EMU sympathies (PSU) by share of urban population, sex, EMU sympathies, table content and measurement month

Urban Heat Island images:MorningAfternoonEveningTacoma Heat Island StudyData collected on 7/25/2018, collected by Dr. Vivek Shandas, Capa StrategiesWhat Earth Economics is working on:Through grant funding, Earth Economics is working on building out an approach and methodology using Urban Heat Island modeling (LANDSAT data) to assume health impacts (mortality rates) on a census tract level, using research on how demographics and UHI impact community health outcomes.Variables:Name: Census Block Group NamePop: Census Block Group populationIncome: Average individual Census Block Group level annual incomeOver 65: Population over age 65Under14: Population under age 14AF: Afternoon temperature (C), averaged to Census Block Group (July 25, 2018). Data collected by Dr. Vivek Shandas using this methodologyPm: Evening temperature (C), averaged to Census Block Group (July 25, 2018)Combtemp: Average of evening and afternoon temperatureHighRiskAgeGroup: Percent of population in a high risk age group for heat related illness (over age 65 and under age 14)Density: Population DensityCity of Tacoma Contact: Vanessa Simpson, Senior Technical GIS Analyst, Environmental Servicesvsimpson@cityoftacoma.org

Чиста вибірка (PSU) за часткою міського населення, статусом партійної симпатії, змістом таблиці та місяцем вимірювання

Abstract

The transition from socialism to a market economy has transformed the lives of many people. What are people's perceptions and attitudes to transition? What are the current attitudes to market reforms and political institutions?

To analyze these issues, the EBRD and the World Bank have jointly conducted the comprehensive, region-wide "Life in Transition Survey" (LiTS), which combines traditional household survey features with questions about respondents' attitudes and is carried out through two-stage sampling with a random selection of households and respondents.

The LiTS assesses the impact of transition on people through their personal and professional experiences during the first 15 years of transition. LiTS attempts to understand how these personal experiences of transition relate to people’s attitudes toward market and political reforms, as well as their priorities for the future.

The main objective of the LiTS was to build on existing studies to provide a comprehensive assessment of relationships among life satisfaction and living standards, poverty and inequality, trust in state institutions, satisfaction with public services, attitudes to a market economy and democracy and to provide valuable insights into how transition has affected the lives of people across a region comprising 16 countries in Central and Eastern Europe (“CEE”) and 11 in the Commonwealth of Independent State (“CIS”). Turkey and Mongolia were also included in the survey.

Geographic coverage

The LITS was to be implemented in the following 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Poland, Romania, Russia, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 1,000 face-to-face household interviews per country were to be conducted, with adult (18 years and over) occupants and with no upper limit for age. The sample was to be nationally representative. The EBRD’s preferred procedure was a two stage sampling method, with census enumeration areas (CEA) as primary sampling units and households as secondary sampling units. To the extent possible, the EBRD wished the sampling procedure to apply no more than 2 stages.

The first stage of selection was to use as a sampling frame the list of CEA's generated by the most recent census. Ideally, 50 primary sampling units (PSU's) were to be selected from that sample frame, with probability proportional to size (PPS), using as a measure of size either the population, or the number of households.

The second sampling stage was to select households within each of the primary sampling units, using as a sampling frame a specially developed list of all households in each of the selected PSU's defined above. Households to be interviewed were to be selected from that list by systematic, equal probability sampling. Twenty households were to be selected in each of the 50 PSU's.

The individuals to be interviewed in each household were to be selected at random, within each of the selected households, with no substitution if possible.

ESTABLISHMENT OF THE SAMPLE FRAME OF PSU’s

In each country we established the most recent sample frame of PSU’s which would best serve the purposes of the LITS sampling methodology. Details of the PSU sample frames in each country are shown in table 1 (page 10) of the survey report.

In the cases of Armenia, Azerbaijan, Kazakhstan, Serbia and Uzbekistan, CEA’s were used. In Croatia we also used CEA’s but in this case, because the CEA’s were very small and we would not have been able to complete the targeted number of interviews within each PSU, we merged together adjoining CEA’s and constructed a sample of 1,732 Merged Enumeration Areas. The same was the case in Montenegro.

In Estonia, Hungary, Lithuania, Poland and the Slovak Republic we used Eurostat’s NUTS area classification system.

[NOTE: The NUTS (from the French "Nomenclature des territoriales statistiques" or in English ("Nomenclature of territorial units for statistics"), is a uniform and consistent system that runs on five different NUTS levels and is widely used for EU surveys including the Eurobarometer (a comparable survey to the Life in Transition). As a hierarchical system, NUTS subdivides the territory of the country into a defined number of regions on NUTS 1 level (population 3-7 million), NUTS 2 level (800,000-3 million) and NUTS 3 level (150,000-800,000). At a more detailed level NUTS 3 is subdivided into smaller units (districts and municipalities). These are called "Local Administrative Units" (LAU). The LAU is further divided into upper LAU (LAU1 - formerly NUTS 4) and LAU 2 (formerly NUTS 5).]

Albania, Bulgaria, the Czech Republic, Georgia, Moldova and Romania used the electoral register as the basis for the PSU sample frame. In the other cases, the PSU sample frame was chosen using either local geographical or administrative and territorial classification systems. The total number of PSU sample frames per country varied from 182 in the case of Mongolia to over 48,000 in the case of Turkey. To ensure the safety of our fieldworkers, we excluded from the sample frame PSU’s territories (in countries such as Georgia, Azerbaijan, Moldova, Russia, etc) in which there was conflict and political instability. We have also excluded areas which were not easily accessible due to their terrain or were sparsely populated.

