Context

The Philippine Statistics Authority (PSA) spearheads the conduct of the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three (3) years, is aimed at providing data on family income and expenditure, including, among others, levels of consumption by item of expenditure, sources of income in cash, and related information affecting income and expenditure levels and patterns in the Philippines.

Content

Inside this data set is some selected variables from the latest Family Income and Expenditure Survey (FIES) in the Philippines. It contains more than 40k observations and 60 variables which is primarily comprised of the household income and expenditures of that specific household

Acknowledgements

The Philippine Statistics Authority for providing the publisher with their raw data

Inspiration

Socio-economic classification models in the Philippines has been very problematic. In fact, not one SEC model has been widely accepted. Government bodies uses their own SEC models and private research entities uses their own. We all know that household income is the greatest indicator of one's socio-economic classification that's why the publisher would like to find out the following:

1) Best model in predicting household income 2) Key drivers of household income, we want to make the model as sparse as possible 3) Some exploratory analysis in the data would also be useful

Additional file 1. List of the identified TIFY family genes in kiwifruit.

Prostate Specific Antigen (PSA) Blood Based Biomarker Market Outlook 2032

The global prostate specific antigen (PSA) blood based biomarker market size was USD XX Billion in 2023 and is projected to reach USD XX Billion by 2032, expanding at a CAGR of XX% during 2024–2032. The market growth is attributed to the rising awareness and understanding of prostate cancer across the globe.



Growing awareness and understanding of prostate cancer among the general population is a significant driver of the prostate specific antigen (PSA) blood based biomarker market. An increase in public understanding of the necessity for early detection and routine screenings is expected to heighten the demand for PSA tests. This increased demand presents a lucrative opportunity for companies to expand their market share and develop advanced and accurate PSA tests.

Impact of Artificial Intelligence (AI) in Prostate Specific Antigen (PSA) Blood Based Biomarker Market

Artificial Intelligence has a significant impact on prostate specific antigen (PSA) blood based biomarker market. The integration of AI in this sector has enhanced the accuracy, speed, and predictive capabilities of PSA tests. AI algorithms, have been instrumental in analyzing complex patterns in PSA levels, thereby improving the detection and diagnosis of prostate cancer. Furthermore, AI has facilitated the development of non-invasive PSA tests, reducing the discomfort and risks associated with traditional invasive procedures.



The use of AI has led to cost savings, as it minimizes the need for unnecessary biopsies and treatments. Moreover, AI-powered predictive models have been pivotal in identifying patients at high risk of prostate cancer, enabling early intervention and potentially improving patient outcomes. The impact of AI on the PSA blood-based biomarker market has been profound, leading to advancements in prostate cancer diagnosis and treatment.

Prostate Specific Antigen (PSA) Blood Based Biomarker Market Dynamics

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This includes all measure of poverty among family and population at the regional level for the years 1991, 2006, 2009, 2012, and 2015. These are Poverty Incidence and Magnitude, Poverty and Food Thresholds, Poverty Gap, Income Gap, and Extent of Poverty. These data were derived from the result of Family Income and Expenditure Surveys and Labor Force Surveys.Map Displays at Scale: 1:6,000,000 to 1:12,000,000. Download detailed metadata about Philippine SDG 1.

Pressure Swing Adsorption (PSA) Market Outlook 2032

The global pressure swing adsorption (PSA) market size was USD 18.85 Billion in 2023 and is likely to reach USD 32.04 Billion by 2032, expanding at a CAGR of 6.6 % during 2024–2032. The market growth is attributed to the high energy efficiency and increasing demand for hydrogen across the globe.



High energy efficiency is expected to boost the market. Several industries strive to reduce their energy consumption and carbon footprint and the adoption of energy-efficient technologies such as PSA has seen a considerable increase. However, the growing demand for hydrogen in various applications, including fuel cells and ammonia production, has driven the market. PSA systems offer an effective solution for hydrogen purification, making them indispensable in industries that require high-purity hydrogen.

Impact of Artificial Intelligence (AI) in Pressure Swing Adsorption (PSA) Market

The use of artificial intelligence is anticipated to expand at a rapid pace in the pressure swing adsorption (PSA) market. AI's influence is evident in the enhancement of operational efficiency and the optimization of sales and marketing strategies. Through predictive analytics, AI aids in forecasting market trends, customer preferences, and potential growth areas, enabling businesses to make informed decisions.

