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. 2024 Apr 29;19(4):e0302001. doi: 10.1371/journal.pone.0302001

Factors associated with miscarriage in Nepal: Evidence from Nepal Demographic and Health Surveys, 2001–2016

Sharadha Hamal 1,*, Yogendra B Gurung 2, Bidhya Shrestha 2, Prabin Shrestha 3, Nanda Lal Sapkota 4, Vijaya Laxmi Shrestha 5
Editor: Ganesh Dangal6
PMCID: PMC11057729  PMID: 38683811

Abstract

Background

Miscarriage is a major public health concern in low and middle-income countries (LMICs) like Nepal. This study aims to examine the factors associated with miscarriage among pregnant women of reproductive age (15–49 years) in the past 15 years.

Methods

There were a total of weighted sample of 26,376 cross-sectional pregnancy data from Nepal Demographic and Health Surveys (NDHS) 2001, 2006, 2011, and 2016 combined together, which was used in the study. Multilevel logistic regression analysis that adjusted for cluster and survey weights was used to identify factors associated with miscarriage among pregnant women of reproductive age in Nepal.

Results

The results showed that maternal age, contraception, tobacco smoking, wealth index, respondents’ educational status, and, caste/ethnicity were found to be strong factors of miscarriage in Nepal. The likelihood of having a miscarriage among older women (≥40 years) was more than 100% (aOR = 2.12, 95% CI [1.73, 2.59]), among non-users of contraception was 88.9% (aOR = 1.88, 95% CI [1.68, 2.11]) (p<005) and non-smoking women had a 19% lower odds of miscarriage (aOR = 0.81, 95% CI [0.69, 0.95]). Respondents from the richest wealth index had 50% (aOR = 1.50, 95% CI [1.22, 1.85]) higher likelihood of miscarriage. Mothers with only primary education had a 25% higher chance of miscarriage (aOR = 1.25, 95% CI [1.09, 1.44]) compared to those with secondary and higher secondary education. In relation to caste/ethnicity, Dalits had 13% lesser likelihood (aOR = 0.87, 95% CI [0.74, 1.02]) and Janajatis had 26% lower chances of a miscarriage than Brahmin/Chettri (aOR = 0.74, 95% CI [0.64, 0.85]).

Conclusion

Findings from this study show that miscarriages are associated with maternal age, use of contraception, smoking, wealth index, caste, and ethnicity. Interventions aimed to improve use of contraceptives, avoiding smoking and pregnancy planning on the basis of maternal age, are needed to prevent miscarriage. Also, women from Brahmin ethinicity and those with the highest income index require greater attention when it comes to miscarriage prevention strategies in Nepal.

Introduction

Miscarriages are the most common public health risk that could occur in every pregnancy [1]. Women who undergo miscarriages continue to suffer in secret, endure painful events, and face societal stigma, which causes them to experience emotional loneliness and mental health issues. It is a terrible experience, especially in nations with low to middle income like Nepal. It is an unintentional termination of a pregnancy before the fetus has reached the seventh month of gestation [2,3]. It is a cultural taboo that receives little attention in the literature [4]. It is estimated that 8% to 15% of all clinically recognized pregnancies and 30% of all pregnancies result in spontaneous loss [2,5]. Statistical differences based on increasing maternal age pose an increasing risk of miscarriage among pregnant women [5]. Many pregnancies end in miscarriage before a women knows about her pregnancy status [2,4,6]. Investigations are usually conducted among those pregnant women having recurrent pregnancies [6]. The level of perinatal and maternal mortality may have been significantly reduced as well as other poor pregnancy outcomes in reproductive women as a result of improvements in the quality of care given during pregnancy. Yet, such improvements haven’t had as much of an impact on the high miscarriage rate, with between 20% to 30% of pregnancies ending in miscarriage [7].

A study by Quenby et.al, revealed that the couple’s age—both very young and older female age (less than 20 years and more than 35 years) as well as older males (more than 40 years)—may be associated with miscarriages [8]. Additionally, among pregnant women in reproductive life, very high or very low Body Mass Index (BMI), black race, prior miscarriages, smoking habits, alcohol consumption, stress, working night shifts, air pollution, and pesticide exposure were associated with miscarriages. Miscarriage involves both modifiable and non-modifiable risk factors [8]. According to a recent systematic review and meta-analysis of research undertaken in 26 different countries [2,9], active smoking along with obesity, are risk factors for miscarriage in Nepal [2]; caffeine use is also related to miscarriage and the presence of non-modifiable risk factors such as maternal age, chromosomal abnormalities, and aberrant uterine architecture are also associated factors for miscarriage. Fetal viability is significantly impacted by modifiable behavioral risk factors as well [2,10]. For instance, alcohol consumption during pregnancy was discovered to be associated with miscarriage [11,12] and smoking during pregnancy was associated with a slightly higher hazard ratio for miscarriage (1.18, 95% CI [0.96, 1.44]) [3,13]. In addition, drinking coffee while pregnant, heavy lifting, mental instability, health problems, and a history of abortion are all significant risk factors for miscarriages [14]. Even while several global and national initiative programs are working hard to reduce pregnancy-related risks and make motherhood safe, women still endure miscarriages. Various Nepal Demographic and Health Surveys (NDHSs) have revealed a shifting trend of miscarriage in Nepal [2], but understanding the causes of miscarriage in Nepal is still crucial.

The underlying etiology of miscarriage is still poorly known, although numerous researchers have worked to uncover possible risk factors [7,15]. There is less research that looks at the factors associated with miscarriage in low and middle-income countries (LMICs) like Nepal, as the majority of the information comes from high income nations. NDHS is the only source that provides data on miscarriage. However, factors associated with miscarriage are yet to be explored and analyzed. Therefore, a detailed examination of the root causes of miscarriage in Nepal is required. The study aims to examine the factors associated with miscarriage in Nepal by using the pooled data from the NDHS 2001, 2006, 2011, and 2016 to explore the various demographic, socio-economic, and maternal characteristics of miscarriage in the past 15 years.

The findings from this study would be helpful from the perspective of healthcare system planning to help government and non-governmental organizations modify current health policy and practices that focuses on miscarriage. This could be a significant step toward improving reproductive health in Nepal and achieving Sustainable Development Goal (SDG) Goal-3 to ensure healthy lives and promote well-being for all at all ages.

Methods

Data sources and sample composition

The datasets for the study were from NDHS 2001[16] 2006 [17], 2011 [18], and 2016 [19]. The NDHS is a nationally representative household survey using multistage cluster sampling designs, stratified by geographical regions, and urban and rural areas. All four surveys sampling methods were similar and routinely collected data to estimate socio-demographic, maternal and child health and mortality, fertility, HIV/AIDS, family planning, nutrition, and so on conducted every five years by the Ministry of Health of Nepal. The NDHS uses standardized techniques that involve standard questionnaires, manuals, and field procedures to collect data that is comparable across nations. Detailed standardized survey methodology and sampling methods are used in gathering the data [16–19]. The NDHS used three different types of surveys, each with information unique to the household, women, and men. The pre-tested, translated questionnaires were used to gather data on a variety of demographic and health variables, including a women’s reproductive health outcome such as miscarriage, in three primary languages: Maithili, Bhojpuri, and Nepali.

When four of the NDHS surveys were pooled together there were a total of 45,055 women of reproductive age between 15 and 49 years, with an average response rate of nearly 97% [16–19]. The selection process for the sample from the NDHS in 2001, 2006, 2011, and 2016 is presented in Fig 1. Women of reproductive age were asked to record all pregnancies that resulted in both live and non-live births. Information on the duration of the pregnancy and the reason for termination was obtained for pregnancies that resulted in non-live births to determine if the pregnancy ended in a miscarriage or an induced abortion. A total weighted sample of 26,376 was obtained by limiting the analysis to pregnancies that terminated within the five years before the survey. This limitation was designed to reduce the mothers’ recall bias, enhancing the study’s internal and external validity.

Fig 1. Flow chart for selection of a sample from NDHS 2001, 2006, 2011, and 2016.

Fig 1

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Outcome variable

Miscarriage is an outcome variable that refers to the spontaneous termination of a pregnancy before the fetus reaches the gestational age of seven months [1]. When a pregnancy ends before 7 months gestation, a miscarriage is classified as 1; 0 otherwise.

