors\u0026rsquo; (age below 16) provided written consent to participant. All the methods were done based on Helsinki declarations. No information obtained from the data set was disclosed to any third person. The study is not an experimental study. Further explanation of how DHS uses data and its ethical standards can be found at: \u003cstrong\u003ehttp://goo.gl/ny8T6X.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used for the present study will be accessible with rational request from responsible corresponding author and every one can access the data set online from www.measuredhs.com.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJ, initiated the research concept, wrote up the research proposal, extract and analyze the data, presented the result and wrote up the manuscript. AH participated in data analysis, interpretation of the result and wrote up of the manuscript. KD, MT, KA and YT participated in data extraction and data analysis. AE, WN, AW, LY, MG and NW involved in the interpretation of the result and discussion. LA, HA, DG and AY involved in the review and finalized the manuscript document. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is not applicable for the present study because this study used a secondary data analysis done by Ethiopian central statistical agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was secured for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to forward our special acknowledgement to measure DHS program for given us to access and use the 2016 EDHS data set.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWoldesenbet, S., et al., Association between viral suppression during the third trimester of pregnancy and unintended pregnancy among women on antiretroviral therapy: Results from the 2019 antenatal HIV Sentinel Survey, South Africa. 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BMJ global health, 2018. 3(2): p. e000707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWellings, K., et al., The prevalence of unplanned pregnancy and associated factors in Britain: findings from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3). The Lancet, 2013. 382(9907): p. 1807\u0026ndash;1816.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetachew, F.D., Level of unintended pregnancy and its associated factors among currently pregnant women in Duguna Fango district, Wolaita zone, southern Ethiopia. Malaysian Journal of Medical and Biological Research, 2016. 3(1): p. 11\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmeyaw, E.K., Prevalence and correlates of unintended pregnancy in Ghana: Analysis of 2014 Ghana Demographic and Health Survey. Maternal health, neonatology and perinatology, 2018. 4: p. 1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYenealem, F. and G. 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Aemro, and C. Teller, Prevalence and determinants of unintended childbirth in Ethiopia. BMC Pregnancy and Childbirth, 2014. 14: p. 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzugbara, C., Household characteristics and unintended pregnancy among ever-married women in Nigeria. Social medicine, 2014. 8(1): p. 4\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabte, D., et al., Correlates of unintended pregnancy in Ethiopia: results from a national survey. PLOS ONE, 2013. 8(12): p. e82987.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe, H.H., et al., The burden of unintended pregnancies in Brazil: a social and public health system cost analysis. International Journal of Women's Health, 2014: p. 663\u0026ndash;670.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen, L., et al., The impact of rapid urbanization and public housing development on urban form and density in Addis Ababa, Ethiopia. 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Odimegwu, Determinants of maternal health care utilization in Nigeria: a multilevel approach. The Pan African medical journal, 2014. 17(Suppl 1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemu, B.W., Third stage of Labor Practice and Associated Factors among Skilled Birth Attendants Working in Gamo and Gofa Zone Public Health Facility, Southern, Ethiopia. Ethiopian Journal of Reproductive Health, 2021. 13(2): p. 10\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohamed, E.A.-E.B., et al., Prevalence, determinants, and outcomes of unintended pregnancy in Sohag district, Egypt. Journal of the Egyptian Public Health Association, 2019. 94: p. 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyalew, H.G., et al., Prevalence and factors associated with unintended pregnancy among adolescent girls and young women in sub-Saharan Africa, a multilevel analysis. BMC Women's Health, 2022. 22(1): p. 464.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYohannes, E. and B. Balis, Unintended pregnancy and associated factors among women who live in Ilu Gelan District, Western Ethiopia, 2021. International Journal of Reproductive Medicine, 2022. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoldesenbet, S., et al., The prevalence of unintended pregnancy and its association with HIV status among pregnant women in South Africa, a national antenatal survey, 2019. Scientific Reports, 2021. 11(1): p. 23740.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoherty, K., et al., Unintended pregnancy in Gaborone, Botswana: A cross sectional study. African Journal of Reproductive Health, 2018. 22(2): p. 76\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulatu, T., A. Cherie, and L. Negesa, Prevalence of unwanted pregnancy and associated factors among women in reproductive age groups at selected health facilities in Addis Ababa, Ethiopia. J Women\u0026rsquo;s Health Care, 2017. 6(392): p. 2167\u0026ndash;420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNigussie, K., et al., Magnitude of unintended pregnancy and associated factors among pregnant women in Debre Markos Town, East Gojjam Zone, Northwest Ethiopia: a cross-sectional study. International journal of women's health, 2021: p. 129\u0026ndash;139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAragaw, F.M., et al., Magnitude of unintended pregnancy and its determinants among childbearing age women in low and middle-income countries: evidence from 61 low and middle income countries. 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BMC Research Notes, 2018. 