Using the stochastic health state function to forecast healthcare demand and healthcare financing: Evidence from Singapore

• Author: Shu Peng Loh,Ngee Choon Chia
• DOI: http://doi.org/10.1111/rode.12528
• Date: 01 August 2018
• Published date: 01 August 2018

SPECIAL ISSUE ARTICLE

Using the stochastic health state function to forecast

healthcare demand and healthcare financing:

Evidence from Singapore

Ngee Choon Chia

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Shu Peng Loh

Department of Economics, National

University of Singapore

Correspondence

Ngee Choon Chia, National University of

Singapore, Department of Economics,

Faculty of Arts and Social Sciences,

Block AS2, Level 6, 1 Arts Link,

Singapore 117570.

Email: ecscnc@nus.edu.sg

Abstract

Typically, healthcare financing for an ageing population

requires projections on healthcare demand and cost. How-

ever, projecting healthcare demand based on projected

elderly does not consider changes in population health

state over time. This paper proposes a new approach to

forecast health variables using a stochastic health state

function and the well‐established Lee–Carter stochastic

mortality model. With the estimated health state at each

age over time, we project the hospitalization rate, health-

care demand, and financing cost for Singapore using his-

torical life tables and hospital admission data. Our

findings show that while hospital insurance claims

increase owing to an aging population, improving health

state could save costs from hospital insurance claims.

This has policy implications: more attention should be

given to preventive healthcare such as health screening to

improve the overall health state of the population.

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INTRODUCTION

Singapore is ageing rapidly, and the demographic landscape is changing. In 2014, 11 percent of

the resident population in Singapore were elderly (aged 65 and above). The elderly population as a

proportion of resident population is expected to increase at an unprecedented rate, reaching 14 per-

cent in 2019 and 20 percent by 2026. While it took Singapore 19 years to transit from an “ageing”

to an “aged”population, the transition to a “super‐aged”population will be much quicker, in less

than 10 years. In contrast, Japan took nearly 25 years to transit from an “ageing”to an “aged”

population structure. Our findings have important policy implications on healthcare financing for

an aging population.

DOI: 10.1111/rode.12528

Rev Dev Econ. 2018;22:1081–1104. wileyonlinelibrary.com/journal/rode © 2018 John Wiley & Sons Ltd

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Ageing population has significant ramifications on government healthcare spending. Fiscal plan-

ning often depends on projected healthcare spending for the elderly, which in turn depends on pro-

jected healthcare demand and costs. Projections on healthcare demand are also based on the

demographic landscape in the future. This paper uses the Lee and Carter (1992) stochastic mortal-

ity model to project population, life expectancy, and mortality and survival probabilities. The Lee–

Carter model has been successfully used to project populations for the G7 countries (Tuljapurkar,

Li, & C. Boe, 2000). However, the Lee–Carter approach does not consider changing health states.

Skiadas and Skiadas (2015) wrote that life tables do not just contain information on births and

deaths, but also convey information on the health status of the population by year of age. Popula-

tions with higher health states would have higher life expectancy. With a specified health state

function, the health state of the population becomes quantifiable and could be used to predict the

demand for healthcare using lifetables. Lifetables convey significant information on the health

status of the population over time and over age groups. The force of mortality operates because of

diminishing vitality or deterioration of human health over the course of time.

Instead of computing healthy life expectancy only, this paper illustrates how the Skiadas health

state function could be used along with the Lee–Carter stochastic population model to project hos-

pital demand and healthcare financing costs. This approach is particularly useful in countries where

detailed micro health data may not be available. We project hospitalization rate, healthcare

demand, and healthcare financing cost in Singapore using data from life tables, aggregated hospital

admission data, and hospital insurance claims. Using the model, the projected 2013 hospital insur-

ance claims are consistent with the actual hospital insurance (Medishield) claims made in 2013.

This paper is divided into five sections. Section 2 briefly describes the “3M”healthcare financ-

ing system in Singapore. Section 3 gives an overview of the Lee–Carter stochastic demographic

model and the Skiadas health state model. We describe how these models can be implemented

using Singapore's mortality and aggregated health data. Section 4 discusses the simulation results

on hospital demand, healthy life expectancy and the financing costs. Sensitivity analyses are con-

ducted using different medical inflation rates and the speed of ageing. Section 5 provides some

concluding remarks and policy implications.

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HEALTH SECTOR AND HEALTH CARE FINANCING IN

SINGAPORE

Since older people experience higher hospitalization incidence and longer average length of hospi-

tal stay, an ageing population puts significant stress on health infrastructure. As can be gleaned

from Figure 1, Singapore's ageing population has already begun to manifest in the rising number

of hospital admissions. Total annual hospital admissions grew by 23 percent between 2006 and

2015. Public sector admissions for older patients aged 65 years and above increased from 28.6 per-

cent in 2006 to 33.4 percent in 2013. Furthermore, the average length of stay for older patients

increased from 7.8 days in 2010 to 8.2 days in 2013.

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Based on the Bloomberg (2014) ranking, Singapore has remarkable health outcomes and its

healthcare system is ranked the best in the world. National healthcare expenditure is well below

other developed nations at about 2.05 percent of GDP. Table 1 shows that unlike many other

countries (with the notable exception of the United States), Singapore's private health expendi ture

is higher than public health expenditure. Although Singapore's healthcare financing and delivery

system is anchored on the philosophy of individual responsibility under the “3M”framework,

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CHIA AND LOH