Study factors

We identified14 possible study variables classified into 3 different categories based on the availability of data from all four NDHS (2001–2016). Survey years 2001, 2006, 2011, and 2016 were the time-dependent confounding variables. The variables at the community level were place of residence (urban and rural), province (Koshi, Madhesh, Bagmati, Gandaki, Lumbini, Karnali, Sudurpaschim), and ecological zones (Mountain, Hill, and Terai). Socio-economic variables such as religion (Buddhist, Hindu and other religion), caste/ethnicity (Brahmin/Chhetri, Dalit, Janajati, Madhesi and others including Muslims), wealth index (Poorest, Poorer, Middle, Richer, Richest), women’s educational status and their partner’s educational level (No education, Primary, Secondary, Higher Secondary), their role as the head of the family (male headed family and female headed family), and their mother’s employment (Not Working, Agriculture, Non Agriculture). Use of contraception, maternal smoking habit, and maternal age are exposure variables used to examine the proximate determinant for the outcome variables. They are grouped as maternal factors in the conceptual framework (Fig 2).

Fig 2. Framework for factors affecting miscarriage in Nepal, adopted from Mosely & Chen’s analytical framework for the study of child survival in developing countries.

Fig 2

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Statistical analysis

The study was based on the deductive technique, and the analysis was carried out using a quantitative approach. The analytical frameworks for miscarriage have been conceptualized with multiple factors based on Mosley and Chen’s analytical framework for child survival in developing countries [20] (Fig 2).

Multilevel logistic regression analysis that adjusted for the cluster and survey weights was used to identify the factors associated with miscarriage in Nepal, taking cluster and survey weights being taken into consideration based on Fig 2 [20,21]. Frequency tabulation was used to characterize the features of the study population. The prevalence with a 95% Confidence Interval (CI) of miscarriage were assessed for all study variables. The chi-square test was used to measure the association between the factor variables and miscarriage during the last 5 years and adjusted for the survey design that includes cluster and urban/rural stratification. Multicollinearity amongst the predictor variables was checked using the Variance Inflation Factor (VIF). The mean value of VIF<10 was the cut-off point [22]. The statistical significance was considered at p-value < 0.05 and 95% confidence intervals (CIs).

Multivariable analysis was conducted by using a three-stage multilevel model (Fig 1) similar to those described to account for the complex hierarchical interrelationships between each blocks of determinants [21,23]. As part of hierarchical technique, we first analyzed variables from the community level block (Place of residence, Province and Ecological Zone) along with the survey year to establish a baseline multivariate model (Model I), Socio-economic Variables (Religion, Caste/Ethnicity, Wealth Index, Education Status of respondent, Education status of partner, Sex of the household head, Maternal occupation) were then fitted into Model I (Model II). In the final model (Model III), the exposure variable within maternal blocks (contraception, maternal smoking habit and maternal age) was analyzed (Model II.).

The factors with p-values 0.05 in each step were kept. In order to avoid any statistical bias, we validated our findings by: (1) backward-eliminating potential risk factors with a p-value of less than 0.20 from the univariate analysis; (2) testing the backward-elimination method by including all of the variables (all potential risk factors); and (3) testing and reporting collinearity. The odds ratios with 95% CI were performed to assess the adjusted risk of the independent variables, and those with p<0.05 were kept in the final model. The goodness of fit of the model was assessed by using Hosmer-Lemshow test. All analyses were performed using SPSS 26 version.

Ethical considerations

NDHS received ethical approval from the Ethical Review Board of the Nepal Health Research Council and the ICF Institutional Review Board. The DHS website offers public access to the NDHS datasets [24]. The NDHS 2001–2016 datasets were made available for download and use after the author requested permission from MEASURE DHS/ICF International, Rockville, Maryland, USA. All research participants were read a pre-structured consent statement, and the interviewer verbally obtained their informed consent (assent on behalf of minors) was recorded by the interviewer.

Results

Overall, 6.5% (1,715) (95% CI [6.2, 6.8]) of the pregnancies resulted in miscarriage during the last 15 years, based on the survey year 2001–2016. Different maternal age groups observed age-specific miscarriage and discovered a greater occurrence among women aged 45 to 49 years having a 10.7% prevalence (95% CI [7.07, 14.21]) followed by a roughly lower frequency of 10.3% among mothers between the ages of 40 to 44 (95% CI [8.30, 12.20]). Mothers aged 15 to 19 years also had a similar prevalence of 10.1% (95% CI [8.70, 11.46]) and a lower prevalence among age group 25–29, 5.8% (95% CI [5.26, 6.26]) whereas about 6.9% (95% CI [5.89, 8.00]) prevalence among age group 35–39, 6.0% prevalence among 30 to 34 age group and 6.1% (95% CI [5.54, 6.57]) prevalence of miscarriage observed among women from 20 to 24 years age groups (Fig 3).

Fig 3. Trends of miscarriage among women of reproductive age group (15 to 49) years based on maternal age.

Fig 3

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The basic profile of pregnant women with a proportion of miscarriage in Nepal in the last 15 years

NDHS data from 2001, 2006, 2011, and 2016 were combined to create a total of 26,376 pregnancies. Table 1 shows the weighted study population, number of miscarriage, miscarriage prevalence and 95% CI of study variables in Nepal. Over the fifteen years, the prevalence of miscarriage among pregnant women considerably rose from 4.9% (95% CI [4.3, 5.3]) in 2001 to 9.1% (95% CI [8.3, 9.8]) in 2016. The prevalence of miscarriage varied according to the age of the mother; which was highest among women older than 40 years (10.3%), followed by mothers younger than 20 years (10.1%), and lower among mothers aged 35 to 39 years (6.9%) and 20 to 34 years (5.9%) respectively. The prevalence of miscarriage was significantly higher for those living in urban areas (8.7%), Karnali Province (10.4%), and the hills (6.7%). Similarly, high prevalence was observed among those following the Hindu religion (6.6%), who belong to Brahmin/Chhetri (7.7%) and are richest in terms of wealth index (7.6%). Contraceptive non-users were also found to have a higher prevalence of miscarriage (7.5%).

Table 1. Weighted study population, miscarriage prevalence and 95% CI of study variables in Nepal (2001–2016), (N = 26376).

Explanatory Variables Unweighted Population Weighted Population
N*(26671) N (26376) Miscarriage % (95%CI)
Survey Year
 2001 7535 7572 4.9 (4.3,5.3)
 2006 6491 6227 5.6 (5.0,6.2)
 2011 6378 6356 6.9 (6.1,7.3)
 2016 6267 6222 9.1 (8.3,9.8
Community Level Factors
Place of residence
 Urban 7324 5431 8.7 (7.9–9.4)
 Rural 19347 20945 5.9 (5.6–6.2)
Province
 Koshi 5155 4545 6.2 (5.5–6.9)
 Madhes Province 7430 8602 5.9 (5.3–6.3)
 Bagmati Province 5642 6914 6.7 (6.1–7.3)
 Gandaki Province 2837 2434 6.2 (5.2–7.1)
 Lumbini Province 3722 2895 7.8 (6.8–8.7)
 Karnali Province 1006 434 10.4 (7.5–13.3)
 Sudurpaschim Province 879 552 8.2 (5.9–10.5)
Ecological zone
 Tarai 12271 13651 6.3 (5.9–6.7)
 Hill 10555 10664 6.7 (6.2–7.1)
 Mountain 3845 2061 6.6 (5.5–7.6)
Socio-economic variables.
  Religion
 Buddhist 1698 1770 4,7 (3.7–5.7)
 Hindu 22954 22290 6.6 (6.3–6.9)
 Others 2019 2316 6.5 (5.47.4)
  Caste/Ethnicity
 Brahmin/Chhetri 8909 7492 7.7 (7.1–8.3)
 Dalit 4381 4173 6.7 (5.9–7.4)
 Janajati 8296 8496 5.4 (4.9–5.8)
 Madhesi and others including Muslims 5085 6216 6.4 (5.8–7.0)
  Wealth index
 Poorest 7294 6298 5.7 (5.1–6.3)
 Poorer 5475 5597 6.3 (5.7–6.9)
 Middle 4976 5427 6.4 (5.7–7.0)
 Richer 4854 4943 6.8 (6.0–7.4)
 Richest 4072 4111 7.6 (6.8–8.4)
  Education Status of Respondents
 No education 14355 14216 5.6 (5.2–5.9)
 Primary 4778 4806 7.5 (6.7–8.2)
 Secondary 1033 5911 7.4 (6.6–8.0)
 Higher Secondary 1507 1442 8.7 (7.1–10.0)
  Sex of household head
 Male 20903 20737 6.5 (6.1–6.7)
 Female 5768 5639 6.7 (6.0–7.3)
  Maternal Working Status
 Not Working 6037 6708 7.1 (6.4–7.7)
 Agriculture 17729 16803 6.0 (5.6–6.3)
 Non-Agriculture 2905 2865 8.2 (7.2–9.2)
Maternal Factors(exposure Variables)
  Contraception
 Using contraceptives 9840 9645 4.7 (4.2–5.1)
 Not using 16831 16730 7.5 (7.1–7.9)
  Smoking
 Smoker 4355 4004 6.3 (5.5–7.0)
 Non-smoker 22316 22372 6.5 (6.2–6.8)
  Maternal Age
 <20 years 1780 1833 10.1 (8.7–11.4)
 20–34 years 21326 21082 5.9 (5.6–6.2)
 35–39 years 2324 2239 6.9 (5.8–8.0)
 >40 years 1241 1222 10.3 (8.6–12.0)
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N* Unweighted Population, N Weighted Population, weighted miscarriage %, CI = Confident Interval.

