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Section II - Part

Published online by Cambridge University Press:  04 September 2021

Jo. M. Martins
Affiliation:
International Medical University, Malaysia
Indra Pathmanathan
Affiliation:
United Nations University - International Institute for Global Health
David T. Tan
Affiliation:
United Nations Development Programme
Shiang Cheng Lim
Affiliation:
RTI International
Pascale Allotey
Affiliation:
United Nations University - International Institute for Global Health

Summary

Information

Figure 0

Table 3.1 Human development, Malaysia, 1970–2015

Sources: World Bank (2019a; 2019b; 2019c). Calculations made by the author.
Figure 1

Table 3.2 Changes in poverty, urbanisation, safe deliveries and infant mortality, Malaysia, 1960–1980

Sources: Supplementary Tables 3.D, 3.J and 3.G; Ministry of Health Malaysia (1982).
Figure 2

Table 3.3 Epidemiological transition and causes of death, peninsular Malaysia, 1982–1990

Source: Suleiman & Jegathesan, n.d.
Figure 3

Table 3.4 Changes in poverty, urbanisation, safe deliveries and infant mortality, Malaysia, 1980–2000

Sources: Supplementary Tables 3.D, 3.G and 3.J; Ministry of Health Malaysia, 1982; 1992; 2002.
Figure 4

Table 3.5 Infant mortality, poverty and rural living, Malaysia, 2000

Sources: Department of Statistics Malaysia, 1992; 2003a; Abbas, 1997); Suleiman & Jegathesan, n.d.; Hatta and Ali, 2013.
Figure 5

Table 3.6 Changes in poverty, urbanisation, safe deliveries and infant mortality, Malaysia, 2000–2016

Sources: Supplementary Tables 3.D, 3.G and 3.J; Ministry of Health Malaysia, 2002; 2012; 2018b.
Figure 6

Table 3.7 Life expectancy by sex and years of age, Malaysia, 1999 and 2017

Sources: Department of Statistics Malaysia, 2000; 2017c.
Figure 7

Table 3.8 Burden of disease and injury, Malaysia, 2014

Source: Institute of Public Health, 2017.
Figure 8

Supplementary Table 3.A Gross domestic product growth, Malaysia, 1960–2017

Source: World Bank, 2019e. Calculations made by the author.
Figure 9

Supplementary Table 3.B Gross domestic product by industry, Malaysia, 1961–2017

Sources: Young et al., 1980; Prime Minister’s Department, 1991a; 1991b; Economic Planning Unit, 1996; 2015; Department of Statistics Malaysia, 2003b; 2010; 2018c.
Figure 10

Supplementary Table 3.C Employment by industry, Malaysia, 1970–2017

Sources: Young et al., 1980; Prime Minister’s Department, 1991a; 1991b; Department of Statistics Malaysia, 1989; 2003b; 2010; 2013; 2017a; Economic Planning Unit, 1996; 2001; 2015.
Figure 11

Supplementary Table 3.D Poverty in Malaysia, 1970–2016

Sources: Roslan, 2001; Ahmad, 2007; Economic Planning Unit, 2016; Department of Statistics Malaysia, 2017a.
Figure 12

Supplementary Table 3.E Population growth, Malaysia, 1960–2017

Source: Department of Statistics Malaysia, 2016b; 2018c. Calculations made by the author.
Figure 13

Supplementary Table 3.F Population by ethnic group, Malaysia, 1957–2010

Sources: Department of Statistics Malaysia, 1989; 1991; 1992; 2003b; 2010; 2013; 2017a; 2018c.
Figure 14

Supplementary Table 3.G Fertility and life expectancy, Malaysia, 1960–2017

Sources: Department of Statistics Malaysia, 1992; 2003a; 2016a; 2017c; 2019; World Bank, 2019f.
Figure 15

Supplementary Table 3.H Age distribution of the population of Malaysia, 1957–2017

Sources: Mahari et al., 2011; Department of Statistics Malaysia, 2013; 2017a; 2018c.
Figure 16

