PUBLIC VERSUS PRIVATE HEALTHCARE SERVICES IN MALAYSIA: THE CASE OF PRICE AND PERFORMANCE OF ULTRA SOUND AND MAMMOGRAM MACHINES 1

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1 PUBLIC VERSUS PRIVATE HEALTHCARE SERVICES IN MALAYSIA: THE CASE OF PRICE AND PERFORMANCE OF ULTRA SOUND AND MAMMOGRAM MACHINES 1 Susila Munisamy and H. Osman-Rani Faculty of Economics and Administration, University of Malaya Abstract This paper introduces the evolving features of public and private healthcare sectors in Malaysia after the 1980s, and explores the diverging trends between the two sub-sectors and their emerging differences. Based on primary data for the year 2006, the study focuses on the differences in price and performance between public and private hospitals by examining the case of ultrasound and mammogram machines. Comparison of the patient charges and waiting times for the use of these machines demonstrated important contrast between the public and private hospitals. The findings show that the waiting time in public hospital is significantly longer compared to private hospitals, and the patient charges are substantially lower in public hospitals. The study also points to the fact that optimal utilization of the medical equipment and the competitive roles of public and private hospitals are crucial to improve the efficiency and quality of health services. Key Words: Hospitals, healthcare services, ultrasound, mammogram, Malaysia JEL Classification Codes: I11, I12, I18 1. Background The development of the health sector in Malaysia has always been an integral part of its socio-economic development. The sector has largely been free of controversy over the policies at least until mid-1980s. Initially, the bulk of healthcare services existed in the public sector and the private sector s share in healthcare was very small. Since 1980s, with rapid economic growth, the demand for healthcare expanded together with the growth of public and private hospitals and clinics. 1 An earlier version of this paper was presented at the Workshop on Healthcare Services in Malaysia: Are there differences in practices, performances and charges between public and private hospitals? organized by the Faculty of Economics and Administration, University of Malaya, Kuala Lumpur, June The present version has benefitted from comments by participants in general and by Professors Goh Kim Leng and Rajah Rasiah in particular.

2 According to Dr. David Quek, the President of Malaysian Medical Association (MMA), in 2008, Malaysia spent about RM35 billion on healthcare (nearly 5% of GNP), slightly more than half of it (RM18.8 billion) in the private sector. Of that amount, Malaysians forked out RM10.8 billion from their own pockets for private healthcare. According to him, they can expect to fork out more in the years to come (The Nut Graph, 6 July 2010). There are economic rationales to justify the complementary roles of government and private healthcare services (World Bank, 1990; Musgrove, 1996). Firstly, to provide equitable access to the poor, and that basic health care is a fundamental right, public investment in the health of the poor sector of the society is an economically efficient and politically acceptable strategy for reducing poverty and alleviating its consequences. Private investments are mainly meant for those who can afford. Secondly, health services are not all private goods to be produced by the private sector. Some actions that promote health are public goods or create large positive externalities, (such as medical information campaign, preventions of transmitted diseases, immunizations, family planning, and skilled delivery care) or what is generally classified under public health. Private markets would hardly be interested to produce them or at best would only produce too little. Thirdly, the market for health care goods and services can fail through imperfect competition among providers, which allows excess profits, inefficient use of resources, poor quality and too little production (Arrow, 1963). One other reason why markets may work poorly is that variations in health risk create incentives for insurance companies to refuse to insure the poor, the very people who most need health insurance (World Bank 1993). Government can help improve how markets function by providing right information about the quality, cost and outcome of health care. Fourthly, competition between the public and the private suppliers can help spur improvements in quality and efficiency of health services in general. Staff disposition, competence and integrity, hours of operation, waiting time, accuracy of diagnosis, and the cultural appropriateness of services, for examples, are important aspects for quality assurance to raise health use and outcomes. 2

3 RM million 2. Development of Health Services in Malaysia Chart 1 displays increasing trends in Malaysian Government spending on health care and public health services from 1965 to The increase was particularly rapid from 1985 (with health expenditure of RM1,129 million) to 2005 (total health expenditure of RM8,723 million). The proportion of development expenditure varies from as low as around 10 per cent in 1980 and 1985 to as high as around 25 per cent in 1990 and Chart 1: Federal Government Expenditure on Public Health and Health Care, (RM million in current prices) DEVELOPMENT EXP ,272 1,220 OPERATING EXP ,017 1,316 2,384 4,131 7,503 Source: Ministry of Finance, Economic Report, selected years. A clearer trend can be seen in Chart 2, where operating and development expenditures combined as a share of total government expenditure declined substantially until around four per cent in the 1980s, and it was only after that it increased continuously until close to seven per cent in However, in terms of percentage of Gross National Product (GNP), it has been more or less stable between 1.5 and 2.0 per cent throughout the years. 2 Government health expenditure has usually been moving along the growth of GNP. 2 Private health expenditures are however not easily available. They are always more difficult to estimate. But even in the public sector, health spending is poorly disaggregated by use. 3

