Identifying the factors associated with cesarean section modeled with categorical correlation coefficients in partial least squares

Factors Associated with the Magnitude Of acUpuncture treatment effectS (FAMOUS): a meta-epidemiological study of acupuncture randomised controlled trials

OBJECTIVE

To identify factors and assess to what extent they impact the magnitude of the treatment effect of acupuncture therapies across therapeutic areas.

DATA SOURCE

Medline, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, Wanfang Database, VIP Database, and China Biology Medicine disc, between 2015 and 2019.

STUDY SELECTION

The inclusion criteria were trials with a total number of randomised patients larger than 100, at least one patient-important outcome and one of two sets of comparisons.

DATA ANALYSIS

The potential independent variables were identified by reviewing relevant literature and consulting with experts. We conducted meta-regression analyses with standardised mean difference (SMD) as effect estimate for the dependent variable. The analyses included univariable meta-regression and multivariable meta-regression using a three-level robust mixed model.

RESULTS

1304 effect estimates from 584 acupuncture randomised controlled trials (RCTs) were analysed. The multivariable analyses contained 15 independent variables . In the multivariable analysis, the following produced larger treatment effects of large magnitude (>0.4): quality of life (difference of adjusted SMDs 0.51, 95% CI 0.24 to 0.77), or pain (0.48, 95% CI 0.27 to 0.69), or function (0.41, 95% CI 0.21 to 0.61) vs major events. The following produced larger treatment effects of moderate magnitude (0.2-0.4): single-centred vs multicentred RCTs (0.38, 95% CI 0.10 to 0.66); penetration acupuncture vs non-penetration types of acupuncture (0.34, 95% CI 0.15 to 0.53); non-pain symptoms vs major events (0.32, 95% CI 0.12 to 0.52). The following produced larger treatment effects of small magnitude (<0.2): high vs low frequency treatment sessions (0.19, 95% CI 0.03 to 0.35); pain vs non-pain symptoms (0.16, 95% CI 0.04 to 0.27); unreported vs reported funding (0.12, 95% CI 0 to 0.25).

CONCLUSION

Patients, clinicians and policy-makers should consider penetrating over non-penetrating acupuncture and more frequent treatment sessions when feasible and acceptable. When designing future acupuncture RCTs, trialists should consider factors that impact acupuncture treatment effects.

Complex Survival System Modeling for Risk Assessment of Infant Mortality Using a Parametric Approach

Pakistan is still one of the five countries contributing to half of the child deaths worldwide and holds a low ratio of infant survival. A high rate of poverty, low level of education, limited health facilities, rural-urban inequalities, and political uncertainty are the main reasons for this condition. Survival models that evaluate the performance of models over simulated and real data set may serve as an effective technique to determine accurate complex systems. The present study proposed an efficient extension of the recent parametric technique for risk assessment of infant mortality to address complex survival systems in the presence of extreme observations. This extended method integrated four distributions with the basic algorithm using a real data set of infant survival without extreme observations. The proposed models are compared with the standard partial least squares-Cox regression (PLS-CoxR), and higher efficiency of these proposed algorithms is observed for handling complex survival time systems for risk assessment. The algorithm is also used to analyze simulated data set for further verification of results. The optimal model revealed that the mother's age, type of residence, wealth index, permission to go to a medical facility, distance to a health facility, and awareness about tuberculosis significantly affected the survival time of infants. The flexibility and continuity of extended parametric methods support the implementation of public health surveillance data effectively for data-oriented evaluation. The findings may support projecting targeted interventions, producing awareness, and implementing policies planned to reduce infant mortality.