Supplementary Table 3.I Dependency rates, Malaysia, 1957–2017

Sources: Mahari et al., 2011; Department of Statistics Malaysia, 2013; 2017a; 2018c. Calculations made by the author.
Figure 17

Supplementary Table 3.J Urban population, Malaysia, 1960–2017

Source: World Bank, 2019 g.
Figure 18

Supplementary Table 3.K Number of people per doctor, Malaysia, 1964–2016

Sources: Prime Minister’s Department, 1965; 1971; Department of Statistics Malaysia, 1992; 2003b; 2013; 2017a.
Figure 19

Supplementary Table 3.L Number of people per nursing personnel, Malaysia, 1964–2016

Sources: Prime Minister’s Department, 1965; 1971; Department of Statistics Malaysia, 1992; 2003b; 2013; 2017b.
Figure 20

Table 4.1 Health indicators in Malaysia, 1957–1990

Source: Suleiman and Jegathesan, n.d..
Figure 21

Table 4.2 Rural Health Services and notional staffing pattern

Source: J. H. W. Wong et al., 2019.
Figure 22

Table 4.3 Number of rural health facilities in Peninsular Malaysia

Sources: Prime Minister’s Department, 1971; 1976; 1981; Ismail, 1974.
Figure 23

Table 4.4 Percentage of institutional deliveries and immunisation coverage, 1970–1990

Source: Suleiman and Jegathesan, n.d..
Figure 24

Figure 4.1 Pap smear slides taken and coverage in Malaysia, 1994–2003.

Source: Ministry of Health Malaysia, 2005.
Figure 25

Figure 4.2 Trends in the utilisation pattern of OPDs.

Source: Suleiman and Jegathesan, n.d.
Figure 26

Table 4.5 Gaps and challenges and action taken in integrating preventive and curative services

Sources: Economic Planning Unit, 1996; Awin, 2003; 2004.
Figure 27

Table 4.6 Quality monitoring and improvement: examples of experiences in primary care

Sources: Suleiman and Jegathesan, n.d.; Ministry of Health Malaysia, 2004.
Figure 28

Figure 4.3 Estimated outpatient visits to clinics per capita per annum, Malaysia, 1930s–2000s.

Note: Private outpatient refers to outpatient visits to both hospitals and clinics.Source:Health Policy Research Associates et al., 2013.
Figure 29

Table 4.7 Top three reasons for encounters in public and private clinics

Source: Clinical Research Centre, 2014.
Figure 30

Table 4.8 Access to and satisfaction with primary care

Sources: Sivasampu et al., 2015; 2016.
Figure 31

Table 4.9 Doctors reporting involvement in health promotion during routine patient encounters

Sources: Sivasampu et al., 2015; 2016.
Figure 32

Table 4.10 Referral experiences reported by doctors

Sources: Sivasampu et al., 2015; 2016.
Figure 33

Table 4.11 Clinical outcomes for the management of diabetes and hypertension

Source: Ministry of Health Malaysia & Harvard T. H. Chan School of Public Health, 2016.
Figure 34

Figure 4-A Expansion of scope in PHC services.

Figure 35

Figure 4-B Approaches to healthcare require supportive practices and systems, which in turn create an ecosystem that is aligned to and facilitates that approach. Shifting a healthcare approach requires comprehensive changes to the healthcare ecosystem.

Figure 36

Figure 4-C Four changes to the PHC clinics’ setting were critical to the ecosystem change: locus of financing and decision-making, scope and alignment of healthcare staff responsibilities, professional development pathways, and facilities and operations.

Figure 37

Figure 4-D Reviewed approach of primary healthcare (REAP-WISE).

Source: Fadzil et al., 2018.
Figure 38

Figure 4-a Employee expectations and unionisation determine the effectiveness of their demands of employers for healthcare benefits.

Figure 39

Figure 4-b MCO-imposed caps on per-visit reimbursement generates hidden costs through multiple visits (R1) or inadequate provision of care (B2). These hidden costs are largely borne by GPs, patients and employers instead of the MCOs.

