Market failure, policy failure and other distortions in chronic disease markets

Background: The increasing prevalence of chronic disease represents a significant burden on most health systems. This paper explores the market failures and policy failures that exist in the management of chronic diseases.
Discussion: There are many sources of market failure in health care that undermine the efficiency of chronic disease management. These include incomplete information as well as information asymmetry between providers and consumers, the effect of externalities on consumer behaviour, and the divergence between social and private time preference rates. This has seen government and policy interventions to address both market failures and distributional issues resulting from the inability of private markets to reach an efficient and equitable distribution of resources. However, these have introduced a series of policy failures such as distorted re-imbursement arrangements across modalities and delivery settings.
Summary: The paper concludes that market failure resulting from a preference of individuals for 'immediate gratification' in the form of health care and disease management, rather than preventative services, where the benefits are delayed, has a major impact on achieving an efficient allocation of resources in markets for the management of chronic diseases. This distortion is compounded by government health policy that tends to favour medical and pharmaceutical interventions further contributing to distortions in the allocation of resources and inefficiencies in the management of chronic disease.

Web search activity data accurately predict population chronic disease risk in the USA

BACKGROUND: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. METHODS: Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. RESULTS: For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81; 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96; 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. CONCLUSIONS: The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.