A Study of Factors Affecting the Renewal of Health Insurance Policy

Health insurance policies are generally one-year policies and to remain part of the insurance poll, policyholders are required to renew their policies each year. Understanding the factors that affect the demand and renewal decisions to continue in health insurance programme is imperative for future growth and development of the insurance sector. We extend our previous work on factors affecting the decision to purchase health insurance to understand the factors affecting the renewal of insurance policy. We find the factors affecting health insurance renewal are not the same as factors affecting health insurance purchase decision. This has implications for insurance providers. The study also suggests customer satisfaction as an important factor influencing the renewal decision of policyholder.

Time series analysis of private healthcare expenditures GDP: cointegration results with structural breaks

This paper analyses the time-series behaviour of private health expenditure and GDP to understand whether there is long-term equilibrium relationship between these two variables and estimate income elasticity of private health expenditure. The study uses cointegration analysis with structural breaks and estimates these relationships using FM OLS (fully modified ordinary least squares) method. The findings suggest that income elasticity of private health expenditures is 1.95 indicating that for every one per cent increase in per capita income the private health expenditure has gone up by 1.95 per cent. The private health expenditure was 2.4 per cent of GDP in 1960 and this has risen to 5.8 per cent in 2003. In nominal terms it has grown at the rate of 11.3 per cent since 1960 and during 1990’s the growth rate is 18 per cent per annum. The study discusses four reasons for this high growth experience. These are: (i) financing mechanisms including provider payment system, (ii) demographic trends and epidemiological transition, (iii) production function of private health services delivery system, and (iv) dwindling financing support to public health system. In developing countries where per se the need for spending on health is high, high levels of private health expenditures pose serious challenge to policy makers. The sheer size of these expenditures once it has risen to high levels can impede control of health expenditures itself. The high private health expenditures are also cause of concern because most of these expenditures are out-of-pocket, insurance mechanisms cover small segment of population, provider payment systems are primarily based on fee-for-services and the professional regulation and accountability systems are weak and non-functioning in many ways. It is not clear whether these expenditures are sustainable as it can have number of undesirable consequences making the health system high cost, unaffordable, and vulnerable to provider payment system.

Analysis of Public Expenditure on Health Using State Level Data

Increasingly the governments are facing pressures to increase budgetary allocations to social sectors. Recently there has been suggestion to increase the government budget allocations to health sector and increase it to 3 per cent of GDP. Is this feasible goal and in what time-frame? Health being State subject in India and much depends on the ability of the State governments to allocate higher budgetary support to health sector. This inter alia depends on what are current levels of spending, what target spending as per cent of income the States assume to spend on health and given fundamental relationship between income levels and public expenditures, how fast expenditures can respond to rising income levels. We present analysis of public expenditures on health using state level public health expenditure data to provide preliminary analysis on these issues. The findings suggest that at state level governments have target of allocating only about 0.43 per cent of SGDP to health and medical care. This does not include the allocations received under central sponsored programmes such as family welfare. Given this level of spending at current levels and fiscal position of state governments the goal of spending 2 to 3 per cent of GDP on health looks very ambitious task. The analysis also suggests that elasticity of health expenditure when SGDP changes in only 0.68 which suggest that for every one percent increase in state per capita income the per capita public healthcare expenditure has increased by around 0.68 per cent.

Selective classification using a robust meta-learning approach

Selective classification involves identifying the subset of test samples that a model can classify with high accuracy, and is important for applications such as automated medical diagnosis. We argue that this capability of identifying uncertain samples is valuable for training classifiers as well, with the aim of building more accurate classifiers. We unify these dual roles by training a single auxiliary meta-network to output an importance weight as a function of the instance. This measure is used at train time to reweight training data, and at test-time to rank test instances for selective classification. A second, key component of our proposal is the meta-objective of minimizing dropout variance (the variance of classifier output when subjected to random weight dropout) for training the metanetwork. We train the classifier together with its metanetwork using a nested objective of minimizing classifier loss on training data and meta-loss on a separate meta-training dataset. We outperform current state-of-the-art on selective classification by substantial margins--for instance, upto 1.9% AUC and 2% accuracy on a real-world diabetic retinopathy dataset. Finally, our meta-learning framework extends naturally to unsupervised domain adaptation, given our unsupervised variance minimization meta-objective. We show cumulative absolute gains of 3.4% / 3.3% accuracy and AUC over the other baselines in domain shift settings on the Retinopathy dataset using unsupervised domain adaptation

