Inequalities in non-communicable diseases in urban Hanoi, Vietnam: Health care utilization, expenditure, and responsiveness of commune health stations

Vu Duy Kien

DepartmentofPublicHealthandClinicalMedicineEpidemiologyandGlobalHealthUmeåUniversity,Sweden2016

Responsible publisher under Swedish law: the Dean of the Medical Faculty This work is protected by the Swedish Copyright Legislation (Act 1960:729) ISBN 978-91-7601-564-3 ISSN 0346-6612 New Series No. 1845 Copyright © 2016 Vu Duy Kien Cover Design by Hans Karlsson Electronic version available at http://umu.diva-portal.org/ Printed by: UmU-tryckservice, Umeå University, Sweden 2016

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“Only a life lived for others is a life worthwhile” -Albert Einstein

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Abstract

Background: Non-communicable diseases (NCDs) are the leading causes of morbidity and mortality among adults in Vietnam. Little is known about the magnitude of socioeconomic inequalities in NCDs and other NCD-related factors in urban areas, in particular among the poor living in slum areas. Understanding these disparities are essential in contributing to the knowledge, needed to reduce inequalities and close the related health gaps burdening the disadvantaged populations in urban areas.

Objective: To examine the burden and health system responsiveness to NCDs in Hanoi, Vietnam and investigate the role of socioeconomic inequalities in their prevalence, subsequent healthcare utilization and related impoverishment due to health expenditures. Methods: A cross-sectional study was conducted among 3,736 individuals aged 15 years and over who lived in 1211 randomly selected households in 2013 in urban Hanoi, Vietnam. The study collected information on household’s characteristics, household expenditures, and household member information. A qualitative approach was implemented to explore the responsiveness of commune health stations to the increasing burden of NCDs in urban Hanoi. In-depth interview approach was conducted among health staff involved in NCD tasks at four commune health stations in urban Hanoi. Furthermore, NCD managers at relevance district, provincial and national levels were interviewed.

Results: The prevalence of self-reported NCDs was significantly higher among individuals in non-slum areas (11.6%) than those in slum areas (7.9%). However, the prevalence of self-reported NCDs concentrated among the poor in both slum and non-slum areas. In slum areas, the poor needed more health care services, but the rich consumed more health care services. Among households with at least one household member reporting diagnosis of NCDs, the proportion of household facing catastrophic health expenditure and impoverishment were the greater in slum areas than in non-slum areas. Poor households in slum areas were more likely to face catastrophic health expenditure and impoverishment. The poor in non-slum areas were also more likely to face impoverishment if their household members experienced NCDs. Health system responses to NCDs at commune health stations in urban Hanoi were weak, characterized by the lack of health information, inadequate human resources, poor financing, inadequate quality and quantity of services, lack of essential medicines. The commune health stations were not prepared to respond to the rising prevalence of NCDs in urban Hanoi.

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Conclusion: This thesis shows the existence of socioeconomic inequalities in the prevalence of self-reported NCDs in both non-slum and slum areas in urban Hanoi. NCDs associated with the inequalities in health care utilization, catastrophic health expenditure and impoverishment, particular in slum areas. Appropriate interventions should focus more on specific population groups to reduce the socioeconomic inequalities in the NCD prevalence and health care utilization related to NCDs to prevent catastrophic health expenditure and impoverishment among the households of NCD patients. The functions of commune health stations in the urban setting should be strengthened through the development of NCDs service packages covered by the health insurance.

Keywords: socioeconomic inequalities, non-communicable diseases, health care utilization, catastrophic health expenditure, impoverishment, health system, commune health stations, Hanoi, Vietnam

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Abbreviation

ASEAN Association of Southeast Asian Nations CHS Commune Health Station CI Confidence Interval COPD Chronic Obstructive Pulmonary Disease CVD Cardiovascular Disease DASP Distributive Analysis Stata Package DBP Diastolic Blood Pressure GDP Gross Domestic Product LMICs Low-and Middle-Income Countries NCDs Non-Communicable Diseases OR Odds Ratio SE Standard Error STEPS STEPwise Approach to Surveillance PCA Principal Components Analysis UN-HABITAT United Nations Human Settlements Programme US$ US Dollars VND Vietnamese currency WHO World Health Organization

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Original Papers

This thesis is based on the following original papers:

1. Kien VD, Minh HV, Giang KB, Dao A, Weinehall L, Eriksson M,

Ng N. Socioeconomic inequalities in self-reported chronic non-communicable diseases in urban Hanoi, Vietnam. Global Public Health. 2016; 4:1-16.

2. Kien VD, Minh HV, Giang KB, Weinehall L, Ng N. Horizontal inequity in public health care service utilization for non-communicable diseases in urban Vietnam. Global Health Action. 2014;7(1):24919.

3. Kien VD, Minh HV, Giang KB, Dao A, Tuan LT, Ng N. Socioeconomic inequalities in catastrophic health expenditure and impoverishment related to non-communicable diseases in urban Vietnam (Submitted).

4. Kien VD, Minh HV, Giang KB, Ng N, Viet N, Eriksson M. Responsiveness of commune health stations to non-communicable diseases in urban Vietnam (Manuscript).

The original papers are published in Open-Access journal, and the authors retain the right of the manuscript.

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Table of Contents Abstract iii

Abbreviation v

Original Papers viTable of Contents viiIntroduction 1

Epidemiological transition and burden of NCDs 1The context of Vietnam 2Economic reforms and health system 3Impacts of economic reforms on health indicators 5Impacts of economic reforms on urbanization 6Economic reform and inequality 6Challenges posed by health patterns, urbanization and inequality 7Inequalities in urban health related to NCDs 8Measuring health inequality and inequity 9Theoretical framework and scope of the study 12

Study Objectives 15Overall objective 15Specific objectives 15

Methods 17Study setting 17Quantitative approach (paper I, paper II, paper III) 19

Study design 19Sampling and data collection 19Variables 21Data management and analysis 24

Qualitative approach (paper IV) 28Study design 28Selection of study participants and data collection 28Data management and analysis 29

Ethical considerations 30

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Results 31Characteristics of participating households and individuals 31Patterns of socioeconomic inequalities in self-reported NCDs and health care utilization (paper I, II) 33Contributions to socioeconomic inequality in self-reported NCDs and health care utilization related to NCDs (paper I, II) 36Inequality in catastrophic health expenditure and impoverishment (paper III) 40Responsiveness of commune health station to NCDs (paper IV) 45

Discussion 50The prevalence of NCDs concentrated among the poor 50NCDs contributed to increasing inequalities in health care utilization 52Catastrophic health expenditure and impoverishment association with NCDs, and concentration within the poor 53Weak responsiveness of commune health station to NCDs 54Methodological considerations 56Strengths of the thesis 58

Conclusion 59Implications for futures studies and policy 60Acknowledgements 62References 65

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Introduction

Epidemiological transition and burden of NCDs

The threat of non-communicable diseases (NCDs) has been an important issue on the global agenda (1, 2). In 2011, heads of states and governments assembled at the United Nations to issue a political declaration on the prevention and control of non-communicable diseases worldwide, with particular attention toward low-resource settings (3). The increasing burden of NCDs was considered as one of the major challenges for development in the twenty-first century (4). The rise of NCDs could threaten economic growth and prosperity in many countries, and may further exacerbate inequalities between countries and different population groups (5). To coordinate and stimulate response to increasing burdens, the World Health Organization (WHO) developed the Global Action Plan for the Prevention and Control of NCDs, which set out nine voluntary global targets to be reached by 2025 that included a target of 25% relative reduction in premature mortality due to NCDs (6).

In the Global NCD Status Reports, the WHO demonstrated that NCDs were a substantial burden globally (2, 7). Four main NCDs, cardiovascular diseases (CVD), diabetes, chronic respiratory diseases, and cancer were considered as causes of approximately 38 million deaths each year (67% of total deaths occurred worldwide) (8). The WHO projected that the annual number of deaths due to infectious diseases would decline, while the annual number of NCD deaths would increase to 52 million by 2030 (9). Although NCDs were always considered burdens concentrated within developed countries, the global epidemic has already been underway, as the rate of NCDs has also been increasing in developing countries, hallmarking an epidemiologic transition (10-12). Globally, NCD deaths have increased in every region since 2000. NCD deaths have increased the most in the South-East Asia region and the Western Pacific region (8). According to the WHO, the NCD deaths in low- and middle-income countries (LMICs) accounted for about 80% of worldwide NCD deaths (13, 14).

To explain the change of diseases’ pattern over time, Abdel Omran proposed a theory of epidemiological transition (12). The theory described the replacement of infectious diseases by NCDs over time due to the advancements in public health and improvements in sanitation. The theory accurately described the shift in demographic- and disease- profiles in Europe and North America occurring from the mid-18th to mid-20th centuries. The epidemiological transition of mortality included three

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different stages: 1) Pestilence and famine, 2) Receding pandemics, and 3) Degeneration and man-made diseases (12). In 1998, after thirty years, Omran updated the theory by moving from a three-stage formulation to a five-stage formulation, and the two additional stages included “declining cardiovascular mortality, ageing, life-styles modification, emerging and resurgent diseases” and “aspired quality of life, with paradoxical longevity and persistent inequities” (15). The Omran’s theory was criticized when being applied in the developing world since it did not take into account the importance of political process, health policy, national and international program provision and environmental control (16-18). However, the epidemiologic transition theory can still be considered to understand population health dynamics in many contexts including Vietnam (19). The context of Vietnam

Vietnam, which official name is the Socialist Republic of Vietnam, is a country in the Southeast Asia, and has a border with China to the north, Laos to the northwest, Cambodia to the southwest, and the East Sea to the east. The country has a long and narrow shape, and it covers a total area of approximately 331,210 km2 (Figure 1).

Figure 1: Maps of Vietnam in the region

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Vietnam is a one-party socialist state officially espousing communism. The current state constitution asserts that the Communist Party of Vietnam takes the center role in all organs of government, politics, and society. As of 2105, the population of Vietnam was about 91.7 million, which made it the country at the world’s 14th most populous country, and the eighth most populous Asian country (20). About 70% of the population lives in rural areas. The country has 54 ethnic groups, of which the Kinh tribe is the majority accounting for more than 85%.

Economic reforms and health system

Vietnam was unified under a common government after years of conflict, but the country remained impoverished and isolated. Since 1986, the government initiated a series of economic and political reforms (known as “Doi Moi”) that helped Vietnam to develop and to be integrated into the world economy. The reform put Vietnam into a transition from a centrally planned economy to a socialist-oriented market economy. After the economic reform, Vietnam has achieved significant results in economic development. The economic growth of Vietnam has been around 7–8% per year (21). The absolute poverty rate has been reduced to below 20% (compared with around 75% in the mid-1980s, 58% in 1993 and 37% in 1998) (22). Vietnam has been categorized a lower middle-income country since 2010.

Together with the economic development, the health system has changed allowing the private sector provision of health services. The Vietnam health system embraces a mix of public and private providers. There are four levels of public service delivery (Figure 2):

• Central level providers (central/regional hospitals, research institutes) administered directly by the Ministry of Health

• Provincial level providers (general/specialized hospitals and preventive medicine centers) administered by the Provincial Health Department

• District level providers (district hospitals and district preventive centers), also administered by the Provincial Health Department.

• Commune level providers (commune health stations) administered by the District Health Centers.

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As of 2013, there were 46 national hospitals, 447 provincial hospitals, 1214 district hospitals and 11,033 commune health stations in the public sector system (23). Overall, there were only 155 private hospitals (23), however, there were many private health clinics and pharmacies, particular in urban areas. The public health care system still plays a vital role in health care, especially in prevention, research and training (24).

Figure 2: Public health system in Vietnam

The main changes within the health system were the introduction of user fees, the legalization of the private health sector in 1989 and the initiation of health insurance schemes at the national level in 1992 (25). Through targeted policy instruments released in recent decades, Vietnam has paid more attention to vulnerable groups, such as children, the elderly, the poor,

Government

Ministry of health

Prevention

Provincial People’s Committees

District People’s Committees

Treatment

Provincial Health

Departments

District health units

Provincial Preventive

Medicine Centers

Provincial hospitals

National hospitals

District health centers

District hospitals

Commune health stations Commune People’s

Committees

Villages Village health collaborators

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and near poor. The poor have received free health care cards since 1995. This policy was later officially realized by the Decision 139/2002/QD-TTg, which established a health care fund for the poor. According to the Decree 36/2005/ND-CP, all children under six years old are to have received free health care services since 2005 (26). The Law of Health Insurance was approved in 2008, and took effect since 2009. After that, all the poor, children under six years old, the elderly from 85 years and above, and people classified as being in need have received free health insurance cards with the same benefits package as all other groups (24, 25). In 2006, the Vietnam government issued Decree 43/2006/ND-CP on financial autonomy, ownership and accountability of public administrative organizations. The decree was then revised by Decree 85/2012-ND-CP, which gave greater autonomy to hospitals in various operational aspects (27). Furthermore, in 2012, the Ministry of Health accepted to significantly increase the fee schedules of more than 400 health care services. Since the fee schedules for health care services increased, it was expected that more people would join health insurance scheme to avoid the burden of direct out-of-pocket payments for the health care services (25). In 2014, the Law on Health Insurance was revised, as an effort to promote the enrollment of whole families, emphasizing the inclusion of all members. To strengthen the health care system and move toward universal coverage of health care, Vietnam has set a target for health insurance coverage of 80% by 2020 (25).