Figure 40

Supplementary Table 4-a Problems encountered by GPs who had contracts with MCOs

Source: Kenny et al., 2017.
Figure 41

Figure 4-c Impact of MCOs on employer–employee–union dynamics changing the prior system (Figure 4-a) in ways that result in lower health benefits for the workforce.

Figure 42

Figure 4-d Pathways toward government regulation of practices related to healthcare benefits are ineffectual due to limited ability of the public and medical professionals to organise (B4 loop) and lack of information on how these practices affect the burden on the public healthcare system (B5 loop).

Figure 43

Figure 5.1 Evolving profile of types of hospitals, number of TB and leprosy beds, and childbirth in hospitals.

Source: Calculations by author based on data from Suleiman and Jegathesan (n.d.).
Figure 44

Figure 5.2 Utilisation of Ministry of Health hospitals in Peninsular Malaysia, 1970 and 1996.

Source: Suleiman and Jegathesan, n.d.
Figure 45

Table 5.1 Regional disparity in secondary care in different regions of Malaysia, 1972

Source: Calculations by author based on data from Abdul et al. (1974).
Figure 46

Table 5.2 Increased availability of specialist care in MoH hospitals, 1970–1997

Source: Suleiman and Jegathesan, n.d.
Figure 47

Table 5.3 Laboratory services increased in sophistication in tandem with the availability of specialist clinicians

Sources: Suleiman and Jegathesan, n.d.;2 Institute of Medical Research (IMR).
Figure 48

Figure 5.3 Dynamics of providing more sophisticated clinical services.

Figure 49

Table 5.4 Rapid growth of private hospitals, 1980–1996

Source: Suleiman and Jegathesan, n.d. (data extracted from Ministry of Health Malaysia annual reports 1981, 1985, 1990 and 1996).
Figure 50

Table 5.5 Distribution of high-cost imaging technology in MoH and private hospitals, 1997

Source: Suleiman and Jegathesan, n.d.
Figure 51

Figure 5.4 Rising demand for medical care outpaced public hospital resources, creating a gap in public sector capacity (B1). The expansion of private sector hospitals (B2 loop) offered a means of bridging this gap with private sector resources. However, private healthcare has drawn on medical personnel from the public sector, becoming another source of pressure on public sector capacity (R1 loop). This is a well-known system archetype known as ‘shifting the burden’, in which actions taken to address the outcomes of a problem (a gap in hospital capacity) can exacerbate the underlying causes of that problem (public hospital human resources).

Figure 52

Figure 5.5 Dynamics of improving clinical outcomes and establishing a quality culture at every level.

Figure 53

Figure 5.6 Dynamics of improving resource utilisation and client satisfaction.

Figure 54

Figure 5.7 Bed occupancy rates in MoH hospitals.

Source: Ministry of Health Malaysia, 2016.
Figure 55

Table 5.6 Referral experiences reported by doctors in public sector health centres

Source: Sivasampu et al., 2015.
Figure 56

Table 5.7 Cataract surgery profiles, 2002 and 2015

Source: Goh et al., 2016.
Figure 57

Figure 5.8 Harnessing technology to improve access to seamless, integrated care.

Figure 58

Figure 5.9 Composition of inpatient care utilisation in public and private sector by socio-economic status.

Source: Health Policy Research Associates et al., 2013.
Figure 59

Table 5.8 Expenditure on and utilisation of public and private hospitals, 2012 and 2017

Source: Ministry of Health Malaysia, 2012; 2018a; 2018b.
Figure 60

Table 5.9 Client satisfaction with hospital services

Source: Institute for Public Health, 2015.
Figure 61

Table 5.10 Selected medical technology in hospitals, 2011

Source: Sivasampu et al., 2013.
Figure 62

Table 5.11 Sources of funds in the private sector, Malaysia, 2012 and 2017

Source: Ministry of Health Malaysia, 2018b.
Figure 63

Figure 5.10 Interactions between the larger ecosystem and the healthcare provider sub-system with its enabling or constraining sub-systems.

