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.

Preliminary figures between January to September 2024 indicated that ischaemic heart disease was the leading cause of death in the Philippines. The number of people who died from this illness was estimated at 75,500. Following this, cancer resulted in the deaths of about 43,000 people. Eating habits Heart diseases have been linked to high meat consumption, among others. In the Philippines, pork has been the most consumed meat type, followed closely by chicken. While pork meat is typically produced domestically, the country also imports pork to supplement its supply. However, plant-based food has started gaining popularity among Filipinos. In fact, a 2024 survey revealed that 69 percent of surveyed Filipinos consumed plant-based products, including meat alternatives. Common diseases in the Philippines Aside from heart and cerebrovascular diseases, the Filipino population is also exposed to infections, diabetes, skin diseases, and illnesses resulting from high meat consumption. In 2020, over 700,000 Filipinos contracted acute respiratory tract infections, followed by over 400,000 diagnosed with hypertension. In areas with high exposure to rain, dengue infections and leptospirosis have also become prevalent.

This shows the percentage of mortality rate of children from day of birth to before reaching 1st birthday (0-11 months) at the regional level for the years 2008, 2013, and 2017. These data were derived from the result of National Demographic and Health Survey of the Philippine Statistics Authority.

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.

Number and percentage of incident cases of prostate cancer and deaths by age group.

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.

Evaluation of regional variation of prostate cancer (PCa) incidence and PCa-specific mortality is essential in the assessment of equity in a national healthcare system. We evaluated PCa incidence and PCa-specific mortality between different municipalities and hospital districts in Finland over 1985–2019. Men diagnosed with PCa in Finland from 1985 through 2019 were retrieved from Finnish Cancer Registry. Age-standardized PCa incidence and mortality rates were estimated by municipality and hospital district as well as municipality urbanization, education, and income level using hierarchical Bayesian modeling. Standard deviations (SD) of the regional rates were compared between periods from 1985–1989 to 2015–2019. We identified 123,185 men diagnosed with any stage PCa between 1985 and 2019. SD of PCa incidence rate (per 100,000 person-years) showed that the total variation of PCa incidence between different municipalities was substantial and varied over time: from 22.2 (95% CI, 17.1–27.8) in 1985–1989 to 56.5 (95% CI, 49.8–64.5) in 2000–2004. The SD of PCa mortality rate between all municipalities was from 9.0 (95% CI, 6.6–11.8) in 2005–2009 to 2.4 (95% CI, 0.9–4.8) in 2015–2019. There was a trend toward a lower PCa-specific mortality rate in municipalities with higher education level. Regional variation in the incidence rate of PCa became more evident after initiation of PSA testing in Finland, which indicates that early diagnostic practice (PSA testing) of PCa has been different in different parts of the country. Variation in the national PCa mortality rate was indeed recognizable, however, this variation diminished at the same time as the mortality rate declined in Finland. It seems that after the initiation period of PSA testing, PSA has equalized PCa mortality outcomes in Finland.

Joinpoint analyses of prostate cancer incidence and mortality trends.

Over the last two observations, the life expectancy has significantly increased in all gender groups Comparing the two different gender groups for the year 2023, the 'life expectancy of women at birth' leads the ranking with 72.82 years. Contrastingly, 'life expectancy of men at birth' is ranked last, with 66.89 years. Their difference, compared to life expectancy of women at birth, lies at 5.93 years. Life expectancy at birth refers to the number of years that the average newborn can expect to live, providing that mortality patterns at the time of their birth do not change thereafter.Find further similar statistics for other countries or regions like Solomon Islands and Costa Rica.