Impacts of economic reforms on health indicators Vietnam has made impressive progress in improving the health status of its people, as evidenced by trends in basic health indicators such as average life expectancy at birth, maternal and child mortality and child malnutrition. The life expectancy at birth in Vietnam has significantly increased from 63 years in 1980 to 73.3 years in 2015 (70.7 for males, and 76.1 for females) (19). Currently, the life expectancy at birth of Vietnam is higher than that of almost all other countries in the Southeast Asian countries, except Singapore and Brunei (19). The maternal mortality ratio in Vietnam has dropped to about 60/100,000 live births in 2015, which was a substantial decline as compared to the ratio of 233/100,00 live births in 1990 (19, 28). The rate of infant mortality decreased sharply from 44.4 infant deaths/1000 live births in 1990 to 14.7 in 2015. The under-five child mortality rate declined from 58/1000 live births in 1990 to 22.1 in 2015. Moreover, the rate of children aged under-five, who are underweight, has continued to decrease from 17.5% in 2010 to 14.1% in 2015 (19).

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Impacts of economic reforms on urbanization Along with the rapid economic growth, Vietnam has been undergoing a dramatic period of urbanization. The urbanization process has been proceeding very rapidly, with the urban share of the population increasing from 21.7% in 1999 to 33.1% in 2014 (29). Urbanization and industrialization processes have led to many adverse environmental impacts, including pollution and various social problems. These processes resulted in the change of habits that are harmful to health, such as increasing consumption of fatty foods, harmful use of alcohol, smoking, and physical inactivity (19). With the rapidly growing rural-urban migration, the urban population will increase significantly in coming years. It is estimated that the proportion of the population living in the urban area of Vietnam will exceed 50% in next 20 years (30). The rapid urbanization will create significant challenges for the health care system, in terms of receiving more patients, more complicated caseloads, and increasing community expectations (19). Some reports on urban-rural disparities in health and living conditions showed that urban populations had better health status as well as living conditions in general than those who lived in rural areas (31-35). However, these reports did not provide disaggregated data comparing health and living conditions among urban dwellers themselves.

Economic reform and inequality

Inequalities in basic health indicators between urban and rural areas, various geographic regions within the country and different social population groups have not declined much, but rather some have even increased in recent years. Generally, urban areas have better health outcomes than rural areas. Plain and delta regions have better health outcomes than the mountainous regions (19). When comparing between the region with best health outcomes and the worst health outcomes, the difference between the life expectancy at birth was 6.4 years (19). The infant mortality in the region with the worst health outcomes was about 2.9 times higher than that in the region with the best outcome, while the difference in the under-five mortality rate was 3.0 times, and child underweight was 2.7 times (19). Another study in Vietnam also showed that though the overall rate of child malnutrition declined, the socioeconomic inequality in child malnutrition had increased between 2000 and 2011 (36). The child malnutrition rate, infant mortality rate, under-five mortality rate, and maternal mortality rate are still high in poor, mountainous, and remote regions, and far behind those of the advanced groups (19).

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Challenges posed by health patterns, urbanization and inequality

Vietnam has suffered a double burden of diseases; in that, the burden of communicable disease threats remain important, while simultaneously the burden of NCDs is increasing. According to hospital-based statistics, the proportion of all visits due to communicable diseases went down from 53% in 1976 to 12% in 2013, but the proportion of all visits attributable to NCDs increased from 45% to 70% in the same period (23, 37). The total NCD deaths were estimated to account for about 73% of total deaths in Vietnam (38). CVD deaths accounted for the highest share of all deaths (33%). Cancer was the next most prevalent cause of deaths in Vietnam, accounting for 18% of total deaths (38). While, chronic obstructive pulmonary diseases (COPD) accounted for 7%, and diabetes accounted for 3% of total deaths (38).

The Vietnamese government approved and implemented the first national program for NCD prevention and control for the period 2002-2010 that covered CVDs, diabetes, cancer and mental and neurological disorders (19). In 2015, the government issued the second national program for prevention of CVDs, cancer, diabetes, COPDs, asthma and other NCDs during the period 2015-2025 (24). Based on the national strategy for NCDs, the specific project for each NCD, such as the National Mental Health Program, National Cancer Control Plan, National Hypertension Program, National Diabetes Project, National Chronic Respiratory Diseases Project, was established (19). Although Vietnam has had policies and programs on general and specific NCD prevention and control, the activities of NCD prevention and control have not yet received adequate priority and political commitment. The scope of the national program on NCDs was small, and coverage constraints limited the population of NCD patients included (19).

In Vietnam, neither a clear definition nor reliable statistics of slum areas are available, and even the term “slum” was sensitive to use in public. However, when applying the UN’s definition (39), one can identify the existence of slum areas in the major cities, especially in Hanoi and Ho Chi Minh city. With the rapid development of urban areas and the concurrent increases in NCDs, understanding the socioeconomic inequality in NCDs and their social factors are necessary for health planning in the future. Moreover, NCDs are chronic and progress slowly, so these diseases need to be follow up and managed over a long-term period. Exploring the responsiveness of commune health stations in terms of providing primary health care services for NCDs is also necessary to address the burden of NCDs in the future. Particularly, there is still a lack of data related to NCDs and their associated factors in urban settings of Vietnam and few if any previous reports seek to elucidate the relationships between social conditions and health.

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Inequalities in urban health related to NCDs

About a half of the world’s population lived in urban areas by the end of 2008, and about two-thirds of the world’s population are predicted to live in urban areas in the next 30 years (30). Most of the urban population growth is expected to be in cities in developing countries. The speed of the urban population growth in developing countries has outpaced the infrastructure requirements, resulting in many countries being faced with a triple threat of infectious diseases, NCDs, and injuries (40). Rapid urbanization has typically been accompanied by an equally alarming increase in urban poverty and the rise of large urban slum areas (41). Evidence showed that the health status and living conditions of urban populations are better than those who live in rural areas (31-35), however, within urban areas, socioeconomic inequalities existed between slum and non-slum areas (41).

Urbanization has created challenges to population health in developing countries (42), since it normally has been accompanied by an increase in urban poverty and/or slum (40, 43). The poor in slum areas were the most vulnerable group that they suffered from a double burden of both communicable and NCDs (44). A study in Nairobi, Kenya showed that within slum areas, NCD contributed significantly to the mortality rate (45). In Nigeria, Daniel et al. reported a high prevalence of hypertension among slum population in Lagos (46). Four main NCD risk factors, such as smoking, alcohol abuse, poor consumption of vegetables, and lack of physical activity, were observed among slum areas (47, 48).

Despite some advantages in terms of health care access in urban areas, urban health has been threatened by inequalities in health. There are certain inequalities in urban health that should be considered, in particular, those related to economics, genders and vulnerable groups (49). The number of poor people living in urban settings (normally living in urban slums, informal settings, or pavement tents) is increasing rapidly and the gaps in social and living conditions between rich and poor are widening while many essential services needed for healthy living are not accessible by them because of both physical and financial barriers (50, 51). Unfortunately, many essential services needed for healthy living are not accessible in such areas because of both physical and financial barriers (52-55). The urban setting also influences the health of vulnerable groups including children, the elderly, migrants, and the disabled (56-58).

NCDs last for a long period, which generally generates financial burden due to health care spending. Ke et al. estimated that about 150 million people suffered from catastrophic health expenditures, and about 100 million

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people became poor because of payment for health care every year (59). Since most of the health care services were paid mainly out-of-pocket, the burden of health care expenditure was the most severe in developing countries (59). Minh et al. reported that the number of households experiencing catastrophic health expenditure and impoverishment increased during 2002-2010 (60). Kwesiga et al. showed that 25% of Ugandan households experienced catastrophic health expenditure, and about 4% experienced impoverishment due to health service payments. In Nepal, about 14% households faced catastrophic health expenditure (61). The corresponding rate of the elderly’s households in China was 26% (62). Recent evidence suggested that NCDs were linked to household catastrophic health expenditure and impoverishment (63). Specifically, several studies showed a higher proportion of catastrophic health expenditure and impoverishment among households with NCD patients (61, 64-66) or households with low socioeconomic status in slum areas (67).

In response to the burden of NCDs, the WHO highlighted that the primary health care should be strengthened to address the issues of NCDs (68, 69). Evidence showed that the population health could benefit, if the NCD intervention were integrated into the primary healthcare services (69). However, a study in Lebanon showed that primary health care centers in Lebanon still lack in responsiveness to health needs related to some NCDs, e.g. CVDs, COPDs and cancer (70). While no studies have examined the health system in Vietnam’s urban areas exclusively, a study conducted in rural areas showed that the capacity of the primary health care system was inadequate to provide NCD services for the population (71). Measuring health inequality and inequity In many countries, some gaps within the country were evident, in that, nearly all children in the richest group accessed to health care services, compared to only about half of children in the poor (42). Similarly, the gaps between countries could be immense, for example, the differences in the life expectancy at birth or children under-five mortality rate among some developed countries and low-income countries (72, 73). Since health inequality occurs at both national and international levels, it currently becomes more and more global concern (73). To address the problem related to inequality in NCDs, in this thesis, I utilized both concepts of health inequality and health inequity. Thus, it is necessary to get a clear definition of these terms in this thesis.

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• Health inequality (or health disparity): Health inequality is defined as the unequal distribution of health status or health determinants between different sub-groups (74).

• Health inequity: Health inequity is defined as the unequal distribution of health status and health determinant that may be unnecessary and avoidable as well as unjust and unfair (74, 75). So “inequity” is a form of inequality when it is considered as unfairness or injustice.

Consequently, “inequity” is inequality when considered as unfairness or injustice. In addition, health inequality could be considered unnecessary and avoidable, without explicitly including ideas about justice and fairness. In this thesis, both terms were used but with the main focus on avoidable and unnecessary health inequalities.

Besides, we also measured horizontal inequity to assess the difference between the inequality in health care use and health care needs. Horizontal equity principle was applied to assess the equity in health care utilization. The definition of “horizontal health equity” would be presented, as followed.

• Horizontal health equity: it refers to equal treatment for equal

medical needs, irrespective of other characteristics such as socioeconomic status, race, place of residence, etc. (76). It is presented as horizontal health inequity index, and if the index equals to zero, it means that equity is reached (more detail is available in Method section of this thesis).

Table 1 shows the summary of the common methods that were used to measure health inequality (77-80). Researchers should base their selection of measurement on the available data, its limitations and the purpose of their study (77). In this study, the data of each household and individuals were available, so we employed concentration index to measure health inequality. The benefit to using the concentration index is that it reflects an experience of the entire population. The concentration index is also sensitive to the distribution of the population across a spectrum of social factors, and better ensures that the inequalities in health related to socioeconomic dimensions are taken into account (80).

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Table 1: Summarizing the methods to measure inequality (77-80)

Measures Summary of the measurement The range Comparing the top and the bottom of

socioeconomic groups. Lorenz curve and Gini coefficient

The Lorenz curve plots the cumulative proportions of the population (starting with the sickest person and ending with the healthiest) against the cumulative proportion of health. If health is equally distributed, the Lorenz curve coincides with the diagonal line. If health is not equally distributed, the Lorenz curve lies above or below the diagonal line. The Gini coefficient is equal to twice the area between the Lorenz curve and the diagonal line. The Gini coefficient ranges from 0 to 1. If the Gini coefficient is 0, meaning that the health is completely equally distributed. If Gini coefficient is 1, meaning that the population’s health concentrates in one person.

Pseudo Lorenz curves and the index of dissimilarity

Similar to Lorenz curve, but using group-level data (e.g. occupation, education etc.).

Slope and relative indices of inequality

The slope of regression line shows the relationship between a group’s health status and its relative rank in the socioeconomic distribution.

Concentration curve and concentration index

Concentration curves are similar to Lorenz curves, but individuals are ranked by the socioeconomic status. The concentration index is calculated the same as Gini coefficient, but it ranges from – 1 to +1. If the concentration index is 0, it means that the health is equally distributed among socioeconomic groups. If the concentration index is negative, it means that the health (normally ill-health) concentrates among the poor. And, if the concentration is positive, it means that the health (normally, good health or health care service utilization) concentrates among the rich.

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Theoretical framework and scope of the study

In this study, we adapted the Social Determinants of Health conceptual framework, which was developed and championed by the Commission on Social Determinants of Health, WHO (81). This theoretical framework was built by reviewing three different prominent theories: 1) psychosocial approaches, 2) social production of disease/political economy of health, and 3) eco-social frameworks. These three theories used the following three main pathways and mechanism to explain causation: 1) “social selection”, or social mobility, 2) “social causation”, and 3) life course perspectives. “Socioeconomic position” mentioned in each theory and related pathways and mechanisms play a central role in the social determinants of health inequities. Health inequities, or avoidable health inequalities, happened due to the conditions in which people grow, live, work, and age, and the systems put in place to deal with illness. On the other hand, the conditions, in which people live and die, could be established and potentially mitigated by political, social and economic forces (81).

Figure 3: Social determinants of health conceptual framework (73)

Figure 3 present the Social Determinant of Health conceptual framework. This framework serves as a guide to explain the interactions between socioeconomic factors, political contexts, and health disparities. Within socioeconomic positions, the population was stratified by income, education, occupation, gender, race/ethnicity and other factors. The socioeconomic position in turn form determinants of health status (intermediary determinants) based on social status, exposure and vulnerability to health-

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related conditions. The illness may impact an individual’s socioeconomic position by reducing income and chance to get a job; or at the macro level, epidemic diseases could disrupt the function of social, economic and political institutions. In the framework, the health system can be a critical component that mediates the individual consequences and could help in reduce health inequality. In addition, the framework describes the linkages of social capital and social cohesion between the structural and intermediary dimensions, suggesting that exploring these issues could also help to intervene in the factors, which socially determine health (73).