AI streamlines the sales process by automating routine tasks, allowing sales representatives to focus on strategic aspects such as building customer relationships and closing deals. Furthermore, AI-powered chatbots have revolutionized customer service in the PSA sales market by providing instant responses to customer queries, improving customer satisfaction and loyalty.



AI plays a crucial role in analyzing large volumes of data to glean insights into market patterns, customer behavior, and competitor strategies. This data-driven approach facilitates the development of effective sales strategies, leading to increased market share and profitability in the PSA sales market.</

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Philippines National Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between 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% 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, the 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

In 2023, a family of five in the Philippines had a poverty threshold of a little 13,873 Philippine pesos per month. That was higher than the monthly poverty threshold in 2018, which amounted to around about 12,000 Philippine pesos.

Additional file 4. One-to-one orthologous relationships between A. eriantha and other five plant species.

Philippines Average Family Income: Region I, Ilocos data was reported at 238,000.000 PHP in 2015. This records an increase from the previous number of 204,000.000 PHP for 2012. Philippines Average Family Income: Region I, Ilocos data is updated yearly, averaging 122,449.000 PHP from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 238,000.000 PHP in 2015 and a record low of 34,031.000 PHP in 1988. Philippines Average Family Income: Region I, Ilocos data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H020: Family Income and Expenditure Survey: Average Annual Income, Expenditure and Saving: By Region.

Prostate Specific Antigen (PSA) Test Market Outlook

The Prostate Specific Antigen (PSA) Test market is poised for substantial growth, with the global market size projected to increase from USD 3.2 billion in 2023 to an estimated USD 5.8 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 6.5% during the forecast period. This growth is fueled by factors such as the increasing prevalence of prostate cancer, rising awareness of cancer screening programs, and technological advancements in diagnostic testing. The PSA test remains one of the most commonly used tools for early detection and monitoring of prostate cancer, which is a significant driver for market expansion.

The primary growth factor for the PSA test market is the increasing prevalence of prostate cancer, which is one of the most common types of cancer among men globally. Prostate cancer's growing incidence has heightened the demand for early detection screenings, making PSA tests crucial. The aging population contributes significantly to this demand, as older men are more susceptible to prostate cancer. Additionally, increased awareness and education about prostate health have led to more men opting for routine screenings. These factors collectively contribute to the rising demand for PSA tests, thereby propelling market growth.

Technological advancements in diagnostic testing have dramatically improved the accuracy and reliability of PSA tests, further bolstering market growth. Innovations such as highly sensitive assays, automated diagnostic platforms, and the development of complexed PSA tests that improve specificity, have enhanced the efficiency of prostate cancer detection. These technological improvements have made PSA testing more accessible and have reduced the number of false-positive results, thus increasing confidence in the tests among healthcare providers and patients alike. This technological progress is also likely to spur additional research and development efforts, further expanding the market.

Another critical factor driving growth in the PSA test market is the increasing support and investment from government and healthcare organizations for cancer screening programs. Many countries have implemented national screening guidelines that recommend regular PSA testing for men over a certain age, often 50 years, or earlier for those with a family history of prostate cancer. These initiatives are designed to reduce cancer mortality rates through early detection and prevention. The implementation of such programs has resulted in a significant rise in the number of PSA tests conducted globally, thereby positively impacting market growth.

From a regional perspective, North America currently holds the largest share of the PSA test market, attributed mainly to a well-established healthcare infrastructure, a high prevalence of prostate cancer, and significant healthcare expenditure. However, the Asia Pacific region is expected to witness the highest growth rate, driven by increasing healthcare awareness, an improving healthcare system, and rising disposable incomes. European countries are also expected to contribute significantly to market growth due to government-supported screening initiatives. The Middle East & Africa and Latin America, though currently having smaller market shares, are anticipated to see steady growth as healthcare infrastructure improves and awareness programs expand.

Test Type Analysis

The PSA test market is segmented into different test types, including Total PSA Test, Free PSA Test, and Complexed PSA Test. The Total PSA Test is the most widely used among these, accounting for the largest market share. This test measures the overall level of prostate-specific antigen in the blood and is a critical tool for initial screening and monitoring of prostate health. The prominence of Total PSA Tests can be attributed to their efficacy in detecting the risk of prostate cancer and their widespread availability in healthcare facilities globally. Efforts to improve the specificity of Total PSA Tests through the development of new assays continue to enhance their market presence.