Mortality from all cancers, directly age-standardised rate, persons, under 75 years, 2004-08 (pooled) per 100,000 European Standard population by Local Authority by local deprivation quintile. Local deprivation quintiles are calculated by ranking small areas (Lower Level Super Output Areas (LSOAs)) within each Local Authority based on their Index of Multiple Deprivation 2007 (IMD 2007) deprivation score, and then grouping the LSOAs in each Local Authority into five groups (quintiles) with approximately equal numbers of LSOAs in each. The upper local deprivation quintile (Quintile 1) corresponds with the 20% most deprived small areas within that Local Authority. The mortality rates have been directly age-standardised using the European Standard Population in order to make allowances for differences in the age structure of populations. There are inequalities in health. For example, people living in more deprived areas tend to have shorter life expectancy, and higher prevalence and mortality rates of most cancers. Cancer accounts for nearly 30% of all deaths among men in England every year and nearly 25% of deaths among women every year1. Reducing inequalities in premature mortality from all cancers is a national priority, as set out in the Department of Health’s Vital Signs Operating Framework 2008/09-2010/112 and the PSA Delivery Agreement 183. However, existing indicators for premature cancer mortality do not take deprivation into account. This indicator has been produced in order to quantify inequalities in cancer mortality by deprivation. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01368

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

Pacing System Analyzer (PSA) Market Outlook

The global Pacing System Analyzer (PSA) market size was valued at approximately USD 1.1 billion in 2023 and is projected to reach USD 2.3 billion by 2032, growing at a CAGR of 8.7% over the forecast period. This remarkable growth can be attributed to several factors, including advancements in cardiac care technologies, increasing prevalence of cardiovascular diseases, and rising aging populations globally.

A significant driver of the PSA market is the advancements in cardiac care technologies. Innovations in medical devices and techniques for managing cardiac conditions have necessitated the use of more sophisticated tools like PSAs. These advancements are driven by the need for accurate diagnosis and effective treatment, which ultimately enhances patient outcomes. The evolution of minimally invasive procedures has further amplified the demand for precise and reliable pacing system analyzers, as they help in verifying the functionality of pacemakers and other cardiac devices during and post-implantation.

Another contributory factor is the increasing prevalence of cardiovascular diseases worldwide. Cardiovascular diseases remain the leading cause of mortality globally, prompting healthcare providers to adopt more advanced diagnostic and therapeutic tools. The growing incidence of heart-related ailments such as atrial fibrillation, bradycardia, and heart failure has led to a higher demand for PSAs. The devices assist in the meticulous monitoring and management of these conditions, ensuring that patients receive timely and appropriate interventions, thereby driving the market growth.

The rising aging population is also a critical growth factor for the PSA market. Older adults are more susceptible to cardiac disorders, necessitating frequent monitoring and management of heart conditions. As the global population continues to age, the demand for advanced cardiac care solutions, including PSAs, is expected to surge. Moreover, the awareness among this demographic about the availability of advanced medical devices and their benefits in improving quality of life plays a crucial role in market expansion.

Paroxysmal Supraventricular Tachycardia (PSVT) is a condition characterized by episodes of rapid heart rate originating above the heart's ventricles. This condition often presents itself suddenly and can cause symptoms such as palpitations, dizziness, and shortness of breath. The management of PSVT has become increasingly critical in the context of cardiac care, as it can significantly impact a patient's quality of life. The advent of advanced pacing system analyzers has facilitated better monitoring and management of such arrhythmias, allowing for timely interventions and improved patient outcomes. As the prevalence of PSVT and other arrhythmias continues to rise, the demand for precise diagnostic tools like PSAs is expected to grow, further driving the market.

From a regional perspective, North America dominates the PSA market, owing to its advanced healthcare infrastructure, higher adoption of cutting-edge medical technologies, and a substantial patient base with cardiovascular ailments. Europe follows suit, driven by similar factors and increasing healthcare expenditure. The Asia Pacific region is witnessing rapid growth due to improving healthcare facilities, rising disposable incomes, and increasing awareness about cardiac health. Emerging markets in Latin America, and the Middle East & Africa also show promising potential due to the rising prevalence of heart diseases and improving healthcare infrastructure.

Product Type Analysis

The PSA market by product type is segmented into Single-Chamber PSA and Dual-Chamber PSA. Single-Chamber PSAs are primarily used to monitor the electrical activity and pacing function in a single chamber of the heart, either the atrium or the ventricle. These devices are essential in managing simpler cardiac conditions where only one chamber requires pacing. The demand for Single-Chamber PSAs is driven by their cost-effectiveness and the relatively straightforward nature of the procedures they are used in. This segment, although smaller in market share compared to dual-chamber PSAs, still holds a significant place due to its specific clinical applications.