Figure 4: The WHO health system framework (82)

Health system responsiveness was defined as “the ability of the health system to meet the population's legitimate expectations regarding their interaction with the health system, apart from expectations for improvements in health or wealth" (83). In this study, we also adapted the WHO health system framework (82) to explore the responsiveness of commune health station to NCDs (paper IV). The health system framework includes six building block: 1) service delivery, 2) health workforce, 3) health information system, 4) access to essential medicines, 5) financing, 6) leadership/governance (Figure 4). These six building blocks are critical for the health system, and may influence general health system responsiveness and the capacities of commune health stations to provide specific health care services. Within the six building blocks, there are some cross-cutting components that interact with each other, e.g. financing and health workforce are inputs, access to essential medicines and service delivery reflect the immediate outputs, and leadership/governance and health information system provide the overall policy and regulation of all other blocks (82).

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Figure 5 shows the conceptual framework in this study, in which we adapted the Social Determinants of Health conceptual framework (73) and the WHO health system framework (82). Four main outcome variables for investigation include the NCD prevalence, health care utilization, catastrophic health expenditure and impoverishment. These outcomes variables interact with each other within the frame of the health system, and they can be used to measure the overall impact the inequalities in NCDs in urban Hanoi. Socioeconomic position and behavioral risk factors are some social factors that influence the distribution of the outcome variables.

Figure 5: Conceptual framework in this study

Health system (Commune health stations)

(paper IV)

NCD prevalence (paper I)

Health care utilization

(paper II)

Catastrophic health expenditure and impoverishment

(paper III)

Behavior risk factor -Smoking -Alcohol

consumption

Socioeconomic position -Gender

-Education -Occupation

-Income

Inequality in NCDs in urban

Hanoi

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Study Objectives Overall objective To examine the burden and health system responsiveness to NCDs in Hanoi, Vietnam and investigate the role of socioeconomic inequalities in their prevalence, subsequent healthcare utilization and related impoverishment due to health expenditures.

Specific objectives

• To estimate socioeconomic inequalities in self-reported NCDs in urban Hanoi, Vietnam.

• To analyze horizontal equity in health care utilization related to NCDs in urban Hanoi, Vietnam.

• To assess socioeconomic inequalities in catastrophic heath expenditure and impoverishment associated with NCDs in urban Hanoi, Vietnam.

• To explore the responsiveness of commune health stations to NCDs in urban Hanoi, Vietnam.

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17

Methods Study setting

This study is a part of the research project entitled “The status of health, health care utilization and health care expenditure of people in urban Hanoi”. The whole project collected information on health-related issues including health behaviors, self-reported NCDs, quality of life, health care utilization, health care expenditure, as well as perceptions of climate change in urban Hanoi. The author of this thesis, one of the members of the research project team, worked closely with other team members to develop the project proposal, and to provide supervision in the collection of data in the field. This thesis focuses on one of the important parts of the project, in seeking to understand the inequalities in NCDs and their associated social factors. In addition, this thesis explored the responsiveness of commune health stations in terms of providing primary health care services to the community for NCDs.

Figure 6: Maps of Hanoi and the study site

The overall research project was conducted in Hanoi- the capital city of Vietnam (Figure 6). In 2008, it was decided that a nearby province and some districts of other nearby provinces be merged into the metropolitan area of Hanoi. Since then, Hanoi became the largest city and the second most populous city in the country. Currently, Hanoi includes 30 districts,

18

including 12 urban districts, one district-level town (Son Tay) and 17 rural districts. As of 2013, Hanoi’s population was estimated to be 6.9 million, of which 2.9 million (42%) lived in urban districts (84). The population density differs greatly between rural district areas and urban district areas. Hanoi’s average population density is about 2000 people/km2; however, the rural areas are at about 1000 people/km2, while the urban population density reaches up to about 35,400 people/km2 in some areas.

In this study, we focused on four inner urban districts located in the center of Hanoi, namely Ba Dinh, Hoan Kiem, Hai Ba Trung and Dong Da districts (Figure 7). These districts represent typical urban areas in Hanoi with both slum and non-slum areas. They account for 1.2 million of the urban population in Hanoi.

Figure 7: The study site in urban Hanoi

This study combined both quantitative and qualitative approaches. The quantitative approach focused inequalities in the NCD prevalence, health care utilization associated with NCDs, and catastrophic health expenditure and impoverishment among household with NCD patients. Since commune health stations are the lowest level in the health system, and the closest to the community, we used a qualitative approach to explore the responsiveness of the commune health stations to the burden of NCDs among the community. The quantitative approach allowed us to measure the phenomena and patterns of association, while qualitative approach helped us to identify the unknown processes, explain how phenomena occur, and assess current capacities and opportunity for improvements (85).

19

Quantitative approach (paper I, paper II, paper III) Study design

A population-based cross-sectional study was employed in this study. The study was conducted from February to March 2013. The sample size calculation was done by applying the method for estimating a population proportion with specified relative precision (86). We used the significance level of 0.05, and the relative precision of 0.4. Furthermore, we anticipated the value of 10% of households would have at least one member with NCDs based on a pilot study that we conducted before the data collection with the participation of 60 households. We controlled for a 30% non-response rate and design effects of 2, in relation to the cluster sampling design. So, we estimated that the targeted sample size was at least 600 households in each of non-slum and slum areas included in quantitative studies.

Figure 8: A slum area and a nearby non-slum area in Hanoi

Sampling and data collection

To do the fieldwork, we conducted a mapping study to determine the list of slum areas in all four abovementioned urban districts. We finally identified the list of 84 slum areas (Figure 8). In Vietnam, there was no clear definition and reliable statistics of slum areas. In this study, we adapted the

20

United Nation definition that a slum area was identified as “groups of households where people lived in temporary houses, or insecurity locations, or narrow spaces or nearby/in polluted environment locations” (39). Thirty slum areas were randomly selected from the list of 84 slum areas adapting the cluster sampling method for assessing immunization coverage (87).

Figure 9: Sampling and sample size for the quantitative approach

4 urban districts of Hanoi

84 slum areas

30 slum areas

611 households

30 non-slum areas

600 households

1750 individuals 1986 individuals

5 households excluded due to

inadequate information

11 households excluded due to

inadequate information

589 households

492 households 528 households

114 households excluded due to no

spending for healthcare

61 households excluded due to no

spending for healthcare

608 households

(Paper I and II)

(Paper III)

21

After selecting a slum area, we chose a nearby non-slum area in the same ward for comparison. In each area, we targeted a cluster containing about 20 households. We defined a household as “a one person or a group of people who shared accommodation and meals for at least six months in the last 12 months.” There were two or three collection teams assigned to gather data in each area. We first selected the household at the center of and area, and then the data collection teams went door-to-door to collect data. In each area, the data collection was completed when the teams had jointly interviewed about 20 households. Figure 9 shows the sampling process in the quantitative approach, and the sample size of each paper (I-III).

Figure 10: Interviewing in a slum household in Hanoi

We conducted face-to-face interviews using structured questionnaires with the heads of households (alternatively the spouse or an adult available at the time of the survey- who knew about that household) (Figure 10). We recruited a group of forty medical students to conduct the survey. The interviewers were trained and supervised by the senior staff of the Center for Health System Research at Hanoi Medical University.

22

Variables Dependent variables

• Self-reported NCDs (paper I): The study participants were asked if the

individual had been diagnosed by doctor or health worker in the last 12 months with any of four NCDs: cardiovascular diseases, chronic respiratory diseases, diabetes or cancer.

• Public health care utilization (paper II): We asked the study

participants about their visits to public health facilities in the 12 months prior to the interviews. Public health facilities included commune health centers, district hospitals, provincial hospitals and national hospitals.

• Catastrophic health expenditures (paper III): Catastrophic expenditure occurred if a household's total out-of-pocket health care payments were equal to or exceeding 40% of household's capacity to pay (88).

• Impoverishment (paper III): Based on the poverty line using the estimation of household subsistence expenditure, impoverishment was defined as “a non-poor household becoming a poor household was impoverished after paying for health care services” (88).

• Variables to estimate catastrophic and impoverishment

o Household capacity to pay was defined as a household’s non-subsistence spending. To estimate household capacity to pay, we subtracted subsistence (or basic necessities) expenditure from the total monthly household expenditure. If households reported that food expenditures were lower than subsistence spending, the non-food expenditures were used as non-subsistence spending.

o Household subsistence spending was the minimum requirement to

maintain basic standard of living. In this study, we used the household's average food expenditure in the 45th to 55th percentile adjusted for the household equivalence scale as a proxy measure for subsistence expenditure.

o The poverty line was defined by the food expenditure of the

household, where the food expenditure share of total household expenditure is at the 50th percentile in the country. Since we did not have reference values for the entire country, we used the household subsistence as a proxy for the poverty line in this study.

23

Independent variables

Individual-level variables • Sex (paper I): 1-male, 2-female. • Female as a head of household (paper III): 1-yes, 2-no • Age (paper I): age of individual by year • Daily smoking (paper I): an individual reported that he/she

smoked daily. • Daily drinking (paper I): an individual reported that he/she drank

alcohol daily. • Hypertension (paper I): an individual reported that he/she was

diagnosed by a doctor or health staff that he/she got hypertension. Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg.

• Marital status (paper I): 1-married, 2-unmarried, 3-divorced/widowed.

• Education (paper I): 1-primary school or less, 2-secondary school, 3-high school, 4-college/university.

• Work status (paper I, II): 1-employed, 2-unemployed, 3-retired. • Health insurance (paper I, II): an individual reported that he/she

got health insurance (1-yes, 2-no). • Age-sex (paper II): 1-male aged 15-29, 2-male aged 30-44, 3-male

aged 45-59, 4-male aged 60 or above, 5-female aged 15-29, 6-female aged 30-44, 7-female aged 45-59, 8-female aged 60 or above

Household-level variables

• Household living areas (paper I, II, III): 1-living in slum areas, 2-living in non-slum areas.

• Household size (paper I): number of people in a household, and categorized by 1-from 1-2 people, 2-from 3-4 people, 3- from 5-6 people, 4-from 7 or above.

• Household with at least one elderly (>60 years old) person (paper III): 1-yes, 2-no

• Household with at least one child <6 years old (paper III): 1-yes, 2-no

• Household with all members having social health insurance (paper III): 1-yes, 2-no

• Socioeconomic status (paper I, II, III): We used the wealth asset index as a proxy for socioeconomic status. The wealth asset index was constructed by principal components analysis (PCA), and separately for slum and non-slum areas (89, 90). We used variables on the ownership of consumer goods, dwelling characteristics, water and sanitation, and other characteristics that are related to the household’s wealth. These variables included construction materials used in the dwelling (e.g., materials for roofs, walls, and floors), access to utilities and infrastructure (e.g., sanitation facilities and

24

sources of water), and ownership of selected durable assets (e.g., TVs, radios, computers, internet access, telephones, mobile phones, VCD/DVD players, refrigerators, washing machines, water heaters, motorbikes, and cars). Categorical variables, such as the material used in dwelling constructions, were broken down into a set of dummy variables. The variables with prevalence below 5% or higher 95% were excluded from the analysis. Eigenvalues >1 was used as criteria for extraction, and varimax (orthogonal) rotation was used to improve component interpretation. Socioeconomic status was categorized into five quintiles: 1-poorest (lowest 20%), 2-near poor (lower 20%), 3-middle (middle 20%), 4-rich (higher 20%), 5-richest (highest 20%).

Data management and analysis The research team read and reviewed all questionnaires to ensure completeness of data. In case of missing or unclear data, the research team called to a household to re-check relevant information (Figure 11). Data entry and management were performed by using Epidata software, version 3.1 (91). An automatic entry check file was created to ensure that consistent and plausible data were entered.

Figure 11: Interviewers at a commune health station before going to the field

25

Socioeconomic inequality in health

Socioeconomic inequality was measured by applying the technique of concentration curves and concentration indices (76, 80). The concentration curve plots the cumulative percentage of interest variables (on the y-axis) against the cumulative percentage of wealth index ranked from the poorest to the richest (on the x-axis). If the interest variables were equally distributed across different socioeconomic levels, the concentration curve would be a linear line at 45-degree (or the line of equality). If the concentration curve lies above or below the line of equality, the interest variables concentrate among the rich (Figure 12) or the poor (Figure 13), respectively. The further away the concentration curve is from the line of equality, the greater the degree of socioeconomic inequality.

Figure 12: Example of concentration curve with the health outcome concentrating among the rich

The concentration index is calculated as twice the area between the concentration curve and the line of equality. The formula for estimating concentration index is

! =2$!%& ℎ, ) (1)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Cummulative%ofhealthvariables

Cumulative%populationrankedbysocioeconomicstatus(pooresttorichest)

LineofEqualityConcentrationcurve

26

where µ is the mean of the interest variable, h is an interest variable of an individual, and r is a rank of the individual by socioeconomic status (76). The concentration index ranges between -1 and +1. The concentration index takes the value of 0, if the health distribution is completely equal. It is a negative, indicating concentration of the interest variable among the poor. Meanwhile, it takes a positive value, indicating the concentration of the interest variable among the rich.

Figure 13: Example of concentration curve with health outcome concentrating among the poor

In paper I, we estimated the concentration index of self-reported NCDs. We estimated the concentration index of public health care utilization in paper II. For the paper III, we estimated the concentration index of catastrophic health expenditure and impoverishment.

