Structural equation modeling on the relationship between maternal characteristics and pregnancy complications: A study based on National Family Health Survey

Abstract

Aim This study aimed to understand the complex relationship between maternal complications and women's characteristics in India. Methods The present study builds structural equation modeling (SEM) using the data from the National Family Health Survey round 4 (2015–16) and includes 190, 898 women who had given their last birth during 5 years preceding the survey. Based on eight questions related to maternal complications, the SEM constructed two latent variables – complications during pregnancy (PREGCOMP) and delivery (DELCOMP). Results Result shows that prolonged labor was highly prevalent (42%) among women, followed by vaginal bleeding (34%) and swelling in legs, body or face (32%). Women from affluent households (β = −0.02 for each‐middle, richer and richest quantile), with higher education (β = −0.02), timing when they received first ANC (β = −0.003) and women who received full ANC (β = −0.01) had a lower risk of PREGCOMP. However, consanguineous marriage (β = 0.01) was positively related to PREGCOMP. Further, women with PREGCOMP (β = 0.20), rural residents (β = 0.02), having exposure to mass media (β = 0.01), and who had facility delivery (β = 0.04) were at high risk of DELCOMP. Women with pregnancy complications are more likely to have delivery complications. [Correction added on 27 November, after first online publication: The statement “who received full antenatal care (ANC) (β = −0.05)” has been removed.] Conclusion Socioeconomic and demographic factors significantly affect both the latent variables. This study concludes that the use of intensive healthcare services reduces health complications. The paper recommends early visits and full ANC checkups to reduce pregnancy complications.

How socioeconomic status, social capital and functional independence are associated with subjective wellbeing among older Indian adults? A structural equation modeling analysis

“…The chi-square test statistic was 4387.67, df = 146, p < 0.001; but because the chi-square test is very sensitive to sample size (n = 9231), we considered the supplementary goodness-of-fit index-RMSEA, which indicated a good fit at 0.046; acceptable level of RMSEA used in this study was < 0.05. Other fit statistics such as CFI, Tucker-Lewis Index (TLI) and Standardized Root Mean Square Residual (SRMR) are considered as model indices [58]. The value of CFI and TLI ≥ 0.95 is considered to be best fit model; however, the value above 0.90 is also acceptable [59].…”

A Review of the National Family Health Survey Data in Addressing India’s Maternal Health Situation

“…Two studies have focused on maternal health through the reproductive health index using NFHS 1 [ 24 ] and NFHS 3 [ 55 ] data. Two other studies have focused on pregnancy complications and adverse pregnancy outcomes on NFHS 4 [ 56 ] and all four rounds of NFHS [ 57 ].…”

“…Four studies have used spatial analysis in understanding maternal health care utilization [ 14 , 15 , 42 , 65 ]. Studies established a strong association between mass media and maternal health in India [ 14 – 16 , 42 , 49 , 56 ]. Usage of phone is positively associated with skilled birth attendance, PNC, usage of modern contraceptives but it is negatively associated with early ANC [ 17 ].…”

Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms

“…Compared with 2 to 4 antenatal care visits, singleton pregnancies with > 4 visits before 24 weeks were associated with increased risk of PTB. Notably, singleton pregnancies with > 4 visits of antenatal care before 24 weeks' gestation might be attributable to maternal complications or mental health problems (Nath et al, 2017;Kumar and Dhillon, 2021).…”