Factors associated with miscarriage in Nepal

Table 2 shows the unadjusted model, and three stages of multilevel models—Model I, Model II and Model III. Model III is resulting in a parsimonious model from Model I and Model-II in the multivariate analysis after adjusting for potential explanatory variables. The adjustment of the variables is to identify the different factors of miscarriage in Nepal. This is based on the hierarchical approach shown in Fig 2. Most of the independent variables revealed associations in the unadjusted model (Table 2). Model adjusted for years of survey, place of residence, province, Ecological Zone, Religion, Caste/Ethnicity, Education status of women, partner’s education, head of household head, maternal occupation, Contraception, maternal smoking habit and maternal age. Fixing multicollinearity by examining variance inflation factors with a cut-off value of <10. Some variables are manually removed from subsequent analysis to achieve model fit (p>0.05).The goodness of fit of the model was assessed by using the Hosmer-Lemshow Test which showed the model was fit with statistically insignificant as the value of the test was 0.448 for Model I, 0.364 for Model II and 0.144 for Model III (Table 2).

Table 2. Factors associated with miscarriage in Nepal, NDHS 2001–2016.

Correlates Unadjusted Model Model I Model II Model III
OR(95%CI) P Value aOR(95%CI) P Value aOR(95%CI) P Value aOR(95%CI) P Value
Survey Year
2001* 1 1 1 1
2006 1.16(1.00–1.35) 0.50 1.14(0.98–1.32) 0.08 1.13(0.97–1.31) 0.11 1.19(1.02–1.39) 0.02
2011 1.41(1.22–1.63) 0.00 1.44(1.24–1.68) 0.00 1.40(1.19–1.65) 0.00 1.50(1.27–1.76) 0.00
2016 1.94(1.70–2.23) 0.00 1.72(1.47–2.02) 0.00 1.72(1.45–2.04) 0.00 1.88(1.58–2.24) 0.00
Community level factors
Rural* 1 1
Urban 1.50(1.35–1.68) 0.00 0.82(0.72–0.94) 0.00 0.89(0.77–1.03) 0.12 0.87(0.76–1.10) 0.75
Province
province 1* 1 1
Madhes Province 0.92(0.79–1.07) .0.33 0.89(0.76–1.03) 0.13 0.88(0.75–1.03) 0.12 0.87(0.74–1.02) 0.95
Bagmati Province 1.08(0.93–1.26) 0.28 1.00(0.85–1.18) 0.95 0.97(0.82–1.14) 0.72 0.96(0.81–1.13) 0.67
Gandaki Province `0.99(0.81–1.22) .0.97 1.03(0.84–1.26) 0.75 0.99(0.80–1.22) 0.97 0.96(0.78–1.18) 0.71
Lumbini Province 1.27(1.06–1.52) 0.00 1.16(0.96–1.39) 0.11 1.12(0.93–1.35) 0.21 1.10(0.91–1.33) 0.30
Karnali Province 1.76(1.26–2.45) 0.00 1.21(0.86–1.70) 0.26 1.15(0.81–1.64) 0.41 1.19(0.84–1.70) 0.31
Sudurpaschim Province 1.34(0.97–1.86) 0.07 0.90(0.64–1.27) 0.57 0.84(0.59–1.19) 0.33 0.88(0.62–1.24) 0.46
Ecological zone
Tarai* 1
Hill 1.06(0.96–1.18) 0.21
Mountain 1.04(0.86–1.26) 0.62
Socioeconomic Factors
Religion
Buddhist* 1 1 1
Hindu 1.42(1.13–1.78) 0.00 1.11(0.87–1.42) 0.36 1.21(0.95–1.55) 0.11
Others 1.38(1.05–1.82) 0.02 1.86(0.86–1.56) 0.31 1.17(0.86–1.58) 0.29
Caste/Ethnicity
Brahmin/Chhetri* 1 1 1
Dalit 0.85(0.73–0.99) 0.04 0.92(0.78–1.08) 0.34 0.87(0.74–1.02) 0.10
Janajati 0.68(0.60–0.77) 0.00 0.73(0.64–0.85) 0.00 0.74(0.64–0.85) 0.00
Madhesi and others including Muslims 0.82(0.72–0.93) 0.00 0.84(0.71–0.98) 0.34 0.82(0.69–0.96) 0.16
Educational level of respondent
No Education 1 1 1
Primary Level Education 1.36(1.19–1.54) 0.00 1.20(1.05–1.38) 0.00 1.25(1.09–1.44) 0.00
Secondary 1.33(1.18–1.51) 0.00 1.00(0.86–1.17) 0.92 1.07(0.92–1.24) 0.37
Higher Secondary 1.59(1.30–1.94) 0.00 0.84(0.65–1.09) 0.20 1.06(0.83–1.35) 0.59
Education status of partner/husband
No education* 1 1
primary 1.10(0.95–1.27) 0.16 0.99(0.85–1.15) 0.93
Secondary 1.18(1.04–1.35) 0.00 0.97(0.83–1.13) 0.71
Higher Secondary 1.67(1.41–1.97) 0.00 1.23(0.99–1.52) 0.61
Wealth Index
Poorest* 1 1 1
Poorer 1.11(0.95–1.29) 0.17 1.12(0.96–1.31) 0.14 1.20(1.02–1.40) 0.02
Middle 1.12(0.97–1.31) 0.11 1.13(0.96–1.33) 0.12 1.23(1.03–1.44 0.01
Richer 1.19(1.02–1.39) 0.02 1.14(0.96–1.36) 0.12 1.30(1.09–1.55) 0.00
Richest 1.35(1.15–1.58) 0.00 1.20(0.97–1.48) 0.85 1.50(1.22–1.85) 0.00
Sex of the household head
Male 1
Female 1.03(0.92–1.16) 0.55
Maternal Factor
Maternal currently working status
Not Working 1 1 1
Agriculture 0.82(0.74–0.92) 0.00 0.99(0.87–1.13) 0.96 1.01(0.89–1.16) 0.78
Non Agriculture 1.17(1.00–1.38) 0.51 1.13(0.95–1.34 0.14 1.23(1.03–1.46) 0.01
Exposure Variables
Contraception
Used* 1 1
Not used 1.64(1.47–1.83) 0.00 1.81(1.61–2.03) 0.00
Smoker
Smoker* 1 1
Non-smoker 1.04(0.90–1.194) 0.57 0.81(0.69–0.95) 0.01
Maternal Age
<20 years 1.78(1.51–2.09) 0.00 1.71(1.44–2.02 0.00
20–34 years* 1 1
35–39 years 1.18(0.99–1.40) 0.05 1.30(1.09–1.56) 0.00
>40 years 1.83(1.51–2.22) 0.00 2.12(1.73–2.59) 0.00
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Model I (Model adjusted for years of survey, Place of residence, Province).

Model II (Model adjusted for Years of survey, Place of residence, province, religion, caste/ethnicity, education status of women/partners, wealth index, maternal occupation).

Model III (Model adjusted for years of survey, place of residence, province, religion, caste/ethnicity, education status of women, wealth index, maternal occupation, contraception, smoking habit, maternal age).

The level of miscarriage has been steadily rising over the past 15 years, from 2001 to 2016. The likelihood of rising miscarriages is by 19% during 2001–2006 (aOR = 1.19, 95% CI [1.02, 1.39]), 50% in 2011 (aOR = 1.50, 95% CI [1.27–1.76]), and the chances increased by 88% in 2016 (aOR = 1.88, 95% CI [1.58–2.24]) since NDHS 2001 (Table 2 and Model III).

There was no statistically significant difference in miscarriage based on place of residence. But women living in urban areas had an odd of 0.87-times lower risk of getting a miscarriage (aOR = 0.87, 95% CI [0.76, 1.10]) compared with women from rural areas (Table 2 and Model III) though it was statistically significant in the bivariate analysis (unadjusted model).