Dual-Chamber PSAs, on the other hand, are designed to monitor the electrical activity and pacing function in both the atrium and the ventricle. These dev

Objectives: To predict castration-resistant prostate cancer (CRPC) and prostate cancer (Pca) death by use of clinical variables at Pca diagnosis and PSA levels after start of gonadotropin-releasing hormone agonists (GnRH) in men with non-metastatic castration sensitive prostate cancer (nmCSPC). Materials and Methods: PSA values for 1603 men with nmCSPC in the National Prostate Cancer Register of Sweden who received GnRH as primary treatment were retrieved from Uppsala-Örebro PSA Cohort and Stockholm PSA and Biopsy Register. All men had measured PSA before (pre-GnRH PSA) and 3–6 months after (post-GnRH PSA) date of start of GnRH. Unadjusted and adjusted Cox models were used to predict CRPC by PSA levels. PSA levels and ISUP grade were used to construct a risk score to stratify men by tertiles according to risk of CRPC and Pca death. Results: 788 (49%) men reached CRPC and 456 (28%) died of Pca during follow-up. Post-GnRH PSA predicted CRPC regardless of pre-GnRH PSA. CRPC risk increased with higher post-GnRH PSA, HR 4.7 (95% CI: 3.4–6.7) for PSA > 16 ng/mL vs 0–0.25 ng/mL and with ISUP grade, HR 3.7 (95%: 2.5–5.4) for ISUP 5 vs ISUP 1. Risk of Pca death in men above top vs bellow bottom tertile of post-GnRH PSA and ISUP grade was HR 4.1 (95% CI: 3.0–5.5). Conclusion: A risk score based on post-GnRH PSA and ISUP grade could be used for early identification of a target group for future clinical trials on additional therapy to GnRH.

Objective: This review aimed to systematically quantify the association between pre-operative total prostate specific antigen (tPSA) and survivorship of prostate cancer (PCa). Methods: Data sources for the review included MEDLINE, PubMed, Cochrane Library, CINAHL, Academic Search Complete, PsycINFO, and relevant reference lists. Databases were searched from inception to June, 2022. The study took place between May 2022 and March 2023. We included studies that applied a quantitative approach to examine the interaction between pre-operative PSA and survivorship of PCa. Pre-operative PSA constituted the independent variable, whereas survivorship of prostate cancer as measured by biochemical recurrence and mortality constitute the outcome variable. A risk of bias assessment was conducted with the aid of a mixed-method appraisal tool. We employed meta-analysis to quantify the association of pre-operative PSA with biochemical recurrence and mortality and computed I2 to assess the degree of heterogeneity. Results: We found a positive weak association between pre-operative PSA and biochemical recurrence (HR = 1.074; 95% CI = 1.042 - 1.106). With a median rise in PSA (≥ 2 ng/mL), the likelihood for biochemical recurrence increase by approximately 7.4%. There was statistically a significant association between PSA and mortality (HR = 1.222, CI 0.917 - 1.630). Conclusions: Biochemical recurrence associates with pre-operative PSA in an inconsistent manner. The sole use of pre-operative PSA in estimating post-prostatectomy biochemical recurrence should be discouraged. There is need for a multi-factorial model which employs a prudent combination of the most important and cost-effective biomarkers in predicting post-prostatectomy biochemical recurrence.

ObjectiveUsing the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients.MethodsPatients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index.ResultsThere was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model.ConclusionsAlthough there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

The slope index of inequality (SII) in circulatory disease mortality for persons under 75 years. The SII gives a single score describing the extent of inequality in each Local Authority, and is broadly comparable between areas. See below for further details on the SII. There are inequalities in health. For example, people living in more deprived areas tend to have shorter life expectancy, and higher prevalence and mortality rates of circulatory disease. Circulatory disease accounts for nearly 40% of all deaths among persons in England every year1. Reducing inequalities in premature mortality from all cancers is a national priority, as set out in the Department of Health’s Vital Signs Operating Framework 2008/09-2010/112 and the PSA Delivery Agreement 183. However, existing indicators for premature circulatory disease mortality do not take deprivation into account. This indicator has been produced in order to quantify inequalities in circulatory disease mortality by deprivation. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01370

