In 2022, ethnic Chinese households had the highest mean monthly household income in Malaysia, at around 10.66 thousand Malaysian ringgit. This was more than three thousand ringgit higher than Bumiputera households. Despite the implementation of affirmative action through Article 153 of the Malaysian constitution, the economic position of the Bumiputera vis-à-vis other ethnicities still left much room for improvement.

Historical policies, ethnicity, and the urban-rural divide The Bumiputera make up the majority of the Malaysian population, yet have one of the lowest average monthly household incomes in Malaysia. This economic disparity could be explained by the effects of colonial policies that kept the Bumiputera largely in the countryside. This resulted in an urban-rural divide that was characterized by ethnicity, with the immigrant Chinese and Indian laborers concentrated in the urban centers, a demographic pattern that is still evident today.

There was a considerable difference in urban and rural household incomes in Malaysia, with urban household income being around 3.6 thousand ringgit more than rural households. This was largely due to the fact that wages in urban areas had to keep up with the higher cost of living there. This thus impacted the average monthly incomes of the largely rural-based Bumiputera and the largely urban-based ethnic Chinese. This visible wealth inequality has led to racial tensions in Malaysia, and it is still one of the problem in the country amidst a new government led by Prime Minister Anwar Ibrahim, who was elected in 2022.

Prevalence of catastrophic health expenditure by levels of socioeconomic status and healthcare provider.

BackgroundPlant-based diets in lower-income countries are often associated with inadequate protein nutrition and adverse health outcomes.ObjectiveTo examine the diversity of protein food sources, in both animal and plant, across diverse socio-demographic groups in Indonesia as compared to Malaysia.DesignThe SCRiPT (Socio Cultural Research in Protein Transition) study was based on population-based samples recruited in Indonesia (N = 1665) and in Malaysia (N = 1604). Data from 24-h in-person dietary recalls in each country were used to construct the frequency counts of protein sources by food group. Protein sources were defined as fish, poultry, red meat (beef, pork, and mutton), eggs, dairy, and plants (cereals, pulses, and tubers). The percent reported frequencies for animal and plant proteins were compared across socio-demographic strata and by country. Analyses were based on one-way Anovas and general linear model regressions adjusting for covariates.ResultsAnimal protein frequency counts were 34% of total in Indonesia, but 50% in Malaysia's. Higher reported consumption frequencies for poultry and red meat in both countries were associated with urban living, greater modernization, and higher socioeconomic status, with stronger social gradients observed in Indonesia. Reported fish consumption was higher in Indonesia than in Malaysia. Fish was more likely to be listed by rural island populations in Indonesia and was associated with lower education and incomes. Consumption frequencies for plant-based proteins were associated with lower socio-economic status in Indonesia and in Malaysia.ConclusionsMore affluent groups in both countries reported higher frequencies for meat, eggs, and dairy as opposed to fish. Greater economic development in Southeast (SE) Asia is associated with more animal protein, particularly from poultry, which may displace fish, the traditional source of high quality protein for the region.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

A cross-sectional study was conducted in a B40 community Perumahan Awam, Sri Johor, Cheras, Malaysia. This study aimed to identify the Body Mass Index (BMI) of children and adolescents ( age 5-17) living in this community and correlate their family socioeconomic status with BMI, eating habits, quality of food intake, and meal skipping. The sample size was calculated using Krecjie and Morgan formula for prevalence studies of a known population. A self-administered online questionnaire via Google® Forms and face to face interviews were done by year three medical students in 2023 over a weekend. (August 5th – 6th 2023). Ethical clearance was provided and written informed consent was given by all participants in this study.

Percentage Share to Malaysia GDP: Pahang data was reported at 4.400 % in 2010. This records a decrease from the previous number of 4.500 % for 2009. Percentage Share to Malaysia GDP: Pahang data is updated yearly, averaging 4.500 % from Dec 2006 (Median) to 2010, with 5 observations. The data reached an all-time high of 4.600 % in 2006 and a record low of 4.400 % in 2010. Percentage Share to Malaysia GDP: Pahang data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A055: 2000 Base: GDP by State and Economic Activity: 2000 Price.