In the majority of cases, the source for this information was the national statistical body for the country in question, or the relevant central electoral committee. In establishing the sample frames and to the extent possible, we tried to maintain a uniform measure of size namely, the population aged 18 years and over which was of more pertinence to the LITS methodology. Where the PSU was based on CEA’s, the measure was usually the total population, whereas the electoral register provided data on the population aged 18 years old and above, the normal voting age in all sampled countries. Although the NUTS classification provided data on the total population, we filtered, where possible, the information and used as a measure of size the population aged 18 and above. The other classification systems used usually measure the total population of a country. However, in the case of Azerbaijan, which used CEA’s, and Slovenia, where a classification system based on administrative and territorial areas was employed, the measure of size was the number of households in each PSU.

The accuracy of the PSU information was dependent, to a large extent, on how recently the data has been collected. Where the data were collected recently then the information could be considered as relatively accurate. However, in some countries we believed that more recent information was available, but because the relevant authorities were not prepared to share this with us citing secrecy reasons, we had no alternative than to use less up to date data. In some countries the age of the data available makes the figures less certain. An obvious case in point is Bosnia and Herzegovina, where the latest available figures date back to 1991, before the Balkan wars. The population figures available take no account of the casualties suffered among the civilian population, resulting displacement and subsequent migration of people.

Equally there have been cases where countries have experienced economic migration in recent years, as in the case of those countries that acceded to the European Union in May, 2004, such as Hungary, Poland and the Baltic states, or to other countries within the region e.g. Armenians to Russia, Albanians to Greece and Italy; the available figures may not accurately reflect this. And, as most economic migrants tend to be men, the actual proportion of females in a population was, in many cases, higher than the available statistics would suggest. People migration in recent years has also occurred from rural to urban areas in Albania and the majority of the Asian Republics, as well as in Mongolia on a continuous basis but in this case, because of the nomadic population of the country.

SAMPLING METHODOLOGY

Brief Overview

In broad terms the following sampling methodology was employed: · From the sample frame of PSU’s we selected 50 units · Within each selected PSU, we sampled 20 households, resulting in 1,000 interviews per country · Within each household we sampled 1 and sometimes 2 respondents The sampling procedures were designed to leave no free choice to the interviewers. Details on each of the above steps as well as country specific procedures adapted to suit the availability, depth and quality of the PSU information and local operational issues are described in the following sections.

Selection of PSU’s

The PSU’s of each country (all in electronic format) were sorted first into metropolitan, urban and rural areas (in that order), and within each of these categories by region/oblast/province in alphabetical order. This ensured a consistent sorting methodology across all countries and also that the randomness of the selection process could be supervised.

To select the 50 PSU’s from the sample frame of PSU’s, we employed implicit stratification and sampling was done with PPS. Implicit stratification ensured that the sample of PSU’s was spread across the primary categories of explicit variables and a better representation of the population, without actually stratifying the PSU’s thus, avoiding difficulties in calculating the sampling errors at a later stage. In brief, the PPS involved the

Medical Power Supply Unit (PSU) Market Outlook

The global medical power supply unit (PSU) market size was valued at approximately USD 960 million in 2023 and is projected to reach around USD 1.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. The rising demand for medical devices and diagnostic equipment due to the increasing prevalence of chronic diseases, aging population, and advancements in medical technology are the primary growth factors driving this market.

One of the primary growth drivers of the medical PSU market is the escalating demand for advanced medical equipment that requires reliable and efficient power supply units. As the medical industry continues to innovate, the need for sophisticated devices such as diagnostic and monitoring equipment, surgical devices, and home healthcare devices has surged. These devices necessitate high-quality power supply units to ensure optimal performance and patient safety. Additionally, the increasing prevalence of chronic diseases such as diabetes, cancer, and cardiovascular diseases has led to a higher demand for diagnostic and therapeutic devices, further propelling market growth.

Another significant growth factor is the aging global population, which has increased the need for healthcare services and medical devices. Older adults are more susceptible to chronic conditions and require ongoing medical attention and monitoring. This demographic shift has led to a rise in hospital admissions, outpatient services, and home healthcare solutions, all of which depend on reliable power supply units. Moreover, advancements in medical technology, such as minimally invasive surgeries and telemedicine, have expanded the utilization of medical devices, thereby driving the demand for PSUs.

The integration of innovative technologies and the trend of digitalization within the healthcare sector have also contributed to the growth of the medical PSU market. The adoption of artificial intelligence (AI), Internet of Things (IoT), and connected devices in healthcare has revolutionized patient care and medical diagnostics. These technologies require stable and uninterrupted power supply to function efficiently, emphasizing the critical role of PSUs. Furthermore, stringent regulatory standards and guidelines for medical devices necessitate high-quality power supply units to ensure compliance and patient safety, thereby boosting market growth.

Regionally, North America holds a dominant position in the medical PSU market due to the presence of advanced healthcare infrastructure, high healthcare expenditure, and significant investments in medical technology. The United States, in particular, is a key contributor to market growth, with a robust demand for medical devices and continuous advancements in healthcare solutions. Europe follows suit, with countries like Germany, France, and the UK being prominent markets due to their well-established healthcare systems and focus on technological innovation. The Asia Pacific region is anticipated to witness significant growth during the forecast period, driven by the increasing healthcare needs of a large and aging population, rising investments in healthcare infrastructure, and growing awareness about advanced medical treatments.

In the context of ensuring uninterrupted power supply, the role of a Medical Grade UPS System becomes increasingly significant. These systems are specifically designed to provide backup power to critical medical equipment during power outages, ensuring that life-saving devices remain operational without interruption. The reliability of a Medical Grade UPS System is paramount in healthcare settings, where even a brief power disruption can have serious consequences. By providing a seamless transition to backup power, these systems help maintain the continuity of patient care and safeguard sensitive medical data. As healthcare facilities continue to adopt advanced technologies and digital solutions, the demand for robust and reliable power backup solutions like Medical Grade UPS Systems is expected to rise, further driving the market growth.