4 Percentage Implicitly, Nelson (2008: 226) argued that the improved health services and health status in Malaysia have been the benefits of growth rather than the causes of growth. Chart 2: Federal Government Spending on Public Health and Health Care as Percentage of Total Government Expenditure and GNP, % of GOVT EXP % of GNP Source: Ministry of Finance, Economic Report, selected years. Nevertheless, for 2009, from the total government development expenditure of RM53.6 billion, 4.9 per cent was for the health sector compared with 21.9 per cent for education and training, 8.5 per cent for security, and 52.0 per cent for economic services. The allocation for health in Malaysia is relatively low even by international standards. Table 1 shows some indications. In terms of health expenditure as a percentage of GDP and government health expenditure as a percentage total government expenditure for the years 2000 and 2006, Malaysia s allocations for health sector were not much different from other developing countries like Afghanistan, Bangladesh, Sri lanka, Philippines, Thailand and Viet Nam. Although higher than Myanmar or Pakistan, were lower than countries like Argentina, China, Brazil, Egypt, Jordan and Zimbabwe. Definitely Malaysia s healthcare sector is lower than the developed countries like Japan, South Korea, Canada, New Zealand, United Kingdom, United States, Germany and France. In terms of public and private share in health expenditure, it is more difficult to generalize. For many developed countries, the government shares tend to be bigger than private (e.g., Australia, France, Germany, Japan, New Zealand and the United Kingdom); but for many developing countries, they can be the opposite (e.g., Afghanistan, 4

5 Bangladesh, China, Egypt, India, and Uganda). While in the case of Malaysia, it is about 50:50, almost similar to countries like Argentina, Brazil, South Korea, Sri Lanka and the United States. That has probably made the debate on public versus private hospitals more critical. Table 1: Health Expenditure Ratios in Selected Countries, 2000 and 2006 Total expenditure on health as % of GDP % of govt expenditure on health % of private expenditure on health Govt expend on health as % of total govt expenditure Country Afghanistan Argentina Australia Bangladesh Brazil Canada China South Korea Egypt France Germany Ghana India Indonesia Japan Jordan MALAYSIA Myanmar New Zealand Nigeria Pakistan Philippines South Africa Sri Lanka Thailand Uganda United Kingdom United States Viet Nam Zimbabwe Source: WHO, World Health Statistics

6 Since the 1990s, the national health policy has remained basically unchanged: While the government will remain a provider of basic health services, the role of the Ministry of Health will gradually shift towards more policymaking and regulatory aspects as well as setting standards to ensure quality, affordability and appropriateness of care. At the same time, the Ministry of Health will ensure an equitable distribution in the provision of health services and health manpower between the public and private sectors (Malaysia, 1993: 244). It is recognized by the Government that in order to ensure that the health care delivery system is efficient, optimal and equitable, coordination between the public and private sectors has to be properly managed (Malaysia, 1993: 224; Malaysia 2006: 430). Healthcare as a social service became part of privatization program since the introduction of the Privatization Master Plan in 1991 (Nik Rosnah, 2005: 36-37). Since 1990s, many medical services have been corporatized or privatized. The private sector has continued to play an active role in complementing public health services. In 1990, there were only 95 government hospitals with a total of 23,223 hospital beds under the Ministry of Health (Malaysia, 1996: 535) but in 2005 the number of hospitals increased by 35 per cent more to 128 and the number of beds by 52 per cent to 35,210. Urban health clinics increased almost four times from 123 in 1990 to 462 in 2005; but rural community and health clinics have remained almost constant at around 2,400 units, but the emphasis had been on upgrading and equipping (Malaysia, 2006: 417). In addition, there are more than 200 mobile units (dispensary services, village health teams, flying doctors and dental clinics) to provide services to remote areas, including into the interiors of Sabah and Sarawak. While hospitals and specialist services in various disciplines are heavily concentrated in urban areas, especially in the Klang Valley, health programs are part of integrated rural development efforts that include investment in clinics, rural roads, and rural schools (Pathmanathan et. al, 2003). Rural health services have also improved to an extent that in 2005 about 85 per cent of the rural population lived within five kilometers of a public health clinic (Nelson, 2008: 220). 6

