Among Canadian provinces, Ontario had the largest number of hospitals with around 300 establishments, as of 2023. Canada has a publicly funded health care system based on a system of taxation, fees and private funding. Current reports estimate that health care expenditures account for over 11 percent of Canada’s gross domestic product (GDP). Health care expenditures for hospitals totaled 88 billion Canadian dollars in 2023. Hospital expenditures in Canada In total, there were 1,017 hospitals in Canada as of 2023. Hospital expenditures per capita appear to be highest in less populated territories. In 2021, the greatest expenditures for hospitals in Canada were staff compensation and supplies. Research, education and other areas accounted for just over ten percent of expenditures during that time. The hospital with the highest research spending in Canada in 2022bwas the University Health Network located in Ontario. Patients in Canadian hospitals In general, the average length of hospital stays in Canada appears to be on the decline, but has rebounded since 2018. Common reasons for hospitalizations in Canada include child birth, COPD, pneumonia, heart failure and mental health disorders. Canadian hospitals perform a large number of surgeries every year. During fiscal year 2022/23, C-sections, knee replacements and hip replacements were the most common surgeries in Canada.

In 2023, of the 946 employer hospitals in Canada, 317 were large with over 500 employees. While Ontario had the most number of micro, medium, and large hospitals, Saskatchewan had the most number of small hospitals who employed 5-99 staff.

This dataset shows the list of reservations for CODVID-19 in capital city and provinces in Cambodia, issued by the General Department of Clean Water of the Ministry of Industry and Handicrafts, and extracted from the official Facebook page of the Cambodian Water Supply Association (CWA).

Hospital Morbidity Survey: Hospital discharges by sex, main diagnosis, province and Autonomous City and Community of hospitalization. National.

The major variables are listed below: Health institution details, type of health services provided, staff profile, absenteeism, staff turnover, vacancies, admission rates, length of stay, average number of visits made by patient, hospital bed occupancy rates, management of HIV/AIDS services, HIV/AIDS care, TB treatment, treatment for sexually transmitted disease, cost of health care, drug availability, laboratory supplies, blood transfusion services. Postal survey Telephone interview Health facilities in the public and private sector in South Africa. The task was to obtain a representative probability sample of 2000 patients, and at most representative probability sample 2000 health professionals who are in contact with patients undergoing treatment at the selected health facilities. The sampling frame was the national DoH's health facilities database (1996). Target population, was selected from two separate sampling frames: - (a) a list of all public clinics in the country (excluding mobile, satellite, part-time and specialized clinics; and (b) a list of all hospitals (public and private) and Private clinics with indication of the number of beds available in each of health facilities from the national DoH database on health facilities (1996). Provinces and health regions within provinces were considered as explicit strata. Provinces formed the primary stratification variable and the health regions the secondary stratification variable. The Primary sampling unit (PSU) was the magisterial districts within each health region in the case of public clinics, Secondary sampling unit (SSU) were clinics and hospitals- drawn using simple random sampling, and Ultimate/final sampling unit the (USU) the professional and non-professional health workers and patients. Measure of size (MOS) for public clinics was a monotonic function of the number of clinics per managerial districts. Selected 167 clinics were allocated disproportionately i.e. proportional to MOS. Allocated sample number of clinics within each province was allocated proportionately to the health regions in the province. MOS for hospitals and private clinics was a monotonic function of the number of beds as in DOH's database. Sample sizes for SSUs: Public clinics (167) Public Hospitals (33) Private Hospitals and clinics (22) Sample sizes for USUs: 1000 patients 500 nursing personnel 200 medical doctors 100 other professional health workers 400 non-professional health workers Public clinics 1000 patients 500 nursing personnel 111 nonprofessional personnel( e.g. cleaners) Public Hospitals 667 patients 333 nursing Personnel 200 medical doctors 67 other professional 222 non-professionals Private Hospitals and clinics

A list of provincial hospitals in Zimbabwe

Hospital Morbidity Survey: Hospital admissions, by sex, main diagnosis, province of hospitalisation and Autonomous Community. National.