Figure 64

Figure 5-A The gap between demand and supply. A simple balancing (B1) loop caused the rapidly increasing demand for dialysis in the 1990s to outpace the ability of the Malaysian public health sector to respond. Factors in the wider system kept the supply rate low and the supply gap large.

Figure 65

Figure 5-B A view of the wider system affecting dialysis demand and supply. The balancing loop in Figure 5-A interacts with a second balancing (B2) loop. The two reinforcing loops (R1a, R1b) show some of the factors that kept private sector involvement low.

Figure 66

Supplementary Table 5-A Haemodialysis in Malaysia: prevalence, 1990–2015

Sources: National Renal Registry, 2003; 2008; 2018.
Figure 67

Figure 5-C Changing the behaviour of the system through new policy. To increase supply, the Malaysian government subsidised private provision and fixed payment rates for dialysis services.

Figure 68

Supplementary Table 5-B Dialysis: price pre-treatment

Source: Lim et al., 2010.
Figure 69

Supplementary Table 5-C Dialysis: financing by sector

Sources: National Renal Registry, 2008; 2018.
Figure 70

Figure 5-D Effect of the rapid expansion of services on the workforce (B3 and B4).

Figure 71

Figure 6.1 Incidence rate of communicable diseases per 100,000 population, Malaysia, 1975–1997.

Sources:Ministry of Health, 1983; Suleiman and Jegathesan, n.d.
Figure 72

Table 6.1 Examples illustrating key features in the spectrum of Malaysian vertical disease control approaches that subsequently merged with mainstream health services (see Supplementary Table 6.c for programme details)

Figure 73

Table 6.2 Percentage coverage of immunisation in Malaysia, 1970–2017

Sources: Suleiman & Jegathesan, n.d.; Ministry of Health, 2010; 2018b.
Figure 74

Table 6.3 Infant and child mortality rates, 1957–2017

Sources: Jayalakshmi, 1994; Department of Statistics, 2009; 2011a; Ministry of Health et al., 2015.
Figure 75

Table 6.4 Prevalence of selected NCD risk factors in Malaysia for adults aged ≥18 years, 1996–2015

Sources: Institute for Public Health, 1996; 2008; 2011; 2015; Department of Statistics, 2011b; Ministry of Health Malaysia & Harvard T. H. Chan School of Public Health, 2016.
Figure 76

Table 6.5 Incidence rate of emerging and re-emerging communicable diseases (per 100,000 population)

Sources: Suleiman and Jegathesan, n.d.; Ministry of Health 2005; 2010; 2018b.
Figure 77

Table 6.6 Illustrative examples of the rapid emergence of and varied challenges posed by emerging diseases in Malaysia

Source: Adapted from Tee et al. (2009).
Figure 78

-

Figure 79

Figure 6-A A criminalisation approach emphasising criminal enforcement, education and rehabilitative efforts failed to reduce the number of new HIV cases from injecting drugs use.

Figure 80

Figure 6-B Stigmatisation and the paradigm of regarding the MoH as the main provider of outreach and services were barriers to a harm reduction programme.

Figure 81

Figure 6-C Commitment to MDG goals and local advocacy were critical enabling factors that overcame barriers to the adoption of the harm reduction approach.

Figure 82

Figure 6-D Success of the pilots created favourable conditions for institutional changes that persisted even after key enabling factors for the adoption of the harm reduction approach (MDGs and local advocacy) receded.

Figure 83

Figure 6-E While harm reduction strategies have reduced HIV in IDUs, the gains are being threatened by the increasing incidence of sexual transmission of HIV.

Figure 84

Figure 7-A The PWD strategy for expanding the water and sanitation network was unable to respond to rural disease burdens in a timely manner.

Figure 85

Figure 7-B Inadequate rural infrastructure investment in sanitation undermined community trust in government actors, hindering educational efforts that attempted to address the sanitation issues.