In the multivariate analysis, the chance of miscarriage was demonstrated to be negligible with regards to respondents from various provinces, despite the fact that provinces exhibit a significant association with miscarriage in bivariate analysis (p<0.05), particularly in the case of Lumbini and Karnali Province (Table 2, unadjusted model and Model III).

In the bivariate analysis (Table 2, unadjusted model), religion was found to be associated with miscarriage, but not in the multivariate analysis (Table 2, Model III). However, in comparison to Buddhists, women who belonged to other religion had a 17% likelihood of miscarriage (aOR = 1.05, 95% CI [0.77, 1.43]) whereas mothers who were Hindu had a 21.0% risk (aOR = 1.21, 95% CI [0.95 1.55]) of getting miscarriage, though it was not significantly associated (p>0.05) at 95% CI.

The likelihood of getting a miscarriage on the basis of caste and ethnicity were shown to be strongly significant in bivariate analysis (p<0.05) with all different sub categories of castes and ethnicities (Table 2, Unadjusted model). In the multivariate analysis (Model III and Table 2) among women belonging to Dalit groups the odd of getting miscarriage was 0.87 times less likely (aOR = 0.87, 95% CI [0.74, 1.02]), and similarly Janajatis also have an odd of 0.74 times less likely to get miscarriage (aOR = 0.74, 95% CI [0.64, 0.85]) as compare to women belonging to Brahmin/Chhetri caste groups, and it was also significantly associated (p<0.05).

Both analysis—bivariate (unadjusted model, Table 2) and multivariate Model III (Table 2) (p<0.05)—showed a significant relationship between the respondents’ education status and their chance of miscarriage. When compared to the reference group of women with no education, only respondents with a primary level of education had 1.25 times greater odds of a miscarriage (aOR = 1.25, 95% CI [1.09, 1.44]) (Model III and Table 2).

With regards to the wealth index, bivariate and multivariate analyses both showed significant relationships (p<0.05 at 95% CI) (Table 2). Women who belonged to different wealth indices had a considerably higher risk of miscarriage. The odds of miscarriage was 1.20 times higher (aOR = 1.20, 95% CI [1.02, 1.40]) for women who were in the poorer index, similarly, 1.23 times higher for Middle index (aOR = 1.23, 95% CI [1.03–1.44]), richer index 1.30 times higher odds (aOR = 1.30, 95% CI [1.09, 1.55]) and women in the richest wealth index have odds of 1.50 times greater (aOR = 1.50, 95% CI [1.22, 1.85]) than the reference group of women belongs to poorest wealth index.

The relationship between smoking and miscarriage was significant in the multivariate analysis but not in the bivariate analysis (p>0.05) (Table 2). Women who did not smoke reported 0.81 times lower risks of getting a miscarriage than those who smoked (aOR = 0.81, 95% CI [0.69, 0.95]) (Table 2 and Model III).

Women who were not using contraception had higher probabilities of miscarriage, in both bivariate and multivariate analyses (Table 2). Women who did not use any form of contraception had a 1.81 times greater risk of miscarriage than those who did (aOR = 1.81, 95% CI [1.61, 2.03]) (Table 2 and Model III).

In both bivariate and multivariate analyses, the likelihood of a miscarriage was greater among women under the age of 20 and mothers older than 40 years old, with a somewhat lower chance among women in the age range of 35 to 39 years compared to the reference group of women aged 20 to 34 years, which is statistically significant as well (p<0.05) (Table 2). The odds of getting a miscarriage was 2.12 times higher among women more than 40 years (aOR = 2.12, 95% CI [1.73, 2.59]); similarly women belonging to less than 20 years younger also have odds of 1.71 times higher (aOR = 1.71, 95% CI [1.44, 2.02]) whereas among women belonging to age groups 35 to 39 years, there was 1.30 times greater odds of getting miscarriage (aOR = 1.30, 95% CI [1.09, 1.56]) with reference groups 20 to 34 years women (Table 2 and Model III).

Discussion

During the last 15 years, from NDHS 2001 to 2016, 6.7% of pregnancies ended in miscarriage. We identified seven factors associated with miscarriage in Nepal namely, maternal age, contraception use, maternal smoking behavior, caste or ethnicity, women pursuing elementary education, maternal occupation, and wealth index. They have a considerable impact on the likelihood of miscarriage among women of reproductive age groups in Nepal.

From the NDHS 2001 to 2016, the trends of miscarriages nearly doubled in Nepal, this findings is consistent with the study on pregnancy outcomes among Indian women which showed that the number of miscarriages among Indian women increased between 2015 and 2021 [25]. But in Finland the annual incidence of miscarriage among women aged 15 to 49 years has decreased significantly between 1998 and 2016 [5], falling by only 1.8% in 2016 from 1998, which is little bit contrast to the findings of our study in Nepal [5]. However, a study in India showed that, there were 6.3% of continuing pregnancies that resulted in miscarriage in last 3 years from 2014 to 2017 [20], and in the Manitoba study, the yearly miscarriage incidence was 11.3%, or around 1 in 9 pregnant women [26]. Though the findings are contextual, the main essence is that the trend of miscarriage is increasing in developing countries like Nepal but decreasing slightly in developed countries like Finland. We observed a U-shaped miscarriage trend with maternal age (Fig 3), with the risk of miscarriage being highest for women older than 40 and younger than 20 years. The rate of miscarriage was highest among women aged 15 to 19 and then decreased as women aged after turning 20, and almost stagnant till 34 years, increasing again as women aged over 35. Our study’s findings are consistent with a birth cohort study from China [27] as well that found a J-shaped relationship between maternal age and spontaneous abortion, with advanced maternal age (>30 years) being significantly associated with miscarriage. However, in contrast, one study from Sudan found that the risk of miscarriage among Sudanese women follows a distinct curve in relation to maternal age, with the curve showing a lower risk for women under 20 years and at 40 years [28].

The study has supported the risk of miscarriage based on maternal age that the probability of miscarriage is higher among younger mothers (15–19 years), and then the probability of miscarriage sharply increased in older mothers (30+ years old) of reproductive age. Significant variations exist between reproductive women’s age-specific groupings. While the increased risk of miscarriage at advanced maternal age could be the result of age-related hormonal changes, the significant increase in miscarriage among young women could be a reflection of biological phenomenon or it could reflect the hidden social context as well as the effect of reproductive immaturity. Further, we are in line with the findings of a Danish study that found that the risk of miscarriage is less than 15% until the age of 34, but increases to 25% between the ages of 35 and 39, 51% between the ages of 40 and 44, and more than 90% for women who are 45 years or older [29]. Chromosomal abnormalities, cessation of the uterine capacity, and depletion of ovarian follicles are all reasons why hormone treatment can be helpful for a woman trying to conceive later in life [30]. As chromosomal abnormalities are the most common cause of first-trimester miscarriage and are discovered in 50% to 80% of pregnancy tissues specimens after spontaneous miscarriage, a correlation between increasing maternal age and a higher incidence of chromosomal abnormality has been established in prior studies [29]. About two third of these are trisomies, and the likelihood of trisomy increases with maternal age. This study’s findings are in line with those of Ford and MacCormac [30], as maternal aging is a significant, immutable factor in aneuploidy. It is linked to an increased risk of a live birth trisomy, particularly Down syndrome, and to a sharp rise in trisomy conceptions, the majority of which end in miscarriage.

The chance of miscarriage is also considerably lower among Dalit and Janajati women in Nepal than it is among Brahmin and Chhetri women, and this finding is important since it indicates that further research is needed to determine why this is the case in Nepal. We identified that women in Nepal with the highest wealth indices have a greater odds of miscarriage than those with the lowest wealth indices. Our findings from this study is supported by one study in Bihar, India which revealed that there was an association between intimate partner violence (IPV) and miscarriage. Women in the lower wealth quartile (Quartile 1) showed no associations between IPV and miscarriage, but women in the higher wealth quartile (Quartile 3) saw an association between IPV and miscarriage [31]. Although, we are not examining intimate partner violence in this study, there is a possibility that it could be a hidden cause for miscarriage among women in the richest wealth quintile since they have higher odds of miscarriage in Nepal. The results are in stark contrast to a study of Danish and Chinese women, which found that those with higher salaries had a reduced incidence of spontaneous abortion than those with lower incomes [32,33]. Further research on contextual barriers for miscarriage is required.