This collection, the second wave of a panel survey, provides household-level retrospective and current data for Peninsular Malaysian women and their husbands and covers traditional topics of demographic research such as fertility, nuptiality, migration, and mortality as well as social and economic factors affecting family decision-making. The overall purpose of the data collection was to study household behavior in diverse settings during a period of rapid demographic and socioeconomic change. Eight survey instruments were used in this study. The tracking instrument, MFLS-2, was used for all households where an interview was attempted, and recorded information such as disposition of survey and questionnaires, number of eligibles, and respondent identifiers. The MF20 instrument, Household Members, was administered to all Panel sample households that were located. It solicited information on the status of the household members and included items such as location, marital status, education, and birthdate. The MF21 form, Household Roster, was used on all households interviewed in the survey. This form collected demographic information on current and very recent household members. The MF22 form, Female Life History, surveyed the Panel women and their selected daughters and daughters-in-law, and the New Sample women. Information collected by this form included pregnancy history and related events, marital, work, and migration histories, family background, and education. The MF23 form, Male Life History, collected data from husbands of the Panel women, selected sons and sons-in-law, and husbands of New Sample women. Data on marital, work, and migration histories, education, and family background were recorded. The MF24 form, Senior Life History, was administered to selected persons aged 50 or more and contained questions on marriages, children living elsewhere, literacy, work experience, migration history, health, and family background. The MF25 form, Household Economy, collected data on household economy from all households interviewed in this wave. Forms MF26 and MF27 were used to generate community-level data subfiles for this collection. Part 97 (MF26DIST--District-Level Data) contains one record for each of the 78 districts of Peninsular Malaysia. This file provides information (most of which pertains to 1988, but some of which dates back to 1970) on health services (e.g., number of hospitals, health centers, and doctors), family planning services (e.g., number of family planning clinics, contraceptive use), birth, death, and fertility rates, number of primary and secondary schools, ethnic distributions, and industrial and occupational distributions. Part 98 (MF26EB--Community-Level Data) contains one record for each of the 398 Enumeration Blocks selected for MFLS-2 and the 52 Primary Sampling Units used in MFLS-1. This file gives the current status of family planning services, general health services, schools, water and sanitation, housing costs, agriculture, transportation, population, urban/rural status, and government programs. Part 99 (MF27COMM--Community-Level Data) offers data for the same units as Part 98 and contains similar information, along with retrospective data on family planning services, health services, schools, and water treatment. Merged files (Parts 106-112) that contain one record per respondent were created by ICPSR using the variables CASE SPLIT PERSON for MF22, MF23, MF24, and MF25 on the New and Senior samples and the Panel and Children samples.

Malaysia Gross Domestic Product (GDP): 2000p: Perak data was reported at 30,167.000 MYR mn in 2010. This records an increase from the previous number of 28,536.000 MYR mn for 2009. Malaysia Gross Domestic Product (GDP): 2000p: Perak data is updated yearly, averaging 27,757.500 MYR mn from Dec 2005 (Median) to 2010, with 6 observations. The data reached an all-time high of 30,167.000 MYR mn in 2010 and a record low of 23,931.000 MYR mn in 2005. Malaysia Gross Domestic Product (GDP): 2000p: Perak data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A055: 2000 Base: GDP by State and Economic Activity: 2000 Price.

Malaysia Gross Domestic Product (GDP): 2000p: Kedah data was reported at 18,637.000 MYR mn in 2010. This records an increase from the previous number of 17,846.000 MYR mn for 2009. Malaysia Gross Domestic Product (GDP): 2000p: Kedah data is updated yearly, averaging 18,007.000 MYR mn from Dec 2005 (Median) to 2010, with 6 observations. The data reached an all-time high of 18,637.000 MYR mn in 2010 and a record low of 15,475.000 MYR mn in 2005. Malaysia Gross Domestic Product (GDP): 2000p: Kedah data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A055: 2000 Base: GDP by State and Economic Activity: 2000 Price.

Malaysia GDP: 2015p: Sarawak: Agriculture data was reported at 16,461.539 MYR mn in 2018. This records a decrease from the previous number of 16,687.232 MYR mn for 2017. Malaysia GDP: 2015p: Sarawak: Agriculture data is updated yearly, averaging 16,659.556 MYR mn from Dec 2015 (Median) to 2018, with 4 observations. The data reached an all-time high of 16,987.941 MYR mn in 2015 and a record low of 16,461.539 MYR mn in 2018. Malaysia GDP: 2015p: Sarawak: Agriculture data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A017: 2015 Base: GDP by State and Economic Activity: 2015 Price.