Abstract

In 2000s the overall performance of the health sector in Zambia has shown some improvement as reflected in the trends of basic health delivery indicators, such as health center outpatient per capita attendance, first antenatal coverage, and fully immunized children under 5 years old. Despite these service improvements, overall health status in the country has stagnated. The disease burden is overrun by the high prevalence of HIV/AIDS, and compounded by high poverty levels and the poor macroeconomic situation in most of the early 2000s.

In 2007, the Ministry of Health, the Ministry of Finance and the World Bank launched a study to identify the different conditions facing health facilities and the factors affecting their capacity to deliver good quality services. Techniques of Public Expenditure Tracking Survey (PETS) and Quantitative Service Delivery Survey (QSDS) were combined in this research.

The study provided data for analysis of: - budget allocation, release, and spending from the Ministry of Health down to the health facility level, - management of infrastructure, utilities, and equipment, including the physical state and functionality of health facilities; basic utilities, transport, and patient amenities; and medical equipment and instruments, - management of health personnel, including staff availability, vacancy, absenteeism, and tardiness; staff turnover; staff workload, use of time, and morale; and staff salary and benefits, - management of drugs and other medical consumables, including the system for distribution; availability of drugs, vaccines, contraceptives, and other medical consumables; and problems associated with these inputs, - clinic and patient management, including capacity of health facilities to deliver services; management and supervision of health facilities; travel and waiting time of patients; and patients' perceptions of quality.

Eighteen hospitals, 90 rural health centers and 40 urban health centers were visited in four provinces. The health facilities were selected using purposive and random sampling techniques.

Geographic coverage

Provinces: Lusaka, Copperbelt, Southern, Northern and Western.

Analysis unit

• Ministry of Health,
• Ministry of Finance,
• Provincial health offices,
• District health offices,
• Health facilities,
• Health facilities employees,
• Patients.

Kind of data

Sample survey data [ssd]

Sampling procedure

Zambia PETS in Health 2007 adopted a multistage sampling frame involving provinces, districts, and health facilities, and within health facilities, health workers and patients.

1) During the first stage of sample selection, five provinces were chosen. Two urban provinces - Lusaka Province, and Copperbelt Province were purposively selected. One rural province - Southern Province - was deliberately included in the sample on the advice of the Ministry of Health. (It was the most highly resourced rural province in terms of the number of health facilities, therefore it took a disproportionately larger share of Zambian government health funds). The two other rural provinces - Northern Province and Western Province - were randomly chosen from the list of remaining six rural provinces.

2) Districts were selected during the second stage of the sampling process using purposive and random sampling. Lusaka province had only four districts, one of which was selected for questionnaire pre-testing. Initially, to save on costs and time, a fixed number of four districts were selected for each province. It is important to note that of the three districts selected by default in Lusaka province, Lusaka Urban is the provincial headquarters and is the most urbanized district in the country; Kafue represents a mix of urban and rural areas; and Luangwa is typically a rural district. In the rest of provinces, it was intended that the provincial capital be purposively selected, plus three other districts selected randomly. Another exception was that given their size, Northern and Southern Provinces were granted five districts and Lusaka was granted three instead of four. Overall, 21 districts were selected, accounting for 29 percent of all districts in the country. The following districts were chosen: - Lusaka Province: Lusaka Urban, Kafue, and Luangwa, - Copperbelt Province: Ndola, Mpongwe, Mufulira, and Chililabombwe, - Southern Province: Livingstone, Siavonga, Namwala, Sinazongwe, and Kalomo, - Western Province: Mongu, Shangombo, Sesheke, - Northern Province: Kasama, Mpika, Nakonde, Chinsali, and Chilubi Island.

3) Facilities were selected during the third stage using the simple random sampling without replacement technique. The complete list of health facilities was drawn from the inventory made by the Central Board of Health (CBOH) in 2002 and published as "Health Institutions in Zambia: A Listing of Health Facilities According to Levels and Locations". The survey aimed to capture a number of facilities in each district commensurate with the district population, with 50 percent lying within 10 kilometers of the central business district and the other 50 percent outside the 10 kilometers radius. Given the distribution of hospital facilities, it was expected (and later observed) that the sampling frame would include the district hospital or a higher-level hospital, whichever existed in the respective districts. The total number of facilities selected represented 13 percent of all health facilities in Zambia.