Decomposition of socioeconomic inequality

We decomposed the concentration index to understand the contribution of different independent variables to the socioeconomic inequality of interest variables (paper I, paper II). A linear regression model on independent variables can be used to predict an interest variable, as follows:

. = / + 122

32 + 4(2)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Cummulative%ofahealthvariable

Cumulative%populationrankedbysocioeconomicstatus(pooresttorichest)

LineofEqualityConcentrationcurve

27

Equation (2) can be transformed to the concentration index for y, and it can be written:

! = 1232/$ !2 + 6!7 $ (3)2

where µ is the mean of y, x; is the mean of x; (independent variable k), C; is the concentration index for x;, and GC> is the generalized concentration for the error term (ε). In Equation 3, the first part is an explained component, while the second part “GC> µ“ is an unexplained component or residual. The component of β;x;/µ is the elasticity that indicates the impact of each C; (concentration index of independent variable k) on the total concentration index (80).

In this thesis, our dependent variables were binary variables (paper I-II), so we could not apply the linear regression model. We, therefore, used the probit regression model to estimate the coefficient β;B to replace the β; in Equation 2. Specifically, after running probit regression model, we estimated the marginal effect of using as the coefficient β;B (76). Horizontal health inequity

To measure the horizontal inequity, we applied the indirect method proposed by O’Donnell et al. (76, 92). The indirect method was used to compare the differences between actual needs and need-standardized distributions for the probability of public health care utilization during a year (paper II). Since non-linear estimation could be used to address the issue of non-utilization of public health care services (92), we applied a probit model with control variables for needs, including age-sex, and self-reported NCDs. The horizontal inequity was calculated as the difference between the concentration index of health care use and that of health care needs. After standardizing by the indirect method, the health care use and the health care needs variables were used to estimate their concentration index by applying Equation 1. A positive value of the horizontal inequity indicates a distribution of health care utilization in favor of the rich, and vice versa. The following is the equation to calculate the horizontal inequity index.

HI = CE − CG(4)

where HI is a horizontal index, CM and CN are the concentration index of health care use and health care needs, respectively.

We used the Distributive Analysis Stata Package (DASP) (paper I, II, III) to estimate concentration index (93). The t-test was used to compare if the values of concentration index differed from zero, and to compare between

28

two concentration indices. The Chi-square or Fisher exacted test was used to compare categorical variables (paper I, II, III). The Mann-Whitney test was used to compare the out-of-pocket payment for health care as a share of household capacity to pay and as a share of total household expenditure between households having members experiencing NCDs and households without members experiencing NCDs (paper III). The t-test was used to distinguish the concentration index from zero and access statistical significance (paper I, III). Multivariate data analysis was conducted with logistic (paper III) and probit (paper I, II) regression models. All the analyses were conducted using the STATA statistical software version 12.1 or 13.1. The level of statistical significance was set to 0.05. Individuals and household with any missing or inadequate data in the variables used in this study were excluded from the data analysis.

Qualitative approach (paper IV) Study design

To understand the responsiveness of primary health care services to NCDs at commune health stations, we employed a qualitative approach rooted in thematic analysis (94). This approach was based on the health care provider’s perspective through in-depth interviews. To collect rich information, in-depth interviews were considered a suitable approach, and appropriate for our study (95). Through in-depth interviews, we asked open-ended questions to key informants to explore the depth of information, and to better understand the broader picture and support our ultimate interest to better address NCDs in urban Hanoi.

Selection of study participants and data collection

The study was conducted between July and August 2015. We purposely selected Hoan Kiem district (representing the old quarter areas) and Hai Ba Trung (representing the old quarter and new urban areas). In these two districts, there were two types of commune health stations (Figure 14): those involved in the national program on NCDs and those, which were not involved. A total of four commune health stations were selected in this study, in both districts we selected two commune health stations: one with an NCD program and one without. At the commune health stations, we interviewed all health staff that involved in NCD activities, such as doctors, doctor assistants, pharmacist assistants and nurses. To supplement the information about NCD activities that we collected from the commune level, we also interviewed NCD managers at national, provincial, and district levels regarding the implementation of primary health care services for NCDs at

29

commune level. A total of 19 in-depth interviews were conducted, of these 15 interviews were with health staff at commune health stations, and four interviews with NCD managers at district, provincial and national levels.

We developed two interview guides: one for NCDs managers at different levels and one for health staff at commune health stations. The interview guides were written in Vietnamese. These guides were similar, but in the guide for NCDs managers was supplemented with a question related to health information systems capacity for tracking NCDs. The interview guides were developed based on the WHO conceptual framework for health systems, and adapted for relevance to the current situation in Vietnam’s primary health care context (82). The interview questions were classified into two sections: the first section focused on personal and professional information such as the participants’ age, work title, work experience and educational background; the second section was designed to explore participant views on primary health care services based on the WHO’s six building blocks (82). We added a question about the vital needs of commune health stations to improve their primary health care service for NCDs.

Figure 14: A commune health station in Hanoi

Our interviews lasted between 45 minutes and 1 hour. The interviews with health staff at commune health stations were conducted in a separate room in those commune health stations, while the interviews with NCDs managers at national, provincial and district levels were conducted at the respondent’s offices. A senior researcher (the author of this thesis) conducted all

30

interviews with the support of one research assistant from the Center for Health System Research, Hanoi Medical University. A briefing session was conducted after each interview, and all reactions of interviewees were taken notes by a research assistant.

Data management and analysis

All interviews were audiotaped and transcribed verbatim into MS Word by a research assistant. The content of transcriptions was reviewed and translated into English by the senior researcher. The data were converted into the relevant format, and entered into the OpenCode version 4.02 software (96).

An initial descriptive coding framework was developed based on the WHO framework for health system (82), implying that data was organized under these pre-determined themes. The verbatim interview transcripts were reviewed several times among the research team, and then codes were developed to capture the content of the responses from each informant (94). The codes were thereafter grouped into sub-themes and organized under our core themes, i.e. the six blocks of the WHO health system framework. An additional theme, describing the needs for improvements in NCD services at primary health care were inductively developed based on our data. At the final step, the research team members discussed and finalized the ultimate list of themes and sub-themes.

Ethical considerations

The ethical board at Hanoi Medical University and the district health centres of Ba Dinh, Dong Da, Hoan Kiem and Hai Ba Trung, Hanoi (Document No.0084/DHYHN-TTNCHTYT, and Document No. 825/QD-DHYHN) approved this study. Participants were informed about the main topics of the study, data collection process as well as the confidentiality of the data. For the in-depth interviews, participants were aware of tape-recording the conversation during the interview, and they could refuse if they did not want to be recorded. In addition, we informed them that only the research team could access the study data, and all results were presented in aggregated format to ensure that no one could trace their personal information. They had known that they had complete rights to withdraw from the study at any time without any consequences.

31

Results Characteristics of participating households and individuals

A total of 1211 households were recruited, and of these 1203 households (608 in slum areas and 595 in non-slum areas) with adequate information were analyzed in this study. Five households in non-slum areas (0.8%) and three households in slum areas (0.5%) lacked the necessary information on household expenditure and were consequently excluded from the analysis. The non-response rate of households was less than 5% in both slum and non-slum areas.

Table 3 shows the household characteristics in slum and non-slum areas. The sex of head of household did not differ statistically significantly between households in slum areas or non-slum areas. However, compared with households in non-slum areas, households in slum areas had a significantly lower proportion of members with self-reported NCDs, the elderly in households, children in households, and all household members with social health insurance. In addition, the household size in slum areas was significantly smaller than that in non-slum areas. Table 3: Characteristics of participating households in the study by area

Slum, n (%) (N=606)

Non-slum, n (%) (N=589)

P-Value (χ2 test)

Household with members with self-reported NCDs

Cardiovascular disease 53 (8.75) 115 (19.5) <0.001 Chronic pulmonary disease 39 (6.4) 34 (5.8) 0.63 Diabetes 52 (8.6) 90 (15.3) <0.001 Cancer 12 (2.0) 11 (1.9) 0.89 Any NCD (at least one member with NCDs) 133 (22.0) 206 (35.0) <0.001

Household with female as household's heads 284 (46.9) 284 (48.2) 0.64 Household size

1-2 people 172 (28.4) 94 (16.0) <0.001 3-4 people 286 (47.2) 258 (43.8) ≥ 5 people 148 (24.4) 237 (40.2)

Household with at least one older people ≥60 years old

263 (43.4) 368 (62.5) <0.001

Household with at least one child <6 years old 153 (25.3) 185 (31.4) 0.02 Household with all members owned social health insurance

275 (45.4) 371 (63) <0.001

Household socioeconomic status (quintile) Poorest (20%) 120 (19.8) 122 (20.7) 0.77 Poor (20%) 123 (20.3) 135 (22.9) Middle (20%) 121 (20.0) 108 (18.3) Rich (20%) 122 (20.1) 114 (19.4) Richest (20%) 120 (19.8) 110 (18.7)

32

A total of 3,815 individuals participated in this study, in which 3,736 individuals (97.9%) with adequate information were included in the study analysis. These individuals belonged to 1203 households that we mentioned above. The non-response rate of individuals was also less than 5% in both slum and non-slum areas.

Table 4: Characteristics of participating individuals by area

Variables Slum areas n(%)

(N=1750)

Non-slum areas n(%)

(N=1986)

P-Value (χ2 test)

NCDs

Cardiovascular disease 53 (3.0) 125 (6.3) <0.05 Chronic respiratory disease 37 (2.1) 32 (1.6) >0.05

Diabetes 52 (3.0) 96 (4.8) <0.05

Cancer 12 (0.7) 10 (0.5) >0.05

Any NCDs 139 (7.9) 230 (11.6) <0.05

Sex (male) 811 (46.3) 934 (47.0) >0.05

Age in years (SD) 44.0 (18.2) 47.1 (18.8) <0.05* Household size

1-2 people 306 (17.5) 178 (9.0) <0.05 3-4 people 815 (46.6) 773 (38.9) 5-6 people 453 (25.9) 835 (42.0) 7+ people 176 (10.0) 200 (10.1)

Education Primary school or less 328 (18.7) 120 (6.0) <0.05 Secondary school 492 (28.1) 359 (18.1) High school 493 (28.2) 539 (27.1) College/University 437 (25.0) 968 (48.7)

Marital status Married 1181 (67.5) 1450 (73.0) <0.05 Unmarried 392 (22.4) 388 (19.5) Divorced/Widowed 177 (10.1) 148 (7.5)

Work status Employed 1060 (60.6) 1092 (55.0) <0.05 Unemployed 333 (19.0) 279 (14.1) Retired 357 (20.4) 615 (30.9)

Household socioeconomic status Poorest (20%) 288 (16.5) 328 (16.5) <0.05 Poor (20%) 316 (18.1) 374 (18.8) Middle (20%) 356 (20.3) 498 (25.1) Rich (20%) 386 (22.1) 399 (20.1) Richest (20%) 404 (23.1) 387 (19.5)

Daily smoking 251 (14.3) 239 (12.0) <0.05 Daily drinking 167 (9.5) 152 (7.7) <0.05 Hypertension 147 (8.4) 246 (12.4) <0.05 Health insurance 1151 (65.8) 1641 (82.6) <0.05 Public health care utilization 373 (21.3) 531 (26.7) <0.05

*: t-test.

33

Table 4 presents a detailed description of the demographic characteristics of participants. The proportion of any self-reported NCDs in non-slum areas was significantly higher among those in non-slum areas (11.6%) than those in slum areas (7.9%). When comparing the prevalence of the four specific diseases between slum and non-slum areas, the prevalence of cardiovascular disease and diabetes were significantly higher in non-slum areas than in slum areas. Regarding NCDs risk factors, the prevalence of daily smoking and drinking among slum areas was significantly higher than that among non-slum areas. However, the prevalence of self-reported hypertension in non-slum areas was significantly higher than in slum areas. People in non-slum areas were more likely to use public health care services than those in slum areas.

Patterns of socioeconomic inequalities in self-reported NCDs and health care utilization (paper I, II)

Socioeconomic inequality was measured by using the concentration index. Table 5 shows the concentration index of self-reported NCDs for slum and non-slum areas using three different methods. All the values were negative, which indicated that self-reported NCDs concentrated among the poor in both slum and non-slum areas.

Table 5: Concentration index of NCDs by area

Method Slum areas (N=1750)

Non-slum areas (n=1986)

Concentration index

95% CI Concentration index

95% CI

Unstandardized -0.10 (-0.19, -0.01) -0.17 (-0.24, -0.09)

Age, age squared, and sex standardized

-0.07 (-0.15, 0.02)

-0.05 (-0.12, 0.02)

Age, age squared, and sex standardized, and controlled by other variables

-0.08 (-0.17, 0.01) -0.11 (-0.18, -0.04)

The socioeconomic inequality in self-reported NCDs was also shown in Figure 15. The figure illustrates the concentration curves, and we found that both concentration curves were above the inequality line, indicating that prevalence of self-reported NCDs concentrated among the poor in both non-slum and slum areas. In other words, these results could be interpreted as those living in lower socioeconomic status households suffered from a higher prevalence of NCDs than those living in households with higher socioeconomic status in both slum and non-slum areas.

34

Figure 15: Concentration curves of self-reported NCDs by area

The marginal effects of each associated factors on self-reported NCDs in slum and non-slum areas are presented in Table 6. The prevalence of self-reported NCDs was associated with older age, living in households with more than seven members, being divorced or widowed, having hypertension and using public health care services in non-slum areas. Older age, being unemployed, having hypertension, and using public health care services were significantly associated with the prevalence of self-reported NCDs in slum areas. There were no significant associations between daily smoking or drinking and self-reported NCDs in either slum or non-slum areas.

35

Table 6: Probability of determinants on self-reported NCDs (marginal effect using probit model) in Hanoi

Variables Slum areas (n=1750)

Non-slum areas (n=1986)

Sex (male) -0.002 0.011 Age in years 0.010* 0.011* Aged square -0.007* -0.006* Household size

1-2 people ref. ref. 3-4 people -0.026 0.023 5-6 people -0.023 0.007 7+ people -0.017 0.062*

Education Primary school or less -0.010 0.024 Secondary school -0.029 0.018 High school -0.030 0.028 College/University ref. ref.