Figure 86

Figure 7-C The paradigm that the MoH mission is limited to healthcare delivery created internal and external barriers to its involvement in rural water and sanitation. However, once those barriers were overcome, its large personnel base and ability to prioritize health outcomes enabled community trust and responsiveness to rural water and sanitation interventions.

Figure 87

Figure 7-a Factors that led to poor clinical waste management. Limited government budgets prevented capital investment necessary for appropriate clinical waste management (dotted arrow). Adequate clinical waste management also requires prioritization by hospital staff; however, this was typically a low priority, with tasks directly related to the delivery of health services taking precedence.

Figure 88

Figure 7-b Inability of the government to allocate sufficient resources for clinical waste management undercut both the enforcement of standards that did exist and the development of further standards necessary for ‘cradle-to-grave’ management.

Figure 89

Figure 7-c Outsourcing of clinical waste services enabled necessary capital investment for clinical waste management, enabling the B1, B2 and B3 loops to function properly. Well-designed governance and information systems were critical to successful implementation.

Figure 90

Table 7-A Comparison of scope of services before and after privatization

Figure 91

Table 8.1 Summary of interacting influences on the evolution of the health workforce, 1960s and 1970s

Figure 92

Table 8.2 Production of allied health personnel (selected categories), 1956–1995

Source: Suleiman and Jegathesan, 2000.
Figure 93

Table 8.3 Evolution of the composition of the health workforce (selected categories) 1955–2015

Sources: Government of the Federation of Malaya, n.d.; World Health Organization, 1977; Ministry of Health Malaysia, 1995a; 1995b; 2016b.
Figure 94

Table 8.4 Selected health staff, utilisation rates and health outcomes

Sources: Calculations by the author derived from data from Pathmanathan et al. (2003) and Suleiman and Jegathesan (2000).
Figure 95

Table 8.5 Summary of interacting influences on the evolution of the health workforce, 1980s and 1990s

Figure 96

Table 8.6 Profile of health worker training programmes

Source: Ismail & Martinez, 1975.
Figure 97

Figure 8.1 Malaysian doctors in the public and private sectors, 1955–2013.

Sources: Calculations by the author derived from data from the Ministry of Health Malaysia (1971; 1974; 1982; 1983; 1984; 1986; 1995a; 2000; 2010) and the Government of the Federation of Malaya (n.d.).
Figure 98

Figure 8.2 Regional disparities in availability of doctors, 1970–2010.

Sources: Calculations by the author derived from data from the Ministry of Health Malaysia (1971; 1974; 1982; 1983; 1984; 1986; 1995a; 2000; 2010) and the Government of the Federation of Malaya (n.d.).
Figure 99

Table 8.7 Number of people per doctor and per nursing staff, 1970–2000

Source: Calculations by author based on data from MoH annual reports from various years.
Figure 100

Table 8.8 Access to health facility (with doctor, medical assistant or community nurse)

Sources: Institute for Public Health, 1986; 1996.
Figure 101

Figure 8.3 Utilisation of outpatient services in Malaysia.

Source: Reproduced from Health Policy Research Associates et al. (2013).
Figure 102

Table 8.9 Summary of interacting influences and the evolution of the health workforce, 2000s and 2010s

Figure 103

Table 8.10 Examples of specialisation and the relevant governance mechanisms

Sources: World Health Organization, 2014; National Specialist Register, n.d.
Figure 104

Figure 8.4 Distribution of selected specialist doctors in Malaysia, 2013.

Source: Ministry of Health Malaysia, 2016b.
Figure 105

Figure 8.5 Reported satisfaction with public and private clinics, 2015.

Source: Institute for Health Systems Research, n.d.
Figure 106

Figure 8.6 Reported satisfaction with public and private hospitals, 2015.

Source: Institute for Health Systems Research, n.d.
Figure 107

Figure 8-A New graduates entering the workforce as HOs.

Source: Ministry of Health Malaysia, 2016, p. 73.
Figure 108

Figure 8-B Doctors’ average career path in Malaysia.

