Does Social Medical Insurance Achieve a Poverty Reduction Effect in China?

This paper aims to explore the impact of social medical insurance (SMI) on poverty reduction (PR) in China. Considering the time-varying characteristics of factors, this paper uses the bootstrap Granger full sample causality and subsample rolling window model to find the relationship between SMI and PR. The results highlight that in some periods, there is a bidirectional causal link between SMI and PR. Influenced by the medical insurance reform and medication measures. Social medical insurance does not have a positive impact on poverty reduction in some periods. These results are supported by the Utility Maximization Model of Insurance Consumption, which highlights that individuals make utility maximization choices when choosing insurance. The effect of medical insurance on poverty alleviation depends on whether an individual's investment in medical insurance can maximize its utility. If the proportion of social medical insurance reimbursement is too low, individuals will give up buying social medical insurance. Thus, the anti-poverty effect of social medical insurance is difficult to achieve. Therefore, authorities need to pay attention to specific contexts and social medical insurance policies and further improve the social medical insurance system to promote the realization of the anti-poverty of social medical insurance.

Practical lessons from a general dentist of more than 50 years private practice plus 2 years of United States Air Force practice

Having practiced over 54 years the art and science of general dentistry, many changes in philosophy and performance have occurred. Some are minor while others very major. This series of observations will treat some in detail while others very briefly. The physical locations have been in the Dallas, Texas area of the USA. Definitions of terms set the stage for discussion of the basis of dentistry. Support for the scientific as well as evidence-based approaches is laid forth. Filling materials have transitioned from amalgam to composite being most prevalent. Fluoride added to local water supplies has decreased the number of decayed/sensitive teeth, the timing of initial decay, and the prognosis for remediation. pH is a major player in the deterioration of tooth structure. New understandings of tooth brushing and oral hygiene have significantly improved the future for continuing dental health. Absence of fluoride in bottled water has taken a front-center stage for helping/hurting chances of keeping teeth free of decay. Fluoride varnishes have widespread acceptance in America. Failure to seek routine dental care has influenced the outcomes for many younger patients, especially those who have graduated high school, gone off to college or into the workforce. Such lack of routine preventive influence raises the costs of care when it is received, often leading to complaints from patients about the high costs of repair. The alternative is prevention with ongoing consistent 6-month recalls/repairs when problems initiate, rather than allowing problems of long duration. The USA dental insurance industry adverse impact on practicing dentists is a vital monologue. Revealing the dental insurance industry as a number one concern of many surveys of practicing dentists is a way of preparing international countries for learning from the flawed USA models. Recent Congressional law, HR 1418, the Competitive Health Insurance Reform Act, will address some of the most critical wrongs by placing the dental insurance industry into antitrust restraints. Current concerns about digital X-ray’s diagnostic potential are revealed. Conservative dentistry is promoted. Results of conservative practice from nearly 50 years are documented with photos and X-rays. Bonded bridge technology is highlighted for its valued impact.

Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers’ health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia.

Trends in total and out-of-pocket cost of cancer care around the first year of diagnosis for the four most common cancers among privately insured nonelderly adults: 2009-2016.

5 Background: To provide a comprehensive evaluation of the trends in treatment pattern, total and out-of-pocket (OOP) costs of cancer care for in the period between 2-month before and 12-month after cancer diagnosis for the privately insured non-elderly adults diagnosed with female breast, colorectal, lung, or prostate cancer. These four cancers represent the four most prevalent cancers in the United States and the 14-month duration captures the most expensive care phase in the cost trajectory of cancer. Methods: We constructed incident cohorts using claims data from the Health Care Cost Institute between 2009 and 2016. We identified treatment modality (cancer-related surgery, systemic therapy, radiation, and other hospitalizations) and calculated associated total and OOP (sum of deductible, coinsurance, and copayment) costs from payment variables. For each cancer, we examined healthcare utilization and cost trends based on the year of diagnosis and conducted logistic regressions to assess the trend in utilization and generalized linear models to evaluate the trend in costs. All estimates are reported in 2020 US dollars. Results: The cohorts consisted of 105,255 breast, 23,571 colorectal, 11,321 lung, and 59,197 prostate cancer patients. Between 2009 and 2016, use of systemic therapy and radiation significantly increased, except for lung cancer. Cancer surgeries significantly increased for breast and colorectal cancer but decreased for prostate cancer, whereas hospitalizations for reasons other than cancer declined for all cancers (p < 0.001). Costs increased for nearly all treatment modalities except for systemic therapy in colorectal and radiation in prostate cancer. Total mean costs per patient had the largest increase in breast cancer (29%, $109,544 to $140,743), followed by lung (11%) and prostate (4%) cancer. Cost increase in colorectal cancer was not statistically significant (P = 0.089). Similar trends were found in median costs. Although not every cancer had significant increase in total costs over time, OOP costs increased > 15% for all cancers, with deductibles accounting for an increasingly proportion. Conclusions: Rising costs of cancer treatments, compounded with increasing cost-sharing increased OOP costs for privately insured, non-elderly cancer patients. Policy initiatives to mitigate financial hardship should consider cost containment as well as insurance reform.