• Sampling of hospitals (1st and 2nd level, 18 in total): The distribution of hospitals in Zambia is such that there is typically one hospital in each district. Provincial centers, which tend to host second level (regional) hospitals, do not have level one (district) hospitals. A few districts like Shangombo and Nakonde may not have any hospitals at all. 19 hospitals (across all three levels of care) were selected by default through the random selection of the districts, as discussed above. The final sample of hospitals consisted of 18 facilities.

• Sampling of health centers (132 in total): With the respective district serving as the sampling cluster for health centers, health centers were randomly selected within each district. The sample size of health centers per district within each province was weighted by the total number of public (government and mission) health centers in the district relative to centers in the other districts.

Patient exit interviews will be conducted on a sample of patients visiting the sample facility during the survey. The sampling procedure will involve picking every 4th-7th patient on the queue, depending on the utilization level at each facility. Prior appointment and consent will be sought while the patient is on the queue. Five patients will be chosen per facility as the budget could not accommodate interviewing a larger sample. Thus, a total of 750 patients will be interviewed.

At least two health workers from each health facility will also be interviewed. Where possible, a simple random sampling procedure will be used in selecting the sample of staff from the authorized establishment data obtainable at the Ministry of Health headquarters. However, data about staff establishment available centrally are often hampered by transfers, resignations, long leave, long term illnesses, and deaths. Thus, only staff present at the time of the survey will be potential interviewees. The in-charge of the health facility will also be interviewed.

Mode of data collection

Face-to-face [f2f]

Research instrument

Information was collected with the help of the following survey instruments:

• Health Facility Questionnaire,
• Patient Questionnaire,
• District Health Management Team Questionnaire.

Other sources of information were also used, including data from the Ministry of Health, the Ministry of Finance, Provincial Health Offices, District Health Offices and Medical Stores, Ltd.

As of 2022, Tanzania had a total of 336 hospitals. Among regions, Dar es Salaam had the largest concentration of hospitals, 53 institutions. The Mwanza and Kilimanjaro regions followed, each with 21 and 20 hospitals, respectively.

The total number of hospitals in China has increased significantly over the last decade from roughly ****** in 2010 to almost ****** in 2023. As of 2023, the region with the highest number of hospitals in China had been Shandong province with ***** hospitals, followed by Henan province with ***** hospitals. In the same year, Chinese hospitals provided about ************* hospital beds in total and almost ************* people had been employed in the medical sector of China. Regional healthcare difference in China Health care institutions in China have suffered from regional disparities in medical resources, especially between economically developed and less developed regions. In 2023, hospitals and other health institutions in Guangdong, the largest province in South Central China, had registered the highest number of hospital visits among Chinese regions, amounting to about *********** visits, while the Autonomous Region of Tibet only registered ************ visits. Increasing medical costsExpenditure for health care provision have taken up a large proportion of people’s incomes in China and increased dramatically over the last 25 years. In 1990, annual health care expenditure per capita in urban areas had amounted to a mere ** yuan, whereas in 2017 more than ***** yuan had been attributed to individual medical costs.