Marital status Married ref. ref. Unmarried -0.013 -0.007 Divorced/Widowed -0.018 0.038*

Work status Employed ref. ref. Unemployed 0.065* -0.022 Retired -0.019 0.007

Household socioeconomic status Poorest (20%) 0.016 0.040 Poor (20%) 0.030 0.019 Middle (20%) 0.020 0.040 Rich (20%) 0.001 0.024 Richest (20%) ref. ref.

Daily smoking 0.017 -0.026 Daily drinking -0.007 0.039 Hypertension 0.056* 0.107* Health insurance 0.014 0.021 Public health care utilization 0.073* 0.060* Wald chi square 219.9 290.3 p-value <0.01 <0.01 Pseudo R2 0.24 0.34

*: Coefficients significantly different from zero at p < 0.05.

For health care utilization, 21.3% of people in slum areas reported visiting any public health care facility during the last 12 months while 26.7% of those in non-slum areas reported the same utilization (Table 7). In non-slum areas, all values of concentration index were negative, indicating that health care utilization concentrated among the poor in non-slum areas. In slum areas, the concentration indices for using district hospital (-0.063) and provincial hospitals (-0.112) were negative, indicating that at district and provincial hospitals, health care utilizations concentrated among the poor. However, these values for using communal health centers (0.044) and national hospitals (0.114) were positive, indicating that health care utilization concentrated among the rich. Generally, the rich were more likely to use health care in slum areas.

36

Table 7: Mean and concentration indices of public health care utilization during the last 12 months by area

Slum areas Non-slum areas

Variables Proportion Concentration index

Proportion Concentration index

Communal health center 0.020 0.044 0.017 -0.157 District hospitals 0.035 -0.063 0.038 -0.020 Provincial hospitals 0.053 -0.112 0.073 -0.049 National hospitals 0.118 0.114 0.148 -0.101 Any health care utilization 0.213 0.034 0.267 -0.074

Contributions to socioeconomic inequality in self-reported NCDs and health care utilization related to NCDs (paper I, II)

Contributions to socioeconomic inequality were measured by decomposing the concentration index based on associated factors (Table 8). In slum areas, the largest contributors to an increase in the inequality in self-reported NCDs were low socioeconomic status (54.7%) and older age (32.8%). In non-slum areas, the largest contributors were older age (48.1%) and lower socioeconomic status (29.7%). Hypertension was considered as a risk factor of NCDs, and the analysis shows that hypertension contributed to about 9% of the increase in inequalities in self-reported NCDs in non-slum areas, but only 2.5% in slum areas. In slum areas, having health insurance and health care utilization contributed to the decrease in the inequality in self-reported NCDs of about 12% and 3.1%, respectively. Table 8: Contributions to concentration indices of self-reported NCDs

Contribution to concentration index

Slum areas, % Non-slum areas, %

Sex 0.3 -0.1 Age 32.8 48.1 Household size 22.2 -4.4 Education -13.7 3.6 Marital status -1.4 3.0 Work status 5.4 2.1 Socioeconomic status 54.7 29.7 Daily smoking 0.4 -0.6 Daily drinking 0.5 1.0 Hypertension 2.5 9.0 Health insurance -11.9 -0.8 Public health care utilization -3.1 7.3 Residual 11.3 2.2 Total 100 100

In slum areas (Table 9), the results of our estimation suggest that the actual health care utilization concentrated among the rich (0.034 - the value was positive), while the health care needs concentrated among the poor (-0.019 – the value was negative). After standardizing for age, sex and NCDs, the value

37

of the wealthiest 20% utilizing public health care services is 0.217, which is 26% higher than the mean of the poorest 20% (0.172). For non-slum areas, as Table 10 shows, the values of actual- and needs-predicted public health care utilization were all negative, indicating that the poor are more likely to have the opportunity to use public health care services in non-slum areas.

We extended our analysis to decompose the effects of need factors and non-need factors in the health care utilization in slum areas (Figure 16). After standardizing the percentage of contribution, the age–sex groups and NCDs pushes the health care utilization in a pro-poor direction by about 40% and 21%, respectively. The direct effect of the socioeconomic status and health insurance on health care utilization would increase the inequality by approximately 37% and 54%, respectively, which pushes the health care utilization among the rich. Education and work status reduced the inequality by approximately 8% and 31%, respectively, and collectively pushed the health care utilization in a pro-poor direction.

38

Tab

le 9

: Dis

trib

uti

ons

of a

ctu

al, n

eed

pre

dic

ted

, an

d n

eed

sta

nd

ard

ized

hea

lth

car

e u

tili

zati

on in

slu

m a

reas

, Han

oi

Pro

bab

ilit

y of

an

y p

ub

lic

hea

lth

car

e u

tili

zati

on

Qu

inti

le

Pro

bit

wit

h c

ontr

ols

Nee

d s

tan

dar

diz

ed

Act

ual

N

eed

pre

dic

ted

D

iffe

ren

t =

pre

dic

ted−a

ctu

al

Wit

h c

ontr

ols

Wit

hou

t co

ntr

ols

Pro

bit

O

LS

P

rob

it

OL

S

Wea

lth

qu

inti

le: 1

- lo

wes

t 20

%

0.1

74

0.2

01

-0.0

27

0.1

72

0.1

72

0.1

76

0.1

76

Wea

lth

qu

inti

le: 2

- lo

wer

20

%

0.2

23

0.2

13

0.0

11

0.2

10

0.2

10

0.2

07

0.2

07

Wea

lth

qu

inti

le: 3

- m

idd

le 2

0%

0

.20

3 0

.19

6

0.0

09

0

.20

7 0

.20

6

0.2

05

0.2

05

Wea

lth

qu

inti

le: 4

- h

igh

er 2

0%

0

.255

0

.19

6

0.0

60

0

.259

0

.259

0

.259

0

.259

W

ealt

h q

uin

tile

: 5 -

hig

hes

t 20

%

0.2

08

0

.19

0

0.0

18

0.2

17

0.2

17

0.2

17

0.2

17

Mea

n

0.2

13

0.1

99

0

.014

0

.213

0

.213

0

.213

0

.213

C

once

ntr

atio

n in

dex

/HI WV

0

.034

-0

.019

0.0

52

0.0

50

0.0

50

0.0

50

Sta

nd

ard

err

or

0.0

28

0.0

10

0

.025

0

.025

0

.025

0

.025

t-

rati

o 1.

214

-1

.88

9

2.

033

1.

99

8

1.9

95

2.0

11

Not

e: O

LS=

Ord

inar

y Le

ast S

quar

e m

odel

; pro

bit=

Pro

bit m

odel

; HI W

V: H

oriz

onta

l ine

quity

inde

x us

ing

the

indi

rect

stan

dard

izat

ion

appr

oach

.

Tab

le 1

0: D

istr

ibu

tion

s of

act

ual

, nee

d p

red

icte

d, a

nd

nee

d s

tan

dar

diz

ed h

ealt

h c

are

uti

liza

tion

in n

on-s

lum

are

as, H

anoi

P

rob

abil

ity

of a

ny

pu

bli

c h

ealt

h c

are

uti

liza

tion

Qu

inti

le

Pro

bit

wit

h c

ontr

ols

Nee

d s

tan

dar

diz

ed

Act

ual

N

eed

pre

dic

ted

D

iffe

ren

t =

pre

dic

ted−a

ctu

al

Wit

h c

ontr

ols

Wit

hou

t co

ntr

ols

Pro

bit

O

LS

P

rob

it

OL

S

Wea

lth

qu

inti

le: 1

- lo

wes

t 20

%

0.3

35

0.3

13

0.0

22

0.2

80

0

.28

1 0

.273

0

.273

W

ealt

h q

uin

tile

: 2 -

low

er 2

0%

0

.271

0

.250

0

.021

0

.278

0

.278

0

.278

0

.279

W

ealt

h q

uin

tile

: 3 -

mid

dle

20

%

0.2

22

0.2

46

-0

.024

0

.234

0

.234

0

.236

0

.236

W

ealt

h q

uin

tile

: 4 -

hig

her

20

%

0.2

62

0.2

39

0.0

22

0.2

80

0

.28

0

0.2

81

0.2

82

W

ealt

h q

uin

tile

: 5 -

hig

hes

t 20

%

0.2

43

0.2

37

0.0

06

0

.26

3 0

.26

2 0

.26

5 0

.26

5 M

ean

0

.26

7 0

.257

0

.010

0

.26

7 0

.26

7 0

.26

7 0

.26

7 C

once

ntr

atio

n in

dex

/HI WV

-0

.075

-0

.06

6

-0

.011

-0

.013

-0

.00

4

-0.0

04

S

tan

dar

d e

rror

0

.022

0

.010

0.0

19

0.0

19

0.0

19

0.0

19

t-ra

tio

-3.3

64

-6

.456

-0.5

80

-0

.66

7 -0

.222

-0

.234

N

ote:

OLS

: Ord

inar

y Le

ast S

quar

es; H

I WV: H

oriz

onta

l ine

quity

inde

x us

ing

the

indi

rect

stan

dard

izat

ion

appr

oach

.

39

Fig

ure

16

: Con

trib

uti

ons

to c

once

ntr

atio

n in

dex

in h

ealt

h c

are

uti

liza

tion

in s

lum

are

as, H

anoi

-40

%

-21%

37%

54

%

-8%

-31%

-60

%

-40

%

-20

%

0%

20

%

40

%

60

%

Ag

e-s

ex

N

CD

sS

oci

oe

con

om

ic

sta

tus

He

alt

h in

sura

nce

Ed

uca

tio

nW

ork

sta

tus

Ne

ed

fa

cto

rsN

on

-ne

ed

fa

cto

rs

%contributiontoconcentrationindexofhealthcareutilization

40

Inequality in catastrophic health expenditure and impoverishment (paper III)

To estimate the catastrophic health expenditure and impoverishment, we included only households that reported to pay for health care in the last 12 months, thus, 492 households in slum areas and 528 households in non-slum areas were included in the analysis (Figure 9). In slum areas, the monthly average out-of-pocket payments for health care ranged from US$ 18.3 to 39.9, while in non-slum areas, it ranged from US$ 35.0 to 52.0. The average monthly out-of-pocket payments for health care were significantly lower among slum households than those among non-slum households within the four lower socioeconomic groups (Figure17).

Figure17:Meansand95%CIofhouseholdout-of-pocketpaymentpermonthforhealthcareservicesbysocioeconomicstatus

Out-of-pocket payments for health care accounted for about 14.5% (in slum areas) and 12.3% (in non-slum areas) of household capacity to pay, while they accounted for about 8.3% (in slum areas) and 7.7% (in non-slum areas) of total health expenditure. These indicators were significantly higher among the households with NCD patients than among households without NCD patients. Within households with NCD patients, the share of out-of-pocket payments for health care services of the household’s capacity to pay in slum households was significantly higher than that of non-slum households (Table 11).

35.038.8

52.046.7

35.5

18.319.6 21.0 23.2

39.3

0

10

20

30

40

50

60

70

Poorest- 20% Poor- 20% Middle-20% Rich-20% Richest-20%Meanan

d95

%CIofh

ousholdou

tofp

ocketp

aymen

tfor

healthca

reservices(US

$)

Householdsocioeconomicstatus

Non-slumareasSlumareas

Exchange rate to convert from US$ to VND for 2013: 20,828 (Source: The State Bank of Vietnam)95% CI: 95% confidence interval

41

The proportion of households facing catastrophic health expenditure and impoverishment in slum areas was significantly higher than that in non-slum areas. In both slum and non-slum areas, households with NCD patients were significantly more likely to face catastrophic health expenditure and impoverishment that those households without NCD patients. In households with NCD patients, the proportion of households facing catastrophic health expenditure and impoverishment among slum households was significantly higher than that of non-slum households. There were no such differences between slum and non-slum areas among households without NCD patients (Table 12).

All the values of the concentration indices were negative, indicating that the proportion of catastrophic health expenditure and impoverishment concentrated among the poor households in both slum and non-slum areas. However, only the concentration indices that were significantly different from zero should be considered (Table 13). The proportion of catastrophic health expenditure significantly concentrated among the poor households in slum areas in both households with NCD patients (concentration index=-0.30, p<0.001) and households without NCD patients (concentration index=-0.37, p<0.01). In slum areas, the proportion of catastrophic health expenditure significantly concentrated among the poor households with NCD patients (concentration index=-0.31, p<0.01). The proportion of impoverishment also significantly concentrated among the poor households in slum areas in both households with NCD patients (concentration index=-0.42, p<0.001) and without NCD patients (concentration index=-0.36, p<0.001).

Table 14 shows the association of catastrophic health expenditures and impoverishment with social factors. The significant factors related to households experiencing catastrophic health expenditure were households with at least one member reporting diagnosis of NCDs (odds ratio [OR] = 2.4; 95% confidence interval [CI]=1.5–3.9), households in slum areas (OR=2.1; 95%CI=1.2-3.5), households with older people (OR=1.9; 95% CI=1.1-3.3), and households belonged to the poorest socioeconomic quintile (OR=4.9; 95% CI=2.0-12.0). Impoverishment due to health care spending significantly associated with households that had at least one member with self-reported NCDs (OR=2.3; 95% CI=1.1-5.0), lived in slum areas (OR=3.9; 95% CI=1.7-9.5), and belonged to the poor socioeconomic quintile (OR=11.2; 95% CI, 1.4-91.4) and the poorest socioeconomic quintile (OR=9.3; 95% CI, 1.2-77.8).

