Factors Influencing Unmet Medical Need among U.S. Adults: Disparities in Access to Health Services

Inequities in access to health services has negative consequences on individual well-being, and imposes financial and emotional burden on patients, families, health care systems, and the public. Inequities engendered from differences in socioeconomic status, health insurance coverage, race, and other characteristics can engender disparities. This study aimed to identify the potential predictors of unmet medical need among the civilian noninstitutionalized U.S. adults. Inability to receive needed medical care or receiving medical care after a delay, due to the associated costs, constructed unmet medical need. This study used a four-year (2014-2017) National Health Interview Survey (NHIS) data (sample size: 296,301 adults) and implemented a conceptual framework to study disparities in access to health services and estimate the relative importance of predisposing, enabling, and need factors as the predictors of unmet medical need. Findings from machine learning and logistics regression models highlight the importance of health insurance coverage as a key contributing factor of health disparities. About 60% of variation in unmet medical need was predictable, with over 90% accuracy, solely with health insurance coverage status. Self-rated health status, family structure, and family income to poverty ratio were other statistically significant predictors. Even after controlling for a wide variety of sociodemographic and health status variables such as age, gender, perceived health status, education, income, etc., health insurance remains significantly associated with unmet medical need (OR: 5.03 , 95%CI: 4.67-5.42). To ensure precise national estimates, proper survey data analysis methods were incorporated to account for the complex sampling method used by NHIS. Furthermore, the enabling factors (health insurance and income) exert much more weight on unmet medical need than predisposing factors and need factors. The findings raise the concerns about the existence and magnitude of disparities in health care access and provide a comprehensive framework to a target population for understanding the sources of health inequities with data-driven evidence. Results can be utilized to address potential areas for designing public policy and program interventions by identifying the relative vulnerability of different population groups for lacking access to affordable health services. Future studies using longitudinal panel data are necessary to establish a causal relationship between the predictors and unmet medical need

Impact of National Health Insurance on health seeking behavior in the Kassena-Nankana district of Northern Ghana

Includes bibliographical references.The National Health Insurance Scheme (NHIS) was introduced in Ghana in 2003 with the aim of mobilizing additional funds for health care, promoting equal access to reasonable health care, pool health risks, prevent impoverishment, and improve the efficiency and quality of health care. The success of the NHIS in improving access to health care since its implementation and the extent to which it has impacted on health seeking behaviour has not been extensively investigated. This study examines health-seeking behaviours of insured and uninsured households on the mutual health insurance scheme on health care access in the Kassena-Nankana District (KND) of northern Ghana and to determine the factors that influence household decision to enrol into the NHIS. The study is a cross sectional survey of 422 household heads randomly selected to represent rural, peri-urban and urban zones of KND. Data was analysed using STATA version 8.0. A binary logit model was used to determine factors that predict household enrolment into the NHIS. The choice of a particular type of provider with multiple outcomes was analysed using a multinomial logit model. Results showed that 72% of household heads were males and the average age was 51 years. Out of the 422 respondents, 64% were insured. Household heads of age 40 years and above, being a female household head, being married, and economic wealth positively influenced enrolment into the national health insurance scheme. Seventy four percent (74%) of the ill among the insured and 48% among uninsured sought care from public facilities while 14% among the insured and 8% among uninsured sought care from private facility. Also, self treatment among the insured was 13% and 44% among uninsured households. Results also showed that being a member of NHIS and being moderately or severely ill were associated with public health facility utilization. Household heads of 60 years or older was negatively associated with use of public health facilities. Similarly, a household that was insured, being a Muslim and the severity of illness of household member were positively associated with the use of private health care. The findings showed that the insured were more likely to use formal care providers than the uninsured. This implies that the NHI in the KND has improved the health seeking behaviour from the hitherto use of informal providers and self treatment to preferred use of formal providers

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life