Modeling the Ranked Antenatal Care Visits Using Optimized Partial Least Square Regression

The frequency and timing of antenatal care visits are observed to be the significant factors of infant and maternal morbidity and mortality. The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least square regression model. A data set collected during 2017-2018 by Pakistan Demographic and Health Surveys is used for modeling purposes. The partial least square regression model coupled with rank correlation measures are introduced for improved performance to address ranked response. The proposed models included PLSρ_s, PLSτ_A, PLSτ_B, PLSτ_C, PLS _D, PLSτ_GK, PLS _G, and PLS _U. Three filter-based factor selection methods are executed, and leave-one-out cross-validation by linear discriminant analysis is measured on predicted scores of all models. Finally, the Monte Carlo simulation method with 10 iterations of repeated sampling for optimization of validation performance is applied to select the optimum model. The standard and proposed models are executed over simulated and real data sets for efficiency comparison. The PLSρ_s is found to be the most appropriate proposed method to model the observed ranked data set of antenatal care visits based on validation performance. The optimal model selected 29 influential factors of inadequate use of antenatal care. The important factors of reduced antenatal care visits included women's educational status, wealth index, total children ever born, husband's education level, domestic violence, and history of cesarean section. The findings recommended that partial least square regression algorithms coupled with rank correlation coefficients provide more efficient estimates of ranked data in the presence of multicollinearity.

Feasibility of a birth-cohort in Pakistan: evidence for better lives study

Background

Evidence for Better Lives Study Foundational Research (EBLS-FR) is a preliminary endeavor to establish the feasibility of a global birth cohort, and within this feasibility study, piloting the research instrument, with participants from eight lower middle-income countries across the globe. It aims to investigate mediators and moderators of child development and wellbeing; it envisages informing policy and practice change to promote child health and wellbeing globally. Pakistan is one of the resource poor lower middle-income country (LMIC) taking part in this global birth cohort; we report the feasibility of establishing such a birth cohort in Pakistan.

Method

From March 2019 to July 2019, 153 third trimester pregnant women were identified, using community health worker registers, and approached for baseline demographics and a number of maternal wellbeing, mental health, support-related information, and stress-related biomarkers from bio-samples in a peri-urban area of Islamabad Capital Territory. One hundred fifty of these women gave consent and participated in the study. From October 2019 to December 2019, we re-contacted and were able to follow 121 of these women in the 8–24 weeks postnatal period. All interviews were done after obtaining informed consent and data were collected electronically.

Results

One hundred fifty (98.0%) third trimester pregnant women consented and were successfully interviewed, 111 (74.0%) provided bio-samples and 121 (80.6%) were followed up postnatally. Their mean age and years of schooling was 27.29 (SD = 5.18) and 7.77 (SD = 4.79) respectively. A majority (82.3%) of the participants were housewives. Nearly a tenth were first time mothers. Ninety-two (61.3%) of the women reported current pregnancy to have been unplanned. Overall wellbeing and mental health were reported to be poor (WHO-5 mean scores 49.41 (SD = 32.20) and PHQ-9 mean scores 8.23 (SD = 7.0)). Thirty-eight (21.8%) of the women reported four or more adverse childhood experiences; 46 (31.3%) reported intimate partner violence during their current pregnancy. During the postnatal follow up visits, 72 (58.0%) of the women reported breastfeeding their infants.

Conclusion

The foundational research demonstrated that Pakistan site could identify, approach, interview, and follow up women and children postnatally, with a high response rates for both the follow up visits and bio-samples. Therefore, a future larger-scale pregnancy birth cohort study in Pakistan is feasible.

The Partial Least Squares Spline Model for Public Health Surveillance Data

Factor discovery of public health surveillance data is a crucial problem and extremely challenging from a scientific viewpoint with enormous applications in research studies. In this study, the main focus is to introduce the improved survival regression technique in the presence of multicollinearity, and hence, the partial least squares spline modeling approach is proposed. The proposed method is compared with the benchmark partial least squares Cox regression model in terms of accuracy based on the Akaike information criterion. Further, the optimal model is practiced on a real data set of infant mortality obtained from the Pakistan Demographic and Health Survey. This model is implemented to assess the significant risk factors of infant mortality. The recommended features contain key information about infant survival and could be useful in public health surveillance-related research.

Correction: Identifying the factors associated with cesarean section modeled with categorical correlation coefficients in partial least squares

[This corrects the article DOI: 10.1371/journal.pone.0219427.].