7 The total population-doctor ratio improved from 2,569:1 to 1,145:1, and the number of doctors (both public and private) increased more than two and a half times from 1990 to 2007 (see Table 2). The number of doctors in both public and private hospitals/clinics combined increased from 7,012 in 1990 to 15,619 in 2000 and 23,738 in Of the total, there were slightly more than 2,000 specialists in 2005 and the latest figure in 2007 was 2,413 (Malaysia, 2008: 78). Table 2: Population: Health Personnel Ratio, 1990, 2000, 2005 and 2007 Type of Personnel Number Population: Health Personnel Ratio Public & Private Sectors: Doctors Dentists Pharmacists Nurses 7,012 1,471 1,239 28,932 15,619 2,144 2,225 31,129 20,105 2,751 4,012 44,120 23,738 3,163 5,730 48,196 2,569:1 12,245:1 14,538:1 n.a. 1,413:1 10,356:1 8,306:1 1000:1 1,300:1 9,497:1 6,512:1 592:1 1,145:1 8,586:1 4,742:1 556:1 Ministry of Health Only: Medical Assistants Lab Technologists Radiographers Physiotherapists 4,903* 508 n.a. * include Laboratory Technologists Source: Malaysia (1996), Malaysia (2008). 6,530 2, ,709 3,302 1, ,948 3,684 1, ,670:1* 35,430:1 n.a. 4,742:1 7,823:1 36,578:1 85,215:1 3,894:1 7,913:1 22,602:1 55,828:1 3,419:1 7,376:1 19,763: 1 47,177: 1 The improvement in income levels and increasing demand for more quality and faster services led to the expansion in the number of private hospitals and maternity and nursing homes from 119 in 1983 to 192 in 1990 (Malaysia, 1991: 352). The number increased further to 225 in 1999 compared with 127 hospitals in the public sector. Private hospitals are extremely biased towards urban areas, where the demands are higher, and mainly for curative services. In 1999, for example, 97.8 per cent of the private hospital beds were in urban areas. In addition, these private hospitals provided specialist services and were equipped with the up-to-date diagnostic and imaging facilities. In 1999, 23 of 27 MRI equipment, 67 out of 86 CT Scanners, 67 per cent of physicians, 66 per cent of surgeons 7

8 and 80 per cent of obstetricians and gynecologists were in the private sector (Malaysia, 2001: 486). Public sector staffing could not keep pace with the demand. There were and are still severe shortages of doctors and specialized equipment. 3 These problems were made worse by flow of doctors from public into private hospitals and clinics, given that doctors in private hospitals earn up to three to four times higher than doctors in public hospitals, and so are other para-specialists like medical assistants, nurses, and lab technologists (Jeyakumar, 2009). At the same time the government has been concerned with the potential of fiscal burden of medical care. This eventually created unequal distribution of medical services not only between public and private hospitals, but also among states in the country, and especially difficult access for communities in rural areas. 4 The disparities between states still remained high as we shall see in Table 3. Malaysian public health care is heavily subsidized. From the outset, public medical care has been relatively free or very minimal fee at clinics or in Third Class hospital wards, which comprised 80 per cent of hospital beds (Nik Rosnah, 2005: 43). In short, as private facilities have expanded, healthcare in Malaysia has become increasingly dualistic: one standard of service for those protected by health insurance or be able to afford private fees out-of-pocket, and less prompt and convenient public service for the less fortunate (Nelson, 2008: 222). The private hospitals tend to be more market and profit oriented, while the public hospitals tend to be more of social concern. Consequently, the private hospitals are expected to charge their patients more for their services compared to public hospitals and consequently are expected to earn higher 3 Measures undertaken to alleviate these shortages included the recruitment of foreign doctors and retired specialists. In 2005, there were 871 foreign doctors and dentists, and 873 retired specialists, doctors and dentists being reemployed on contract basis (Malaysia, 2006: 422). 4 Consequently, the Private Healthcare Facilities and Services Act was enacted in 1998, among others, to improve access to healthcare, correct imbalances in standards and quality of care as well as rationalize medical charges in the private health sector to more affordable levels. (Malaysia, 2001: 486). In this regard, the National Strategic Plan for Quality in Health was formulated in 1998, and the National Implementation Plan for Quality launched. To allow for greater coordination and integration in the implementation of health promotion activities, the Health Promotion Foundation, comprising the public and private sectors, NGOs and consumers was formed in A healthy lifestyle campaign with the theme Be Healthy for Life was implemented in

9 profits (Horwitz, 2009). This is particularly so with the recent development of health tourism. 5 Health tourism also plays an important role in contributing to curative services by private health providers. Patient customers are willing to pay more not only because they can afford, but also because they can expect better quality services. Private hospitals are expected to provide better equipment and faster services (or shorter waiting time). On the basis that the medical profession is not simply to make profits but as importantly to help the sick, the Health Ministry s Director General, Tan Sri Dr. Mohd Ismail Merican, suggested that the Government should revise the fees charged by private hospitals and clinics. According to him, The exorbitant fees charged by private hospitals has been brought to my attention many times. The current schedule drawn by the Malaysian Medical Association (MMA) is not as comprehensive as it should be. (The Star, Tuesday 8 June 2010). In reply, Dr. Chong Su Lin the CEO of the private Sunway Medical Centre says that running a hospital business is not as lucrative as imagined. The net profit margins are only about 5%. She disagrees that private hospital fees are exorbitant. She further argues that the government needs to decide if it wants to be more socialist and provide everything, or more capitalist and allow market forces to dictate prices. According to her, Until that is decided, we can t plan a strategy. (Ding Jo-Ann, 2010). To complicate matters, Dr. David Quek says that the government in fact owns some of the large corporations that have entered the private healthcare business. For example, Khazanah Nasional owns 60% the Pantai-Gleneagles group, while KPJ is wholly-owned by the Johore state government. Since they have entered the market, they have been aggressively pushing profit margins higher and higher. (Ding Jo-Ann, 2010). The reality is that, in Malaysia, government regulation is imposed, including regulation of privately delivered health services to ensure safety, quality and fairness. Government regulation of insurance is another important aspect. Such discussions and debates would underscore the need for a study on differences in practices, performances and charges between public and private hospitals. 5 Under the Eighth Malaysia Plan period ( ), for example, 450,000 foreign patients obtained treatment from private hospitals for various illnesses as well as utilized diagnostic and therapeutic services such as endoscopy, haemodialysis and magnetic resonance imaging. In 2007, more than 340,000 medical tourists, 85-90% from ASEAN countries, brought in RM253.8 million, seeking predominantly curative treatments (MGCC, 2009). 9