STROBE checklist. (DOC)

IntroductionAs the rate of end-stage kidney disease rises, there is an urgent need to consider the catastrophic health expenditure of post-transplantation care. Even a small amount of out-of-pocket payment for healthcare can negatively affect households’ financial security. This study aims to determine the association between socioeconomic status and the prevalence of catastrophic health expenditure in post-transplantation care.MethodA multi-centre cross-sectional survey was conducted in person among 409 kidney transplant recipients in six public hospitals in the Klang Valley, Malaysia. Catastrophic health expenditure is considered at 10% out-of-pocket payment from household income used for healthcare expenditure. The association of socioeconomic status with catastrophic health expenditure is determined via multiple logistic regression analysis.Results93 kidney transplant recipients (23.6%) incurred catastrophic health expenditures. Kidney transplant recipients in the Middle 40% (RM 4360 to RM 9619 or USD 1085.39 –USD 2394.57) and Bottom 40% (RM 9619 or > USD 2394.57) income group. Kidney transplant recipients in the Bottom 40% and Middle 40% income groups were more susceptible to catastrophic health expenditure at 2.8 times and 3.1 times compared to higher-income groups, even under the care of the Ministry of Health.ConclusionUniversal health coverage in Malaysia cannot address the burden of out-of-pocket healthcare expenditure on low-income Kidney transplant recipients for long-term post-transplantation care. Policymakers must reexamine the healthcare system to protect vulnerable households from catastrophic health expenditures.

Malaysia GDP: 2000p: Johor: Construction data was reported at 1,758.000 MYR mn in 2010. This records an increase from the previous number of 1,633.000 MYR mn for 2009. Malaysia GDP: 2000p: Johor: Construction data is updated yearly, averaging 1,476.500 MYR mn from Dec 2005 (Median) to 2010, with 6 observations. The data reached an all-time high of 1,758.000 MYR mn in 2010 and a record low of 1,426.000 MYR mn in 2007. Malaysia GDP: 2000p: Johor: Construction data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A055: 2000 Base: GDP by State and Economic Activity: 2000 Price.

Malaysia (DC)Annual Percentage Growth: Sabah data was reported at 3.000 % in 2013. This records a decrease from the previous number of 4.200 % for 2012. Malaysia (DC)Annual Percentage Growth: Sabah data is updated yearly, averaging 3.700 % from Dec 2006 (Median) to 2013, with 8 observations. The data reached an all-time high of 10.700 % in 2008 and a record low of 1.400 % in 2011. Malaysia (DC)Annual Percentage Growth: Sabah data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A039: 2005 Base: GDP by State and Economic Activity: 2005 Price.

Malaysia Annual Percentage Growth: Kedah data was reported at 4.400 % in 2010. This records an increase from the previous number of -1.800 % for 2009. Malaysia Annual Percentage Growth: Kedah data is updated yearly, averaging 4.400 % from Dec 2006 (Median) to 2010, with 5 observations. The data reached an all-time high of 8.900 % in 2007 and a record low of -1.800 % in 2009. Malaysia Annual Percentage Growth: Kedah data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A055: 2000 Base: GDP by State and Economic Activity: 2000 Price.

(DC)Percentage Share to Malaysia GDP data was reported at 100.000 % in 2013. This stayed constant from the previous number of 100.000 % for 2012. (DC)Percentage Share to Malaysia GDP data is updated yearly, averaging 100.000 % from Dec 2005 (Median) to 2013, with 9 observations. The data reached an all-time high of 100.000 % in 2013 and a record low of 100.000 % in 2013. (DC)Percentage Share to Malaysia GDP data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A039: 2005 Base: GDP by State and Economic Activity: 2005 Price.

Malaysia GDP: 2000p: Johor: Manufacturing data was reported at 17,992.000 MYR mn in 2010. This records an increase from the previous number of 15,048.000 MYR mn for 2009. Malaysia GDP: 2000p: Johor: Manufacturing data is updated yearly, averaging 17,681.000 MYR mn from Dec 2005 (Median) to 2010, with 6 observations. The data reached an all-time high of 18,134.000 MYR mn in 2007 and a record low of 15,048.000 MYR mn in 2009. Malaysia GDP: 2000p: Johor: Manufacturing data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A055: 2000 Base: GDP by State and Economic Activity: 2000 Price.

Medical characteristics of kidney transplant recipients by household income.

Percentage Share to Malaysia GDP: Kedah data was reported at 3.300 % in 2010. This records a decrease from the previous number of 3.400 % for 2009. Percentage Share to Malaysia GDP: Kedah data is updated yearly, averaging 3.400 % from Dec 2006 (Median) to 2010, with 5 observations. The data reached an all-time high of 3.600 % in 2007 and a record low of 3.300 % in 2010. Percentage Share to Malaysia GDP: Kedah data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.A055: 2000 Base: GDP by State and Economic Activity: 2000 Price.

Logistic regression model of factors associated with obesity among school adolescents in Terengganu, Malaysia (n = 3798).