In 2024, the total population of the Philippines was at approximately 114.17 million inhabitants. For the foreseeable future, the Filipino population is expected to increase slightly, despite a current overall downward trend in population growth. The dwindling Filipino population For now, the population figures in the Philippines still show a steady increase and the country is still one of the most densely populated countries in the Asia-Pacific region, however, all signs point to a decline in the number of inhabitants in the long run: Just like the population growth rate, the country’s fertility rate, for example, has also been decreasing for years now, while the death rate has been increasing simultaneously.   Poor healthcare to blame One of the reasons for the downward trend is the aging population; fewer babies are born each year, while life expectancy at birth has been steady over the years. Another reason is poor healthcare in the country: The Philippines have a high tuberculosis incidence rate, a highly infectious disease, and are among the countries with a high probability of death from noncommunicable diseases as well.

Background: Previous reviews of geographical disparities in the prostate cancer continuum from diagnosis to mortality have identified a consistent pattern of poorer outcomes with increasing residential disadvantage and for rural residents. However, there are no contemporary, systematic reviews summarizing the latest available evidence. Our objective was to systematically review the published international evidence for geographical variations in prostate cancer indicators by residential rurality and disadvantage.Methods: Systematic searches of peer-reviewed articles in English published from 1/1/1998 to 30/06/2018 using PubMed, EMBASE, CINAHL, and Informit databases. Inclusion criteria were: population was adult prostate cancer patients; outcome measure was PSA testing, prostate cancer incidence, stage at diagnosis, access to and use of services, survival, and prostate cancer mortality with quantitative results by residential rurality and/or disadvantage. Studies were critically appraised using a modified Newcastle-Ottawa Scale.Results: Overall 169 studies met the inclusion criteria. Around 50% were assessed as high quality and 50% moderate. Men from disadvantaged areas had consistently lower prostate-specific antigen (PSA) testing and prostate cancer incidence, poorer survival, more advanced disease and a trend toward higher mortality. Although less consistent, predominant patterns by rurality were lower PSA testing, prostate cancer incidence and survival, but higher stage disease and mortality among rural men. Both geographical measures were associated with variations in access and use of prostate cancer-related services for low to high risk disease.Conclusions: This review found substantial evidence that prostate cancer indicators varied by residential location across diverse populations and geographies. While wide variations in study design limited comparisons across studies, our review indicated that internationally, men living in disadvantaged areas, and to a lesser extent more rural areas, face a greater prostate cancer burden. This review highlights the need for a better understanding of the complex social, environmental, and behavioral reasons for these variations, recognizing that, while important, geographical access is not the only issue. Implementing research strategies to help identify these processes and to better understand the central role of disadvantage to variations in health outcome are crucial to inform the development of evidence-based targeted interventions.

The number of people killed or seriously injured in road traffic accidents divided by the population of the area in thousands multiplied by 100. This indicator contains data from all ‘types’ of road user, including pedestrians, pedal cyclists, motorcyclists, car users, and other vehicle users. Motor vehicle traffic accidents are a major cause of preventable deaths and morbidity, particularly in younger age groups. For children and for men aged 20-64 years, mortality rates for motor vehicle traffic accidents are higher in lower socioeconomic groups. For instance, there would be 600 fewer deaths in men aged 20-64 years from motor vehicle traffic accidents each year if all men had the same death rates as those in social classes I and II combined. There is evidence that some groups, like children, old people and potential cyclists, avoid roads because they are dangerous, which can reduce casualties but lower the quality of life. Ideally, casualty data need to be combined with other information. For example, a rise in journeys on foot and bicycle combined with a fall in accidents would indicate real progress. One of the Department for Transport’s PSA targets is to reduce the number of people killed or seriously injured in Great Britain in road accidents by 40 % by 2010 and the number of children killed or seriously injured by 50 % by 2010, compared with the averages for 1994-1998. Legacy unique identifier: P01050

aLog rank test (univariate analysis) or Wald test (multivariate analysis).bAnalyzed as a continuous variable.cStratification based on limited representation of Gleason 6 and 4+4 cases.dStratifies pathologic stage based on organ confinement.