10 Health programs in Malaysia, like the experiences in many developing countries (Golladay and Liese, 1980; World Bank, 1993), have been biased toward the urbanized population, and to oversophisticated curative treatment rather than more basic, widespread preventive treatment. Problems of health infrastructure development and health personnel availability vary between rural and urban areas and among states. Table 3 gives an indication of the quantitative difference between the public and private health sectors in terms of doctors according to state distribution in Not only the population-doctor ratio tends to vary across states, but also the percentage of private doctors. Of the total number of doctors in 2005, almost half (47.5%) of them were from the private sector. The high percentages are from the more developed states of Selangor (66.3%), Penang (55.8%), Perak (54.2), Johor (51.2%), and Melaka (49.7%). Table 3: Number of Doctors by State, 2005 Number of Doctors State Public Sector 1 Private % of MOH Non- Total Doctor Private MOH Doctors Johor Kedah Kelantan Melaka N. Sembilan Pahang Perak Perlis Penang Sabah Sarawak Selangor ,055 2, Terengganu Kuala Lumpur 1, ,125 1, Total No. of Doctors 1, , ,525 1,109 1,113 3, ,926 Ratio of Population to Doctors 1,794:1 1,872:1 1,596:1 1,051:1 1,191:1 1,786:1 1,509:1 1,655:1 963:1 2,719:1 2,078:1 1,512:1 2,145:1 396:1 Malaysia 8,368 1,531 9,899 8, ,842 1,387:1 Note: 1 Includes MOH (= Ministry of Health), and other government agencies, local authorities and universities under non-moh. Source: Ninth Malaysia Plan, Table

11 The Federal Territory of Kuala Lumpur has a slightly lower than average percentage of private doctors (45.9%) not because it lacks the number of private doctors (the ratio of private doctors to population is below the national average of the total number of private doctors). Kuala Lumpur has the second largest number of private doctors (1,801), next to Selangor, but it has the biggest number of public doctors (2,125), double that of Selangor. There are about five to seven times as many doctors available per capita in Kuala Lumpur as in the poorer states of Sabah, Sarawak, Kedah and Terengganu. Almost every two out of five doctors in Malaysia are in Kuala Lumpur and Selangor. In general, the population per doctor is higher the lower the economic index of the states in Malaysia (Chart 3), and the percentage of private doctors has a positive relationship with the rate of urbanization by state (Chart 4). On this inequity issue, the World Health Report 2008 indicates that inequities in access to care and in health outcomes are usually greatest in cases where health is treated as a commodity and care is driven by profitability. According to the report, the results are predictable: unnecessary tests and procedures, more frequent and longer hospital stays, higher overall costs, and exclusion of people who cannot pay. 11

12 % of Private Doctors Population per Doctor Chart 3: The relationship between population per doctor and economic index of states in Malaysia, ,000 2,500 Sabah 2,000 T'ganu r = ,500 Kelantan Selangor 1,000 Melaka 500 KL Economic Index Note: Because the test statistic of t = is in the region of rejection [critical t (df=12, α=0.01) = ±3.055], the null hypothesis that there is no relationship between the two variables is rejected, and we conclude that there is a significant negative relationship between population per doctor and economic index. Source: Calculated from data in Malaysia (2006), Tables 17-1 and Chart 4: The relationship between percentage of private doctors and the rate of urbanization by state, Penang Selangor KL 30.0 Kedah Perlis r = Urbanization (%) Note: The coefficient r = is significantly different from zero at the 1% level of significance. The higher the rate of urbanization, the higher the percentage of private doctors. Source: Calculated from data in Malaysia (2006), Tables 17-5 and