Product Type Analysis

The medical power supply unit market is segmented by product type into AC-DC power supply and DC-DC power supply. The AC-DC power supply units are extensively used in medical devices due to their capability to convert alternating current (AC) from the mains to direct current (DC) required by the electronic circuits within the devices. These

Abstract

The 1997 Jordan Population and Family Health Survey (JPFHS) is a national sample survey carried out by the Department of Statistics (DOS) as part of its National Household Surveys Program (NHSP). The JPFHS was specifically aimed at providing information on fertility, family planning, and infant and child mortality. Information was also gathered on breastfeeding, on maternal and child health care and nutritional status, and on the characteristics of households and household members. The survey will provide policymakers and planners with important information for use in formulating informed programs and policies on reproductive behavior and health.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN AND IMPLEMENTATION

The 1997 JPFHS sample was designed to produce reliable estimates of major survey variables for the country as a whole, for urban and rural areas, for the three regions (each composed of a group of governorates), and for the three major governorates, Amman, Irbid, and Zarqa.

The 1997 JPFHS sample is a subsample of the master sample that was designed using the frame obtained from the 1994 Population and Housing Census. A two-stage sampling procedure was employed. First, primary sampling units (PSUs) were selected with probability proportional to the number of housing units in the PSU. A total of 300 PSUs were selected at this stage. In the second stage, in each selected PSU, occupied housing units were selected with probability inversely proportional to the number of housing units in the PSU. This design maintains a self-weighted sampling fraction within each governorate.

UPDATING OF SAMPLING FRAME

Prior to the main fieldwork, mapping operations were carried out and the sample units/blocks were selected and then identified and located in the field. The selected blocks were delineated and the outer boundaries were demarcated with special signs. During this process, the numbers on buildings and housing units were updated, listed and documented, along with the name of the owner/tenant of the unit or household and the name of the household head. These activities took place between January 7 and February 28, 1997.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 1997 JPFHS used two questionnaires, one for the household interview and the other for eligible women. Both questionnaires were developed in English and then translated into Arabic. The household questionnaire was used to list all members of the sampled households, including usual residents as well as visitors. For each member of the household, basic demographic and social characteristics were recorded and women eligible for the individual interview were identified. The individual questionnaire was developed utilizing the experience gained from previous surveys, in particular the 1983 and 1990 Jordan Fertility and Family Health Surveys (JFFHS).

The 1997 JPFHS individual questionnaire consists of 10 sections: - Respondent’s background - Marriage - Reproduction (birth history) - Contraception - Pregnancy, breastfeeding, health and immunization - Fertility preferences - Husband’s background, woman’s work and residence - Knowledge of AIDS - Maternal mortality - Height and weight of children and mothers.

Cleaning operations

Fieldwork and data processing activities overlapped. After a week of data collection, and after field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman where they were registered and stored. Special teams were formed to carry out office editing and coding.

Data entry started after a week of office data processing. The process of data entry, editing, and cleaning was done by means of the ISSA (Integrated System for Survey Analysis) program DHS has developed especially for such surveys. The ISSA program allows data to be edited while being entered. Data entry was completed on November 14, 1997. A data processing specialist from Macro made a trip to Jordan in November and December 1997 to identify problems in data entry, editing, and cleaning, and to work on tabulations for both the preliminary and final report.

Response rate

A total of 7,924 occupied housing units were selected for the survey; from among those, 7,592 households were found. Of the occupied households, 7,335 (97 percent) were successfully interviewed. In those households, 5,765 eligible women were identified, and complete interviews were obtained with 5,548 of them (96 percent of all eligible women). Thus, the overall response rate of the 1997 JPFHS was 93 percent. The principal reason for nonresponse among the women was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are subject to two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the result of mistakes made in implementing data collection and data processing (such as failure to locate and interview the correct household, misunderstanding questions either by the interviewer or the respondent, and data entry errors). Although during the implementation of the 1997 JPFHS numerous efforts were made to minimize this type of error, nonsampling errors are not only impossible to avoid but also difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The respondents selected in the 1997 JPFHS constitute only one of many samples that could have been selected from the same population, given the same design and expected size. Each of those samples would have yielded results differing somewhat from the results of the sample actually selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, since the 1997 JDHS-II sample resulted from a multistage stratified design, formulae of higher complexity had to be used. The computer software used to calculate sampling errors for the 1997 JDHS-II was the ISSA Sampling Error Module, which uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics, such as fertility and mortality rates.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

Our seagrass collection mainly comes from the Southern coastline of Thailand, cover 12 out of 13 species found in Thai waters, there was no H. minor in our collection. Recently, we reported a new record of H. major in Thai waters, and identify key characters to distinguish those overlapping morphological characteristic between H. ovalis and H. major (Tuntiprapas et al., 2015). We have extensive collections of H. ovalis, with a total of 266 samples covered not only in Thai waters but also Johor, Malaysia and Hong Kong, we are also interested in population genetic and ecological adaptations of this very common and high plasticity of this species.

Abstract

The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey.

The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique.

The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National coverage Domains: Urban/rural; Regions (East Forest, West Forest, East Savannah, West Savannah)

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample selection process for the CILSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households.

A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time.

It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

Sampling Procedures for Block 1 Data

The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.

The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type.

Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census.

Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.)

Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Bias in the Selection of Households within PSUs, Block 1 Data

Analysis of the four years of the CILSS data revealed that household size (unweighted), dropped by 24 percent between 1985 and 1988. Three possible explanations were considered: (1) area l demographic change; (2) non-sampling measurement errors were involved; or (3) some sort of sampling bias. Investigation ruled out the first two possibilities. The third possibility clearly was an issue because the sampling frame and listing procedures had indeed changed in midstream and this was likely to have had an effect. In fact, the investigation found that the substantial part of the drop in household size over the years occurred between the first and second panel data sets in 1987, i.e. the tail end of Block 1 data and the start of Block 2 data. From this, it is reasonable to assume that differences in the sampling frame and sampling procedures between the two blocks were indeed responsible.