42

Tab

le 1

1: O

ut-

of-p

ocke

t p

aym

ent

for

hea

lth

car

e as

a s

har

e of

hou

seh

old

cap

acit

y to

pay

an

d t

otal

hou

seh

old

exp

end

itu

re

Ove

rall

Hou

seh

old

s w

ith

at

leas

t on

e m

emb

er w

ith

NC

Ds

Hou

seh

old

s w

ith

out

any

mem

ber

wit

h N

CD

s P

-val

uea

Ou

t-of

-poc

ket

pay

men

t fo

r h

ealt

h c

are

as a

sh

are

of h

ouse

hol

d’s

cap

acit

y to

pay

, % (

SE

)

Slu

m a

reas

14

.5 (

0.9

) 22

.4 (

2.1)

12

.1 (

1.0

) <

0.0

01

Non

-slu

m a

reas

12

.3 (

1.0

) 15

.9 (

1.2)

10

.3 (

1.3)

<

0.0

01

P-v

alu

eb

0.2

9

0.0

2 0

.21

O

ut-

of-p

ocke

t p

aym

ent

for

hea

lth

car

e as

a s

har

e of

tot

al h

ouse

hol

d e

xpen

dit

ure

, % (

SE

) S

lum

are

as

8.3

(0

.5)

13.2

(1.

3)

6.8

(0

.4)

<0

.00

1 N

on-s

lum

are

as

7.7

(0.5

) 10

.3 (

0.9

) 6

.2 (

0.6

) <

0.0

01

P-v

alu

eb

0.9

5 0

.11

0.6

3

SE: s

tan

dar

d e

rror

. a:

Man

n-W

hit

ney

tes

t to

com

par

e be

twee

n h

ouse

hol

ds

wit

h a

t le

ast

one

mem

ber

wit

h a

nd

wit

hou

t N

CD

s b:

Man

n-W

hit

ney

tes

t to

com

par

e be

twee

n h

ouse

hol

ds

in s

lum

an

d n

on-s

lum

are

as

Tab

le 1

2: P

atte

rn o

f ca

tast

rop

hic

hea

lth

exp

end

itu

re a

nd

imp

over

ish

men

t of

hou

seh

old

s

O

vera

ll

Hou

seh

old

wit

h a

t le

ast

one

mem

ber

wit

h N

CD

s H

ouse

hol

d w

ith

out

any

mem

ber

wit

h N

CD

s

P-v

alu

ea

Cat

astr

oph

ic h

ealt

h e

xpen

dit

ure

, % (

SE

)

Slu

m a

reas

10

.0 (

1.4

) 20

.7 (

3.8

) 6

.6 (

1.3)

<

0.0

01

Non

-slu

m a

reas

6

.6 (

1.1)

10

.5 (

2.2)

4

.5 (

1.1)

<

0.0

1 P

-val

ueb

0

.05

0.0

1 0

.20

Imp

over

ish

men

t, %

(S

E)

Slu

m a

reas

5.

1 (1

.0)

10.3

(2.

8)

3.5

(0.9

) <

0.0

1 N

on-s

lum

are

as

1.5

(0.5

) 1.

6 (

0.9

) 1.

5 (0

.7)

0.9

4c

P-v

alu

eb

<0

.01

<0

.01

0.0

9

SE

: sta

nd

ard

err

or.

a : χ

2 t

est

to c

omp

are

betw

een

hou

seh

old

s w

ith

at

leas

t on

e m

embe

r w

ith

an

d w

ith

out

NC

Ds

b: χ

2 t

est

to c

omp

are

betw

een

hou

seh

old

s in

slu

m a

nd

non

-slu

m a

reas

c :

Fis

her

’s e

xact

tes

t.

43

Tab

le 1

3: C

once

ntr

atio

n in

dex

of

hou

seh

old

cat

astr

oph

ic h

ealt

h e

xpen

dit

ure

an

d im

pov

eris

hm

ent

Ove

rall

Hou

seh

old

s w

ith

at

leas

t on

e m

emb

er w

ith

NC

Ds

Hou

seh

old

s w

ith

out

any

mem

ber

wit

h N

CD

s

P-v

alu

ea

Cat

astr

oph

ic h

ealt

h e

xpen

dit

ure

, con

cen

trat

ion

ind

ex (

SE

)

Slu

m a

reas

-0

.35

(0.0

7)**

* -0

.30

(0

.09

)***

-0

.37

(0.1

2)**

0

.60

N

on-s

lum

are

as

-0.2

9 (

0.1

0)*

* -0

.31

(0.1

2)**

-0

.19

(0

.17)

0

.54

P

-val

ueb

0

.60

0

.94

0

.38

Imp

over

ish

men

t, c

once

ntr

atio

n in

dex

(S

E)

Slu

m a

reas

-0

.40

(0

.08

)***

-0

.42

(0.1

2)**

* -0

.36

(0

.10

)***

0

.70

N

on-s

lum

are

as

-0.2

3 (0

.23)

-0

.26

(0

.35)

-0

.21

(0.3

0)

0.9

2 P

-val

ueb

0

.50

0

.65

0.6

3

SE: s

tan

dar

d e

rror

. a:

In

dep

end

ent

t-te

st t

o co

mp

are

betw

een

hou

seh

old

s w

ith

at

leas

t on

e m

embe

r w

ith

an

d w

ith

out

NC

Ds.

b:

In

dep

end

ent

t-te

st t

o co

mp

are

hou

seh

old

s in

slu

m a

nd

non

-slu

m a

reas

wit

hin

eac

h c

olu

mn

. *p <

0.0

5; *

*p <

0.0

1; *

**p <

0.0

01

(t-t

est

to c

omp

are

the

con

cen

trat

ion

ind

ex w

ith

0).

44

Tab

le 1

4:

Ass

ocia

ted

fac

tors

of

hou

seh

old

cat

astr

oph

ic h

ealt

h a

nd

im

pov

eris

hm

ent

of h

ouse

hol

ds,

ass

esse

d u

sin

g m

ult

ivar

iab

le lo

gist

ic r

egre

ssio

n a

nal

ysis

Cat

astr

oph

ic h

ealt

h e

xpen

dit

ure

Im

pov

eris

hm

ent

OR

(9

5% C

I)

P-

Val

ue

OR

(9

5% C

I)

P -

Val

ue

Hou

seh

old

wit

h a

t le

ast

one

mem

ber

wh

o re

por

ted

dia

gnos

is o

f N

CD

s

Yes

2.

4 (

1.5-

3.9

) <

0.0

1 2.

3 (1

.1-5

.0)

0.0

3 N

o 1

1

L

ocat

ion

of

hou

seh

old

S

lum

are

as

2.1

(1.2

-3.5

) <

0.0

1 3.

9 (

1.7-

9.5

) <

0.0

1 N

on-s

lum

are

as

1

1

Hou

seh

old

wit

h f

emal

e as

hou

seh

old

's h

ead

s

Y

es

0.9

(0

.6-1

.5)

0.7

8

1.9

(0

.9-4

.1)

0.8

0

No

1

1

Hou

seh

old

siz

e

1-

2 p

eop

le

1

1

3-4

peo

ple

0

.7 (

0.4

-1.3

) 0

.28

0

.6 (

0.2

-1.4

) 0

.24

≥

5 p

eop

le

0.7

(0

.3-1

.4)

0.3

1 0

.8 (

0.3

-2.3

) 0

.64

H

ouse

hol

d w

ith

at

leas

t on

e ol

der

peo

ple

≥6

0 y

ears

old

Y

es

1.9

(1.

1-3.

3)

0.0

3 0

.7 (

0.3

-1.7

) 0

.47

No

1

1

Hou

seh

old

wit

h a

t le

ast

one

chil

d <

6 y

ears

old

Y

es

0.6

(0

.3-1

.2)

0.0

9

0.9

(0

.3-2

.5)

0.8

0

No

1

1

Hou

seh

old

wit

h a

ll m

emb

ers

own

ed s

ocia

l hea

lth

insu

ran

ce

Yes

1.

1 (0

.6-1

.8)

0.8

2

1.2

(0.6

-2.7

) 0

.67

No

1

1

Hou

seh

old

soc

ioec

onom

ic s

tatu

s (q

uin

tile

)

P

oore

st (

20%

) 4

.9 (

2.0

-12.

0)

<0

.01

11.2

(1.

4-9

1.4

) 0

.02

Poo

r (2

0%

) 2.

0 (

0.7

-5.2

) 0

.14

9

.3 (

1.2-

77.8

) 0

.04

M

idd

le (

20

%)

2.0

(0

.8-5

.2)

0.1

4

6.8

(0

.8-5

9.1

) 0

.07

Ric

h (

20%

) 1.

8 (

0.7

-4.8

) 0

.21

3.2

(0.3

-33.

8)

0.2

9

Ric

hes

t (2

0%

) 1

1

45

Responsiveness of commune health station to NCDs (paper IV)

Table 15 shows characteristics of participants in the qualitative study. The age of participants ranged between 25 and 54. It is typical that females predominate among health staff at the commune health stations, so in this study, most of the participants were female (17/19). Most of the participants had worked more than five years (17/19). Table 15: Description of respondents by age group, sex, job title, work experience and qualification

Characteristic Number (N=19) Age group (years)

25-29 5 30-34 5 35-39 2 40-44 2 45-49 3 50-54 2

Sex Male 2 Female 17

Work experience (years) <5 2 5-9 7 10-14 3 15-19 3 >20 4

Work title NCD manager at national level 1 NCD manager at provincial level 1 NCD manager at district level 2 NCD manager at commune level 4 Head of commune health center 4 Staff of commune health center 7

Qualification Medical doctor 7 Traditional medicine doctor 1 Doctor assistant 4 Pharmacist/pharmacist assistant 3 Nurse 4

Table 16 presents the overall themes describing the responsiveness of commune health stations to NCDs. These themes were developed based on our data and the six building blocks in the WHO health system framework.

46

Table 16: Summary of finding themes identified in the qualitative study

Six building blocks Themes found in this study

Service delivery Unsystematic, limited and inadequate

Leadership/governance Unawareness and weak implementation of national strategy

Health information Limited, fragmented and inadequate for planning

Health workforce Insufficient number and capacity of NCD workforce

Financing Lack of budget for NCD services

Access to essential medicine Either lacking or insufficient

The primary health care services for NCDs were implemented on a small scale. Currently, the national target programs on NCDs are implemented in only a few communes within a district, and cover a small proportion of the population. Most health staff at commune health stations showed that they knew about the burden of NCDs among the population. However, they lacked autonomy to implement the primary health care services needs for addressing NCDs at their commune health stations. In addition, some health staff at commune health stations strongly demonstrated that they were so busy with many tasks already, such that they did not want to provide NCD services at their commune health stations.

“Because services related to management of NCDs have not been implemented systematically at primary health care level, the coverage was limited, so the proportion of patients, high-risk people accessing health care services were low, it doesn't ensure the equity in health.” –(NCD manager at the national level).

“As I told you, we cannot implement health care service for NCDs. We have many tasks, and we lack a doctor here.”-(Head of a commune health station at a commune health station without any national program on NCDs).

The strategy was issued by the Prime Minister for the period from 2015 to 2025, including programs for addressing cancer, CVDs, diabetes, COPDs, asthma and other NCDs. The strategy was also covered risk factors of NCDs, including smoking, alcohol abuse, food safety and lack of physical activity. Although the national strategy was acknowledged at higher levels, NCD managers and health staff at the commune level were unaware of it. In fact, all participants at commune health stations did not know about the national strategy for NCDs. Even in commune health stations participating in the national program for hypertension or diabetes, health staff did not even know about the national strategy on NCDs.

47

It was noted that the information about NCDs was limited, fragmented and inadequate, since the national targeted programs collected data and even so, only sporadically and on a small scale. In addition, the national targeted programs on NCDs covered only some NCDs. Moreover, there is insufficient coordination of information between the curative and preventative health systems. At the district level, the lack of data on NCDs was also noted. Commune health stations send NCDs reports to the district level based on the NCD target programs. However, since the commune health stations staff have to manage multiple priorities, the quality of the reports typically suffers from divided attention. NCD managers at commune health stations were assigned to collect NCDs information and prepare relevant reports on NCDs. Information was collected from a limited number of patients, who were enrolled in the NCD programs.

“We don’t have much information because we are not a curative facility. We have information on NCDs from four commune health stations participating in the national program, and our two clinics for health insurers. For other commune health stations, we don’t have data on NCDs” –(NCD manager at the district level).

“For me, the NCD reports are so passive. For example, if people come here, and we diagnose them with a disease, then we record their information in our ledges”- (Head of a commune health station without any national program for NCDs).

Insufficient workforce dedicated to NCDs at commune health stations in terms of number and capacity is widely cited by most of the respondents. Some respondents mentioned the misallocation of health staff at commune health stations: while there are a lot of nurses, midwives and pharmacist assistants, but medical doctors are often lacking. In particular, there were an insufficient number of medical doctors for NCDs at commune health stations. Individually, some participants described that their capacity on NCDs was not enough to meet the requirement of the population. Thus, they noted that they needed more specialized training on NCDs.

“…[at commune health stations] there are a lot of nurses, midwife and pharmacist assistant, but we lack medical doctors. Generally, there is one medical doctor per commune health station, but some commune health stations do not have medical doctors. In addition, a medical doctor at commune health stations is normally a head of commune health stations with many tasks. Actually, we need responsible staff who are medical doctor or doctor assistant.” -NCD manager at provincial level” –(NCD manager at the provincial level).