13 The Malaysia healthcare industry, which includes products and equipment, was estimated at around US$1.3 billion in 2005 and more than double (around US$3 billion) in About 400,000 different types of medical devices are being used annually. The majority of high-tech medical devices are imported (US Commercial Service, 2010). The field of diagnostic imaging has advanced by leaps and bounds during the past 25 years. The diagnostic imaging includes magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), as well as ultrasound, digital mammography and molecular imaging. While a significant technological advance, diagnostic imaging is also the fastest growing medical expenditure. The equipment is generally expensive. The high cost of acquisition, as experienced in the United States, may create pressure to increase the volume of imaging done (PHC, 2004). Today, Malaysia is still struggling with the impact of private provision on the public system, emerging dualism, equity issues and pricing concerns (Nelson, 2008). 3. Objective of Study Given the large size and dynamic nature of healthcare industry, and the big number of products and equipment involved, this paper attempts to examine as a case study of the price and efficiency of utilization of two advanced diagnostics imaging services, that is ultrasound and mammography, in public and private hospitals in Malaysia. Private providers are often regarded more technically efficient than the public sector and offer a service that is perceived to be of higher quality but with higher price (World Bank, 1993: 4). Ultrasonography is used to image the parts of the human body for various indications while mammography is used for a single indication, i.e. breast cancer detection. Ultrasound has become a valuable tool to use along with mammograms because it is widely available, non-invasive, and costs less than other options. The value of ultrasound tests depends on the operator s level of skill and experience. Although ultrasound is less sensitive than MRI, it has the advantage of costing less and being more available. With 13

14 the advancement in technology, today s ultrasound and mammogram machines are different from those used 20 years ago. Studies on ultrasound and mammogram have received much attention particularly in the United States (Warner et al. 2001; Berg et al. 2008; Farria et al. 2005). Some of the problems raised have been high cost equipment and treatment, and the difficulty in recruiting and retaining radiologists and radiologic technologists. Challenges to keep the breast imaging centres economically viable are mounting, particularly due to the low rates that allow only the survival of efficiently run centres (Feig, 2000). Studies on ultrasound and mammogram services in Malaysia are grossly lacking. A study by Lau, Ng and Abdullah (2001), for instance, shows that even if radiological facilities are available in Malaysian hospitals, they did not seem to meet the quality standards of American College of Radiology and other international guidelines for radiographic viewing conditions. More specifically, this paper is to investigate if differences exist in the performance of ultrasonography and mammography services between the public and private hospitals. Three criteria were used: (a) mean patient waiting times for use of ultrasound and mammogram machines, (b) mean number of times each machine was used in a day, and (c) mean patient charges for each use of the machines. Accordingly, the following hypotheses were tested: (a) Considering that private hospitals are expected to be more efficient than public hospitals, the patient waiting times for use of ultrasound and mammogram machines in private hospitals are lower than in public hospitals; (b) Considering that more patients can get access to public hospitals than private hospitals, the number of times each machine is used in a day will be higher in public than in private hospitals; and (c) Considering that private hospitals are likely to be profit-oriented and heavily subsidized public hospitals are more for social services, the patient charges for each use of machines are higher in private than in public hospitals. 14

15 While there is no presumption that one type of health provider public or private (forprofit or not-for-profit) is likely to be better than any other, the differences between them will support their complementary roles in the development of healthcare services in the country. 4. Data and Methodology This section describes the sampling procedure and the method of data collection, the specification of variables for analysis and the statistical data analysis used in the study. The data analyzed are from information collected via a survey using a questionnaire. The survey was supported by the Malaysian Medical Association (MMA) and approved by the Ministry of Health (MOH) of Malaysia and the Directors of government hospitals of each state in Peninsular Malaysia. Despite the fact that the data collected contained only hospital-level aggregate information (there was no patient-level data) we also obtained the ethical approval from the Ethics Committee. The sample was drawn from public and private hospitals in Peninsular Malaysia for the year 2006 that had more than 5 beds. Small hospital with a capacity less than 6 beds and small clinics were excluded from the study. The study also excluded hospitals which specialized in specific healthcare services such as maternity, leprosy, mental illness and ophthalmology. This led to a population of 90 public hospitals listed under the Ministry of Health, Malaysia and 99 private hospitals listed as members of the Association of Private Hospitals, Malaysia. The structured sampling procedure took into account the ownership status and the capacity of the hospitals which were classified into three categories, i.e beds, beds and above 100 beds. Table 4 below provides the breakdown of the population and sample size of hospitals selected, by capacity and ownership. 15

16 The breakdown of hospital population shows that close to half the population of private hospitals were small hospitals that fell in the >5-50 beds category. In contrast, 2/3 of the public hospitals were large hospitals with the capacity of >100 beds. The rightmost column shows the sample size of the public and private hospitals. The sample design called for random selection of hospitals by ownership and capacity by the three categories noted above. Our sample was purposely constructed to oversample the larger government hospitals (a higher bed count increased the incidence of being selected). Table 4: Public and Private Hospital Population and Sample, Peninsular Malaysia, 2006 Capacity of Population Sample Hospital (Bedcount) Public (%) Private (%) Public (%) Private (%) >5-50 6(6.7) 45(45.5) 4(9.3) 13(32.5) (26.7) 17(17.2) 8(18.6) 12(30.0) >100 60(66.7) 37(37.4) 31(72.1) 15(37.5) Total Source: Healthcare Survey (2009) A questionnaire was designed by a multidisciplinary study team. It was piloted in 3 public and 3 private hospitals and refined to improve its quality and design. The survey was carried out in early 2009, and the refined questionnaire was sent to randomly selected private and public hospitals within the categories defined above. A total of 40 private hospitals and 43 public hospitals returned the completed questionnaire. However, not all the questions were answered by all the hospitals and thus the analysis has to be based on the hospitals that responded to the particular question. Descriptive analyses are used to compare private and public hospitals characteristics. The analysis compares the mean values across hospital ownership type. Further, a multivariate regression analyses is performed to study the association between hospital ownership and bed occupancy rate with hospital charges, waiting time and number of 16