The listing procedures for Block 1 data indicate d that the selection of households within PSUs was likely to have been biased toward the selection of larger dwellings. Based on a discussion with Christopher Scott, statistical consultant, Demery and Grootaert explain as follows: "In the selected primary sampling units, where the listing of households was to occur, enumerators were instructed to start the listing process at a random location in the primary sampling unit and from this point to select every nth household, that is, with a given fixed "step" until sixty-four households were listed. There are two sources of potential bias in this listing procedure. First, the selection of the starting point might not have been random, but subject to motivated bias on the part of the enumerator (such as the selection of a point where there are numerous dwellings or that is easily accessible). Second, in practice, enumerators counted doors to achieve the "step", rather than counting actual households. This method leads to sample

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XVI. APPENDIX 5). The microdata are available on CD-ROMs. These microdata files present detailed expenditure and income data from the Interview component of the CE for 2007 and the first quarter of 2008. The Interview survey collects data on up to 95 percent of total household expenditures. In addition to the FMLY, MEMB, MTAB, and ITAB_IMPUTE files, the microdata include files created directly from the expenditure sections of the Interview survey (EXPN files). The EXPN files contain expenditure data and ancillary descriptive information, often not available on the FMLY or MTAB files, in a format similar to the Interview questionnaire. In addition to the extra information available on the EXPN files, users can identify distinct spending categories easily and reduce processing time due to the organization of the files by type of expenditure. Estimates of average expenditures in 2007 from the Interview Survey, integrated with data from the Diary Survey, will be published in the report Consumer Expenditures in 2007 (due out in 2009). A list of recent publications containing data from the CE appears at the end of this documentation. The microdata files are in the public domain and, with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Interview Survey, 2007."

Analysis unit

Consumer Units

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U.S. civilian population. Eligible population includes all civilian noninstitutional persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2007 and 2008 samples is composed of 91 areas. The design classifies the PSUs into four categories: • 21 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 38 "X" PSUs, are medium-sized MSA's. • 16 "Y" PSUs are nonmetropolitan areas that are included in the CPI. • 16 "Z" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2007 survey is generated from the 2000 Census of Population 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (EDs) from the Census that fail to meet the criterion for good addresses for new construction, and all EDs in nonpermit-issuing areas are grouped into the area segment frame. Interviewers are then assigned to list these areas before a sample is drawn. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. The Interview Survey is a panel rotation survey. Each panel is interviewed for five consecutive quarters and then dropped from the survey. As one panel leaves the survey, a new panel is introduced. Approximately 20 percent of the addresses are new to the survey each month.

Mode of data collection

Computer Assisted Personal Interview [capi]

Power Supply Unit (PSU) Market Outlook

The global Power Supply Unit (PSU) market size was valued at approximately USD 28.5 billion in 2023 and is projected to reach around USD 40.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 4.0% during the forecast period. The market's growth is driven by factors such as the rising demand for energy-efficient power supplies and the increasing adoption of PSUs in various industries.

One of the primary growth factors of the PSU market is the escalating demand for consumer electronics. As the global population continues to grow and urbanize, there is a burgeoning demand for devices such as smartphones, laptops, and gaming consoles. Each of these devices requires reliable and efficient power supplies, thereby driving the need for advanced PSUs. Moreover, the trend towards miniaturization in electronics has necessitated the development of smaller, more efficient power supply units, further fueling market growth.

Another significant driver is the expansion of industrial automation and the Internet of Things (IoT). Industries worldwide are increasingly integrating automation systems to enhance productivity and efficiency. These systems require robust and reliable power supplies to function optimally. Additionally, the proliferation of IoT devices, which rely on continuous and efficient power, is propelling the demand for PSUs. As industrial sectors adopt more sophisticated technologies, the need for high-performance power supply units is expected to rise accordingly.

The telecommunications sector is also a major contributor to the growth of the PSU market. With the advent of 5G technology and the increasing reliance on cloud services, data centers and telecom infrastructure require high-performance power supply units to ensure uninterrupted service. This growing dependency on robust power solutions in telecommunications infrastructure is anticipated to significantly boost the PSU market. Furthermore, the rising investments in the development of smart cities, which require efficient power distribution systems, are expected to contribute to market expansion.

Regionally, the Asia-Pacific region is expected to witness substantial growth in the PSU market. This growth can be attributed to the rapid industrialization and urbanization in countries like China and India. Additionally, the increasing adoption of consumer electronics and the expanding IT and telecommunications sectors in the region are driving the demand for power supply units. North America and Europe are also expected to exhibit significant growth, driven by advancements in technology and the increasing focus on energy efficiency. Meanwhile, regions such as Latin America and the Middle East & Africa are projected to experience moderate growth, supported by infrastructural developments and increasing industrial activities.

Product Type Analysis

The PSU market can be segmented by product type into AC-DC Power Supply, DC-DC Power Supply, and Uninterruptible Power Supply (UPS). Each of these segments caters to different needs and applications, ensuring a diverse market landscape. The AC-DC Power Supply segment dominates the market due to its widespread use in consumer electronics and industrial applications. These power supplies convert alternating current (AC) from the main power source to direct current (DC), which is required by most electronic devices. The demand for AC-DC power supplies is fueled by the increasing use of electronic gadgets and the need for reliable power solutions in various industries.

DC-DC Power Supply units, on the other hand, are essential in applications where the input and output power sources are both DC. These units are commonly used in automotive and telecommunications applications, where they play a crucial role in voltage conversion and regulation. The growing adoption of electric vehicles (EVs) and the expansion of telecom infrastructure are significant factors driving the demand for DC-DC power supplies. As these sectors continue to evolve, the need for efficient and reliable DC-DC power supply units is expected to increase.