11: p. 1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbita, Z. and D. Girma, Exposure to mass media family planning messages and associated factors among youth men in Ethiopia. Heliyon, 2022. 8(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcharya, P., R. Gautam, and A.R. Aro, Factors influencing mistimed and unwanted pregnancies among Nepali women. Journal of biosocial science, 2016. 48(2): p. 249\u0026ndash;266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazzaq, S., et al., Unintended pregnancy and the associated factors among pregnant females: Sukh Survey-Karachi, Pakistan. JPMA. The Journal of the Pakistan Medical Association, 2021. 71(11 (Suppl 7)): p. S50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassie, T., et al., Magnitude and factors associated with unintended pregnancy among pregnant women in Addis Ababa, Ethiopia. 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Obstetrics \u0026amp; Gynecology, 2017. 130(3): p. 609\u0026ndash;615.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagnus, A., Poverty in the United States: An analysis of Its Measurement and the Long-term Social and Economic Costs, 2020, University of Denver.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnand, A., S. Mondal, and B. Singh, Changes in Socioeconomic Inequalities in Unintended Pregnancies Among Currently Married Women in India. Global Social Welfare, 2023: p. 1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarder, A., et al., Prevalence of unintended pregnancy and its associated factors: evidence from six south Asian countries. PLOS ONE, 2021. 16(2): p. e0245923.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWobse, B.A. and T.A. Gashaw, Multilevel modeling of unintended current pregnancy: In the case of Ethiopian Demographic and Health Survey, 2016. Digital Health, 2023. 9: p. 20552076231173306.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Magnitude, Unintended Pregnancy, Multilevel Analysis, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-4137645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4137645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite growing utilization of family planning in Ethiopia, many pregnancies in rural areas are still unintended and it remains the main global public and reproductive health challenges with devastating impact on women and child health and general public. Hence, this study was sought to determine the prevalence and associated factors of unintended pregnancy in rural women of Ethiopia.\u003c/p\u003e \u003cp\u003eThis study used the 2016 Ethiopian Demography and Health Survey data. Total weighted samples of 974 reproductive-aged rural women were included in the analysis. Multilevel mixed logistic regression analysis was employed to consider the effect of hierarchal nature of EDHS data by using stata version 14 to determine individual and community level factors. Variables significantly associated with unintended pregnancy were declared with adjusted odds ratio with 95% CI at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The prevalence of unintended pregnancy in rural women was 31.66%( 95%CI: 28.8%, 34.66%). Have no media exposure (AOR: 2.67, 95%CI: 1.48, 4.83), not working (AOR: 0.33, 95%CI: 0.21, 0.52), household size of one to three (AOR: 0.44 95%CI: 0.2, 0.96), primiparous (AOR: 0.41, 95%CI: 0.17, 0.99), poor women (AOR: 2.4, 95%CI: 1.24, 4.56), didn\u0026rsquo;t have intention to use contraceptive (AOR: 0.24, 95%CI: 0.14, 0.44) were individual factors associated to unintended pregnancy. Large central region (AOR: 4.2, 95%CI: 1.19, 14.62) and poor community level (AOR: 4.3, 95%CI: 1.85, 10.22) were community level factors associated to unintended pregnancy. The present study prevalence of unintended pregnancy in rural women was high. Maternal occupation, household size, media exposure, parity, women wealth, intention to use contraceptive, region and community level wealth were factors statistically associated with unintended pregnancy. Hence, demographer and public health practitioners give great emphasis to set strategies to increase accessibility women to media and improve women financial capacity, and strengthen availability of maternal health service to decrease unintended pregnancy adverse outcome in rural areas.\u003c/p\u003e","manuscriptTitle":"Magnitude of unintended pregnancy among rural reproductive women in Ethiopia: A Multilevel analysis using 2016 EDHS data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 13:16:29","doi":"10.21203/rs.3.rs-4137645/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-24T04:43:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-23T14:47:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-21T14:08:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266308178125870997917291320202260481165","date":"2024-10-14T07:40:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277229668944414958967883869000757982215","date":"2024-10-10T23:16:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291800711663716599148368523495247040707","date":"2024-06-30T21:55:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-15T18:08:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-14T20:12:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-04T10:08:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-04T10:03:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-20T13:50:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"917a91ec-464e-4bde-aaab-dd69d5452ee0","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30422281,"name":"Health sciences/Health care"},{"id":30422282,"name":"Health sciences/Health occupations"},{"id":30422283,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-01-06T16:22:11+00:00","versionOfRecord":{"articleIdentity":"rs-4137645","link":"https://doi.org/10.1038/s41598-024-81067-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-05 15:57:10","publishedOnDateReadable":"January 5th, 2025"},"versionCreatedAt":"2024-04-09 13:16:29","video":"","vorDoi":"10.1038/s41598-024-81067-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-81067-w","workflowStages":[]},"version":"v1","identity":"rs-4137645","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4137645","identity":"rs-4137645","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}