Learning on non-stationary data with re-weighting

Many real-world learning scenarios face the challenge of slow concept drift, where data distributions change gradually over time. In this setting, we pose the problem of learning temporally sensitive importance weights for training data, in order to optimize predictive accuracy. We propose a class of temporal reweighting functions that can capture multiple timescales of change in the data, as well as instance-specific characteristics. We formulate a bi-level optimization criterion, and an associated meta-learning algorithm, by which these weights can be learned. In particular, our formulation trains an auxiliary network to output weights as a function of training instances, thereby compactly representing the instance weights. We validate our temporal reweighting scheme on a large real-world dataset of 39M images spread over a 9 year period. Our extensive experiments demonstrate the necessity of instance-based temporal reweighting in the dataset, and achieve significant improvements to classical batch-learning approaches. Further, our proposal easily generalizes to a streaming setting and shows significant gains compared to recent continual learning methods

Governance of private sector corporate hospitals and their financial performance: preliminary observations based on analysis of listed and unlisted corporate hospitals in India

This paper analyses financial performance of corporate hospitals in India. While studying the financial performance of hospitals in our previous work we observed that there are some distinct differences between unlisted and listed hospitals. It is hypothesised that corporate hospitals which are listed on the stock exchanges are likely to be more aware about corporate governance issues and ensure better utilisation of resources and meet expectation of various stakeholders. We study the differences in listed and unlisted hospitals in this paper. The findings suggest that operating cost ratio of listed hospitals is significantly different and lower from the unlisted hospitals. We also find that borrowings of unlisted hospitals are much higher than listed hospitals because they have no access to capital markets to raise money. This increase the financial vulnerability of unlisted hospitals as their ability to service the debt is low. We discuss the implications of these results.

Factoring affecting the Demand for Health Insurance in a Micro Insurance Scheme

Health insurance schemes are increasingly recognised as preferable mechanisms to finance health care provision. In this direction micro health insurance schemes and community based health insurance schemes are assuming significant importance in reaching large number of people. However, at the community level despite low premiums the penetration of health insurance is small. The objective of this paper is to analyse factors determining the demand for private health insurance in a micro insurance scheme setting. The study uses two-stage model to examine this issue. First, we determine the factors which affect the insurance purchase decisions and at second level we focus on studying factors which affect the amount of insurance purchase using Heckman two-stage estimation procedure. The data of this study is based on survey and collection of primary data from the Anand district of Gujarat where Charotar Arogya Mandal is offering a health insurance scheme. The results indicate that income and healthcare expenditure are significant determinants of health insurance purchase. Age, coverage of illnesses and knowledge about insurance were also found to be affecting health insurance purchase decision positively. For the decision regarding amount of health insurance purchase, income was found to be having significant but non-linear relationship. In addition, number of children in the family, age, and perception regarding future healthcare expenditure were also found to be significant. The study discusses implications of these results.

Financial Performance of Private Sector Hospitals in India:Some Further Evidence

This paper analyses financial performance of private hospitals. The study is based on financial statement data of private hospitals for the years 1999 to 2004. Using 25 key financial ratios, the study finds six key financial dimensions. These are: fixed assets age, current assets efficiency, operating efficiency, financial structure, surplus/profit appropriation, and financial profitability/operating cost ratio. The findings suggest that over the years hospitals have shown marginal improvement in financial performance. Though the total amount of debt is not high, it is the cost of debt and ability to service the debt which is making debt burden high for hospitals. The financial risks in this sector are high because of lower profitability and lower operating efficiencies. We discuss the implications of the results.