The Uninterruptible Power Supply (UPS) segment is also experiencing significant growth. UPS systems provide backup power in the event of a power outage, ensuring the continuous operation of critical systems in data centers, hospitals, and other essential services. The increasing reliance on digital infrastructure and the need for uninterrupted power supply in various sectors are driving the demand for UPS systems. With the growing importance of data cen

Chronic inflammation associated with inflammatory bowel disease (IBD) results in increased oxidative stress that damages the colonic microenvironment. Low levels of serum bilirubin, an endogenous antioxidant, have been associated with increased risk for Crohn’s disease (CD). Therefore, the aim of this study was to examine whether total serum bilirubin levels are associated with ulcerative colitis (UC). We identified a retrospective case-control population (n = 6,649) from a single tertiary care center, Penn State Hershey Medical Center (PSU) and a validation cohort (n = 1,996) from Virginia Commonwealth University Medical Center (VCU). Cases were age- and sex-matched to controls (PSU: CD n = 254, UC n = 187; VCU: CD n = 233, UC n = 124). Total serum bilirubin levels were obtained from de-identified medical records and segregated into quartiles. Logistic regression analysis was performed on each quartile of total serum bilirubin compared to the last quartile (highest bilirubin levels) to determine the association of total serum bilirubin with UC. Similar to CD patients, UC patients demonstrated reduced levels of total serum bilirubin compared to controls at PSU and VCU. The lowest quartile of total serum bilirubin was independently associated with UC for the PSU (OR: 1.98 [95% CI: 1.09–3.63]) and VCU cohorts (OR: 6.07 [95% CI: 3.01–12.75]). Lower levels of the antioxidant bilirubin may reduce the capability of UC patients to remove reactive oxygen species leading to an increase in intestinal injury. Therapeutics that reduce oxidative stress may be beneficial for these patients.

Abstract

The GHS is an annual household survey, specifically designed to measure various aspects of the living circumstances of South African households. The key findings reported here focus on the five broad areas covered by the GHS, namely: education, health, activities related to work and unemployment, housing and household access to services and facilities.

Geographic coverage

The scope of the General Household Survey 2007 was national coverage.

Analysis unit

The units of anaylsis for the General Household Survey 2007 are individuals and households.

Universe

The survey covered all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the GHS 2007 was based on a master sample (MS) that was designed during 2003 and used for the first time in 2004. This master sample was developed specifically for household sample surveys that were conducted by Statistics South Africa between 2004 and 2007. These include surveys such as the annual Labour Force Surveys (LFS), General Household Survey (GHS) and the Income and Expenditure Survey (IES).

A multi-stage stratified area probability sample design was used. Stratification was done per province (nine provinces) and according to district council (DC) (53 DCs) within provinces. These stratification variables were mainly chosen to ensure better geographical coverage, and to enable analysts to disaggregate the data at DC level.

The design included two stages of sampling. Firstly PSUs were systematically selected using Probability Proportional to Size (PPS) sampling techniques. During the second stage of sampling, Dwelling Units (DUs) were systematically selected as Secondary Sampling Units (SSUs). Census Enumeration Areas (EAs) as delineated for Census 2001 formed the basis of the PSUs. EAs were pooled when needed to form PSUs of adequate size (72 dwelling units or more) for the first stage of sampling. The following criteria were used for PSU formation:

• No overlapping between any two PSUs; • Complete coverage of the sampling population; • Fully identifiable (e.g. in the case of a household survey, information on the geographical boundaries of the PSU should enable the exact location of the PSU); • Secondary sampling units (SSUs) must be clearly identifiable within PSUs; • Updated information on the number of SSUs within all the PSUs had to be available; • PSUs must be sufficiently large in respect of the number of SSUs included to enable the forming of a predetermined number of clusters of SSUs, with the size of a cluster equal to the sample take of SSUs within a PSU, taking all types of surveys into consideration; and • PSUs must also be sufficiently small to facilitate the listing and also regular updating of the SSUs within them.

A PPS sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 000 PSUs were selected. In each selected PSU a systematic sample of ten dwelling units was drawn, thus, resulting in approximately 30 000 dwelling units. All households in the sampled dwelling units were enumerated.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GHS 2007 questionnaire collected data on: Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, access to services, transport, household assets, land ownership, agricultural production Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, disability, access to social services, mortality. Women's characteristics: fertility

Response rate

29 311 (84,0%) of the expected 34 902 interviews were successfully completed. This response rate is 2,0% points down from the 86,0% response rate as reported in the GHS 2006 report. It was not possible to complete interviews in 5,1% of the sampled dwelling units because of reasons such as refusals or absenteeism. An additional 10,9% of all interviews were not conducted for various reasons such as the sampled dwelling units had become vacant or had changed status (e.g.,. they were used as shops/small businesses at the time of the enumeration, but were originally listed as dwelling units).

Sampling error estimates

Estimation and use of standard error The published results of the General Household Survey are based on representative probability samples drawn from the South African population, as discussed in the section on sample design. Consequently, all estimates are subject to sampling variability. This means that the sample estimates may differ from the population figures that would have been produced if the entire South African population had been included in the survey. The measure usually used to indicate the probable difference between a sample estimate and the corresponding population figure is the standard error (SE), which measures the extent to which an estimate might have varied by chance because only a sample of the population was included. There are two major factors which influence the value of a standard error. The first factor is the sample size. Generally speaking, the larger the sample size, the more precise the estimate and the smaller the standard error. Consequently, in a national household survey such as the GHS, one expects more precise estimates at the national level than at the provincial level due to the larger sample size involved. The second factor is the variability between households of the parameter of the population being estimated, for example, the number of unemployed persons in the household.