When compared to women who utilized contraception, we identify that the likelihood of miscarriage was greater in the group of women who did not use contraceptions. Previous studies, found that a pattern of declining miscarriage incidence with increasing years of oral contraceptive (OC) use [34], and our study also shows similar results. But only for women over the age of 30, there was a significant relationship between the length of OC usage and miscarriage. The preservation of ovarian follicles caused by OC was formerly thought to be the cause of the 15% decrease in miscarriage rates among long-term pill users aged 30 or older and the following decrease in spontaneous abortion [34]. However, the current study’s findings on the association between using contraception and miscarriage include all respondents who used both hormonal and non-hormonal methods of contraception.

Non-smoker women reported less chances of having a miscarriage than the reference group of respondents who smoked. The reason for this might be that smoking during pregnancy may influence the growth retardation of the fetus and the chance of having a miscarriage is increased. Active smoking increases the chance of miscarriage [2,9]. Another study, also found that smoking while pregnant increased the chance of miscarriage considerably [3,13].

The study has multiple benefits since its findings can add to the body of knowledge already available about the causes of miscarriage in Nepal. The study used data from nationally representative household surveys that were population-based and had a high response rate (>90%). The data were merged together to create a large sample size of miscarriage that was reported within 5 years preceding survey. Finally, this study applied appropriate statistical adjustments to data obtained from 4 nationally representative surveys and was able to identify the significant factors associated with miscarriage in Nepal.

This study has some limitations as well. First, this study is based on secondary data, and due to its cross-sectional nature, this paper is unable to establish a causal relationship between variables and occurrence of miscarriage. Second, the information on miscarriage is from retrospective data based on self-report from mothers which could be a potential source of recall and misclassification bias. Third, this study was not able to include important confounders such as the use of caffeine and alcohol, and obesity which have been previously identified as important modifiable risk factors for miscarriage in Nepal. Finally, miscarriage and other pregnancy complications might share underlying causes, which could be biological conditions or unmeasured common risk factors, hence, care should be taken in interpreting and applying the findings of this study.

Conclusion

Our analyses examined factors associated with miscarriage in Nepal using pooled population-based surveys for the years 2001 to 2016.Miscarriage has increased significantly in Nepal. The likelihood of an increasing trend is close to two times higher in the data in NDHS 2016 than in NDHS 2001. Our study show that miscarriages are associated with maternal age, use of contraception, smoking, wealth index, caste, and ethnicity. Interventions aimed to improve use of contraceptives, avoiding smoking and pregnancy planning on the basis of maternal age, are required to prevent miscarriage. Also, women who follow the Brahmin ethinicity and those with the highest income index require greater attention when it comes to miscarriage prevention strategies in Nepal.

Obesity in mothers is a significant contributor to miscarriage, and other studies have already identified it as a contributing factor in Nepal. However, the relationship of miscarriages with intimate partner violence is an important area that needs to be studied.

It is necessary to conduct more research to determine why miscarriage rates are rising in Nepal despite the nation’s numerous safe motherhood and child health initiatives, as well as why elite (those with the highest wealth index) have a greater risk of miscarriage, along with intimate partner violence among highest wealth index. Further research is also necessary on the perceptions of women who have gone through a miscarriage in Nepal to examine the psychological impact among women of reproductive women, as this study was only able to quantify of the rates of miscarriages and the factors that affect it.

Acknowledgments

The authors are thankful to measure DHS ICF International, and Rockville, Maryland, USA for granting access to the datasets used in this analysis. The authors would also like to acknowledge Dr. Meeta S. Pradhan for her efforts in editing the language.

Data Availability

"Data may be obtained from DHS (https://dhsprogram.com/data). To obtain data from the DHS website, a data requisition application is required. The authors confirm that others may acquire the data in the same way the authors did and that the authors did not receive any special consideration from MEASURE DHS/ICF International. The titles of the data set used for this study were: NPKR81FL, NPIR81FL, and NPGR81FL."

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Pradip Chouhan

11 Jul 2023

PONE-D-23-17092Factors Affecting Miscarriage in Nepal: Evidence from Nepal Demographic and Health Surveys, 2001-2016PLOS ONE

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Reviewer #1: Thank you for providing me with the opportunity to review the manuscript titled "Factors Affecting Miscarriage in Nepal: Evidence from Nepal Demographic and Health Surveys, 2001-2016." I have carefully evaluated the manuscript and would like to acknowledge its contribution to the field. This is an interesting study which attempts to identify the factors associated with miscarriage in Nepal using data from four consecutive rounds of Nepal DHS.

However, I would also like to highlight several limitations that should be considered.

1. To begin with, I have an objection with the title of this study. The term "factors affecting" may not be the most suitable for a study that is not experimental in nature. When describing the relationship between variables in an observational study, it is more appropriate to use terms such as "associated with" or "associated factors" instead of "affecting."

By replacing "factors affecting" with "factors associated with," the revised title accurately reflects the nature of the study as an observational analysis of the relationships between variables rather than a direct causal investigation.

2. In line 84-88, you mentioned "The study found that the couple's age —" Could you please clarify which study you are referring to?

3. I would suggest that the authors use caution when using terms such as "have a role" or "were linked to," as they imply a causal relationship that can only be established through experimental studies.

4. In line 90, the authors have already stated that active smoking increases the chance of miscarriage. Therefore, there is no need to repeat this information in line 97.

5. Could you please provide further clarification on your statement in line 105-106: "Less conclusive and reliable data do exist, particularly in low- and middle-income nations like Nepal"? What specifically do you mean by "less conclusive" and "reliable data" in this context, as Nepal DHS is already there?

6. Based on the current conceptual framework of your study, which includes exposure variables (health service variables) and confounding variables, it seems that structural equation modeling (SEM) could be a suitable statistical analysis technique to address the complex relationships involved. SEM allows for the examination of both direct and indirect associations among variables, which is beneficial when dealing with multiple predictors and outcome variables.

Moreover, SEM can accommodate latent variables, which are constructs inferred from multiple observed indicators. This feature of SEM can be particularly valuable if your conceptual framework includes latent constructs that need to be considered.

Considering the complexity of your study's conceptual framework and the potential benefits of SEM, I would like to inquire if you have the necessary expertise and resources to perform a SEM analysis. If so, using SEM could provide a more comprehensive representation of your current conceptual framework and allow for a thorough investigation of the direct associations between the health service variables (exposure variables) and the outcome variable while considering the influence of confounding variables.

7. I would like to suggest considering the use of either multilevel modeling (MLM) or structural equation modeling (SEM) based on the hierarchical and nested structure of your data, as observed in the conceptual framework (Figure 2) and the design of the Demographic and Health Surveys (DHS).

MLM allows for the analysis of data with a hierarchical structure, such as individual-level, household-level, and community-level variables. It takes into account the dependencies and correlations within the nested data structure, providing a robust approach to analyzing such data. Given the presence of individual and community-level variables in your study, MLM could help account for the potential clustering effects and assess the influence of both individual and contextual factors on the outcome variable.

Alternatively, SEM could also be a suitable method for your study, considering its ability to handle complex relationships and pathways among multiple variables. SEM can accommodate latent variables and provide insights into direct and indirect associations. If there are latent constructs in your conceptual framework or if you aim to examine the direct associations between health service variables and the outcome variable while considering confounding variables, SEM could be a valuable approach.

I recommend selecting either MLM or SEM based on your convenience, expertise, and the specific goals of your study. Both methods offer unique advantages, and choosing the most suitable method will enhance the rigor and validity of your analysis.

8. Based on the authors' construction of separate models in lines 174-181, demonstrating an understanding of the hierarchy of variables, I recommend performing a multilevel modeling (MLM) analysis. MLM can effectively capture the nested structure of the data, allowing for examination of individual-level and higher-level effects. This approach will provide a comprehensive understanding of the factors influencing miscarriage and enhance the validity of the findings.

9. In the manuscript, it is not explicitly stated whether the authors checked for multicollinearity among the independent variables. However, I suggest that the authors perform a check for multicollinearity by examining the Variance Inflation Factors (VIFs) of the independent variables.

10. In ethical consideration section, authors should state that the access to the DHS datasets can be obtained through the DHS program's official website (https://dhsprogram.com/) by following the necessary protocols and permissions outlined by the program.

By providing this information, readers can be directed to the official DHS website where they can find further details on data access and the procedures to obtain the dataset for their own research purposes.

.

11. In Table 1, it is not explicitly stated whether the percentages are weighted. To ensure clarity and transparency in the reporting of results, I suggest including a footnote in Table 1 to indicate whether the percentages are weighted or unweighted.

Additionally, it would be beneficial to mention in the statistical analysis section of the study whether the percentages presented in the tables are weighted or unweighted.

