■机构分娩确保在有利环境中在熟练的医疗保健人员的监督下进行分娩。对于像印度这样的国家,新生儿和产妇死亡率高,实现机构分娩的100%覆盖率是首要政策优先事项。在这方面,公共卫生机构发挥着关键作用,鉴于他们仍然是大多数人的首选,由于现有的医疗保健障碍。虽然这一领域的研究集中在私营医疗机构,有有限的研究,尤其是在印度的背景下,看看公共卫生设施中机构出生的推动者。在这项研究中,我们希望确定印度公共卫生机构机构出生的重要预测因素。
■我们依靠全国家庭健康调查(NFHS-5)概况数据进行分析。在这项研究中,我们的因变量(DV)是公共卫生机构中机构出生的百分比。我们首先使用Welch的t检验来确定城市和农村地区在DV方面是否存在任何显着差异。然后,我们使用多元线性回归和偏F检验来确定预测DV变化的最佳拟合模型。我们在这项研究中生成了两个模型,并使用Akaike的信息标准(AIC)和调整后的R2值来确定最佳拟合模型。
■我们发现,在公共卫生设施中机构出生的平均百分比方面,城乡之间没有显着差异(P=0.02,α=0.05)。最佳拟合模型是具有中等效应大小(Adjusted2=0.35)和AIC为179.93的交互模型,低于竞争模型(AIC=183.56)。我们发现家庭健康保险(β=-0.29)和在熟练的医疗保健人员(β=-0.56)的监督下进行的家庭分娩是印度公共设施中机构出生的重要预测因素。此外,我们观察到低体重指数(BMI)和肥胖对DV有协同影响。我们的发现表明,低BMI与肥胖之间的相互作用对印度公共卫生机构的机构出生有很大的负面影响(β=-0.61)。
■为家庭提供健康保险可能不会提高印度公共卫生设施的利用率。在公共医疗服务存在其他障碍的地方。因此,重要的是要考虑将现有的获取障碍降至最低的干预措施。虽然从政策角度来看,最终目标应该是长期实现机构分娩的100%覆盖率,在印度的背景下,短期战略是有意义的,特别是管理在机构之外的分娩过程中出现的并发症。
UNASSIGNED: Institutional births ensure deliveries happen under the supervision of skilled healthcare personnel in an enabling environment. For countries like India, with high neonatal and maternal mortalities, achieving 100% coverage of institutional births is a top policy priority. In this respect, public health institutions have a key role, given that they remain the preferred choice by most of the population, owing to the existing barriers to healthcare access. While research in this domain has focused on private health institutions, there are limited studies, especially in the Indian context, that look at the enablers of institutional births in public health facilities. In this study, we look to identify the significant predictors of institutional birth in public health facilities in India.
UNASSIGNED: We rely on the National Family Health Survey (NFHS-5) factsheet data for analysis. Our dependent variable (DV) in this study is the % of institutional births in public health facilities. We first use Welch\'s t-test to determine if there is any significant difference between urban and rural areas in terms of the DV. We then use multiple linear regression and partial F-test to identify the best-fit model that predicts the variation in the DV. We generate two models in this study and use Akaike\'s Information Criterion (AIC) and adjusted R2 values to identify the best-fit model.
UNASSIGNED: We find no significant difference between urban and rural areas (P = 0.02, α =0.05) regarding the mean % of institutional births in public health facilities. The best-fit model is an interaction model with a moderate effect size (Adjusted2 = 0.35) and an AIC of 179.93, lower than the competitive model (AIC = 183.56). We find household health insurance (β = -0.29) and homebirth conducted under the supervision of skilled healthcare personnel (β = -0.56) to be significant predictors of institutional births in public facilities in India. Additionally, we observe low body mass index (BMI) and obesity to have a synergistic impact on the DV. Our findings show that the interaction between low BMI and obesity has a strong negative influence (β = -0.61) on institutional births in public health facilities in India.
UNASSIGNED: Providing households with health insurance coverage may not improve the utilisation of public health facilities for deliveries in India, where other barriers to public healthcare access exist. Therefore, it is important to look at interventions that minimise the existing barriers to access. While the ultimate objective from a policy perspective should be achieving 100% coverage of institutional births in the long run, a short-term strategy makes sense in the Indian context, especially to manage the complications arising during births outside an institutional setting.