Abstract

The HIES gathers information related to demographic characteristics of the members of the surveyed households, expenditure on food and non-food items and income received by each household member from all the different sources in a compulsory manner. Starting from the HIES 2006/07, the survey questionnaire was further expanded beyond the collection of demographic, income and expenditure information. It has been introduced 7 new sections to collect almost all the other household information that helps to understand the correct living standards of the households. Those newly introduced areas covered by the HIES starting from the HIES 2006/07 are as follows. 1. School education (aged 5-19 years) 2. Health information 3. Inventory of durable goods 4. Access to infrastructure facilities 5. Household debts and borrowings 6. Housing, sanitary and disasters 7. Land and agriculture holdings

Geographic coverage

National - excluding Mannar, Kilinochchi and Mullaithivu districts in the Northern province

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design of the survey is two stage stratified and the Urban, Rural and the Estate sectors in each district of the country are the selection domains thus the district is the main domain used for the stratification. The sampling frame is the list of housing units prepared for the Census of Population and Housing (CPH) 2001 and the HIES 2009/10 will be the last HIES sampled from this sampling frame as the DCS is all set to conduct the CPH in 2011 based on whole newly prepared set of census blocks, which has been almost completed by now.

Selection of Primary Sampling Units Primary sampling units (PSUs) are the census blocks selected for the survey and the sampling frame, which is the collection of all the census blocks prepared in 2001 in Sri Lanka, is used for the selection of the PSUs at the first stage of the selection.

The PSU selection is done within all the independentselection domains that are assigned different sample size allocations to total the targeted sample size of 2,500 PSUs. The method of selection of the PSUs at the first stage is systematic with a selection probability given to each census block proportionate to the number of housing units available in the census blocks within the selection domains (PPS).

The selected PSUs are updated to include newly built housing units and to exclude demolished or vacated housing units, which are no longer considered as housing units according to the survey definitions, to capture variation of natural growth and to make necessary adjustments for the same. The PSU updating operation in field is generally done less than one month prior to the survey and it was carried out for the 12 months starting from June 2009 to May 2010 to support the scheduled 12 survey months started from July 2009 to June 2010 for the HIES 2009/10.

Selection of Secondary Sampling Units Secondary Sampling Units (SSUs) or Final sampling units (FSUs) are the housing units selected at the second stage from the 2,500 PSUs selected at the first stage. From each PSU, 10 SSUs (housing units) are systematically selected giving each housing unit in the PSU an equal probability to be selected for the survey. The total sample of size 25,000 housing units is resulted at the end of the sampling process and this sample represents the whole country in different probabilities depend on the different sample sizes allocated for the selection domains.

Sample allocation Allocation of the number of PSUs or determining the sample sizes for the districts is made proportionate to the number of housing units and the standard deviations of the mean household expenditure values reported in the respective districts in previous surveys (Neymann Allocation). Sector allocation of the district sample is made proportionate to the square root of the sizes of the respective selection domains (Urban, Rural and Estate sectors in the district). The sample of PSUs within the selection domain is equally distributed among the 12 survey months and the monthly sample too is equally dispersed among all the weeks in the month assigning a specific week for each PSU for the survey activities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of nine parts. i. Demographic Characteristics ii. School education (aged 5 to 20 years) iii. Health vi. Household expenditure v. Household income vi. part a : Inventory of durable goods vi. part b : Debts of the household vii. Access to facilities in the area viii. Information about housing ix. Agriculture holdings and livestock

Sampling error estimates

Estimation, Standard error, and coefficient of variation table is available in the final survey report.

Polysulphone (PSU) Market Outlook

The global Polysulphone (PSU) market size was valued at approximately USD 1.2 billion in 2023 and is expected to reach USD 1.8 billion by 2032, growing at a CAGR of 4.5% during the forecast period. This market is being driven by several growth factors, including the increasing demand for high-performance thermoplastics in various industries such as healthcare and automotive. The unique properties of polysulphone, such as its exceptional thermal stability and resistance to oxidation and hydrolysis, make it ideal for applications that require stringent regulatory compliance and long-term reliability, fueling its demand across multiple sectors.

One of the primary growth drivers is the burgeoning medical device industry, which is increasingly relying on high-performance materials like PSU. Medical devices require materials that can withstand repeated sterilization and offer excellent mechanical and chemical resistance, and PSU fits the bill perfectly. The growing aging population and advancements in medical technology have spurred the demand for a range of medical devices, from surgical instruments to diagnostic equipment, where PSU is extensively used. The healthcare sector's demand for PSU is expected to continue growing as new medical technologies emerge and the need for reliable and safe medical devices increases globally.

In the automotive sector, the push towards lightweight and fuel-efficient vehicles has led to increased use of advanced materials like polysulphone. The material's high impact resistance and dimensional stability at elevated temperatures make it suitable for under-the-hood applications and other components in modern vehicles. As automotive manufacturers strive to meet stringent emission standards and improve fuel efficiency, the demand for PSU-based components is anticipated to rise. Additionally, the transition towards electric vehicles is likely to create new opportunities for PSU, as it can be used in battery components and other electrical systems.

The electronics industry is another significant contributor to the PSU market growth. With the increasing miniaturization of electronic devices and the demand for high-performance components, PSU's properties make it an attractive choice for various electronic applications. Its ability to maintain mechanical properties at elevated temperatures and resist chemical degradation makes it suitable for manufacturing connectors, insulators, and other critical components in electronic devices. As consumer electronics continue to evolve, and new technologies such as 5G and IoT gain traction, the demand for high-performance materials like PSU is expected to grow concurrently.

The Polyarylsulfones Sales have been witnessing a steady increase, driven by the material's exceptional properties that cater to high-performance applications. These polymers, known for their thermal stability and resistance to chemicals, are increasingly being adopted in industries such as automotive and electronics. The demand for polyarylsulfones is particularly strong in regions with advanced industrial activities, where the need for durable and reliable materials is paramount. As industries continue to innovate and seek materials that offer both performance and longevity, the sales of polyarylsulfones are expected to grow, contributing significantly to the overall market dynamics.