12. The interpretation of odds ratios (ORs) in the paper (line 225-227) appears to be incorrect.

For example, if the adjusted odds ratio (AOR) is reported as 1.42, it indicates a 42% higher odds of a particular outcome occurring in a specific group compared to the reference group, rather than an absolute probability or a percentage increase.

Similarly, in your study, for the odds ratio of 2.06 for pregnant women over 40 years, it means that their odds of experiencing a miscarriage are 2.06 times higher than the reference group of pregnant women aged 20 to 34 years, rather than a 100% higher chance.

To ensure accurate interpretation, it is important to clarify in the paper that odds ratios represent relative changes in odds and not absolute probabilities or percentage increases. I recommend revising the results section accordingly to reflect the correct interpretation of odds ratios.

13. I would like to suggest reevaluating the placement of Table 2 in the manuscript. Since Table 2 primarily presents the results of unadjusted odds ratios, which are not intended to be interpreted or discussed extensively, it may be more appropriate to consider moving it to an appendix or removing it altogether.

14. Could you please explain the rationale behind including the variables in Table 2 (ecological zone, wealth index, sex of household head, and smoker) in further models, despite their lack of significant association with the outcome variable in the bivariate analysis?

In typical practice, bivariate analysis is often employed to identify variables that demonstrate a significant one-to-one relationship with the outcome variable. Variables that do not exhibit a significant association are typically excluded from further analysis. However, in your study, it appears that these variables have been included in subsequent models to compute adjusted odds ratios.

I recommend considering the removal of the statistically insignificant variables (ecological zone, wealth index, sex of household head, and smoker) from further models in your analysis. This approach helps mitigate potential issues related to multicollinearity and overfitting, ensuring that the final models accurately capture the predictors that have a meaningful impact on the outcome.

15. In lines 281-283 of the manuscript, it is mentioned that the authors compare their results with the findings of a study conducted in Finland regarding the annual incidence of miscarriage. I would like to inquire about the specific reason for exclusively comparing the results with Finland and not considering any other countries, particularly low- and middle-income countries (LMICs).

16. The discussion section of the manuscript requires significant improvement to enhance its clarity and logical flow. It is important to establish a coherent storyline that aligns with the study's objectives and effectively presents the key findings.

To address this, I recommend that the authors begin the discussion section by clearly stating the main objective of the study.

Subsequently, they should present the key findings in a structured and organized manner, discussing each finding in relation to the research objective and existing literature.

This will provide readers with a clear understanding of the significance and implications of the study's results. It is essential to provide a concise interpretation of the findings, highlighting their implications and contributions to the field.

Additionally, the authors should address any limitations or challenges encountered during the study and discuss their potential impact on the results and conclusions. Finally, the discussion section should be concluded by summarizing the main findings, their implications, and any recommendations for future research or practice.

By following these guidelines, the authors can significantly enhance the clarity, coherence, and overall quality of the discussion section, ensuring that it effectively communicates the study's objectives, findings, and implications to the readers.

17. In line 310, the authors use the term "Madhes Pradesh," while in other places it is referred to as "Madhes Province." This inconsistency in terminology can potentially confuse international readers.

To ensure clarity and avoid confusion, I recommend using a consistent and standardized term throughout the manuscript. Consider using the commonly recognized term "Madhes Province" to maintain consistency and make it easier for international readers to understand and follow the discussion.

18. I have noticed that the term "Dalit" is used in the manuscript to refer to a specific group of people. It is important to acknowledge that using such terminology can be value-laden and may carry certain connotations. I would like to suggest either using alternative terminology that is more neutral and inclusive or providing a clear justification for the use of the term "Dalit" in a footnote.

19. I have observed that in lines 313-316, the authors are restating their results within the discussion section of the manuscript. It is important to note that the discussion section should focus on the interpretation of the major findings and their implications, rather than reiterating the numerical results.

To enhance the quality and clarity of the discussion, I recommend the authors refrain from presenting the numerical results again in this section. Instead, they should emphasize the significance of the major findings and provide a comprehensive interpretation of their implications. Comparisons with relevant previous studies can be made to highlight similarities or differences in findings and to contribute to the existing body of knowledge.

By avoiding the repetition of numerical results and focusing on the interpretation and contextualization of the major findings, the discussion section will become more informative and insightful.

20. I have noticed that Reference 30 in the manuscript requires modification.

21. I recommend adding a footnote to each table in the manuscript to provide the full form of any abbreviations used.

22. The manuscript requires thorough proofreading and editing before it can be considered for publication. There are multiple grammatical errors throughout the paper that need to be addressed. These errors hinder the clarity and readability of the manuscript. Therefore, I recommend that the authors carefully review and revise the paper to ensure it meets the standards of scholarly writing.

Reviewer #2: Thank you for providing me with the opportunity to review the manuscript titled "Factors

Affecting Miscarriage in Nepal: Evidence from Nepal Demographic and Health Surveys,

2001-2016." (PONE-D-23-17092). After carefully reading the manuscript, I would like to

recommend the following changes for the improvement of the article.

1. The authors are advised to specify "The study found that the couple's age —" (Line

84). Proper citation is needed here.

2. The authors are asked not to repeat the same things. The smoking behaviour and

miscarriage are represented in lines 90 and 97.

3. The calculated percentages in Table 1 are not clearly mentioned whether these are

weighted or unweighted. Authors are advised to mention it.

4. Authors are asked to provide the rationale behind considering few variables like

ecological zone, wealth index, sex of household head etc. in this study.

5. The discussion section should focus on the explanation of the major findings and their

implications. So, the authors are advised to refrain from presenting the numerical

results.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

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Attachment

Submitted filename: JS REVIEW.docx

pone.0302001.s001.docx (14.3KB, docx)
PLoS One. 2024 Apr 29;19(4):e0302001. doi: 10.1371/journal.pone.0302001.r002

Author response to Decision Letter 0

31 Aug 2023

The authors would like to thank the reviewers for their fair reviews and detailed list of constructive suggestions for the improvement of the manuscript. We greatly appreciate the opportunity to submit revisions of our paper based on the feedback received. We have revised the paper by providing more details based on comments received. Below, we have responded to each of the reviewers’ comments and the corresponding changes we made in the paper.

Reviewer 1 (R1) concerns:

1. To begin with, I have an obligation with the title of this study. The term "Factors affecting" may not be the most suitable for the study that is not experimental in nature. When describing the relationship between variables in an observational study. it is more appropriate to use terms such as "associated with" or "associated factors" instead of "affecting" by replacing "factors affecting" with "factors associated with" the revised the title accurately between variables rather than direct causal investigation.

Our Response: We revised the title by replacing "factors affecting" with "factors associated" in our title.

2. In line 84-88, you mentioned "The study found that the couple's age —" Could you please clarify which study you are referring to?

Our Responses: This is cited from research titled "Miscarriage Matters: The Epidemiological, Physical, Psychological, and Economic Costs of Early Pregnancy Loss," which was published in Lancet 397: 1658–67 in 2021. According to the study, a couple's age means females aged less than 20 years and more than 35 years and with a male (husband) older than 35 years, there is a worldwide risk of miscarriage. We have provided the reference of the study as well.

3. I would suggest that the authors use caution when using terms such as "have a role" or "were linked to," as they imply a causal relationship that can only be established through experimental studies.

Our responses: we fixed the phrases "have a role" and "were link to" by substituting it with the phrase associated with.

4. In line 90, the authors have already stated that active smoking increases the chance of miscarriage. Therefore, there is no need to repeat this information in line 97.

Our Responses: We have fixed the duplication of information in lines 90 and 97.

5. Could you please provide further clarification on your statement in line 105-106: "Less conclusive and reliable data do exist, particularly in low- and middle-income nations like Nepal"? What specifically do you mean by "less conclusive" and "reliable data" in this context, as Nepal DHS is already there?

Our Responses: We do concur that Nepal DHS is present. However, the DHS lacks sufficient data on miscarriage. It has restrictions on a number of significant elements, such as certain biological ones, which may directly contribute to miscarriage. In Nepal, there have been little studies on miscarriage.

6. Based on the current conceptual framework of your study, which includes exposure variables (health service variables) and confounding variables, it seems that structural equation modeling (SEM) could be a suitable statistical analysis technique to address the complex relationships involved. SEM allows for the examination of both direct and indirect associations among variables, which is beneficial when dealing with multiple predictors and outcome variables.

Moreover, SEM can accommodate latent variables, which are constructs inferred from multiple observed indicators. This feature of SEM can be particularly valuable if your conceptual framework includes latent constructs that need to be considered.