Figure 109

Table 8-A Rapid increase in medical schools and new medical graduates

Sources: (a) https://en.wikipedia.org/wiki/List_of_medical_schools_in_Malaysia(b) Planning Division. 2016. Human Resources for Health country profiles 2015: Malaysia. Ministry of Health.
Figure 110

Figure 8-C Meeting the demand. Pressure to meet the gap between supply and demand for medical education led the government to make policy changes that rapidly increased the capacity for medical education, with potential compromises in education standards.

Figure 111

Figure 8-D Lack of capacity planning. Employment planning did not reflect student intake rates. The dotted arrow indicates this lack of information flow and the missed opportunity to adjust the capacity of the health system to receive new medical graduates. The delay mark on the dotted arrow reflects the time required for the system to adapt to increase capacity.

Figure 112

Figure 8-E Impact of the bottleneck on the HO experience. Mismatch in graduation and HO intake rates created long waiting periods for employment. Inadequate entry competence of HOs and high HO-to-specialist ratios extended their training period, further reducing the availability of HO posts and reinforcing the longer waiting time for employment.

Figure 113

Figure 8-F The specialist bottleneck. The pool of public sector specialists limits the capacity to train HOs and medical officers and further their career progression, which in turn limits the pool of public sector specialists.

Figure 114

Figure 8-G Systems responses to the crisis. New measures were taken to increase training capacity (B3) and to restrict medical education intakes to prevent continued escalation of the HO crisis (B4a and B4b). However, the gaps in the flows of information that prevented anticipation and proactive response to the change in the number of medical graduates has not been addressed. Thus the health system remains vulnerable to future shifts in the production of medical graduates.

Figure 115

Table 9.1 Total and per capita expenditure on health, Malaysia, 1997–2016

Source: Adapted from Ministry of Health Malaysia, 2019.
Figure 116

Figure 9.1 Public and private health financing sources, Malaysia, 1997–2017.

Source:Ministry of Health Malaysia, 2019.
Figure 117

Table 9.2 Public and private health expenditure, Malaysia, 1997–2017

Source: Adapted from Ministry of Health Malaysia, 2019.
Figure 118

Table 9.3 Licensed private health care facilities, Malaysia, 2007–2017

Source: Ministry of Health Malaysia, 2007; 2010; 2018a.
Figure 119

Figure 9-A Concerns over sustainable health care financing and quality of care are creating an impetus to improve hospital performance. In response, efforts are being made to understand performance shortfalls to increase capacity for improving hospital performance, creating a balancing loop that should reduce the performance gap (B1). However, limited levels of understanding of hospital performance drivers hinder this and have created a demand for further tools to improve understanding. One potential tool is adopting a case-mix approach to accounting, which would generate the necessary data to facilitate comparisons of treatment performance across hospitals, improving understanding (B2 loop). Case-mix accounting achieves this by tracking costs per medical case instead of aggregating costs into line items.

Figure 120

Figure 9-B Institutional pressures keep generic accounting approaches in place over the adoption of the case-mix approach. The pre-existing adoption of generic accounting approaches has created ways of thinking and acting among administrative personnel that would be disrupted by the adoption of case-mix accounting. This creates a dominant reinforcing loop (R1) that competes against another reinforcing loop (R2) that would support case-mix accounting. Even when parallel case-mix accounting systems are created and maintained, improvements in hospital performance are limited, as the data are not used at national level to allocate resources and evaluate hospital-level performance.

Figure 121

Figure 10.1 Reinforcing loop showing how compliance with data collection improves the quality of data, enabling positive impacts on health outcomes. When health system personnel are able to observe these impacts, the intrinsic motivation improves the level of compliance in data collection. Conversely, when this connection is not made, data collection can be perceived as a box-ticking exercise, compromising the quality of data collected.

Figure 122

Figure 10-A For telehealth functions that cut across health facilities, the more health facilities adopt and operate within a particular interoperable telehealth standard, the greater the benefit for other facilities to adopt that standard, creating a reinforcing cycle (R1 loop). However, when there is not a critical mass of health facilities operating to a single standard, the proliferation of incompatible telehealth standards can occur, as facilities seek to set the definitive standard or simply meet locally relevant needs with locally available resources.