Regionally, Asia Pacific holds a significant share of the PSU market, driven by the rapid industrialization and urbanization in countries like China, India, and Japan. The region's booming automotive and electronics industries, coupled with substantial investments in healthcare infrastructure, are major factors contributing to its market dominance. North America and Europe also represent substantial markets for PSU, where advanced healthcare systems and a strong focus on automotive innovation play crucial roles in driving demand. The growth prospects in these regions are further bolstered by ongoing research and development activities aimed at enhancing material properties and expanding PSU applications.

Product Type Analysis

The Polysulphone market is segmented by product type into unfilled polysulphone, glass fiber reinforced polysulphone, and others. Unfilled polysulphone is a pure form of the material, known for its excellent thermal and mechanical properties. This variant is widely utilized across various industries due to its inherent stability and resistance to h

The Sanborn Fire Insurance Company was established to aid in assessing the risk of insuring properties. Sanborn maps provide a wealth of additional information such as the footprint/size, number of floors, roofing materials, amenities, business types/land uses, etc. Pennsylvania State University owns and has digitized approximately 30,000 sheets of Sanborn maps depicting towns and cities across the state of Pennsylvania.

Penn State Library's digital collection for the State College area: Digital Collection

This project is a collaboration between the Donald W. Hamer Center for Maps and Geospatial Information, Eberly Family Special Collections Library, and the Preservation, Conservation, and Digitization department at Penn State University Libraries.

Work was completed by interns supported by the Bednar Internship program at the University Libraries.

The global medical power supply unit (PSU) market is experiencing robust growth, projected to reach $1104 million in 2025 and maintain a healthy Compound Annual Growth Rate (CAGR) of 7.3% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing adoption of advanced medical equipment in hospitals and clinics globally fuels the demand for reliable and efficient PSUs. Miniaturization trends in medical devices, demanding smaller and more efficient power solutions, further contribute to market growth. Furthermore, stringent regulatory requirements regarding safety and performance standards for medical PSUs create a demand for high-quality products, benefiting established players with strong regulatory compliance capabilities. The rising prevalence of chronic diseases and an aging global population also significantly impact market growth, as these factors lead to increased demand for diagnostic, monitoring, and therapeutic medical devices, all of which require reliable power supplies. Segment-wise, AC-DC power supplies currently hold a larger market share compared to DC-DC power supplies, but the latter segment is projected to witness faster growth due to its applications in portable and battery-powered medical devices. Geographically, North America and Europe currently dominate the market, owing to advanced healthcare infrastructure and high per capita healthcare expenditure. However, Asia-Pacific is anticipated to show significant growth in the forecast period, driven by increasing healthcare investments and rising disposable incomes in developing economies like India and China. Competition in the medical PSU market is intense, with a mix of established global players like Advanced Energy, Delta Electronics, and MEAN WELL, and regional manufacturers. Successful players are those that can offer a diverse product portfolio catering to varied application needs, demonstrate strong technological capabilities in terms of efficiency and miniaturization, and maintain robust supply chains to meet the increasing demand. The market is also seeing increasing adoption of innovative technologies such as digital power solutions and improved power density designs to enhance efficiency and reduce the overall size of PSUs, creating opportunities for manufacturers willing to invest in R&D. Potential challenges include fluctuations in raw material prices and maintaining consistent quality control across manufacturing processes to meet stringent industry standards. However, the long-term outlook for the medical PSU market remains highly positive, fueled by continuous technological advancements and the ever-increasing demand for reliable power in the medical device sector.

Abstract

The Côte d'Ivoire Living Standards Survey (LSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (LSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN The principal objective of the sample selection process for the LSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households. A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time. It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

(b) SAMPLE FRAME 1. Sampling Procedures for Block 1 Data The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type. Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

1. Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census. Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.) Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The Household Questionnaire was almost entirely pre-coded, thus reducing errors involved in the coding process. Also, the decentralized data entry system allowed for immediate follow-up on inconsistencies that were detected by the data entry program. Household and personal identification codes were recorded in each section, facilitating merging data across sections

Sampling error estimates

(a) ACCURACY The general consensus is that the quality of the LSS household data is very good. An informal review of data quality conducted by Ainsworth and Mehra (1988) assessed the 1985 and 1986 LSS data in terms of their accuracy, completeness, and internal consistency. The LSS household data were found to score high marks on each of these three counts. One measure of data quality is the extent to which individuals in question respond for themselves during the interview, rather than having proxy responses provided for them by other household members. The investigation of CILSS household survey data for 1985 and 1986 showed that 93 percent of women responded for themselves to the fertility section and that 79 to 80 percent of all adult household members responded for themselves to the employment module. The percent of children responding for themselves to the employment module was far less, 43 to 45 percent. Nevertheless, these rates were found to be higher than for the Peru Living Standards Survey (29 percent).

(b) COMPLETENESS

Investigation of several variables and modules in the LSS (sex, age, parental characteristics, schooling, health, employment, migration, fertility, farming and family business), found that missing data in the household survey are rare. Rates for missing data were found to be close to 0 (0.01 to 0.05 percent) in many cases, but in any case, no higher than 0.76 percent.

Polysulfone (PSU) Market Outlook

The global polysulfone (PSU) market size was valued at approximately $2.5 billion in 2023 and is projected to reach around $4.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 6% during the forecast period. The growth of this market is driven by its increasing demand in various high-performance applications owing to PSU's excellent thermal stability, chemical resistance, and mechanical properties. These attributes make polysulfone highly suitable for industries that require robust materials capable of operating in extreme conditions. The expanding medical devices sector, coupled with advancements in water treatment technologies, are significant growth factors contributing to the increasing adoption of PSU globally.