17 times an advanced diagnostic machine is used per day. The regression model used takes on the following form: Y 1 * OWN 2 * BOCC The dependant variable, Y, used in the regression are the mean time a patient waited before using the diagnostic machine, the mean patient charge for each use of machine, and the mean number of times a machines was used per day. Three separate regressions were run for each dependent variable. The key explanatory variable used in the paper is ownership status (OWN). OWN is a dummy variable indicating the hospital ownership type: 1 represents public ownership, and 0 indicates private ownership. In this study, the fully government owned hospitals were classified as public hospitals while all other hospitals including the public-private partnerships (such as Sime Darby Medical Centre and Pantai Hospital) were classified as private hospitals. Bed occupancy rate (BOCC) was introduced as a variable to control for effects of size and age. BOCC is an intensity of use variable and is defined as the percentage of occupied beds in total beds count in the hospital. Initial analysis using the Pearson correlation method found that the control variables age and bed count were correlated with the explanatory variable of OWN and thus were dropped from the regression analysis. The variables OWN and BOCC were not strongly related to each other, implying the absence of multicollinearity. The coefficients β 1 and β 2 capture the effect on Y of ownership type and bed occupancy rate, respectively, controlling for other factors; and ε is the error term.location dummies were not used as the majority of the hospitals in this study were in urban areas. Ordinary least squares (OLS) regression is used in all the multivariate regressions because the dependent variables are continuous rather than categorical variables. Data were entered into Excel spreadsheet and all analyses were performed in Excel, SPSS 17 and Eviews 6. 17

18 5. Statistical Findings 5.1 Comparing Hospital Characteristics Table 4 provides the characteristics of the sample of hospitals in this study for the year As shown in Table 5, 43 (52%) of the hospitals in our sample are governmentowned and 40 (48%) are private hospitals. The hospitals are classified into 4 categories according to their size or capacity i.e. small (> 5-50 beds), medium ( beds), large ( beds) and very large (> 500 beds). Thirteen public hospitals were very large with a capacity exceeding 500 beds while there were no private hospitals in this category. The larger the hospital, the bigger the number of inpatient admissions. In comparison to the private hospitals, the government hospitals are much larger in terms of bed and inpatient admissions. The rightmost columns provide the availability of two advanced diagnostics imaging equipments at the hospitals i.e. the ultrasound and mammogram. There is no significant difference between the number of diagnostic imaging equipment available at the public and the private hospitals within each category of bed count. The larger hospitals tend to own more ultrasound machines with a maximum of 15 and 17 by the private and public hospitals, respectively. With regards to the mammogram machines, not all hospitals were equipped with it. Of those who were equipped with mammogram machines, each owned only one unit except for a very large public hospital with > 500 beds that owned three units. This suggests the greater use of ultrasound machines as a diagnostic tool by the physicians. Besides the fact that ultrasound imaging is used for various applications whilst the mammogram is used only for the detection of breast cancer, ultrasound is convenient to use, comfortable for patients, easily available and non-invasive with no risk of radiation as compared to using the mammogram machine (College of Radiology, 2008). 18

19 Table 5: Characteristics of the sample Capacity Number of of hospital hospitals (bedcount) Average number of in-patients admissions Maximum Number of Ultrasound machines Maximum Number of Mammogram machines Public Private Public Private Public Private Public Private > ,430 1, ,783 6, ,899 16, > , N Source: Healthcare Survey (2009) Table 6 provides the descriptive statistics of the variables used in this study in an aggregate manner. The overall means are provided and the mean values are calculated as the average of individual hospital rates in the sample 6. In comparison, public hospitals have a higher average value of age, bed count, inpatient admissions and average length of stay than their private counterpart. The average government hospital in the sample was 47 years old, had 403 beds with an admission of 21,800 patients compared to 20 years, 114 beds and 7,800 inpatient admissions of the private hospitals. In other words, public hospitals are on average about three times bigger than private hospitals. The average length of stay was 3 and 2 days for the public and private hospitals, respectively. The bed occupancy rate is an average of the rate of the different clinical departments in the hospital. The overall difference in bed occupancy rate is modest between the public and private hospitals with the median showing a slightly higher occupancy rate at the public hospitals (64% compared to 61% at the private hospitals). The overall average bed occupancy rate of 59% is sub-optimal (assuming the optimal bed occupancy rate is 80%). This suggest that the hospitals should expand the departments that have a high bed occupancy rate and shift some of the departments with a low bed occupancy rate to a smaller location and re-profile the hospital beds for other clinical branches. 6 This may cause aggregation bias and thus the statistical results must be interpreted with caution. 19