Figure 123

Figure 10-B The push for the adoption of telehealth could increase the number of facilities adopting an interoperable telehealth standard or lead to the proliferation of incompatible standards. Due to the lack of technical guidance and enforcement, a proliferation of incompatible standards occurred.

Figure 124

Figure 10-C The proliferation of incompatible telehealth standards actually increases the cost of adopting interoperable standards (R3 loop) due to health facility operations and structures coming to rely on incompatible telehealth software.

Figure 125

Figure 10-D The lack of a critical mass of health facilities operating on the same telehealth standard reduces benefits for certain functions, such as health information exchange. The lack of immediate benefits to health facilities discourages the adoption of telehealth, which in turn makes it difficult to achieve a critical mass (R4 loop).

Figure 126

Figure 11.1 Number of received reports of ADR.

Sources:Ministry of Health Malaysia, 2010; 2011; 2012a; 2013; 2014; 2015; 2016; 2017.
Figure 127

Figure 11.2 Number and ratio of pharmacists per 10,000 population.

Sources:Ministry of Health Malaysia, 1995; 1996; 1997; 1998; 1999; 2001; 2019a; Suleiman & Jegathesan, n.d.
Figure 128

Figure 11.3 Number and ratio of assistant pharmacists per 10,000 population.

Source:Ministry of Health Malaysia, 2019a.
Figure 129

Figure 11.4 MoH medicine expenditure, 2008–2017.

Source:Pharmaceutical Services Programme, Ministry of Health Malaysia, 2018.
Figure 130

Figure 11.5 Number of outpatient prescriptions received, 2011–2017.

Source:Pharmaceutical Services Programme, Ministry of Health Malaysia, 2018.
Figure 131

Table 11.1 Price comparisons in private sector outlets

Source: Pharmaceutical Services Programme, Ministry of Health Malaysia, 2018.
Figure 132

Figure 11.6 Export and import value of pharmaceutical products to Malaysia, 2013 and 2017.

Source:Department of Statistics Malaysia, 2018.
Figure 133

Table 11.2 Export and import value of pharmaceutical products to Malaysia by product category, 2013–2017

Source: Department of Statistics Malaysia, 2018.
Figure 134

Figure 11-A The registration and regulation of traditional medicines was in response to the adverse health impacts from the improper manufacture and use of traditional medicines and has successfully reduced poor practice and consequent outcomes.

Figure 135

Figure 11-B Regulation of traditional medicines creates costs to traditional medicine businesses, which some actors attempt to bypass (R1), creating a race to close regulation loopholes (B1) and enforce existing regulations (B2).

Figure 136

Figure 11-C Creating benefits for traditional medicine businesses for compliance with regulation can reward good actors and reduce attempts to bypass regulation.

Figure 137

Table 12.1 Differing imperatives influenced the system behaviour in formulating health legislation

Figure 138

Table 12.2 Illustrative features of leadership during Malaysia’s experience in introducing HPV immunisation

Source: Buang et al., 2018.
Figure 139

Figure 12-A The paradigm that affordable medical treatment should be a right has led to the creation of tools meant to limit the price of treatment (B1 loop). These tools have provided governments with important leverage to negotiate treatment prices with suppliers (B2 loop).

Figure 140

Figure 12-B Reliance on the private sector for developing treatment solutions creates a competing paradigm that distrusts interference with market mechanisms (R1 loop). This paradigm undermines the availability of price control tools (B3 loop).

Figure 141

Figure 12-C Advocates for market-driven development of medical products have pushed for trade agreements, IPR protection and the use of political pressure and sanctions that increase the risk of using price control tools to limit government actions to control treatment prices (B4 loop). For governments to successfully utilise these tools, they must take a variety of actions to mitigate against these risks.

Figure 142

Table 12-A Stakeholder concerns and contributions

Figure 143

Table 12-B Leadership characteristics and outcomes

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