A structural equation model to explore sociodemographic, macroeconomic, and health factors affecting life expectancy in Oman

Abstract

Introduction the factors determining life expectancy (LE) are crucial for policymakers to study in implementing an effective and accurate intervention in society. In Oman, the available data over the past four decades were not extracted to develop a statistical model to understand how the sociodemographic (SD), macroeconomic (ME), and health-status and resources (HSR) factors affecting LE. The study was aimed at creating a representative model to explain the factors affecting LE in Oman and examine the direct and indirect effects of SD, ME, and HSR in LE in Oman. Methods the research was a retrospective, ecological, time-series study design to collect the annual published data on SD, ME, and HSR in Oman from all available resources from 1978 to 2018. The data were then analyzed with structural equation modeling (SEM) method using IBM® SPSS® Amos 24 for the study of their impacts in LE. Results in Oman, using SEM, the SD, ME, and HSR significantly and directly affected LE by the estimate of -0.92 (p < 0.001), -0.15 (p < 0.001), and 0.23 (p < 0.001) respectively. Conclusion the study was the first attempt to analyze all the different aspects of LE comprehensively in Oman. In the case of Oman, the health resource is an important factor that need to be addressed to increase or to maintain the current LE. Hence, during social hardship or economic recession, health-related support by the government should be continued or even improved because of its positive effect on LE.

Factors Impacting Life Expectancy in Bahrain: Evidence from 1971 to 2020 Data

“…The theoretical model proposed for this study was based on a conceptual framework from previous studies in ASEAN countries 35–37 and Oman. 8 The proposed model consisted of a list of MVs reflecting the respective LV. 58 Based on a recent systematic review, all these MVs significantly affect LE.…”

“…CB-SEM is involved in the confirmatory approach by determining how well a proposed model can estimate the covariance matrix from the dataset. As with previous studies related to LE, research had been conducted in ASEAN countries 35–37 and Oman 8 using CB-SEM. PLS-SEM is an exploratory approach to explaining the variance in the MVs within the proposed model.…”

“…7 A recent study in Oman highlighted that every country needs a specific model for LE projection according to specific indicators. 8 Hence, this study aimed to develop an explanatory model to examine the impact of health status and resources (HSR), macroeconomic (ME), and sociodemographic (SD) factors on LE in Bahrain. A robust model will be of great benefit to the policymakers in terms of the evaluation of population health comprehensively.…”

The Impact of Sociodemographic, Macroeconomic, and Health Status and Resources on Infant Mortality Rates in Oman: Evidence from 1980 to 2022

“…Additionally, a data cleaning procedure was undertaken and is considered one of the steps conducted to ensure the data's quality. Principally, imputation procedures will only be attempted on the missing random explanatory variables [45]. Furthermore, any item in this study that was found to have 20% missing data or more will be excluded and is considered ineligible to be entered into the analysis procedure entirely.…”

“…The proposed conceptual model was developed based on previous studies [44][45][46][47] and the results from a systematic review [29]. Overall, this study intends to examine the effect of multiple explanatory variables on IMR in Oman using structural equation modeling (SEM).…”