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The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in India. Further, it attempts to analyze the extent to which the SED determinants can serve as predictive indicators for the prevalence of NCDs.

The study uses three rounds of unit-level National Sample Survey self-reported morbidity data for the analysis. A machine learning model was constructed to predict the prevalence of NCDs based on SED characteristics. In addition, probit regression was adopted to identify the relevant SED variables across the cluster of states that significantly impact disease prevalence.

Overall, the study finds that the disease prevalence can be reasonably predicted with a given set of SED characteristics. Also, it highlights age as the most important factor across a cluster of states in understanding the distribution of disease prevalence, followed by income, education, and marital status. Understanding these variations is essential for policymakers and public health officials to develop targeted strategies that address each state’s unique challenges and opportunities.

The study complements the existing literature on the interplay of SEDs with the prevalence of NCDs across diverse state-level dynamics. Its predictive analysis of NCD distribution through SED factors adds valuable depth to our understanding, making a notable contribution to the field.

The purpose of this paper is to analyze the main global transformations of the healthcare sector and their underlying causes and effects.

The study focuses on several important trends including the increasing life expectancy and aging, the rise of non-communicable diseases, the risks of pandemics, medication expenditure, the globalization of healthcare and technological innovations such as digitization, robotic and nanomedicine.

The analysis indicates that the world populations will be much older in the near future and the healthcare sector will witness significant growth opportunities. The aging populations will put more pressure on healthcare systems and increase the incidence of non-communicable diseases. In a globalized world, the risks of global pandemics are expected to increase. The surge in the medication expenditure will put much pressure on healthcare systems, insurers, patients, employers and providers. The healthcare sector is characterized by its above-average growth in the USA and much of the developed world. Therefore, the share of healthcare in gross domestic product will continue to rise. The digitization and globalization of healthcare may involve major disruptions in the location and the type of care. New materials particularly nanoparticles will be used to enhance the precision, quality and efficacy of diagnostics, medications and treatments.

The paper touches on several areas including demographic, clinical, financial, managerial and technological transformations and examines their implications for the healthcare sector.

Elderly patients suffering from non-communicable disease face a dilemma in the selection of healthcare providers. This study attempts to identify the key variables playing a crucial role and identify the appropriate healthcare destination with the help of a combination of Decision-making Trial and Evaluation Laboratory (DEMATEL) and analytic hierarchy process (AHP) techniques. The primary objective is to introduce the DEMATEL and AHP as efficient decision-making methods to choose the right healthcare provider for elderly patients suffering from non-communicable diseases.

An integrative approach utilizing DEMATEL and AHP is used to reach the ideal solution for healthcare provider selection decisions. The DEMATEL approach is used to segregate the cause and effect variables. Similarly, the AHP is used to identify the weights of the top five cause-inducing variables, and the paired comparison method is used to select the healthcare provider.

The variables such as dependency on family members, easily accessible services, and patient autonomy play a vital role in the selection decision of healthcare providers in elderly patients suffering from non-communicable diseases.

In terms of priority, home healthcare should be considered the preferred provider for elderly patients suffering from non-communicable diseases followed by neighbourhood registered medical practitioners and hospitals.

This is the first of its kind study which has attempted to solve the healthcare provider selection decision with the combined approach of DEMATEL and AHP.

Twenty-first century has dawned with substantial achievements in population health outcome indicators in India. However, very little is known on patterns in causes of death in India. The paper aims to discuss this issue.

In this paper, data was drawn from two sources namely, National Family Health Survey (NFHS-1, 1992-1993 and NFHS-2, 1998-1999) and published reports of Survey of Cause of Death (Rural). Three-years moving average causes-of-death estimates were calculated based on World Health Organization classification of causes of death. Negative binomial regression models were fitted to capture the effect of socio-demographic and behavioural determinants of patterns in causes of death.

The leading causes of death were heart diseases, tuberculosis, asthma, paralysis, prematurity and cancer. Three-fifth of the deaths to children under the age of ten was from communicable, maternal, perinatal and nutritional conditions. On the other hand, about two-third persons aged 45 years and above were dying from non-communicable diseases. Female were at greater risk of dying from non-communicable diseases (IRR: 1.22, 95 per cent CI: 1.11-1.34, p < 0.001).

The epidemiologic transition in India has produced a shift in mortality from communicable, maternal, perinatal and nutritional conditions to non-communicable diseases, with little or no role played by injuries regardless of the level of all-cause mortality. Coupled with the effects of population age structures, other factors were also responsible for the bulk of the inter-regional disparities. These factors include differences in the populations’ health risks associated with the natural or built environments, prevalence of behavioural risk factors, or gaps in the capacities of health systems to respond to specific disease challenges, social stratification and others.

This paper described the trends, patterns and geographic variability in India’s causes-of-death profile in terms of communicable diseases, non-communicable diseases and injuries, and socio-economic and demographic determinants of patterns in the profile.