Free PSA Tests, which measure the proportion of PSA not bound to proteins in the blood, are also gaining traction in the market. These tests are particularly valuable in differentiating prostate cancer from benign prostatic hyperplasia (BPH), a common non-cancerous enlargement of the prostate gland. By providing additional diagnostic information, Free PSA Tests help reduce unnecessary biopsies, which are invasive and costly. The increasing demand for more precise and less invasive

Abstract

The 2017 Philippines National Demographic and Health Survey (NDHS 2017) is a nationwide survey with a nationally representative sample of approximately 30,832 housing units. The primary objective of the survey is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS 2017 collected information on marriage, fertility levels, fertility preferences, awareness and use of family planning methods, breastfeeding, maternal and child health, child mortality, awareness and behavior regarding HIV/AIDS, women’s empowerment, domestic violence, and other health-related issues such as smoking.

The information collected through the NDHS 2017 is intended to assist policymakers and program managers in the Department of Health (DOH) and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents) and all women age 15-49 years resident in the sample household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the NDHS 2017 is based on a two-stage stratified sample design using the Master Sample Frame (MSF), designed and compiled by the PSA. The MSF is constructed based on the results of the 2010 Census of Population and Housing and updated based on the 2015 Census of Population. The first stage involved a systematic selection of 1,250 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 20 or 26 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the pre-selected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on domestic violence.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used for the NDHS 2017: the Household Questionnaire and the Woman’s Questionnaire. Both questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, universities, and international agencies.

Cleaning operations

The processing of the NDHS 2017 data began almost as soon as fieldwork started. As data collection was completed in each PSU, all electronic data files were transferred via an Internet file streaming system (IFSS) to the PSA central office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the PSU. Secondary editing involved resolving inconsistencies and the coding of openended questions; the former was carried out in the central office by a senior data processor, while the latter was taken on by regional coordinators and central office staff during a 5-day workshop following the completion of the fieldwork. Data editing was carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage, because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for more effective monitoring. The secondary editing of the data was completed by November 2017. The final cleaning of the data set was carried out by data processing specialists from The DHS Program by the end of December 2017.

Response rate

A total of 31,791 households were selected for the sample, of which 27,855 were occupied. Of the occupied households, 27,496 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,690 women age 15-49 were identified for individual interviews; interviews were completed with 25,074 women, yielding a response rate of 98%.

The household response rate is slightly lower in urban areas than in rural areas (98% and 99%, respectively); however, there is no difference by urban-rural residence in response rates among women (98% for each).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Philippines National Demographic and Health Survey (NDHS) 2017 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the NDHS 2017 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample 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.

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% 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, the NDHS 2017 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final 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

See details of the data quality tables in Appendix C of the survey final report.

Additional file 3. Segmentally and tandemly duplicated kiwifruit TIFY gene pairs.

This study was designed to analyze the impact of four televised public service announcements (PSAs) aired for three months in Lima, Ohio. The researchers sought to answer three specific research questions: (1) Were the PSAs effective in transferring knowledge to citizens about the police? (2) Did the PSAs have an impact on resident satisfaction with the police? and (3) Did the PSAs have an impact on the behavior of citizens interacting with the police? To assess public attitudes about the Lima police and to determine whether the substance of the PSAs was being communicated to the residents of Lima, three waves of telephone interviews were conducted (Part 1). The first telephone interviews were conducted in April 1996 with approximately 500 randomly selected Lima residents. These were baseline interviews that took place before the PSAs aired. The survey instrument used in the first interview assessed resident satisfaction with the police and the services they provided. After completion of the Wave 1 interviews, the PSAs were aired on television for three months (June 5-August 28, 1996). After August 28, the PSAs were removed from general circulation. A second wave of telephone interviews was conducted in September 1996 with a different group of randomly selected Lima residents. The same survey instrument used during the first interviews was administered during the second wave, with additional questions added relating to whether the respondent saw any of the PSAs. A third group of randomly selected Lima residents was contacted via the telephone in January 1997 for the final wave of interviews. The final interviews utilized the identical survey instrument used during Wave 2. The focus of this follow-up survey was on citizen retention, over time, of the information communicated in the PSAs. Official data collected from computerized records maintained by the Lima Police Department were also collected to monitor changes in citizen behavior (Part 2). The records data span 127 weeks, from January 1, 1995, to June 7, 1997, which includes 74 weeks of pre-PSA data and 53 weeks of data for the period during the initial airing of the first PSA and thereafter. Variables in Part 1 include whether respondents were interested in learning about what to do if stopped by the police, what actions they had displayed when stopped by the police, if they would defend another person being treated unfairly by the police, how responsible they felt (as a citizen) in preventing crimes, the likelihood of calling the police if they were aware of a crime, perception of crime and fear of crime, and whether there had been an increase or decrease in the level of crime in their neighborhoods. Respondents were also asked about the amount of television they watched, whether they saw any of the public service announcements and if so to rate them, whether the PSAs provided information not already known, whether any of the PSA topics had come up in conversations with family or friends, and whether the respondent would like to see more PSAs in the future. Finally, respondents were asked whether the police were doing as much as they could to make the neighborhood safe, how responsive the police were to nonemergency matters, and to rate their overall satisfaction with the Lima Police Department and its various services. Demographic variables for Part 1 include the race, gender, age, marital status, level of education, employment status, and income level of each respondent. Variables in Part 2 cover police use-of-force or resisting arrest incidents that took place during the study period, whether the PSA aired during the week in which a use-of-force or resisting arrest incident took place, the number of supplemental police use-of-force reports that were made, and the number of resisting arrest charges made.