Considering the complexity of your study's conceptual framework and the potential benefits of SEM, I would like to inquire if you have the necessary expertise and resources to perform a SEM analysis. If so, using SEM could provide a more comprehensive representation of your current conceptual framework and allow for a thorough investigation of the direct associations between the health service variables (exposure variables) and the outcome variable while considering the influence of confounding variables.

Our responses: We have conceptualized our conceptual framework again on the basis of Mosley and Chen's analytical framework as described by the study (Factors associated with perinatal mortality in Nepal: evidence from Nepal demographic and health survey 2001–2016). And carried out multilevel logistic regression analysis adjusted for cluster and survey weight used to identify the significant factor associated with miscarriage in Nepal.

7. I would like to suggest considering the use of either multilevel modeling (MLM) or structural equation modeling (SEM) based on the hierarchical and nested structure of your data, as observed in the conceptual framework (Figure 2) and the design of the Demographic and Health Surveys (DHS).

MLM allows for the analysis of data with a hierarchical structure, such as individual-level, household-level, and community-level variables. It takes into account the dependencies and correlations within the nested data structure, providing a robust approach to analyzing such data. Given the presence of individual and community-level variables in your study, MLM could help account for the potential clustering effects and assess the influence of both individual and contextual factors on the outcome variable.

Alternatively, SEM could also be a suitable method for your study, considering its ability to handle complex relationships and pathways among multiple variables. SEM can accommodate latent variables and provide insights into direct and indirect associations. If there are latent constructs in your conceptual framework or if you aim to examine the direct associations between health service variables and the outcome variable while considering confounding variables, SEM could be a valuable approach.

I recommend selecting either MLM or SEM based on your convenience, expertise, and the specific goals of your study. Both methods offer unique advantages, and choosing the most suitable method will enhance the rigor and validity of your analysis.

Our Responses: Please look at the response 8 for details.

8. Based on the authors' construction of separate models in lines 174-181, demonstrating an understanding of the hierarchy of variables, I recommend performing a multilevel modeling (MLM) analysis. MLM can effectively capture the nested structure of the data, allowing for examination of individual-level and higher-level effects. This approach will provide a comprehensive understanding of the factors influencing miscarriage and enhance the validity of the findings.

Our Responses: On the basis of our expertise multilevel logistic regression analysis was used to identify the factors associated with miscarriage in Nepal, taking cluster and survey weights being taken into consideration based on Fig 2 in our revised manuscript. Multivariable analysis was conducted by using a three-stage multilevel model (Figure 2) similar to those described to account for the complex hierarchical interrelationships between each block of determinants [21, 23] in the reference list of the revised manuscript. As part of the hierarchical technique, we first analyzed variables from the community level block (Place of residence, Province, and ecological Zone) along with the survey year to establish a baseline multivariate model (model I), Socioeconomic Variables (Religion, Caste/Ethnicity, Wealth Index, Education Status of the respondent, Education status of the partner, sex of the household head, maternal occupation) were then fitted into model 1 (model 2). In the final model (model 3), the exposure variables within maternal blocks (conception, maternal smoking habit, and maternal age)) were analyzed with model 2. All the variables are weighted by women's individual sample weight V005 in the DHS data set. These are described in Table 1 and Table 2 of the revised manuscript.

9. In the manuscript, it is not explicitly stated whether the authors checked for multicollinearity among the independent variables. However, I suggest that the authors perform a check for multicollinearity by examining the Variance Inflation Factors (VIFs) of the independent variables.

Our Responses: We have examined the Variance Inflation Factors (VIF) and tested for multi-collinearity.

10. In ethical consideration section, authors should state that the access to the DHS datasets can be obtained through the DHS program's official website (https://dhsprogram.com/) by following the necessary protocols and permissions outlined by the program.

By providing this information, readers can be directed to the official DHS website where they can find further details on data access and the procedures to obtain the dataset for their own research purposes.

Our Responses: We have made corrections in response to your input.

11. In Table 1, it is not explicitly stated whether the percentages are weighted. To ensure clarity and transparency in the reporting of results, I suggest including a footnote in Table 1 to indicate whether the percentages are weighted or unweighted. Additionally, it would be beneficial to mention in the statistical analysis section of the study whether the percentages presented in the tables are weighted or unweighted.

Our responses: Prior drafts of the manuscripts had unweighted percentages, however, in the revised draft, we weighted the percentage and added a footnote accordingly.

12. The interpretation of odds ratios (ORs) in the paper (line 225-227) appears to be incorrect.

For example, if the adjusted odds ratio (AOR) is reported as 1.42, it indicates a 42% higher odds of a particular outcome occurring in a specific group compared to the reference group, rather than an absolute probability or a percentage increase.

Similarly, in your study, for the odds ratio of 2.06 for pregnant women over 40 years, it means that their odds of experiencing a miscarriage are 2.06 times higher than the reference group of pregnant women aged 20 to 34 years, rather than a 100% higher chance.

To ensure accurate interpretation, it is important to clarify in the paper that odds ratios represent relative changes in odds and not absolute probabilities or percentage increases. I recommend revising the results section accordingly to reflect the correct interpretation of odds ratios.

Our Responses: We have made necessary correction in revised version of the manuscript.

13. I would like to suggest reevaluating the placement of Table 2 in the manuscript. Since Table 2 primarily presents the results of unadjusted odds ratios, which are not intended to be interpreted or discussed extensively, it may be more appropriate to consider moving it to an appendix or removing it altogether.

Our Response: Separate bivariate analysis tables were removed, however, final table 2 was included as an unadjusted model in the revised manuscript for comparison of analysis.

14. Could you please explain the rationale behind including the variables in Table 2 (ecological zone, wealth index, sex of household head, and smoker) in further models, despite their lack of significant association with the outcome variable in the bivariate analysis?

In typical practice, bivariate analysis is often employed to identify variables that demonstrate a significant one-to-one relationship with the outcome variable. Variables that do not exhibit a significant association are typically excluded from further analysis. However, in your study, it appears that these variables have been included in subsequent models to compute adjusted odds ratios. I recommend considering the removal of the statistically insignificant variables (ecological zone, wealth index, sex of household head, and smoker) from further models in your analysis. This approach helps mitigate potential issues related to multicollinearity and overfitting, ensuring that the final models accurately capture the predictors that have a meaningful impact on the outcome.

Our responses: We conducted reanalysis of the revised manuscript from the weighted sample. The factors with p-values of 0.05 in each step were kept. In order to avoid any statistical bias, we validated our findings by (1) backward-eliminating potential risk factors with a p-value of less than 0.20 from the univariable analysis; (2) testing the backward-elimination method by including all of the variables (all potential risk factors); and (3) testing and reporting collinearity. The odds ratios with 95% CI were performed to assess the adjusted risk of the independent variables, and those with p< 0.05 were kept in the final model. The goodness of fit of the model was assessed by using Hosmer- Lemshow test. We kept smoking in the final model even though bivariate analysis had a p>0.05 significance level because smoking would be a proximate determinant that is mentioned as a maternal factor in our conceptual framework and was already established as a factor for miscarriage by other studies. However, we wanted to check the validity of these variables in our study, so we checked for multicollinearity and discovered that there was no multicollinearity due to these variables. Therefore, we used these factors in our analysis.

15. In lines 281-283 of the manuscript, it is mentioned that the authors compare their results with the findings of a study conducted in Finland regarding the annual incidence of miscarriage. I would like to inquire about the specific reason for exclusively comparing the results with Finland and not considering any other countries, particularly low- and middle-income countries (LMICs).

Our responses: Along with the study from Finland, we have added one study from a developing nation. Through this, we can discuss and compare our study findings both from developed and developing nations. We have now incorporated a new study from low-and middle-income countries (LMICs) in the discussion.

16. The discussion section of the manuscript requires significant improvement to enhance its clarity and logical flow. It is important to establish a coherent storyline that aligns with the study's objectives and effectively presents the key findings. To address this, I recommend that the authors begin the discussion section by clearly stating the main objective of the study.

Subsequently, they should present the key findings in a structured and organized manner, discussing each finding in relation to the research objective and existing literature. This will provide readers with a clear understanding of the significance and implications of the study's results. It is essential to provide a concise interpretation of the findings, highlighting their implications and contributions to the field. Additionally, the authors should address any limitations or challenges encountered during the study and discuss their potential impact on the results and conclusions. Finally, the discussion section should be concluded by summarizing the main findings, their implications, and any recommendations for future research or practice. By following these guidelines, the authors can significantly enhance the clarity, coherence, and overall quality of the discussion section, ensuring that it effectively communicates the study's objectives, findings, and implications to the readers.