20 Table 6: Descriptive Statistics Public Hospital Private Hospital Age Mean Median SD N Bed-count Mean Median SD N Inpatient admissions Mean 21, ,818 Median 12,892 5,608 SD 25, ,499 N Average length of stay (days) Bed occupancy rate (%) Average charges per use (RM) Average Waiting time (minutes) Average number of times used per machine per day Source: Healthcare Survey (2009) 7 Mean Median SD N Mean Median SD N Ultrasound machine Mammogram machine Public Private Public Private Mean Median SD N Mean Median SD N Mean Median SD N Although 43 public hospitals and 40 private hospitals were selected in the sample, not all have ultrasound and mammogram machines. Thus, the final analysis involves lesser than 30 observation, and so the results should be taken as indicative, but not conclusive. 20

21 As expected, the average patient charge for each the use of the ultrasound and mammogram machines is substantially higher in the private hospitals compared to the public hospitals. The patient charge for an ultrasound is four to five times higher than in the public hospital while mammography costs twice as much in the private hospitals. On the other hand, the public hospitals waiting times for the use of the diagnostic machines are significantly longer than its private counterpart and its variation is also very large. It is worth noting that the preparation and ancillary task before the examination of the patient with the diagnostics imaging machine is included in the waiting time. Regarding the number of times the diagnostics machines were used, on average the public hospitals performed more diagnostics test using the ultrasound and mammogram machines. This may be because the public hospitals of comparable size treat a larger number of patients than private hospitals. However, the median levels did not differ between the public and private hospitals. Considering that, on average, radiographers can only perform three to five ultrasounds and mammograms per hour, the ultrasound and mammogram machines appear to be under-utilized. 8 This could be one of the causes of the longer waiting time to use these diagnostics imaging equipments in the public hospital. 5.2 Two-tailed t -test and Mann-Whitney U Test We conducted univariate statistical analysis involving a single variable to test for equality of variances and to assess whether the means of two types of hospitals are statistically different from each other. We carried out the Levene s F statistics for the equality of variance, the parametric two-tailed t -test and the non-parametric Mann-Whitney U test to examine statistical differences in the means of the variables between private and public hospitals. The results are reported in Table 7. 8 In Malaysia, mammograms are normally performed by a trained female radiographer in mammography facilities licensed by the Ministry of Health. 21

22 Table 7: The Results of Levene s Test for Variance, Two Tailed t -Test and Mann- Whitney U Test for Differences in Group Means Levene's Test for Equality of Variances t-test for Equality of Means F p-value t p-value (2-tailed) Mann-Whitney U Test for Equality of Means z p-value (2-tailed) Age (years) Bed-count No. of inpatient admissions Average length of stay (days) Bed occupancy rate (%) Average charge per use of ultrasound (RM) Average waiting time for ultrasound (minutes) Average number of times ultrasound used per day Average charge per use of mammogram (RM) Average waiting time for mammogram (minutes) Average number of times mammogram used per day The variance are significantly different for the variables age, bed-count, inpatient admission, bed occupancy rate, average charges per use of ultrasound, average waiting time for ultrasound and average waiting time for mammogram machine at the 0.01 level. No significant difference in variance was observed for average length of stay, average charge per use of mammogram and average number of times the mammogram machine was used per day. 22

23 In terms of the differences in the means of variables, the t tests reveal a significant statistical difference between the public and private hospital in terms of age, bed-count, inpatient admissions, average charges per use of ultrasound and average waiting time for the ultrasound machine at the 0.01 level, and average length of stay and average waiting time for the mammogram machine at the 0.05 level. It can be concluded that the public hospitals are older, larger, have more inpatient admissions with longer inpatient-days than the private hospitals. The private hospitals have significantly lower waiting times but higher charges for the use of both the ultrasound and mammogram machines compared with their public counterpart. However, there is no statistical difference in the average number of times the ultrasound and the mammogram machines were used in both the public and private hospitals. A similar picture is portrayed by the non-parametric Mann-Whitney U tests. The results imply that a further look into the differences between the public and private hospitals under study is imperative. 5.3 Multivariate Regression Analysis In order to analyze the effects of ownership and bed occupancy rate of the hospitals on patient charges, frequency of use of advanced diagnostic imaging machines and waiting times to use the diagnostic machines, the multivariate regression is used in this study. A separate regression model was run for each of the dependant variables as well as for each diagnostic machine, i.e. the ultrasound and the mammogram machine. The explanatory variables of ownership and bed occupancy rate remained the same in all the models. The statistical results substantiate the findings in the previous section. a) The Ultrasound Machine Table 8 reports results of the multivariate regression analysis for the ultrasound machine. The regression analysis yielded in the prediction Models 1 to 3. 23