Abstract

The 2013 NDHS is designed to provide information on fertility, family planning, and health in the country for use by the government in monitoring the progress of its programs on population, family planning and health.

In particular, the 2013 NDHS has the following specific objectives: • Collect data which will allow the estimation of demographic rates, particularly fertility rates and under-five mortality rates by urban-rural residence and region. • Analyze the direct and indirect factors which determine the level and patterns of fertility. • Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. • Collect data on health, immunizations, prenatal and postnatal check-ups, assistance at delivery, breastfeeding, and prevalence and treatment of diarrhea, fever and acute respiratory infections among children below five years old. • Collect data on environmental health, utilization of health facilities, health care financing, prevalence of common non-communicable and infectious diseases, and membership in the National Health Insurance Program (PhilHealth). • Collect data on awareness of cancer, heart disease, diabetes, dengue fever and tuberculosis. • Determine the knowledge of women about AIDS, and the extent of misconception on HIV transmission and access to HIV testing. • Determine the extent of violence against women.

Geographic coverage

National coverage

Analysis unit

• Household
• Individuals/ persons
• Woman age 15 to 49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample selection methodology for the 2013 NDHS is based on a stratified two-stage sample design, using the 2010 Census of Population and Housing (CPH) as a frame. The first stage involved a systematic selection of 800 sample enumeration areas (EAs) distributed by stratum (region, urban/rural). In the second stage, 20 sample housing units were selected from each sample EA, using systematic random sampling.

All households in the sampled housing units were interviewed. An EA is defined as an area with discern able boundaries consisting of contiguous households. The sample was designed to provide data representative of the country and its 17 administrative regions.

Further details on the sample design and implementation are given in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2013 NDHS used three questionnaires: Household Questionnaire, Individual Woman’s Questionnaire, and Women’s Safety Module. The development of these questionnaires resulted from the solicited comments and suggestions during the deliberation in the consultative meetings and separate meetings conducted with the various agencies/organizations namely: PSA-NSO, POPCOM, DOH, FNRI, ICF International, NEDA, PCW, PhilHealth, PIDS, PLCPD, UNFPA, USAID, UPPI, UPSE, and WHO. The three questionnaires were translated from English into six major languages - Tagalog, Cebuano, Ilocano, Bicol, Hiligaynon, and Waray.

The main purpose of the Household Questionnaire was to identify female members of the sample household who were eligible for interview with the Individual Woman’s Questionnaire and the Women’s Safety Module.

The Individual Woman’s Questionnaire was used to collect information from all women aged 15-49 years.

The Women’s Safety Module was used to collect information on domestic violence in the country, its prevalence, severity and frequency from only one selected respondent from among all the eligible women who were identified from the Household Questionnaire.

Cleaning operations

All completed questionnaires and the control forms were returned to the PSA-NSO central office in Manila for data processing, which consisted of manual editing, data entry and verification, and editing of computer-identified errors. An ad-hoc group of thirteen regular employees from the DSSD, the Information Resources Department (IRD), and the Information Technology Operations Division (ITOD) of the NSO was created to work fulltime and oversee data processing operation in the NDHS Data Processing Center that was carried out at the NSO-CVEA Building in Quezon City, Philippines. This group was responsible for the different aspects of NDHS data processing. There were 19 data encoders hired to process the data who underwent training on September 12-13, 2013.