The medical devices industry has shown a burgeoning demand for polysulfone materials due to their biocompatibility and ability to withstand repeated sterilization. With the rise in healthcare expenditure and the growing need for advanced medical equipment, the use of PSU in manufacturing medical components such as dialyzers, surgical instruments, and other healthcare products has seen a substantial increase. Additionally, the aging population and the rise in chronic diseases necessitate the production of long-lasting and reliable medical devices, further fueling the demand for PSU. This demand is supported by stringent regulatory standards mandating the use of high-performance materials in medical applications, ensuring patient safety and device efficacy.

In the automotive and aerospace sectors, the lightweight yet durable nature of polysulfone makes it an ideal candidate for replacing metal parts, thus contributing to weight reduction and fuel efficiency. As the automotive industry progresses towards electric and hybrid vehicles, the need for lightweight materials becomes more pronounced, offering PSU a significant growth opportunity. Furthermore, the aerospace industry's relentless pursuit of materials that can endure extreme temperatures without compromising on safety or performance continues to drive the demand for PSU. The material's ability to maintain structural integrity in harsh environments makes it a preferred choice for various aerospace applications such as interior components and structural elements.

The water treatment industry is another major sector propelling the growth of the PSU market, driven by increasing concerns over water scarcity and the need for efficient water purification systems. Polysulfone membranes, with their superior chemical resistance and durability, are extensively used in filtration applications for desalination and wastewater treatment. As global efforts to ensure clean water access intensify, the demand for advanced filtration technologies using PSU is expected to rise. The ongoing investments in infrastructure development and the implementation of stringent environmental regulations further enhance the market prospects for PSU in water treatment applications.

Regional outlook for the PSU market indicates a varying degree of growth across different geographical areas. North America and Europe are expected to retain a significant share of the market due to the presence of established healthcare, automotive, and aerospace industries. The rapid industrialization in the Asia Pacific region, particularly in China and India, is anticipated to result in the highest regional growth, driven by the burgeoning automotive and electronics sectors. Meanwhile, Latin America and the Middle East & Africa are expected to witness moderate growth as they increasingly adopt advanced materials to support their developing industries. The regional distribution of market growth highlights the global demand for PSU as it meets the specific needs of both developed and developing economies.

Product Type Analysis

The polysulfone market is segmented based on product type into standard PSU, glass-filled PSU, and others. Standard PSU, known for its clarity and chemical resistance, is widely used across industries such as medical and electronics. It offers a balance of properties, including good hydrolytic stability and high temperature resistance, making it suitable for applications that require transparency and durability. Its versatility extends to household and food processing applications, where its food-grade quality and resistance to repeated sterilization cycles make it a preferred choice. The demand for standard PSU is expected to remain steady, bolstered by its adaptability to meet diverse industrial needs.

Glass-filled polysulfone is a reinforced variant, offering

ObjectivesThis study aimed to assess the accuracy of bedside ultrasound in predicting resting energy expenditure (REE) in critically ill patients.MethodsWe studied critically ill patients admitted to our hospital’s ICU between November 2021 and March 2023 who underwent REE, cardiac ultrasound, and muscle ultrasound evaluations. General demographic information and ultrasound data (including cardiac output, biceps brachii and quadriceps femoris thickness) were collected to estimate REE (REE-US). Simultaneously, REE was measured using indirect calorimetry (REE-IC). Correlations between REE-US and established equations (Harris-Benedict, Penn State University (PSU), Mifflin, ASPEN standard) as well as REE-IC were evaluated. Additionally, the feasibility and application of ultrasound for REE prediction across different disease conditions in critically ill patients were analysed.ResultsNinety-seven critically ill patients with 124 ultrasound measurements were included. The Penn State University formula showed the highest correlation with REE-IC (r = 0.779, p 

Abstract

The employment/unemployment data are required at very short intervals to monitor the program made in the employment generating policies of the government. To satisfy this need, Sri Lanka Labour Force Survey was designed as a quarterly basis survey to measure the levels and trends of employment, unemployment and labour force in Sri Lanka. Thus the survey is repeated four times each year since the first quarter of 1990.

Key objectives of the survey - To study the economically active / inactive population. - To analyze employment by major industry group and employment status. - To determine unemployment rates by level of education and by age group - To study the informal sector employment. - To determine the underemployment rates by sector and by major industries

Geographic coverage

National coverage. ( Excluding Nothern and Eastern provinces )

Analysis unit

Individuals from the population aged 10 years or more

Universe

Working age population (10 years and above) living in the non-institutional households in Sri Lanka

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey is conducted quarterly to produce estimates of employment, unemployment, labour force participation and basic demographic characteristics. The scope of coverage includes all households in Sri Lanka.

The list of housing units created for Demographic Survey 1994 were taken as sample frame. Sample lists were selected from the above frame taken as Primary Sampling Unit (PSU). A systematic Sample of 15 housing units per PSU was selected on final sampling units.

The total annual sample size is 15915 housing units equaly, distributed as 265 PSUs per each quarter and 15 housing units (SSUs) per PSU. The allocation to the domains aims at ensuring approximately equal of reliability from domain to domain.

The 1061 PSUs were selected by systematic Sampling from the PSUs created for Demographic Survey - 1994. A listing operation was conducted in each selected PSU to provide a frame for the second stage of selection. The selection of housing units within PSU was systematic with random start.

Sampling deviation

Even though survey covers all Districts in the island, in the year 2002, due to situation of the Northen and the Eastern provinces the findings of those two have not been included in the report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire covered questions under four main headings such as

Identification Information Control data Personal Information Labour force Information Questions common to all employed persons

Current survey concepts and methods are very similar to those introduced at the beginning of the survey in 1990. However, some changes have been made over the years to improve the accuracy and usefulness of the data. [Questionnaire is attached in the External Resources section].