24 Table 8: Multivariate Regression Results for the Ultrasound Machine Dependent Variables Explanatory Variables: Ultrasound machine Model 1 Model 2 Model 3 Charge per Average Number of patient per waiting times used SE SE usage time per day (Y1) (Y2) (Y3) SE Ownership type (OWN) *** *** Bed occupancy rate (BOCC) * Constant 66.69** N Adjusted R F- statistics 15.61*** 8.19*** 0.88 Note : ***Significant at 1% level, **Significant at 5% level, *Significant at 10% level Model 1 explores the association of ownership and bed occupancy rate of a hospital with the charge per patient to use an ultrasound machine. The regression analysis yielded a multiple coefficient of determination (adjusted R 2 ) of 0.39 which means that 39% percent of the variations in the charges per patient are explained by ownership and bed occupancy rate. The overall regression model was significant at the 0.01 level (p=0.00) with F- statistics of The derived model was Y1 = *OWN *BOCC The results of Model 1 indicate that ownership type was a very significant variable in explaining variations in charge per patient for usage of an ultrasound machine, at the 0.01 level (p=0.000), while bed occupancy rate did not have a significant impact. If a hospital is of public ownership, the expected charge per patient per usage of an ultrasound machine would be RM lesser than a private hospital with the same bed occupancy 24

25 rate. Thus, it can be concluded that public hospitals charge patients much lower for each time use of an ultrasound machine compared to private hospitals, holding the bed occupancy rate constant. The coefficient of the variable bed occupancy rate is not significantly different from zero. The bed occupancy rate does not contribute to the explanation to the variability in the average waiting time to use an ultrasound machine as this variables is not significant at all (p > 0.1). Model 2 explores the correlates of the mean waiting time for the use of the ultrasound machine. The regression analysis yielded a multiple coefficient of determination (adjusted R 2 ) of 0.23 which means that 23% percent of the variations in the average waiting time to use the ultrasound machine are explained by ownership and bed occupancy rate. The overall regression model was significant at the 0.01 level (p=0.00) with F-statistics of The derived model was Y2 = *OWN *BOCC The coefficients of the variables show that ownership is the most important predictor variable for the waiting time to use the ultrasound machine, while bed occupancy rate makes a small contribution. Bed occupancy rate has a small significance at the 0.1 level (p=0.08). The coefficient of the variable bed occupancy rate indicates that a one percentage increase in rates of bed occupancy in a hospital increases the waiting time for ultrasound machine by 10.83%. Logically, a higher bed occupancy rate indicates an increase in the patient demand for the ultrasound machine and therefore will lengthen the waiting time. The waiting time will also depend on the number of machines available, downtime and the availability of trained staff to operate the machines. The variable on ownership is found to be highly significant at the 0.01 level (p= ). Controlling for the effect of bed occupancy, public hospitals have a higher waiting time compared to their private counterparts. Model 3, that explore the correlates of the frequency of use of the ultrasound machine in a day, did not provide a statistically significant set of results with F-statistics of

26 Although the model fit was not significant, the sign of coefficient of the variable ownership shows that the number of times the ultrasound machine was used is higher in public hospitals. However, the difference is not statistically significant. b) The Mammogram Machine Table 9 presents the results of the multiple regression analysis for the mammogram machine. The regression analysis yielded in the prediction Models 4 to 6. Table 9: Multivariate Regression Results for the Mammogram Machine Dependent Variables Explanatory Variables: Mammogram machine Model 4 Model 5 Model 6 Charge per Average Number of patient per waiting times used SE SE usage time per day (Y4) (Y5) (Y5) SE Ownership type (OWN) Bed occupancy rate (BOCC) *** Constant N Adjusted R F- statistics 5.95*** 5.47** Note : ***Significant at 1% level, **Significant at 5% level, *Significant at 10% level Model 4 investigates the relationship between ownership and bed occupancy rate with the charge per patient to use a mammogram machine. The analysis provided a significant regression model with F value of 5.95 at the 0.01 level (p=0.00) as below Y4 = *OWN +0.39*BOCC 26

27 The results of model 4 indicates that the ownership structure of a hospital is again significant at the 0.01 level (p=0.0057) in predicting the charge imposed on each patient for a one time use of a mammogram machine. As expected, a hospital of public ownership charges a patient RM lesser, compared to a private hospital, to utilize the mammogram machine once. Just as in the case of the ultrasound machine, the bed occupancy rate is not a significant predictor of the charge in using a mammogram machine. The coefficient of the variable bed occupancy rate is not significantly different from zero. Model 5 investigates the correlates of the average waiting time for using the mammogram machine. The analysis provided a significant regression model with F value of 3.64 at the 0.05 level (p=0.04). The derived model is Y5 = *OWN *BOCC Turning to model 5, the signs of the coefficient show that a hospital that is of public ownership increases the average waiting time to use a mammogram machine by 1400 minutes (23 hours). Public hospitals have higher occupancy rate and, thus, require a longer waiting time to use its services. However, both the ownership and bed occupancy rate do not contribute to the explanation to the variability in the average waiting time to use an ultrasound machine as these variables were not significant at all (p > 0.1). Model 6, that explore the correlates of the number of times a mammogram machine is used in a day, did not provide a statistically significant set of results with F-statistics of Although the model fit was not significant, the sign of the coefficient of the variable ownership shows that the number of times the mammogram machine was used is higher in public hospitals. However, the difference is not statistically significant. The coefficient of the variables ownership and bed occupancy rate are not significantly different from zero and thus does not explain the variability in the number of times the mammogram machine was used. 27