48

“… I think that our capacity [on NCDs] would not meet the requirement of patients because patients do not want to visit us. Their demand is higher than that of our capacity”- (Doctor assistant at a commune health station without any national program on NCDs).

All participants emphasized that there was insufficient public financing for primary health care services of NCDs. The budget for NCDs was from the national target programs, and only for commune health station participating in the program. Currently, there is no mechanism to use health insurance at the primary health care level for NCDs. NCD managers at district level noted the lack of a budget for NCDs, as well. At commune health stations participating in the national program, this led to the shortage of medicines as well as reduced coverage of primary health care for NCDs at commune health stations. Some health staff at commune health stations felt that the budget did not affect NCD service provision because patients still need to receive medicines from a district level. In addition, they mentioned a lack of autonomy to decide the budget for NCDs at commune health stations.

“We have always wanted to get an additional budget. Actually, the budgets for NCD were always limited. For example, there weren’t enough medicines. So even if patients want it, we cannot provide medicines”- (NCD manager at the district level).

Some medicines for NCDs were included in the essential drug list at commune health stations. However, there was a lack of equipment and rapid test kit for NCDs at the commune health stations. Although some medicines for NCDs were on the list, health staff at commune health stations reported that they did not have any medicine for NCDs. For commune health stations with the national target programs on hypertension, medicines were distributed from the district level, and then delivered to directly patients who were enrolled in the program. For the national programs on diabetes, commune health stations provided only screening for a limited population.

“…We do not have medicines at our commune health stations. What we have here are some emergency medicines”- (Pharmacist assistants at commune health stations without any national target program on NCDs).

“…We receive medicines for hypertension from the district level for those who were enrolled in the program [national target program on hypertension] in our commune. There is no medicine for other NCDs because we do not provide any NCD services here” – (Pharmacist assistants at commune health stations with the national target program on NCDs).

49

Figure 18 shows the summary of recommendations of participants to improve primary health care services for NCDs at commune health stations. Most of the participants showed that they needed additional budgets as well as human resource, training and equipment. The availability of NCD medication at commune health stations was also important. Especially, some participants suggested that the health insurance should be implemented at commune health stations in urban Hanoi. The development of a service package for NCDs for commune health stations should be considered.

Figure18:VitalneedstoimproveprimaryhealthcareforNCDsincommunehealthstations

VitalneedsforCHSs

BudgetEquipment

Invovlinginhealth

insuranceHealthservice

packageforNCDs

Medicines

Doctor,andtraining

50

Discussion

Recently, there has been a lack of reliable data on the association between socioeconomic status and health among urban dwellers (19). Particularly, the relationship between socioeconomic inequalities and NCDs in urban Vietnam, with special attention toward disparity in slum and non-slum areas separately has been scarcely studied (19). In addition, health care services utilization, catastrophic health expenditure and impoverishments related to NCDs have been the major concerns in Vietnam as the country pursues the national health care systems transition towards universal health coverage (24, 25). This thesis is among the few population-based studies on NCDs conducted in urban areas, including both slum and non-slum areas of urban Hanoi, Vietnam.

Figure19:SupervisingdatacollectorsataslumhouseholdinHanoi

The prevalence of NCDs concentrated among the poor

This thesis provides evidence that the prevalence of self-reported NCDs was significantly higher among individuals in non-slum areas (11.6%) than those in slum areas (7.9%) in Hanoi. The socioeconomic inequality in self-reported NCDs occurred in both slum and non-slum areas. The estimation of the concentration index showed that the socioeconomic inequality concentrated among the poor, indicating that the poor were more likely to get NCDs in

51

both slum and non-slum areas. The situation would be worse in slum areas since slum population was more vulnerable to poor health outcomes (42). The low prevalence of NCDs was observed in this study is because our target population was people starting as young as 15 years old. Other studies in Vietnam reported a higher prevalence because they included people other chronic diseases or chronic health problems (e.g. chronic joint problems and hypertension), and represented older age groups (97, 98). However, looking at the age-specific groups, the prevalence of NCDs in this study is quite similar to other studies (97, 98).

A number of studies on socioeconomic inequalities in health were conducted, but few of these focused on NCDs (99-101). The prevalence of NCDs concentrating among the poor was also found in a study in Thailand (102). A study in Iran showed that mental health problems also concentrated among the poor in urban areas (103). Our findings are consistent with these two studies, in that the elderly and those with lower socioeconomic status experience significant disparities in NCDs (102, 103). Although no significant association between self-reported NCDs and socioeconomic status has been observed, we could still identify the problem of socioeconomic inequality by using the concentration index. In this study, the value of concentration index of self-reported NCDs was negative, meaning that the distribution of NCDs concentrated among the poor.

NCD risk behaviors have been shown to have a strong association with the incidence of NCDs (2). However, this study showed that risk behaviors, such as smoking and drinking, played an insignificant role in terms of the gaps of socioeconomic inequalities in self-reported NCDs in urban Hanoi. In this study, we might not detect the magnitude of smoking and drinking because these factors would have long-term effects on health. In addition, people normally quit smoking and stop drinking, when they have been diagnosed with NCDs.

In some studies, hypertension was considered as one type of NCD (104, 105), but WHO’s guidelines and reports specified hypertension as a risk factor for NCDs, particularly cardiovascular diseases, rather than an NCD in and of itself (7, 13, 106). In addition, within the STEPwise approach to surveillance (STEPS) of NCD risk factors, hypertension was also considered a risk factor variable to NCDs (107). In this study, we found that hypertension was associated significantly with self-reported NCDs, and it contributed to about 9% of increased socioeconomic inequality in NCDs in non-slum areas.

52

NCDs contributed to increasing inequalities in health care utilization

In our study, we focused on the public health care services because they dominated the health care provision in Vietnam. In addition, all national programs on NCDs are integrated into the public health care facilities in Vietnam (108). We found that public health care utilization was strongly and significantly associated with self-reported NCDs among respondents in both slum and non-slum areas. However, health care utilization might lead to NCD diagnosis and vice versa, and within this cross-sectional study, the association does not imply causation.

Figure20:AgroupofslumhouseholdsinHanoi

The probit model was used to understand the association of some key factors of the use of public health care service utilization. Sex–age, NCDs, and health insurance were significantly associated with public health care services utilization. The elderly were more likely to use health care services as compared to younger people. The status of NCDs strongly associated with the increase of public health care utilization. Having health insurance was more likely to increase the use public health care services. The results of the probit model were supported by the decomposition of the concentration index for public health care utilization. In slum areas, self-reported NCDs contributed to pushing health care utilization in a pro-poor direction. Thus, this result suggests that to achieve horizontal equity in the utilization of public health care services, policy should focus on preventive interventions

53

for NCDs and targeting the poor population in slum areas. Social health insurance can be a financial mechanism for reducing inequity in health care utilization (25); however, we found that social health insurance contributed to pushing utilization in a pro-rich direction. Hence, appropriate policy should be considered to promote the use of health insurance among poor and slum population.

Recently, more than a half of the world’s population has been living in urban areas. According to the UN’s projection, about two- thirds of the world’s population will live in urban areas in the next 30 years (30). The rapid increase of urban population occurred mostly in the cities of developing countries, including The Association of Southeast Asian Nations (ASEAN) (40). Rapid urbanization could lead to the extension and the new formulation of slum areas (109). These changes may create major challenges for the health care system, particularly when the burden of NCDs is increasing. Hence, it is necessary to establish an appropriate system to aid in understanding the impact and implications of policies in the health care sector in terms of health care service utilization. Catastrophic health expenditure and impoverishment association with NCDs, and concentration within the poor

The proportion of catastrophic health expenditure and impoverishment significantly concentrated among the poor households with and without NCD patients in slum areas. In non-slum areas, the proportion of impoverishment significantly concentrated among the poor households with NCD patients only. Catastrophic health expenditure and impoverishment had a significant association with households that had NCD patients, lived in slum areas and belonged to the poorest socioeconomic group. Poor households, households with the elderly, or households with NCD patients normally need greater health care services, but often lack financial resources. These households are more likely to be confronted with catastrophic health expenditure and impoverishment (110). Within the urban setting, slum households are considered to be disadvantaged in terms of having the lower quality of living conditions (58).

The findings of general catastrophic health expenditure and impoverishment were quite consistent with the figures in Minh et al.'s study (60). However, looking at households with NCD patients, the proportion of household facing catastrophic health expenditure and impoverishment are much higher than those found in Minh et al.'s study. Some other studies explored catastrophic health expenditure and impoverishment in all urban, or just in slum areas (61, 64, 65, 67, 111, 112). The existence of catastrophic health expenditure

54

and impoverishment was associated with households having NCD patients (61, 64-66), and households with low socioeconomic status (67). The results of this study were also consistent with previous studies and showed that NCDs and lower socioeconomic status increase the risk of households facing catastrophic health expenditure and impoverishment (62, 113). Wang et al. found that poorer households with the elderly having NCDs experienced more catastrophic health expenditure (62).

Health insurance would be an important mechanism for financial protection of population (110). However, in this study, we found that having health insurance had no association with the reduction of catastrophic health expenditure and impoverishment. A possible explanation for this might be that the consumption of prescription drugs in Vietnam has increased since 2006 (108). In addition, more than half of the drugs prescribed by doctors were not on the essential medicines list of the health insurance fund. In many cases, patients had to pay out-of-pocket for the drugs, which were expensive and not covered by health insurance (25). In other settings, health insurance was shown to decrease catastrophic health expenditure and impoverishment, e.g. in Thailand (64), China (65) and Nigeria (67). Another study in Vietnam showed that health insurance did not decrease catastrophic health expenditure and impoverishment (60). Our finding was also consistent with other studies that health insurance had a modest impact on the reduction of catastrophic health expenditure and impoverishment in Vietnam (113-116).

Weak responsiveness of commune health station to NCDs

The burden of NCDs was realized by the Vietnam government (117), and the country has been developing and implementing several policies and strategies for prevention and control of NCDs and their risk factors (19, 118). The national target programs on NCDs were established and have been updated for five or ten years. The national target programs on NCDs have been conducted nationally, but the coverage of these programs has been limited and they still lack prioritization from the local authorities (19). NCD managers at higher levels were aware of the national strategy on NCDs, but most participants at commune health stations, those responsible for enacting the day-to-day patient education and management, were unaware of the national strategy on NCDs. In addition, commune health stations were lacking autonomy to implement NCD services, so they were not up-to-date on their knowledge and practices related to NCDs. The lack of policy dissemination from higher levels to grassroots level might prevent the interest of local authorities in conducting NCD prevention activities for the community (119).

55

Health information on NCDs at commune health stations was inadequate for planning activities. Participants at both high level and commune health stations mentioned the insufficiency of data on NCDs at the commune level. NCD data did not reflect the current situation, and were collected passively. This finding is consistent with a report about NCDs in Vietnam that quality and timeliness of NCD reports did not meet the needs for management and planning (19). It is essential to develop a good health information system because it would contribute to strengthening people’s health, fair distribution of resources, and improving management capacity (69).

Health workforce is very critical to implement health care services on NCDs at commune health stations. Thus, the health workforce needs to be strengthened to provide effective health care services for NCD patients (119). At commune health stations in Hanoi, participants mentioned that there was an insufficient health workforce, with the absence of skilled and specialized providers, so it has been impossible to provide primary health care services at commune health stations. In addition, due to the multitude of health programs implemented at a commune health station, the health staff would not have sufficient time to focus on NCD service provision. In a study in a rural setting, Minh et al. also found that the quality and quantity of health staff were insufficient at primary health care level (71). Human resources for NCDs should be planned based on the need of the population, determining by monitoring and implementation of NCD strategies (70, 71, 120).

Health financing is one of the key input components to the health system (82) that impacts the implementation of NCD interventions. In this study, we found that the lack of a budgeted resources prohibited staff from conducting NCD interventions commensurate to the burden of disease within the community. Although a state budget for NCD prevention, screening, and diagnosis were allocated to some commune health stations participating in the national target program on NCDs (e.g. hypertension or diabetes), the budget has been very limited. Currently, health insurance services have not been implemented at commune health stations in urban Hanoi (19), so commune health stations did not have any other budget to implement NCD services for people in their communes.

Almost no primary health care services on NCDs were conducted at commune health stations. Although there were a few communes involved in the targeted national programs on hypertension or diabetes, screening services and treatment were implemented for a limited population only. NCD patients require long-term care, primary care can deliver better health outcomes with lower cost (121). Thus, it is recommended that that primary health care services for NCDs should be implemented at the primary health

56

level (69). To address the burden of NCDs in urban areas (122), and low utilization of health care services at commune level (123), Vietnam needs to strengthen the primary health care services to achieve better management of NCD patients at commune health stations. In addition, evidence showed that to successfully address the burden of NCDs, the NCDs programs should be integrated into other health programs at primary level (6, 70, 121, 124).

Ensuring NCD patients access to essential medicines at primary health level also helps to control and manage NCDs successfully is also essential (69). Although most drugs needed for NCD treatment were in the essential drug list (19), no NCD treatment drugs were available at urban commune health stations. At commune health stations participating in the national target programs on NCDs, NCD treatment drugs were delivered to limited registered patients only. Since commune health stations do not participate in the national health insurance scheme, NCD patients cannot use their health insurance cards at these commune health stations. Therefore, the responsiveness of commune health stations to NCDs would be impacted by the lack of NCD treatment drugs (70, 120, 125). In return, the lack of NCD treatment drugs prevented commune health stations from providing primary health care services for NCDs (69).