Data entry started on September 16, 2013. The computer package program called Census and Survey Processing System (CSPro) was used for data entry, editing, and verification. Mr. Alexander Izmukhambetov, a data processing specialist from ICF International, spent two weeks at NSO in September 2013 to finalize the data entry program. Data processing was completed on December 6, 2013.

Response rate

For the 2013 NDHS sample, 16,732 households were selected, of which 14,893 were occupied. Of these households, 14,804 were successfully interviewed, yielding a household response rate of 99.4 percent. The household response rates in urban and rural areas are almost identical.

Among the households interviewed, 16,437 women were identified as eligible respondents, and the interviews were completed for 16,155 women, yielding a response rate of 98.3 percent. On the other hand, for the women’s safety module, from a total of 11,373 eligible women, 10,963 were interviewed with privacy, translating to a 96.4 percent response rate. At the individual level, urban and rural response rates showed no difference. The principal reason for non-response among women was the failure to find individuals at home, despite interviewers’ repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2013 National Demographic and Health Survey (NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2013 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between the results of all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey data.

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, the 2013 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2013 NDHS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replications method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of weighted cases in the group or subgroup under consideration.

Further details on sampling errors calculation are given in Appendix B of the final report.

Data appraisal

Data quality tables were produced to review the quality of the data: - 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: The tables are presented in APPENDIX C of the final report.

Findings from a national sample survey of parents of disabled children. The primary purpose of the survey is to measure parental experience of services for disabled children, and provides the 2008-09 baseline for the national performance indicator 5 for the Public Service Agreement on Child Health and Wellbeing (PSA 12). The secondary purpose of the survey is to provide baselines in 30 local authority areas for the National Indicator Set for local authorities (NI 54), and also NHS Vital Signs indicators for Primary Care Trusts (VSC33) in PCT areas which have boundaries exactly coterminous with these LA areas. The data is being collected through a questionnaire to parents asking for their views of health, social care and education services for their disabled child as experienced in the preceding 12 months. The framework for the questionnaire is the Aiming High for Disabled Children core offer standards on information, transparency, assessment, participation and feedback. The data will provide an overall national figure on parental experience (between 0 and 100). The data will also provide an overall local figure on parental experience for 30 local areas participating in the first data collection.

Source: British Market Research Bureau (BMRB)

Publisher: Department for Children Schools and Families (DCSF)

Geographies: County/Unitary Authority

Geographic coverage: England

Time coverage: 2008/09, 2009/10

Type of data: Survey

ABSTRACT Objective: To verify the association between the prostate-specific antigen (PSA) serum concentration and the presence of risk factors for prostate diseases in adult patients with urogenital infections. Materials and methods: Analytical cross-sectional study of PSA in 60 patients aged 40 to 65 years, from January to December 2013. PSA quantification was performed in serum by solid-phase chemiluminescence in two label cycles: 1. mouse monoclonal antibody (mAb); and 2. goat polyclonal antibody (pAbs). After each cycle the free antigen was removed. The readings were taken on the INMULITE ONE Siemens® automated equipment. From the PSA, 0.0-2.5 ng/ml was used to calculate the antigen distribution trend, and a bivariate statistical analysis of PSA was as opposed to previous exams, and endogenous and environmental risk factors detected. Results: Patients between 50-60 years of age, with a family history of urogenital and sexually transmitted infections, toxic habits, and exposure to occupational risk prevailed. Sixty-two percent of patients presented normal PSA levels, and the remaining presented slightly elevated and very high PSA levels. In the statistical analysis, a significant association (p < 0.001) of PSA was found as opposed to previous tests. A significant association (p = 0.001) was also found between PSA versus age, family history, personal pathological history, toxic habits, and risk exposure. Conclusion: The high association rate found between PSA versus age and other risk factors could be used as a predictive value for prostate cancer (Pca) or other prostate disorders.