Location of public health facilities generated from the Western Cape Department of Health's Sinjani database Master Data Systems (MDS) view. The dataset ( DOH_Facilities_PublicHealthSites) is updated on a weekly basis via a scheduled Python script. A definition query on DOH_Facilities_PublicHealthSites defines Hospitals is (Classification = 'District Hospital' or Classification = 'Tertiary Hospital' or Classification = 'Regional Hospital' or Classification = 'Military Hospital' or Classification = 'National Central Hospital')Publication DateFeature layer updated daily from Sinjani MDS viewAttribute DefinitionsOBJECTID: Internal feature number.NAT_CODE: Six digit unique code obtained from the National Department of Health for facilities. The code is generated by the DHIS system.This code is used in the Mom Connect project.ID: Sinjani Internal facility identifierNAME: Name that the organisational unit is registered on Sinjani with. It may differ from actual name in the case of private facilities in order to standardise naming conventions on the system.DISTRICT_ID: Sinjani Internal district identifierDISTRICT: District that the facility falls within based on the gazetted health boundaries. District is between province and sub-district in the organisational unit hierarchy. In the Metro, 2 sub-districts form 1 Sub-structure (NTSS = Northern and Tygerberg; KESS = Khayelitsha and Eastern; KMSS = Klipfontein and Mitchells Plain; SWSS = Southern and Western).SUB_DISTRICT_ID: Sinjani Internal sub-district identifierSUB_DISTRICT: Sub-district that the facility falls within based on the gazetted health boundaries. Sub-district is between facility and district in the organisational unit hierarchy. In the Metro, 2 sub-districts form 1 Sub-structure (NTSS = Northern and Tygerberg; KESS = Khayelitsha and Eastern; KMSS = Klipfontein and Mitchells Plain; SWSS = Southern and Western).DATEIN: The date on which the facility was/will be in use by the department. This could be either active (operational and reporting) or non-reporting (operational but not reporting).DATEOUT: The date after which the facility will not be in use by the department. The facility has therefore suspended operations.STATUS_CODE: Internal Sinjani unique status identifier.LONGITUDE:Longitude values in decimal degrees.CLASSIFICATION_ID: Internal Sinjani unique classification identifier.CLASSIFICATION: Facility classification refers to the type of facility. The classification is determined by the services that are offered, the hours the facility is open, whether it is private of public.CATEGORY_ID: Internal Sinjani unique category identifier.CATEGORY: Facility category refers to the groupSHORT_NAME: Facility short name generated by DHIS. Often used in reports to reduce space used.AUTHORITY: Facility authority refers to who managers, funds and resources the facility. This may be different to who owns the property.EMAIL: The main email address of the facility that will be routed to the intended recipientCONTACT_NAME: Name of person who is the head of the facility.AUTHORITY_ID: Internal Sinjani unique authority identifier.LATITUDE: Latitude values in decimal degreesASSOCIATED_FACILITY: The associated facility is the public health facility that the school is associated with. Association means that the health facility is responsible for the services provided at the school.ASSOCIATED_FACILITY_CODE: Internal Sinjani unique associated facility short code.URBAN_CODE: Internal Sinjani unique urban identifier. URBAN Environmental classification as Urban, Rural or Peri-Urban.HEALTH_FACILITY_SHORT_CODE: Three digit unique code generated by Sinjani system. This code is used in all health systems to identify facilities.SHAPE :Feature geometry.EMIS: Unique code for each school generated by Department of Education.TELNO: The main phone number of the facility e.g. switchboard that will be routed to the intended recipientQUINTILE_ID: Internal Sinjani unique school quintile identifier.QUINTILE_NAME: The quintile that a school is allocated to. The options are: Quintile 1; Quintile 2; Quintile 3; Quintile 4; Quintile 5POSTAL_CODE: Postal code of the address to which mail may be delivered Postal code per suburb as per the master reference postal box code list: List of South African postal codes as published by the South African Post Office. Available at the following link: https://www.postoffice.co.za/Questions/postalcode.html or in the document named South African Postal Codes Master Reference List v1.0. Excludes street codes used for physical addresses. For informal addresses use the postal code of the nearest city/town. For international addresses free text is allowed.TIER: The ART capturing system in use by facilities.OLD_ADDRESS: Lines 1-4 of the physcial address - concatenatedPO_BOX_OR_PRIVATE_BAG: Lines 1-4 of the address to which mail may be deliveredPOSTAL_SUBURB_OR_TOWN: Town or suburb of the address to which mail may be deliveredTOWN: Physical address. City or Town name as per the Area column in the master reference postal code list. For informal addresses this needs to be the nearest town/city. For international addresses free text is allowed.ADDRESS_TYPE: Physical address. Type of address as per SANS 1883-1:2009. Options are: Building; Farm; Informal; PO Box/Private Bag; SAPO poste restante; Site; StreetSTREET_NUMBER: Physical address. Number of the unit, farm on the street.STREET_NAME: Physical address. Name of the street or road. For farm or site type addresses, use a road name, e.g. R364.SUBURB: Physical address. Suburb name as per the Suburb column in the master reference postal code list. For farm type addresses record the District name. For international addresses free text is allowed.DHIS_UID: Unique identifier generated by the DHIS system (NDOH system) for each facility.PROVINCE: Physical address. Name of the province as per the Master Province Reference Data list. For international addresses free text is allowed.STREET_POSTAL_CODE: Physical address. Postal code per suburb as per the master reference postal street code list: List of South African postal codes as published by the South African Post Office. Available at the following link: https://www.postoffice.co.za/Questions/postalcode.html or in the document named South African Postal Codes Master Reference List v1.0. Excludes box codes used for postal box type addresses. For informal addresses use the postal code of the nearest city/town. For international addresses free text is allowed.OPEN_DAYS: The days of the week that the facility is open / operating.OPEN_TIME: The hours of the day that the facility is open / operating.THROUGHPUT_REPORT_REQUIRED: An indicator to flag a facility accordig to whether it requires a throughput report (summary report of Clinicom data) to be generated by the BI system or not.SUBSTRUCT: The grouping of two sub-districts within the Cape Town Metropolitan district according to the organisational and management structure. Only applicable to the Cape Town Metro district. KESS = Khayelitsha and Eastern sub-districtsNTSS = Northern and Tygerberg sub-dsitrictsSWSS = Southern and Western sub-districtsKMPSS = Mitchells Plain and Klipfontein sub-districtsN/A (Rural) = Applied to all rural sub-districtsMETRO_RURAL: The grouping of districts into Metro Health Services (MHS) and Rural Health Services (RHS) according to the organisational and management structure. Metro = City of Cape Town Metropolitan Municipality Rural = Cape Winelands District Municipality; Central Karoo District Municipality; Garden Route District Municipality; Overberg District Municipality; West Coast District MunicipalityDATE_MODIFIED: The date when the facility data was most recently modified in the systemStatus: Includes only records with a Status = 'A' (active) Status of the facility in terms of whether it is operating or not and whether it is submitting data on Sinjani Active - facility is actively submitting data on Sinjani AND is operational. Data is expected on a routine basis. Absence of data results in a missing data report. Suspended - facility is no longer actively submitting data on Sinjani AND is not operational. Not reporting - facility is not actively submitting data on Sinjani, BUT remains operational.