Methodological considerations

The definition of slum has been a major concern in identifying slum areas in the urban setting. Even though the urban poor were mentioned in a study in Hanoi and Ho Chi Minh city (35), no clear definition of slum areas was available in Vietnam. The definition of slum areas was also controversial in some contexts that might create confusion about what counted as a slum area, and might not reflect the real situation of slum areas (126, 127). In addition, a slum area was called “Khu ổ chuột” in Vietnamese, which was a sensitive word in the community. People feel discriminated and stigmatized if their living areas are considered as slum areas. In this thesis, we adapted the definition of slum areas proposed by the United Nations Human Settlements Programme (UN-HABITAT) to detect slum areas in urban Hanoi. Although this slum definition was used for the first time in a study in Vietnam, it had been applied in several reports and studies (58, 126, 128, 129). Based on the adapted slum definition (39), we detected many slum areas in urban Hanoi. However, since the definition of slum areas was not standardized in Vietnam, our estimation might underestimate the real situation of slum areas.

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For measuring socioeconomic inequality, we used concentration indices and concentration curves. These measures were computed based on the distribution of the interest health variables and the distribution of socioeconomic status of households. The measurement of socioeconomic status could influence the results of concentration curve and concentration index (76). Since the income was unreliable in the context of developing countries like Vietnam, we used the household asset index as the proxy for socioeconomic status (130). While further work is needed to develop a standardized method within all countries, this interim measure was recommended to be used in developing countries (131). In addition, we used the decomposition method to identify the contributions of factors to inequality of an interest variable in this thesis. However, the decomposition method is a deterministic approach (132), so there may be other factors such as health system related determinants that contribute to inequalities in our interest variables that could not be identified in this study.

The status of NCDs and health care use were assessed by self-reported questionnaires, which could be easily misclassified by recall bias. In addition, the quantitative interviews were conducted in a face-to-face manner with only the head of household, so the head of household might underestimate the prevalence of NCDs and health care user for other household members. However, these biases could be minimized with well-trained data collectors and strong quality control procedures by the supervisory team during the fieldwork. The methods of self-report and interviewing the head of household had been applied in many national representative surveys that showed reliable results in the context of Vietnam (31-33, 133-137). In addition, the status of NCDs and health care use were special events of household members, so the head of household would remember. Thus, with well-trained data collectors, this information could be collected more accurately.

Focusing on only public health care services was also the concern in this thesis. In Vietnam, public health facilities include commune health stations, district hospitals, provincial hospitals and national hospitals, which play a significant role in providing health care services for the population. Actually, public health care services dominated the health system in Vietnam, especially for NCDs. In addition, public health care facilities participate fully in the national social health insurance scheme, while private health care facilities have not been allowed to participate, or they do not want to participate.

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Strengths of this thesis

We used the concentration index to measure the socioeconomic inequality in self-reported NCDs. The concentration index was recommended in understanding the socioeconomic inequality in health outcome because it reflects the experience of the whole population rather than a specific socioeconomic status group. In addition, the concentration index can address the changes among the population in terms of socioeconomic status (80). The decomposition of concentration index allows the identification of potential groups that are most affected by socioeconomic inequality (76).

In the context of Vietnam, income and consumption data are difficult to collect, and they lack direct measures of living standards or socioeconomic status. However, the measure of socioeconomic status is crucial to estimate socioeconomic inequalities of health. It is easy to collect and reasonable to predict socioeconomic status, so household assets and other characteristics (e.g. household sanitation, water treatment etc.) were recommended to be used to construct alternative measures of household socioeconomic status in developing countries (76). To do this, we used the method of principal components analysis (PCA) to construct wealth index as a proxy for socioeconomic status. In some studies, PCA was also recommended to be used for estimation of wealth (130).

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Conclusion

This thesis demonstrates the importance of socioeconomic inequalities in the distribution of NCDs and related disease burden in urban Hanoi, Vietnam, where the actionable inequality data for policy decisions had previously been scarce. Fueled in part by rapid urbanization, the epidemiological transition is occurring quickly and creating challenges for inhabitants, especially the poor and other vulnerable population groups, who may be less able to manage problems related to NCDs.

• The poor were more likely to suffer from NCDs than those better-off.

These socioeconomic inequalities in the prevalence of self-reported NCDs were discovered to exist in both non-slum and slum areas of urban Hanoi, Vietnam.

• In slum areas, healthcare utilization favored the rich, as they were more likely to use services than the poor. In addition, the poor need more health care services due to NCDs, but they actually still used less than they needed.

• Catastrophic health expenditure and impoverishment were massive problems among households with members reporting an NCD diagnosis, particularly households in slum areas.

• Health system components for NCDs at commune health stations in urban Hanoi are weak, characterized by the lack of health information, sparse human resources, poor financing, inadequate quality and quantity of services, and the lack of essential medicines.

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Implications for futures studies and policy

This thesis illustrates the problems related to NCDs in terms of socioeconomic inequalities in an urban setting. Although urban areas are considered as an affordable place to live, a number of people, who live in the urban poor and/or slums showed health disparities as compared to the better-off. The WHO has urged countries to address the growing disparities in health between different groups in urban areas (40, 73). Insufficient infrastructure and services may influence the health of the poor and slum dwellers in urban setting. Rapid urbanization may create challenges for the health care system, particularly when the burden of NCDs is increasing. Thus, it is necessary for developing countries like Vietnam to monitor the socioeconomic inequalities in NCDs and other NCD related factor, not only in rural and difficult areas, but also in urban areas, especially in slum areas.

The results from this thesis enrich the knowledge and understanding of the socioeconomic inequalities in the prevalence of NCDs in urban Hanoi, Vietnam. The disparities in the prevalence of self-reported NCDs aligned with socioeconomic inequalities in both non-slum and slum areas of urban Hanoi. In detail, interventions related to NCD prevention and management should focus more on the population groups most vulnerable, such as the poor, the elderly, and those with a larger households in slum areas. For, non-slum areas, the interventions should not only focus on the poor and the elderly, but also people with hypertension.

For health care utilization, this thesis may aid in identification of targets for policies to improve the use of public health care services in urban areas of Vietnam. The horizontal inequity in health care utilization was shown in favor of the rich in slum areas. Having NCDs could contribute to pushing health care utilization in a pro-poor direction. Thus, to achieve horizontal equity in the utilization of public health care services, appropriate policies to promote access to health care services should be focused on NCD patients and the poor in slum areas.

The proportion of catastrophic health expenditure and impoverishment was relatively high among households with members reporting an NCD diagnosis, particularly households in slum areas. To address these issues, financial assistance should also be considered together with NCD prevention intervention. The financial interventions to prevent catastrophic health expenditure and impoverishment should begin by targeting poor households in slum areas whose members suffered from NCDs. In addition, these interventions should also address households with the elderly.

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Health insurance is a financial mechanism that provides social protection for people against unexpected illness or injury. The results from this thesis showed that expanding the coverage of health insurance would be necessary for reducing inequalities in NCDs in slum areas. However, the results show that social health insurance contributes to pushing health care utilization to favor the rich in slum areas. Thus, appropriate policies should be considered to improve the health care access of poor people in slum areas through the utilization social health insurance.

Currently, health system components for NCDs at commune health stations in urban Hanoi are weak, so it is necessary to encourage commune health stations to become more involved in providing primary health care services for NCDs. To do so, several policies should be considered for providing more funding for NCD related-services, workforce development for NCDs, and relevant equipment and medicines for NCDs at commune health stations. In addition, a service package for NCDs at commune health stations should be developed and adopted so that NCD patients can access it through their health insurance.

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Acknowledgements

This thesis would have been impossible without the financial support for fieldwork from the Center for Health System Research, Hanoi Medical School. I would also like to thank the Swedish Research School for Global Health and the Umeå, Centre for Global Health Research, with support from FORTE, the Swedish Council for Working Life and Social Research (Grant No. 2006-1512) for granting me a 6-month scholarship to visit Umeå in 2013 and 2014. I would also like to thank the Swedish Centre Party Donation from Global Research Collaboration through the Unit of Epidemiology and Global Health in the Department of Public Health and Clinical Medicine at Umeå University, Sweden for supporting my research. Many thanks to HealthSpace in Asia for covering publication fee for my paper II, as well as funding a trip to present my work at an international conference in Cape Town, South Africa.

I would like to express my sincere gratitude to Assoc. Prof. Hoang Van Minh, my co-supervisor, my boss and a "brother" for your unlimited support and guidance during my Ph.D. studies. You have inspired me to step into a career in research and changed my life. Especially, I thank you for introducing me to Prof. Nawi Ng. Thank you so much, Assoc. Prof Minh, for trusting in me and providing me with this valuable opportunity to attend Umeå.

My deepest gratitude goes to my main advisor Prof. Nawi Ng for accepting me as your student and bringing me to Umeå. Thank you so much for your extraordinary support of my Ph.D. studies and related research. I am very thankful for your patience, motivation, and immense knowledge, as well as your valuable advice, guidance, and encouragement throughout my studies. I could not have imagined having a better supervisor.

My immense thanks also go to Prof. Lars Weinehall, my co-supervisor, who contributed a great deal of comments and suggestions, helping me to develop and implement my Ph.D. research. I received enormous benefit from our discussions relating to my research. Thanks for always wishing me your best regards and providing continued encouragement.

I would like to thank Assoc. Malin Eriksson, my co-supervisor for guiding me in the area of qualitative methods. Thank you for always giving me timely advice throughout my study process, even while you had many things at hand.

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My gratitude to Prof. Anneli Ivarsson for your guidance as my examiner. I enjoyed discussing with you about the progress of my doctoral studies. I really appreciated all your comments and suggestions, which have helped me not only during the Ph.D. journey, but also surely as I embark on my future career.

My sincere gratitude to Birgitta Åström for your great support during my Ph.D. program, in particular, your enthusiasm and patience with the arrangements for my visits to Umeå. Thanks to Ulrika Harju and Lena Mustonen for helping me with logistic and administrative issues. Many thanks to Göran Lönnberg and Wolfgang Lohr for supporting me with many IT issues. My great thanks to teachers, researchers, and colleagues at the Unit of Epidemiology and Global Health: Prof. Peter Byass, Prof. Stig Wall, Prof. Urban Janlert, Prof. Lars Lindholm, Prof. Urban Janlert. Prof. Anna-Karin Hurtig, Prof. Miguel San Sebastian, Dr. Kjerstin Dahlblom, Assoc. Prof. Klas-Göran Sahlen, Dr. Maria Nilsson, Dr. Barbara Schumann and Assoc. Prof. Joacim Rocklöv.

Thank you very much, Assoc. Prof. Kim Bao Giang, my colleague, my co-authors and a "sister" for always providing me with helpful support and encouragement. Thanks to Amy Dao, Le Thanh Tuan and Phan Hoai Viet, my friends and my co-authors, for contributing and sharing this work with me. Many thanks to Dr. Lucia D'Ambruoso, my former co-supervisor for providing references to help me develop and implement the research on the health system.

I would like to thank the health staff at district health centers in Dong Da, Hai Ba Trung, Ba Dinh, and Hoan Kiem districts for providing support and preparing fieldwork for this study. Great thanks I would also like to extend to my colleagues at Center for Health System Research at Hanoi Medical University for supporting my fieldwork: Tran Tuan Anh, Ngo Tri Tuan, Nguyen Thanh Thuy, Nguyen Thi Thanh Thao, Nguyen Thi Tuyet My, Le Hong Chung, Doan Thu Huyen, Nguyen Thi Thu Trang and Le Quynh Trang. Furthermore, many thanks to the medical students, who were data collectors for my research project.

Special thanks to Dr. Mikkel Quam and Ms. Tran Bich Phuong for your comments and helping to edit the language of this thesis. Many thanks to my colleagues from Center for Population Health Sciences at Hanoi School of Public Health for sharing my duties, so that I have more time to work on my thesis: Nguyen Bao Ngoc, Vu Thi Quynh Mai, Tran Thu Ngan and Nguyen Thi Van.

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Thanks to Dr. Jennifer Stewart Williams and Dr. Brian Williams for introducing me to an interesting statistical course in Wollongong Australia, and for your kindness in taking care of me while I was in Sydney. Many thanks to Juan Antonio Cordoba and your family members for inviting me to a beautiful city- Cádiz, for introducing me to your culture and for sharing your interest in health inequity research. I would like to thank Paola Dr. Mosquera Mendez and Dr. Per Gustafsson for inviting me to your lovely home during my stay in Umeå and for sharing Swedish food that made my day.

Many thanks to my dear friends in the Unit of Epidemiology and Global Health for your kindness, friendship and exchange of ideas and knowledge: Ailiana Santosa, Alison Hernandez, Aditya L. Ramadona, Curt Löfgren, Dickson Mkoka, Gladys Mahiti, Julia Schröders, Masoud Vaezghasemi, Anna Myleus, Kamila Al-Alawi, Kanyiva Muindi, Kateryna Karhina, Maquines Sewe, Moses Tetui, Pamela Tinc, Phan Minh Trang, Regis Hitimana, Sulistyawati Suyanto, Thaddaeus Egondi, Vijendra Ingole, Utamie Pujilestari, Linda Sundberg, Prasad Liyanage, Jing Helmersson, Nitin Gangane, Tesfay Gebrehiwet, Hagos Godefay Debeb, Fredinah Namatovu, Bharat Randive, Natanael Sirili, Ryan Wagner, Daniel Eid Rodriguez and Joseph Zulu. Thanks to all my Vietnamese friends here in Umeå: Chi Thuy, Phuoc, Tinh, Liem, Nga, Huyen, Chi, Van, Huong, Tam, Trang and Hung.

My deepest thanks to my wife Mai Anh and my beloved daughter Chau Anh, who have always been with me during my studies. Further thanks to my family: my parents, my parents-in-law, my sister and my sisters-in-law for their love and support.

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