ObjectivesPsoriatic arthritis (PsA) and cutaneous psoriasis (PsO) are different phenotypes of psoriatic disease (PsD), whose underlying specific mechanisms remain incompletely understood. As cytokines are key elements to induce and tune up immune responses to drive inflammatory diseases, our objective was to assess whether clinical features, disease phenotype and PsA and PsO activity were associated with a particular ex vivo cytokine production profile.MethodsForty-eight patients (37 PsA and 11 PsO) and 11 healthy subjects (HS) were studied. Cytokine production by peripheral blood mononuclear cells (PBMC) that were either unstimulated, or stimulated with LPS or anti-CD3/CD28 antibodies, were analysed by multiplex assay in the culture supernatants.ResultsCytokine signature of PsD includes a high level of TNFα in supernatants of LPS-stimulated PBMC, higher levels of IL-6 and lower levels of IFN-γ and IL-17A after CD3-CD28 stimulation, as well as higher spontaneous IL-1RA and TNFα production compared to HS. High body mass index (BMI) was associated with lower levels of IL-1β, and metabolic syndrome with lower levels of IFN-γ after LPS stimulation. In PsD, dermatological activity was related with higher IL-17A level, while rheumatic activity was linked with lower levels of IFN-γ and TNFα. Comparing each PsD subtype to HS, IL-1β and IL-6 productions are higher when using LPS stimulation in PsO patients with higher levels of IL-1β and IL-1α in peripheral PsA patients after CD3/CD28 stimulation. LPS stimulation induced high levels of IL-17A in peripheral PsA compared to axial PsA. PsA patients with axial PsA share some features with PsO but shows a distinct cytokine pattern compared to peripheral PsA.ConclusionPsO and the different PsA subtypes exhibit distinct ex vivo cytokine production profiles and common features of the so-called PsD. Analysis of IL-1 cytokine family and IL-6 seems to be of particular interest to distinguish PsO and peripheral PsA since it depends on monocytes in PsO and T-lymphocytes in peripheral PsA. Peripheral cytokine profiles are influenced by rheumatic and dermatological activity of the disease, and also by metabolic syndrome features. Our results highlight the crucial role of immune cell interactions with different patterns of interaction depending on clinical phenotype.

Pseudomonas syringae pv. actinidiae (Psa) is a bacterial pathogen of kiwifruit. This pathogen causes leaf-spotting, cane dieback, wilting, cankers (lesions), and in severe cases, plant death. Families of diploid A. chinensis seedlings grown in the field show a range of susceptibilities to the disease with up to 100% of seedlings in some families succumbing to Psa. But the effect of selection for field resistance to Psa on the alleles that remain in surviving seedlings has not been assessed. The objective of this work was to analyse, the effect of plant removal from Psa on the allele frequency of an incomplete-factorial-cross population. This population was founded using a range of genotypically distinct diploid A. chinensis var. chinensis parents to make 28 F1 families. However, because of the diversity of these families, low numbers of surviving individuals, and a lack of samples from dead individuals, standard QTL mapping approaches were unlikely to yield good results. Instead, a modified bulk segregant analysis (BSA) overcame these drawbacks while reducing the costs of sampling and sample processing, and the complexity of data analysis. Because the method was modified, part one of this work was used to determine the signal strength required for a QTL to be detected with BSA. Once QTL detection accuracy was known, part two of this work analysed the 28 families from the incomplete-factorial-cross population that had multiple individuals removed due to Psa infection. Each family was assigned to one of eight bulks based on a single parent that contributed to the families. DNA was extracted in bulk by grinding sampled leaf discs together before DNA extraction. Each sample bulk was compared against a bulk made up of WGS data from the parents contributing to the sample bulk. The deviation in allele frequency from the expected allele frequency within surviving populations using the modified BSA method was able to identify 11 QTLs for Psa that were present in at least two analyses. The identification of these Psa resistance QTL will enable marker development to selectively breed for resistance to Psa in future kiwifruit breeding programs.

Abstract: The new ruralities have as a major feature a high degree of socio-economic and socio-cultural heterogeneity, based on the coexistence of farmers and new residents in rural areas. As landowners, both become the target of new environmental policies, including payment for water-related ecosystem services (PSA-Water). The paper analyzes the central component of PSA-Water, the payment, from the perspective of equity in the distribution of financial resources in the context of new ruralities. In 2013, we interviewed 77 farmers participating in three major Brazilian projects of PSA-Water. The projects were implemented in areas where landowners have different socioeconomic profiles. The degree of dependence on the rural property for the social reproduction of the household is a central component of this differentiation. PSA projects allocated larger amounts to landholders less dependent on the property for their livelihoods, but proportionally to the income of the households, contributed more to landholders more dependent on their lands. Two features of the design of projects tend to make invisible the heterogeneity of landholders. First, to adopt mainly environmental criteria to define the amount of the payment; and second, to take being a landholder (any type) as the main criterion for eligibility.