Our Responses: We have made an effort to tailor our discussion to your suggestions in the discussion area so that it would clearly convey the study's goals, findings, and implications to readers.

17. In line 310, the authors use the term "Madhes Pradesh," while in other places it is referred to as "Madhes Province." This inconsistency in terminology can potentially confuse international readers.

To ensure clarity and avoid confusion, I recommend using a consistent and standardized term throughout the manuscript. Consider using the commonly recognized term "Madhes Province" to maintain consistency and make it easier for international readers to understand and follow the discussion.

Our Response: we have made corrections by consistently and evenly employing words. Making "Madhesh Province" rather than "Madesh Pradesh" to minimize confusion.

18. I have noticed that the term "Dalit" is used in the manuscript to refer to a specific group o

Attachment

Submitted filename: Response to Reviewers.docx

pone.0302001.s002.docx (25.4KB, docx)

Decision Letter 1

Pradip Chouhan

25 Oct 2023

PONE-D-23-17092R1Factors Associated with Miscarriage in Nepal: Evidence from Nepal Demographic and Health Surveys, 2001-2016PLOS ONE

Dear Dr. Sharadha Hamal,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Dec 09 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: No

Reviewer #4: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: This is a great piece of work in a topic where limited research are done, especially in resource poor settings like Nepal. This manuscript may benefit from some minor revisions as described below.

Abstract

Methods: write factors associated instead of significant factors

Results: please remove the first sentence to reflect more on your study title.

Conclusion: please be specific with your results when you draw the conclusion

Introduction: Please look at English grammar throughout.

Methods: Please combine exposure variables and confounding variables into one heading under study factors.

Analysis: remove ‘Method of analysis’ and replace with ‘Statistical analysis’

Figure/table headings: Please re-write your fig/table headings (for example: Table 2: Factors associated with miscarriage in Nepal, NDHS 2001-2016)

Please be consistent with the choice of word. For example, if your title is factors associated with miscarriage, please use them throughout rather than replacing it with word such as predictor.

Reviewer #4: The paper have been designed with the help of DHS 2001-2016, but the recent demographic data of Nepal (DHS-2022) is available and the analysis can be done with the help of recent published data which will have more applicability in terms of present date. Therefore, the latest data should be incorporated in the study. More factor variables (obesity status of women) can be used as explanatory variables against miscarriage.

The study shows that miscarriage rate of Nepal from 2001 to 2016 is increased by 4.2 percentage points. Proper explanation is very necessary regarding the gradual increasing of miscarriage rate from 2001 to 2016, in spite of having improved medical facilities day by day.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: Yes: Pramesh Ghimire

Reviewer #4: No

**********

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PLoS One. 2024 Apr 29;19(4):e0302001. doi: 10.1371/journal.pone.0302001.r004

Author response to Decision Letter 1

8 Dec 2023

The authors would like to thank the reviewers for their fair reviews and detailed list of constructive Suggestions. We greatly appreciate the opportunity to submit a major revision of our paper for your renewed consideration. We have given more detail based on comments we are receiving from you. Below, we respond to reviewers’ comments and point to the corresponding changes we made in the paper.

Reviewer (R3) concerns:

Reviewer #3: This is a great piece of work in a topic where limited research are done, especially in resource poor settings like Nepal. This manuscript may benefit from some minor revisions as described below.

Abstract

Methods: write factors associated instead of significant factors

Results: please remove the first sentence to reflect more on your study title.

Conclusion: please be specific with your results when you draw the conclusion

Introduction: Please look at English grammar throughout.

Methods: Please combine exposure variables and confounding variables into one heading under study factors.

Analysis: remove ‘Method of analysis’ and replace with ‘Statistical analysis’

Figure/table headings: Please re-write your fig/table headings (for example: Table 2: Factors associated with miscarriage in Nepal, NDHS 2001-2016)

Please be consistent with the choice of word. For example, if your title is factors associated with miscarriage, please use them throughout rather than replacing it with word such as predictor.

Our responses

Abstract

Methods: We have changed factor associated instead of significant factors in methods section as per your suggestion.

Results: We removed first sentence from result section.

Conclusion: We revised our prior conclusion and were more specific with the findings of our study in new revised manuscript.

Introduction: We worked with a professional paper editor to revise our article and make grammatical and sentence structure corrections.

Methods: We made the changes you suggested put in one heading under study factors.

Analysis: We made correction and replacing methods of analysis in analysis section.

Figure/table headings: In response to your comments, we reworked our figure/table title to be shorter and more concise.

Please be consistent with the choice of word. For example, if your title is factors associated with miscarriage, please use them throughout rather than replacing it with word such as predictor.

We have tried to become consistent in our word choice throughout the article. We changed the word predictor to the factor associated with it to maintain consistency across the study.

Reviewer #4 :( concerns)

Reviewer #4: The paper have been designed with the help of DHS 2001-2016, but the recent demographic data of Nepal (DHS-2022) is available and the analysis can be done with the help of recent published data which will have more applicability in terms of present date. Therefore, the latest data should be incorporated in the study. More factor variables (obesity status of women) can be used as explanatory variables against miscarriage. The study shows that miscarriage rate of Nepal from 2001 to 2016 is increased by 4.2 percentage points. Proper explanation is very necessary regarding the gradual increasing of miscarriage rate from 2001 to 2016, in spite of having improved medical facilities day by day.

Our Responses:

The authors would like to thank you for your suggestions for the study. Of course, the most recent data DHS-2022 report is already accessible. The study used data from the NDHS 2001 to the NDHS 2016 since the study is based on the Millennium Development Goal era, which is from 2000 to 2015, the finding would assist to establish strategies for achieving Sustainable Development Goal (SDG) Goal-3 to ensure healthy lives and promote well-being for all at all ages. And also we have sufficient sample size around 26,376 which is sufficient enough to calculate power. It is like similar proportion of Miscarriage in DHS 2022 in Nepal is about 9.4% almost near to equal level of proportion with DHS 2016. The data was released after the COVID-19 pandemic, therefore it is possible that it was impacted by the COVID-19 pandemic. Thank you for your suggestion; I will include it in a future publication as I am preparing my next paper on miscarriage based on DHS 2022 data. The variable obesity status of women has previously been proven as a risk factor for miscarriage in Nepal by Pramesh et al on an association between obesity and miscarriage in Nepal based on data from 2001 to 2016, we were unable to include it in our study. The DHS data shows that miscarriage is increasing in Nepal and there are various reasons associated with it. Limited access to maternal health care services, Poor quality of care, malnutrition, poor maternal health are the factors contributing to increased miscarriage in Nepal, however this is still an area that needs to be explored.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0302001.s003.docx (16.3KB, docx)

Decision Letter 2

Ganesh Dangal

21 Feb 2024

PONE-D-23-17092R2Factors Associated with Miscarriage in Nepal: Evidence from Nepal Demographic and Health Surveys, 2001-2016PLOS ONE

Dear Dr. Hamal,

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Ganesh Dangal, MD, FICS, FRCOG

Academic Editor

PLOS ONE

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Reviewer #3: All comments have been addressed

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Reviewer #3: Thanks for addressing all the comments. The revised version seems much better shape. I am sure that this manuscript will have greater impact to reduce miscarriage in Nepal.

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PLoS One. 2024 Apr 29;19(4):e0302001. doi: 10.1371/journal.pone.0302001.r006

Author response to Decision Letter 2

25 Mar 2024

We have reviewed the list of references. There are no any references that have been retracted. One of the references that we cited earlier have been removed.

Attachment

Submitted filename: Response to Editor.docx

pone.0302001.s004.docx (14.2KB, docx)

Decision Letter 3

Ganesh Dangal

27 Mar 2024

Factors Associated with Miscarriage in Nepal: Evidence from Nepal Demographic and Health Surveys, 2001-2016

PONE-D-23-17092R3

Dear Dr. Hamal,

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Ganesh Dangal, MD, FICS, FRCOG

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: JS REVIEW.docx

    pone.0302001.s001.docx (14.3KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0302001.s002.docx (25.4KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0302001.s003.docx (16.3KB, docx)
    Attachment

    Submitted filename: Response to Editor.docx

    pone.0302001.s004.docx (14.2KB, docx)

    Data Availability Statement

    "Data may be obtained from DHS (https://dhsprogram.com/data). To obtain data from the DHS website, a data requisition application is required. The authors confirm that others may acquire the data in the same way the authors did and that the authors did not receive any special consideration from MEASURE DHS/ICF International. The titles of the data set used for this study were: NPKR81FL, NPIR81FL, and NPGR81FL."

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