Hospital Morbidity Survey: Hospital admissions, by main diagnosis, five-year age group, province of hospitalisation and Autonomous Community. National.

ObjectiveThe National Drug Price Negotiation (NDPN) has significantly reduced the prices and improved the nationwide availability of novel anticancer drugs (NADs) in China. However, geographical disparities in their availability remain concerning. This study aims to assess these spatial variations and temporal changes, and the determinants using geographic information system (GIS) and spatial statistical methods.MethodsTwo cross-sectional datasets were used corresponding the implementation date of the 2023 NDPN list (1 January 2024) and 9 months after (1 October 2024). Data on drug-providing institutions were extracted from National Healthcare Security Administration (NHSA) platform. Drug availability was measured by the weighted supply number of drug-providing institutions per 1,000 cancer patients, analyzed separately for hospitals and retail pharmacies. Kernel density estimation (KDE) was used to visualize spatial distribution. The Theil index assessed inequality, and Moran’s index measured spatial clustering. Multiple linear regression (OLS) and geographically weighted regression (GWR) were employed to examine the influence of economic development and healthcare infrastructure on drug availability.ResultsA total of 71 NADs in the 2023 NDPN list were analyzed. By October, drug-providing institutions had become more concentrated in the eastern coastal provinces compared to January. Availability improved in both hospitals and retail pharmacies, with higher levels observed in eastern and central provinces, with lower in the western provinces, especially in the Southwest. Inequality declined and spatial clustering increased for both hospital-based and overall availability across provinces (Theil index, hospital: 0.074–0.062, overall: 0.045–0.044; Moran’s I, hospital: 0.315–0.362, overall: 0.452–0.453). Both OLS and GWR models showed a significant and strengthening association between availability (in hospitals and overall) and GDP per capita [e.g., hospital: OLS coef, 0.787–0.833, p < 0.001; GWR mean coef (SD), 0.795 (0.047)−0.834 (0.044); overall: OLS coef, 0.744–0.794, p < 0.01; GWR mean coef (SD), 0.726 (0.119)−0.763 (0.161)]. Retail pharmacy-based availability was positively associated with the number of local chain pharmacies [OLS coef, 0.098–0.122, p < 0.05; GWR mean coef (SD), 0.084 (0.006)−0.107 (0.010)].ConclusionThe availability of price-negotiated NADs increasingly concentrated in economically developed and medically advanced eastern provinces, while remaining lower in southwest. Efforts should target economically underdeveloped areas.

List of pilot counties for the construction of CCMC in Sichuan Province.

Description: The data set contains child patients' data - demographic, morbidity, behavioural, environmental and data on health facilities (i.e. name, type, and province and health district). Patient biographic data (age, sex, race, residence, nationality, refugee status, place of birth, language, type of dwelling, education, employment; religion, orphan hood status, marital status of parents, etc.). Data on history of hospitalisation of patient and data on patients health status like weight loss, diarrhoea etc. and symptoms/diseases that had prompted patients to seek medical and health care. Furthermore environmental data on pollution, living on farms and access to clean drinking water and food was collected as well as data on HIV status. The data contains 108 variables and 415 cases. Abstract: The Nelson Mandela / HSRC study of HIV/AIDS (2002) reported an estimated prevalence of 4.5 million among persons aged two years and older. Given the overall impact of HIV/AIDS on South African society, and the need to make policies on the management of those living with the disease, it was important that studies were undertaken to provide data on the impact on the health system. This study was undertaken by the HSRC in collaboration with the national School of Public Health (NSPH) at the Medical University of South Africa (MEDUNSA) and the Medical Research Council (MRC). It was commissioned by the National Department of Health (DoH) to assess the impact of HIV/AIDS on the health system and to understand its progressive impact over time. The PIs sought to answer the following questions To what extent does HIV/AIDS affect the health system? What aspects or sub-systems are most affected? How is the impact going to progress over time? To answer the questions, a stratified cluster sample of 222 health facilities representative of the public and private sector in South Africa were drawn from the national DoH database on health facilities (1996). A nation-wide, representative sample of 2000 medical professionals including nursing professionals; other categories of nursing staff; other health professionals and non-professional health workers was obtained. In addition to this a representative probability sample of 2000 patients was obtained. Data collection methods included interviews using questionnaires and clinical measurements where either a blood specimen or an oral fluid (Orasure) specimen was collected. An anonymous linked HIV survey was conducted in the Free state, Mpumalanga, North West and Kwazulu-Natal. Oral fluids were tested for HIV antibodies at three different laboratories and results were linked with questionnaire data using barcodes. The child questionnaire contains the child patient's biographical data, hospitalisation history of patients seen at clinics, in-patients interviewed in a hospital, health status, environment. Clinical measurements Face-to-face interview All child patients (younger than 15 years) in public and private health facilities in South Africa. (Note: In hospitals only patients in medical and paediatric wards were included.). The task was to obtain a representative probability sample of 2000 patients, and at most representative probability sample 2000 health professionals who are in contact with patients undergoing treatment at the selected health facilities. The sampling frame was the national DoH's health facilities database (1996). Target population, was selected from two separate sampling frames: - (a) a list of all public clinics in the country (excluding mobile, satellite, part-time and specialized clinics; and (b) a list of all hospitals (public and private) and Private clinics with indication of the number of beds available in each of health facilities from the national DoH database on health facilities (1996). Provinces and health regions within provinces were considered as explicit strata. Provinces formed the primary stratification variable and the health regions the secondary stratification variable. The Primary sampling unit (PSU) was the magisterial districts within each health region in the case of public clinics, Secondary sampling unit (SSU) were clinics and hospitals- drawn using simple random sampling, and Ultimate/final sampling unit the (USU) the professional and non-professional health workers and patients. Measure of size (MOS) for public clinics was a monotonic function of the number of clinics per managerial districts. Selected 167 clinics were allocated disproportionately i.e. proportional to MOS. Allocated sample number of clinics within each province was allocated proportionately to the health regions in the province. MOS for hospitals and private clinics was a monotonic function of the number of beds as in DOH's database. Sample sizes for SSUs: Public clinics (167) Public Hospitals (33) Private Hospitals and clinics (22) Sample sizes for USUs: 1000 patients 500 nursing personnel 200 medical doctors 100 other professional health workers 400 non-professional health workers Public clinics 1000 patients 500 nursing personnel 111 nonprofessional personnel( e.g. cleaners) Public Hospitals 667 patients 333 nursing Personnel 200 medical doctors 67 other professional 222 non-professionals Private Hospitals and clinics

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

In 2024, there were an estimated **** thousand hospitals across the Philippines. In contrast, there were six hospitals in Brunei that year.

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

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

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